Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization Design on Active Guard Ring to Improve Latch-Up Immunity of CMOS Integrated Circuits

A new optimization design of an active guard ring has been proposed to improve latch-up immunity of CMOS integrated circuits and been successfully verified in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> 1.8-/3.3-V CMOS technology. Codesigned with the on-chip electrostatic discharge (ESD) protection devices (gate-ground nMOS and gate-VDD pMOS) equipped at the input–output (I/O) pad, the overshooting/undershooting trigger current during latch-up test can be conducted away through the turned-on channels of the ESD protection MOSFET’s to the power rails (<inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {DD}}$ </tex-math></inline-formula> or <inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {SS}}$ </tex-math></inline-formula>). Therefore, the trigger current injecting from the I/O devices (that directly connected to the I/O pad) through the substrate to initiate the latch-up occurrence at the internal circuit blocks can be significantly reduced. Thus, the latch-up immunity of the whole chip can be effectively improved under the same placement distance between the I/O cells and the internal circuit blocks. The new proposed design is a cost-efficient solution to improve latch-up immunity and also to mention good ESD robustness of the I/O cells.
Highly Reliable a-Si:H TFT Gate Driver With Precharging Structure for In-Cell Touch AMLCD Applications

This paper presents a novel bidirectional gate driver circuit based on hydrogenated amorphous silicon thin-film transistors (TFTs), especially for use in active-matrix liquid-crystal displays with in-cell touch technology. The proposed circuit exploits the time-division driving method to implement the in-cell touch panels with a high report rate and prevent the distortion of touch signals. The proposed structure employs a highly reliable precharging circuit to ameliorate the degradation of the driving TFT during the long-term touch-sensing stages. The output TFTs in the precharging structure are activated only once at the end of the touch-sensing stage in a frame, effectively extending the lifetime of the circuit. The experimental and simulated results illustrate that the proposed gate driver circuit generates uniform output waveforms regardless of whether the circuit operates in forward and backward transmission or stops in any row. To verify the high reliability of the proposed gate driver circuit, long-term stress tests at 85 °C are conducted. After the proposed circuit is operated for 720 h, the falling time of the output waveforms after the touch-sensing stage slightly varies by only $0.51~\mu \text{s}$ , confirming that the output TFT in the proposed precharging structure with a low duty ratio of 0.15% can achieve the high reliability of the proposed circuit. Therefore, the proposed gate driver circuit is highly feasible for use in high-report-rate in-cell touch panels.
Mathematical Models of Radicalization and Terrorism

The rapid spread of radical ideologies has led to a world-wide succession of terrorist attacks in recent years. Understanding how extremist tendencies germinate, develop, and drive individuals to action is important from a cultural standpoint, but also to help formulate response and prevention strategies. Demographic studies, interviews with radicalized subjects, analysis of terrorist databases, reveal that the path to radicalization occurs along progressive steps, where age, social context and peer-to-peer exchanges play major roles. To execute terrorist attacks, radicals must efficiently communicate with one another while maintaining secrecy; they are also subject to pressure from counter-terrorism agencies, public opinion and the need for material resources. Similarly, government entities must gauge which intervention methods are most effective. While a complete understanding of the processes that lead to extremism and violence, and of which deterrents are optimal, is still lacking, mathematical modelers have contributed to the discourse by using tools from statistical mechanics and applied mathematics to describe existing and novel paradigms, and to propose novel counter-terrorism strategies. We review some of their approaches in this work, including compartment models for populations of increasingly extreme views, continuous time models for age-structured radical populations, radicalization as social contagion processes on lattices and social networks, agent based models, game theoretic formulations. We highlight the useful insights offered by analyzing radicalization and terrorism through quantitative frameworks. Finally, we discuss the role of institutional intervention and the stages at which de-radicalization strategies might be most effective.
Power-Efficient Approximate SAD Architecture with LOA Imprecise Adders

Approximate computing is a highly promising approach to reduce the computational effort in video encoders. Its use is even more relevant and advantageous when high resolution videos must be processed in real time using battery powered devices. In this scenario, it is essential to reduce power dissipation and silicon area. In particular, the distortion metric calculation module is one of the most time demanding and the Sum of Absolute Differences (SAD) is usually the most used distortion metric, mainly when dedicated hardware is considered. To overcome this demand, this paper presents a power-efficient SAD architecture compliant with current video encoders based on the usage of Lower-Part-OR Adders (LOA). The attained results showed that important power (17.99%) and area (30.56%) savings can be reached, with an increase of only 0.3% in BD-rate. When compared with state of the art related works, the designed architecture reaches the best area and power dissipation results.
Single Image Deraining: A Comprehensive Benchmark Analysis

