Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Min-consensus for Second-order Nonlinear System with Disturbances

This paper investigates the distributed min-consensus for second-order nonlinear multi-agent systems with external disturbances. A discontinuous integral sliding mode (ISM) protocol combined with finite-time stability theory is applied and a novel min-consensus algorithm is proposed in this paper. To eliminate the chattering phenomenon due to the discontinuous control, a continuous ISM consensus protocol is proposed. It is shown that output of the agents reach a common min-consensus of their initial states. The validity of the min-consensus algorithm is illustrated by a numerical example.
A New Approach For Microcalcification Enhancement In Digital Breast Tomosynthesis Reconstruction

We propose a novel approach aiming to improve the detectability of microcalcifications in Digital Breast Tomosynthesis (DBT) volumes. Hence, our contribution is twofold. First, we formulate the clinical task through a detectability function based on an approach inspired from mathematical model observers. Second, we integrate this new developed clinical-task term in a cost function which is minimized for 3D reconstruction of DBT volumes. Experimental results carried out on both phantom and real clinical data show that the proposed clinical term allows the visibility of microcalcifications to be significantly improved, while preserving an overall high quality of the fully reconstructed volume.
Work in Progress: Systematic Derivation of Accurate Analytic Markov Channel Models for Industrial Control

The motivation of this work is to systematically derive an accurate Markov channel model suitable to represent any industrial communication protocol. This model accounts for both channel and control dynamics. Our analytic model is derived by taking into account multiple interferers and physical phenomena characterizing a communication link. Our finite-state Markov channel model matches both average packet error probability and worst case bursty behavior of the accurate analytic model of the channel.
An Approach of Load Balancers for Low-cost CPPSs in Software-defined Networking Architecture

Cyber-Physical Production Systems (CPPS) and Industrial Internet of Things (IIoT) will improve the flexibility of the future production systems in the newest smart factories. In advanced manufacturing industries, a large number of machines, goods, control systems, and information systems will be interconnected. For this reason, it is necessary to implement load balancing technologies that improve responsiveness and increase the availability of applications, particularly when low-cost devices are used. This paper presents a novel architecture approach for load balancing using Software-Defined Networking (SDN). The proposal is based on IEC-61499 standard over OPC UA in industrial communications, and it is aimed at optimizing resources, maximizing throughput, minimizing response time, and avoiding overload in low-cost devices.
Nanoporous Li4Ti5O12 Material for the Electrode of Lithium Ion Battery

ABSTRACT Nanoporous Li4Ti5O12 material has been synthesized by the sol–gel method using carbon nanobeads (CNBs) incorporated at precursor level. CNBs were prepared by chemical vapour deposition (CVD) process using acetylene gas as the carbon source. XRD analysis of the material shows that there is no change in the crystalline phase due to addition of CNBs. The average pore size in the synthesized material lies in the range of 60–140 nm. Brunauer–Emmett–Teller (BET) analysis shows that there is more than three-fold increase in surface area of the porous material as compared to the material without porosity. Electrochemical analysis has been done to examine the performance of nanoporous Li4Ti5O12 for the application in lithium ion battery.
Probabilistic k m -anonymity

—Set-valued dataset contains different types of items/values per individual, for example, visited locations, purchased goods, watched movies, or search queries. As it is relatively easy to re-identify individuals in such datasets, their release poses signiﬁcant privacy threats. Hence, organizations aiming to share such datasets must adhere to personal data regulations. In order to get rid of these regulations and also to beneﬁt from sharing, these datasets should be anonymized before their release. In this paper, we revisit the problem of anonymizing set-valued data. We argue that anonymization techniques targeting traditional k m -anonymity model, which limits the adversarial background knowledge to at most m items per individual, are impractical for large real-world datasets. Hence, we propose a probabilistic relaxation of k m -anonymity and present an anonymization technique to achieve it. This relaxation also improves the utility of the anonymized data. We also demonstrate the effectiveness of our scalable anonymization technique on a real-world location dataset consisting of more than 4 million subscribers of a large European telecom operator. We believe that our technique can be very appealing for practitioners willing to share such large datasets.
Probability of error for the fixed-length lossy coding of general sources

We are interested in the error exponent of the fixed-length lossy source coding, where the sources are general sources, in the sense of Han and Verdd, including all nonstationary and/or nonergodic sources. The aim of the paper is to establish a unified formula for the minimum (0, r)-achievable rate which is the minimum achievable coding rate under asymptotic constraints of the form cn(D) e--nr (n + CO), where r is the prescribed error exponent, E % ( D ) is the probability of the distortion exceeding a lebel D , and n is the code-length. For the stationary memoryless source with finite alphabet, Marton (1974) obtained a formula for the reliability function which is the inverse function of the minimum achievable rate. Recently, Ihara and Kubo proved that the Marton's formula remains true for the stationary memoryless Gaussian source under mean-squared fidelity criterion. In the paper, it is shown tha t a formula similar t o the Marton's formula holds for the general sources. The error exponent of correct decoding is also investigated and a formula for the minimum achievable rate of correct decoding in the lossy coding is established.
Robust joint audio-video localization in video conferencing using reliability information

