Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust three degrees of freedom based on
 H
 ∞ controller of voltage/current loops for DG unit in micro grids

This study proposes a cascaded voltage-current robust control strategy for a distributed generator (DG) in microgrids. The main objective of the proposed controller is to improve the performance and power quality of a DG by injecting simultaneously a good sinusoidal voltage/current to the different loads connected to the DG. The proposed cascaded voltage/current controller consists of three degree-of-freedom controller, which is designed by using H ∞ control theory based on mixed sensitivity specifications. The choices of appropriate weighting functions W t , W u , W y that satisfy the preset goals to get a robust controller and a transform to a standard H ∞ control design problem based on pre-compensator, feedforward and feedback connections are presented. The 3DOF-controller is based on MATLAB R /SimPowerSystems and using RT-EVENTS-toolbox. The controller is evaluated under different scenarios: transient responses with a resistive local load, and loads disturbances in steady-state responses with resistive, resistive inductive, resistive capacitive, and non-linear loads. Simulation and experimental results of resulting waveforms from a DG unit are presented, it confirms the effectiveness of the proposed method in effectively rejecting perturbation and robustness to loads disturbances; both good reference tracking and good transient response, and finally significantly lead to a very low total harmonic distortion.
Low-overhead constant envelope precoding in multi-cell massive MIMO systems with pilot contamination

The authors consider downlink transmission in a multi-cell massive multiple-input and multiple-output (MIMO) system and focus on decreasing the feedback overhead caused by cell cooperation in constant envelope precoding (CEP). In the literature, the single-cell case for CEP with perfect channel state information (CSI) has been studied. Here, considering full cooperation among the cells, they develop CEP for the multi-cell case. They devise a low overhead centralised construction for CEP which employs limited cooperation among the cells, providing higher system throughput. To achieve the minimum feedback overhead, a distributed realisation of CEP is proposed where each cell locally performs CEP. Furthermore, a new optimisation problem is solved to compensate for the effects of pilot contamination. In addition, to reduce the computational complexity, a relaxed iterative form is presented for the limited cooperation and the distributed scenarios. Numerical results show that in the proposed structures, despite a tremendous decrease in the system overhead, the performance confronts merely an insignificant degradation, <10%, compared to the full-cooperation case. Moreover, in the presence of imperfect CSI, the performance of the distributed structure whose imperfect CSI is compensated for, approaches the performance of this structure in the perfect CSI scenario.
The Inﬂuence of Familiarity on Affective Responses to Natural Scenes – A Study of Kansei in Japanese Advertising –

: This kansei study explored how familiarity with image-word combinations inﬂuences affective states. Stimuli were obtained from Japanese print advertisements (ads), and consisted of images (e.g., natural-scene backgrounds) and their corresponding headlines (advertising copy). Initially, a group of subjects evaluated their level of familiarity with images and headlines independently, and stimuli were ﬁltered based on the results. In the main experiment, a different group of subjects rated their pleasure and arousal to, and familiarity with, image-headline combinations. The Self-Assessment Manikin (SAM) scale was used to evaluate pleasure and arousal, and a bipolar scale was used to evaluate familiarity. The results showed a high correlation between familiarity and pleasure, but low correlation between familiarity and arousal. The characteristics of the stimuli, and their effect on the variables of pleasure, arousal and familiarity, were explored through ANOVA. It is suggested that, in the case of natural-scene ads, familiarity with image-headline combinations may increase the pleasure response to the ads, and that certain components in the images (e.g., water) may increase arousal levels.
Cosmic Mass and the Electromagnetic and Strong Interactions

It seems that- quanta of the angular momentum and the strong interaction range - both are connected with the large scale structure of the universe. In the expanding universe `quanta' increases with increasing mass of the universe. By any chance if the noticed empirical relation is found to be true and valid, `rate of decrease in fine structure ratio' is a measure of cosmic rate of expansion. Considering the integral nature of number of protons (of any nucleus), integral nature of `hbar' can be understood.
Sans Tracas: A Cross-platform Tool for Online EEG Experiments.