We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images.This dataset highlights diverse data sources and image contents, and is divided into three subsets (rain streak, rain drop, rain and mist), each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on the dataset shed light on the comparisons and limitations of state-of-the-art deraining algorithms, and suggest promising future directions.
Subgraph Robustness of Complex Networks Under Attacks

Network measures derived from empirical observations are often poor estimators of the true structure of system as it is impossible to observe all components and all interactions in many real world complex systems. Here, we study attack robustness of complex networks with data missing caused by: 1) a uniform random sampling and 2) a nonuniform random sampling. By introducing the subgraph robustness problem, we develop analytically a framework to investigate robustness properties of the two types of subgraphs under random attacks, localized attacks, and targeted attacks. Interestingly, we find that the benchmark models, such as Erdős-Rényi graphs, random regular networks, and scale-free networks possess distinct characteristic subgraph robustness features. We show that the network robustness depends on several factors including network topology, attack mode, sampling method and the amount of data missing, generalizing some well-known robustness principles of complex networks. Our results offer insight into the structural effect of missing data in networks and highlight the significance of understanding different sampling processes and their consequences on attack robustness, which may be instrumental in designing robust systems.
Convolutional Neural Network With Second-Order Pooling for Underwater Target Classification

Underwater target classification using passive sonar remains a critical issue due to the changeable ocean environment. Convolutional neural networks (CNNs) have shown success in learning invariant features using local filtering and max pooling. In this paper, we propose a novel classification framework which combines the CNN architecture with the second-order pooling (SOP) to capture the temporal correlations from the time-frequency (T-F) representation of the radiated acoustic signals. The convolutional layers are used to learn the local features with a set of kernel filters from the T-F inputs which are extracted by the constant-Q transform (CQT). Instead of using max pooling, the proposed SOP operator is designed to learn the co-occurrences of different CNN filters using the temporal feature trajectory of CNN features for each frequency subband. To preserve the frequency distinctions, the correlated features of each frequency subband are retained. The pooling results are normalized with signed square-root and $l_{2}$ normalization, and then input into the softmax classifier. The whole network can be trained in an end-to-end fashion. To explore the generalization ability to unseen conditions, the proposed CNN model is evaluated on the real radiated acoustic signals recorded at new sea depths. The experimental results demonstrate that the proposed method yields an 8% improvement in classification accuracy over the state-of-the-art deep learning methods.
Fast learning synapses with molecular spin valves via selective magnetic potentiation

We studied LSMO/Alq3/AlOx/Co molecular spin valves in view of their use as synapses in neuromorphic computing. In neuromorphic computing, the learning ability is embodied in specific changes of the synaptic weight. In this perspective, the relevant parameter is the conductance of the molecular spin valve, which plays the role of the synaptic weight. In this work we demonstrated that the conductance can be changes by the repeated application of voltage pulses. We studied the parameter space of the pulses in order to determine the most effective voltage and duration of the pulses. The conductance could also be modified by aligning the magnetizations of the ferromagnetic electrodes parallel or anti parallel to each other. This phenomenon, known as magnetoresistance, affects high conductance devices while leaving low conductance devices unaffected. We studied how this weight update rule affected the speed of reward-based learning in an actor-critic framework, compared to a linear update rule. This nonlinear update performed significantly better (50 learning trials; Epochs to reach a performance goal of 0.975 was 896+/-301 in the nonlinear case and 1076+/-484 in the nonlinear case; Welch t-test: p<0.05). The linear update resulted in more learning trails with very long convergence times, which was largely absent in the nonlinear update.
Finite-Time Connectivity-Preserving Consensus for Second-Order Nonlinear Multiagent Systems

In this paper, the finite-time consensus problem for second-order nonlinear multiagent systems under communication constraints is investigated, where each agent has a limited sensing range. A distributed discontinuous control protocol is proposed to make a team of agents reach consensus in finite time when disturbances exist in multiagent systems. Then, for multiagent systems without disturbances, a new piece-wise continuous protocol is presented to make the agents achieve consensus in finite time. Furthermore, it is shown that initial interaction patterns are preserved under these control protocols. Finally, some numerical examples are provided to illustrate the theoretical results.
Early Detection of Mental Stress Using Advanced Neuroimaging and Artificial Intelligence