This paper proposes a new method for performing joint audio-video localization that explores the reliability of the individual localization estimates. The reliability information is estimated from the audio and video data separately. The proposed method uses this reliability information in conjunction with a simple summing voter to dynamically discriminate erroneous outputs from the audio and video localizers while performing fusion on the localization results. Based on the voter output, a majority rule is then used to make the final decision of the active talker's current location. The results show that adding the reliability information during fusion improves localization performance when compared with audio only, video only and joint audio-video using straight summing fusion localization methods. The computational complexity of the proposed method is comparable to the existing ones.
An 1 GHz class E LDMOS power amplifier

A class E power amplifier working at 1 GHz and with an LDMOS transistor as switching element has been developed. The circuit is implemented with lumped and distributed elements. An output power of 6.2 W at 69% drain efficiency with a gain of 11 dB was obtained at 1 GHz. Both simulations and measurements of the amplifier are presented within this paper. This is to our knowledge the highest efficiency and output power reported for a class E amplifier at 1 GHz.
Ultra wide band 24 GHz automotive radar front-end

The recent approval granted by the FCC for the use of Ultra Wide Band (UWB) signals for vehicular radar applications has provided a gateway for production of these sensors as early as in 2004. However, the rules governing the allowable spectral occupancy create significant constraints on the sensors operation. The implications for waveform design and the consequent limitation on system architecture, including antenna design and receiver architecture are discussed. Other practical considerations such as available semiconductor technology with low-cost plastic packaging are reviewed. This is developed into a methodology for developing a single board sensor with integrated antenna. Results are presented for a specification compliant antenna, and a low-cost plastic package for 24 GHz ICs. Finally, the required IC architecture for a transceiver is presented, along with measured results of a single-chip homodyne I/Q down-conversion receiver fabricated in SiGe.
Ferroelectric- and liquid crystal- tunable microwave phase shifters

This paper introduces two different low-cost, voltage-controlled tunable phase shifter devices based on (1) coplanar waveguide on a ferroelectric thick film ceramics and Al/sub 2/O/sub 3/-microwave substrate and (2) inverted-microstrip line with standard and novel liquid crystal (LC) in-between. For the first type, the differential phase shift can be controlled continuously and exceeds 360/spl deg/ at 38 GHz for a maximum dc voltage of 100 V, corresponding to an electrical field strength of 3.3 V//spl mu/m. The best phase shifter figure-of-merit values, i.e. the quotient of differential phase shift and insertion loss, amounts to 15 to 28/spl deg//dB at 24 GHz for a maximum field strength of 10 V//spl mu/m. This can be increased up to 50/spl deg//dB at 24 GHz for very high field strengths above 20 V//spl mu/m. In contrast, with comparatively low control voltage of 40 V (0.18 V//spl mu/m), the figure-of-merit of the LC type phase shifter amounts to 16/spl deg//dB at 20 GHz for a standard nematic LC K15 and 68/spl deg//dB for an advanced LC mixture.
A seamless coordinator switching (SCS) scheme for wireless personal area network (WPAN)

In the emerging wireless personal area networks (WPAN), one of the active devices in a piconet is designated as a coordinator, which plays critical roles, e.g., to provide the basic timing for the piconet and manage the QoS requirements/power saving modes. To warrant maintenance of the coordinating function under some unpredictable situations such as sudden power-off or fast movement, a coordinator must be seamlessly switched. This paper proposes a new SCS (seamless coordinator switching) scheme to maintain the piconet by selecting a coordinator in the subsequent superframe without incurring the re-initialization overhead.
Land use mapping and monitoring in the Netherlands using remote sensing data

A database with land use change in The Netherlands was created using visual interpretation of Landsat TM imagery. The real land use changes were separated from other changes in the database by storing the real changes in a separate binary mask with code "1" representing "change" and code "0" representing "no-change". The database was validated using a stratified random sample of 394 points. The change database has a producer's accuracy of 0.759. However, the current methodologies for validating a change database are not perfect and further research is necessary for proper validation.
All-optical quantum sensing of rotational Brownian motion of magnetic molecules.