While different neuroimaging modalities have been proposed to detect mental stress, each modality experiences certain limitations. This study proposed novel approaches to detect stress based on fusion of EEG and fNIRS signals in the feature-level using joint independent component analysis (jICA) and canonical correlation analysis method (CCA) and predected the level of stress using machine-learning approach. The jICA and CCA were then developed to combine the features to detect mental stress. The jICA fusion scheme discovers relationships between modalities by utilizing ICA to identify sources from each modality that modulate in the same way across subjects. The CCA fuse information from two sets of features to discover the associations across modalities and to ultimately estimate the sources responsible for these associations. The study further explored the functional connectivity (FC) and evaluated the performance of the fusion methods based on their classification performance and compared it with the result obtained by each individual modality. The jICA fusion technique significantly improved the classification accuracy, sensitivity and specificity on average by +3.46% compared to the EEG and +11.13% compared to the fNIRS. Similarly, CCA method improved the classification accuracy, sensitivity and specificity on average by +8.56% compared to the EEG and +13.03% compared to the fNIRS, respectively. The overall performance of the proposed fusion methods significantly improved the detection rate of mental stress, p<0.05. The FC significantly reduced under stress and suggested EEG and fNIRS as a potential biomarker of stress.
Survey of Authentication Schemes for Health Monitoring: A Subset of Cyber Physical System

Cyber Physical System (CPS) is an emerging field and further considerable research topic in multiple domains. Health monitoring is one of sub-fields in Cyber Physical System and relies heavily on wireless environment for connectivity. Due to prevailing interest in this field, security of the data is key concern when it is accessed from remote location. As it is being utilized by medical practitioner to make health related decisions risk of intrusion is always present. Any breach can cause adverse effects to patient’s health. Therefore, importance of data authenticity, freshness and encumbrance free access becomes vital in this perspective. The multiple security solutions have been found in literature. However, security solutions depend severely on authentication schemes. An adequate authentication scheme can ensure protection against cyber adversaries. The main objective of this paper is to present state-of-the-art review on security challenges, security requirements and current authentication schemes available in literature pertaining to health monitoring. These authentication schemes are analyzed on predefined parameter/features in order to testify efficacy of schemes.
Accurate 3D Face Reconstruction With Weakly-Supervised Learning: From Single Image to Image Set

Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency. However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce. In this paper, we propose a novel deep 3D face reconstruction approach that 1) leverages a robust, hybrid loss function for weakly-supervised learning which takes into account both low-level and perception-level information for supervision, and 2) performs multi-image face reconstruction by exploiting complementary information from different images for shape aggregation. Our method is fast, accurate, and robust to occlusion and large pose. We provide comprehensive experiments on MICC Florence and Facewarehouse datasets, systematically comparing our method with fifteen recent methods and demonstrating its state-of-the-art performance. Code available at https://github.com/Microsoft/Deep3DFaceReconstruction
Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This optimization problem is studied in this paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where DC is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where DC is performed at both the mobile users and the fog server. We propose three algorithms to overcome the strong coupling between the offloading decisions and the resource allocation. Numerical results show that the proposed optimal algorithm for DC at only the mobile users can reduce the WEDC by up to 65% compared to computation offloading strategies that do not leverage DC or use sub-optimal optimization approaches. The proposed algorithms with additional DC at the fog server lead to a further reduction of the WEDC.
Left-to-Right Dependency Parsing with Pointer Networks

We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building n attachments, with n being the length of the input sentence. Similarly to the recent stack-pointer parser by Ma et al. (2018), we use the pointer network framework that, given a word, can directly point to a position from the sentence. However, our left-to-right approach is simpler than the original top-down stack-pointer parser (not requiring a stack) and reduces transition sequence length in half, from 2n-1 actions to n. This results in a quadratic non-projective parser that runs twice as fast as the original while achieving the best accuracy to date on the English PTB dataset (96.04% UAS, 94.43% LAS) among fully-supervised single-model dependency parsers, and improves over the former top-down transition system in the majority of languages tested.
A Learning Framework for Distribution-Based Game-Theoretic Solution Concepts

The past few years have seen several works exploring learning economic solutions from data; these include optimal auction design, function optimization, stable payoffs in cooperative games and more. In this work, we provide a unified learning-theoretic methodology for modeling such problems, and establish tools for determining whether a given solution concept can be efficiently learned from data. Our learning theoretic framework generalizes a notion of function space dimension --- the graph dimension --- adapting it to the solution concept learning domain. We identify sufficient conditions for efficient solution learnability, and show that results in existing works can be immediately derived using our methodology. Finally, we apply our methods in other economic domains, yielding learning variants of competitive equilibria and Condorcet winners.
Préparation de diglycérol et triglycérol par polymérisation directe du glycérol en présence de catalyseurs mésoporeux basiques

La reaction d’etherification directe du glycerol en di- ou triglycerol est une voie interessante de transformation du glycerol. En effet, les polyglycerols mais aussi les esters de polyglycerols trouvent de nombreuses applications comme tensioactifs dans l’industrie chimique. Toutefois les procedes industriels ne conduisent qu’a des melanges de polyglycerols en utilisant des catalyseurs homogenes tres polluants. Ainsi l’objectif de ce travail est la synthese directe d’une coupe de polyglycerols de faible degre de polycondensation (di + triglycerol) a partir de glycerol en presence de solides mesoporeux basiques et en absence de solvant. La partie essentielle de cette etude a consiste en la synthese et la modification de solides mesoporeux afin de les rendre actifs, selectifs et stables dans la reaction d’etherification du glycerol. Ces modifications se font soit par incorporation soit par impregnation de differents elements alcalins ou alcalino-terreux. Les resultats catalytiques obtenus ont montre que les catalyseurs impregnes par du cesium sont les plus actifs pour la preparation de polyglycerols mais qu’ils permettent surtout l’obtention d’une coupe (di + tri) avec une selectivite proche de 100%.
The Selection of Pollen and Seed Dispersal in Plants

Because plants are mostly sessile, dispersal of pollen and seeds during reproduction determines changes in gene frequencies within populations and plant distributions on a broader scale. Selection favours a mixture of local and distant dispersal and for nearly all plants this is achieved with a single type of dispersal structure because dispersal is relatively ineffective on average whatever the dispersal mode; this ineffectiveness probably accounts for the rarity of dimorphic dispersal structures. Empirical studies show that most dispersal is local, but far-dispersal events, because of their potential to increase fitness though colonisation of unoccupied habitat patches, are probably what largely determines the selection of dispersal structures. The effectiveness of dispersal can be described in terms of distance, and the ability to reach favourable habitats. Effectiveness in achieving distance, a result of the interaction of the dispersal mode and agent with the environment, results in the typical leptokurtotic dispersal curve. There is no evidence that the shape of the dispersal curve itself is selected.
Some Luteovirus Diseases in Tasmania Caused by Beet Western Yellows and Subterranean Clover Red Leaf Viruses

A range of crop plants, pasture legumes and weeds, mostly with yellows symptoms similar to those caused by luteoviruses, were collected from the field around Tasmania and checked for infection with beet western yellows virus (BWYV) and subterranean clover red leaf virus (SCRLV) using aphids and indicator plants. BWYV was recovered from 216 of 897 plants tested, representing 30 different species and including 16 not previously recorded as natural hosts. SCRLV was recovered from 163 of 637 plants, representing twelve species including four not previously recorded as natural hosts. BWYV was isolated most often from composites and crucifers, while SCRLV was recovered most frequently from legumes. Eight plants were found infected with both viruses together. In host range studies, Tasmanian isolates of BWYV caused symptoms in lettuce, subterranean clover and sugar beet like those seen on these plants in the field from which the virus was isolated, and were thus similar to isolates of BWYV from North America. The Tasmanian isolates of BWYV were also closely related serologically to Californian isolates of BWYV; these were serologically quite distinct from isolates of SCRLV, and both these groups were serologically distinct from legume yellows virus and from potato leaf roll virus.
Impact of waste discharges on the water quality and interstitial community structure of two Mediterranean rivers

The hydrochemical and biological status of two Mediterranean rivers, one in Morocco near Marrakech (Rheraya) and the other in Italy near Florence (Mugnone), is compared by means of the evolution of several physico‐chemical and biotic factors at nine sampling stations in the Mugnone and five in the Rheraya streams. There is a general downstream increase of water mineralization and organic matter content. However, this chemical and biological continuum is disturbed in the middle section of the rivers by the discharge of treated wastewater in the case of the Mugnone and raw wastewater in the case of the Rheraya. Chemical analyses show a clear increase in the values of parameters related to organic loading below the discharge sites. In this sector, the abundance and richness of stygobitic species, mainly Crustacea Peracarida, are very low. Thus the impact of these wastewater discharges on the interstitial milieu of these two streams is reflected in modifications of both the chemical quality of the water and the biotic community structure.