Sensing local environment through the motional response of small molecules lays the foundation of many fundamental technologies. The information of local viscosity, for example, is contained in the random rotational Brownian motions of molecules. However, detection of the motions is challenging for molecules with sub-nanometer scale or high motional rates. Here we propose and experimentally demonstrate a novel method of detecting fast rotational Brownian motions of small magnetic molecules. With electronic spins as sensors, we are able to detect changes in motional rates, which yield different noise spectra and therefore different relaxation signals of the sensors. As a proof-of-principle demonstration, we experimentally implemented this method to detect the motions of gadolinium (Gd) complex molecules with nitrogen-vacancy (NV) centers in nanodiamonds. With all-optical measurements of the NV centers' longitudinal relaxation, we distinguished binary solutions with varying viscosities. Our method paves a new way for detecting fast motions of sub-nanometer sized magnetic molecules with better spatial resolution than conventional optical methods. It also provides a new tool in designing better contrast agents in magnetic resonance imaging.
Fitting prediction rule ensembles to psychological research data: An introduction and tutorial.

Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive performance and interpretability. Starting from a decision tree ensemble, like a boosted tree ensemble or a random forest, PREs retain a small subset of tree nodes in the final predictive model. These nodes can be written as simple rules of the form if [condition] then [prediction]. As a result, PREs are often much less complex than full decision tree ensembles, while they have been found to provide similar predictive performance in many situations. The current article introduces the methodology and shows how PREs can be fitted using the R package pre through several real-data examples from psychological research. The examples also illustrate a number of features of package pre that may be particularly useful for applications in psychology: support for categorical, multivariate and count responses, application of (non)negativity constraints, inclusion of confirmatory rules and standardized variable importance measures. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
Convergence Rate of 풪(1/k) for Optimistic Gradient and Extragradient Methods in Smooth Convex-Concave Saddle Point Problems

We study the iteration complexity of the optimistic gradient descent-ascent (OGDA) method and the extra-gradient (EG) method for finding a saddle point of a convex-convex unconstrained min-max problem. To do so, we first show that both OGDA and EG can be interpreted as approximate variants of the proximal point method. We then exploit this interpretation to show that both algorithms produce iterates that remain within a bounded set. We further show that the function value of the averaged iterates generated by both of these algorithms converge with a rate of $\mathcal{O}(1/k)$ to the function value at the saddle point. Our theoretical analysis is of interest as it provides a simple convergence analysis for the EG algorithm in terms of function value without using compactness assumption. Moreover, it provides the first convergence rate estimate for OGDA in the general convex-concave setting.
Spam Classification Using Machine Learning Techniques - Sinespam

Most e-mail readers spend a non-trivial amount of time regularly deleting junk e-mail (spam) messages, even as an expanding volume of such e-mail occupies server storage space and consumes network bandwidth. An ongoing challenge, therefore, rests within the development and refinement of automatic classifiers that can distinguish legitimate e-mail from spam. Some published studies have examined spam detectors using Naive Bayesian approaches and large feature sets of binary attributes that determine the existence of common keywords in spam, and many commercial applications also use Naive Bayesian techniques. Spammers recognize these attempts to prevent their messages and have developed tactics to circumvent these filters, but these evasive tactics are themselves patterns that human readers can often identify quickly. This work had the objectives of developing an alternative approach using a neural network (NN) classifier brained on a corpus of e-mail messages from several users. The features selection used in this work is one of the major improvements, because the feature set uses descriptive characteristics of words and messages similar to those that a human reader would use to identify spam, and the model to select the best feature set, was based on forward feature selection. Another objective in this work was to improve the spam detection near 95% of accuracy using Artificial Neural Networks; actually nobody has reached more than 89% of accuracy using ANN.
Terahertz Band: The Last Piece of RF Spectrum Puzzle for Communication Systems

Ultra-high bandwidth, negligible latency and seamless communication are envisioned as milestones that will revolutionize the way by which societies create, distribute and consume information. The remarkable expansion of wireless data traffic has advocated the investigation of suitable regimes in the radio spectrum to satisfy users’ escalating requirements and allow the exploitation of massive capacity and massive connectivity. To this end, the Terahertz (THz) frequency band (0.1-10 THz) has received noticeable attention in the research community as an ideal choice for scenarios involving high-speed transmission. As such, in this work, we present an up-to-date review paper to analyze key concepts associated with the THz system architecture. THz generation methods are first addressed by highlighting the recent progress in the devices technology. Moreover, the recently proposed channel models available for propagation at THz band frequencies are introduced. A comprehensive comparison is then presented between the THz wireless communication and its other contenders. In addition, several applications of THz communication are discussed taking into account various scales. Further, we highlight the milestones achieved regarding THz standardization activities. Finally, a future outlook is provided by presenting and envisaging several potential use cases and attempts to guide the deployment of the THz frequency band.
Enhancing Spectral Utilization by Maximizing the Reuse in LTE Network

Need for increased spectral efficiency is key to improve the quality of experience for next-generation wireless applications like online gaming, HD Video, etc.,. In our work, we consider an LTE Device-to-device (D2D) network where LTE UEs have primary access to the spectrum and D2D pairs have secondary access. To enhance spectral efficiency, BS can offload the traffic by activating multiple D2D pairs within the serving cell. This ensures that the same radio resource will be reused across the primary LTE UEs and different D2D pairs. In this context, we propose to enable more D2D secondary users in the serving cell, by utilizing neighboring BS spectrum to fairly co-exist with neighboring LTE primary users. We model the system and show via extensive simulations, that the above configuration guarantees good throughput for the D2D pairs in the serving cell while ensuring that the primary LTE throughput demand is not compromised.
The Keys to Decidable HyperLTL Satisfiability: Small Models or Very Simple Formulas

HyperLTL, the extension of Linear Temporal Logic by trace quantifiers, is a uniform framework for expressing information flow policies by relating multiple traces of a security-critical system. HyperLTL has been successfully applied to express fundamental security policies like noninterference and observational determinism, but has also found applications beyond security, e.g., distributed protocols and coding theory. However, HyperLTL satisfiability is undecidable as soon as there are existential quantifiers in the scope of a universal one. To overcome this severe limitation to applicability, we investigate here restricted variants of the satisfiability problem to pinpoint the decidability border. First, we restrict the space of admissible models and show decidability when restricting the search space to models of bounded size or to finitely representable ones. Second, we consider formulas with restricted nesting of temporal operators and show that nesting depth one yields decidability for a slightly larger class of quantifier prefixes. We provide tight complexity bounds in almost all cases.
Multichannel Loss Function for Supervised Speech Source Separation by Mask-based Beamforming

In this paper, we propose two mask-based beamforming methods using a deep neural network (DNN) trained by multichannel loss functions. Beamforming technique using time-frequency (TF)-masks estimated by a DNN have been applied to many applications where TF-masks are used for estimating spatial covariance matrices. To train a DNN for mask-based beamforming, loss functions designed for monaural speech enhancement/separation have been employed. Although such a training criterion is simple, it does not directly correspond to the performance of mask-based beamforming. To overcome this problem, we use multichannel loss functions which evaluate the estimated spatial covariance matrices based on the multichannel Itakura--Saito divergence. DNNs trained by the multichannel loss functions can be applied to construct several beamformers. Experimental results confirmed their effectiveness and robustness to microphone configurations.
Three Algorithms for Solving High-Dimensional Fully Coupled FBSDEs Through Deep Learning

Recently, the deep learning method has been used for solving forward–backward stochastic differential equations (FBSDEs) and parabolic partial differential equations, as it has good accuracy and performance for high-dimensional problems. In this article, we mainly solve fully coupled FBSDEs through deep learning and provide three algorithms, and the numerical results show remarkable performance, especially for high-dimensional cases.
Approximately optimal subset selection for statistical design and modelling

We study the problem of optimal subset selection from a set of correlated random variables. In particular, we consider the associated combinatorial optimization problem of maximizing the determinant of a symmetric positive definite matrix that characterizes the chosen subset. This problem arises in many domains, such as experimental designs, regression modelling, and environmental statistics. However, finding the exact solution to this optimization problem is shown to be computationally intractable for large datasets. In this paper, we establish an efficient polynomial-time algorithm using the determinantal point process to approximate the optimal solution to the problem. We demonstrate the advantages of our methods by presenting examples from D-optimal designs of experiemnts for regression modelling and maximum entropy based designs for spatial monitoring networks.
Pure infiniteness and paradoxicality for graph $C^*$-algebras

We obtain necessary and sufficient conditions for pure infiniteness of the path groupoid $C^*$-algebra of a row-finite graph without sinks. In particular we show that for such a path groupoid $\mathcal{G}_E$, the properties of being essential principal and the existence of a basis of $(\mathcal{G}_E^a, 2, 1)$-paradoxical sets for the topology are not only sufficient, but also necessary.
A Distance-Heuristic Tree Building Approach in Application Layer Multicast

In the application layer multicast (ALM), clustering nearby nodes can effectively improve the multicast performance. However, it is difficult for the ALM solution to quickly and accurately position the newcomer, because group members have no direct knowledge of underlying network topology. Additionally, ALM delivery trees with different performances are built when group members join the group in different join sequences. To alleviate the above problems, this paper proposes a distance-heuristic tree building protocol (called DHTB). DHTB uses our proposed distance-constrained cluster model and close-member-first-receive (CF) rule. In the model, most nearby nodes are grouped into some distance-constrained clusters, with little cluster organization and maintenance overhead. The CF rule arranges or rearranges the locations of group members according to related distances, and effectively positions the newcomer with the help of on-demand landmarks. Both the distance-constrained cluster model and CF rule are distance-heuristic. Therefore DHTB can alleviate the join sequence problem, and build the ALM tree with desirable performance.