Driven by a desire to democratize electroencephalography (EEG) research, we created Sans Tracas - a cross-platform web application for running EEG experiments online. A collaborative effort between cognitive neuroscientists and HCI researchers, the platform is designed via a multidisciplinary lens to be easy to use by researchers and study participants alike. For researchers, the platform allows users to augment any behavioural study deployed on online platforms with EEG data recordings from the commercially-available InteraXon Muse EEG device. For end-users who have access to the Muse, the platform is focused on having them perform entire EEG studies on their own from the comfort of their homes. We conducted a pilot study to test the usability of our developed platform. The results suggest that participants found the platform easy to use, useful, and had fun participating in EEG experiments independently and are now more interested in EEG and Brain Computer Interface (BCI) research than before. We contribute to HCI by presenting the design, development, and preliminary evaluation of a cross-platform application that allows users to conduct EEG experiments online using low-cost, commercially-available devices. This platform is a first step towards enabling greater access to research involving electroencephalography around the world.
Introducing the Intranet of Things for Education

This demonstration presents an approach to give students the ability to create an “Intranet of Things” to record, analyse and share environmental data using a transparent cloudlet. The Internet of Things (IoT) is an important subject for students to explore. Many commercial sensors are IoT devices, but these can be unusable in schools due to practical reasons of controlled access to whitelisted web services as well as ethical aspects of sharing children's data with third parties. We will empower children and teachers how to code and setup devices outside the school network, that can sense and record temperature, light and movement data inside the school buildings. The system allows data from the whole school to be easily shared, collated and analysed without using the Internet, but while still educating students about IoT and cloud concepts. We will demonstrate this “Intranet of Things” with live data collected from the conference venue.
Multiple controller deployment scheme based on modified cuckoo search algorithm

Software defined networks (SDN) is a novel network architecture, which decouples the control layer and data layer of the original network architecture. This network architecture utilizes controller to control data transmission. With the expansion of network scale, a single controller is unable to manage the whole network, while multiple controllers need to be deployed. Consequently, how to deploy multiple controllers appropriately is a hot topic in SDN. Furthermore, many intelligent algorithms have been applied to solving this problem. In this paper, we employ Cuckoo Search Algorithm (CSA) to improve the ability of multi-controller deployment. In order to improve the local search ability of the algorithm, the standard CS algorithm is improved in this paper. Firstly, weight coefficient is introduced to adjust the convergence speed of the algorithm. Secondly, all individuals sort according to their function values, after sorting the population can be divided into two parts. The experimental results demonstrate that the modified CSA performs better than other algorithms in multi-controller deployment.
COVID-normal workplaces: Should working from home be a ‘collective flexibility’?

Working from home expanded rapidly during the COVID-19 pandemic. This Controversy examines how working from home was framed and regulated pre-pandemic. We contrast this with the changes made to Australia's industrial award system during the pandemic to increase flexibilities around working from home, in response to a collective need for a responsive safety net. We argue that the conception and regulation of working from home is shifting from an individual flexibility, to a ‘collective flexibility’ available to a wide array of workers, collectively negotiated and governed by increased regulation. While industrial instruments were varied to accommodate public health requirements and the need of organisations and employees to work from home, these shifts were temporary. We argue that working from home provisions should rightly be regulated as a collective entitlement. We therefore consider possible ways forward in regulating this form of working, drawing from international developments. We raise these issues to extend debates around how working from home can be regulated to benefit employers and employees as we move towards COVID-normal.
Extraction and analysis of natural disaster-related VGI from social media: review, opportunities and challenges

Abstract The idea of ‘citizen as sensors’ has gradually become a reality over the past decade. Today, Volunteered Geographic Information (VGI) from citizens is highly involved in acquiring information on natural disasters. In particular, the rapid development of deep learning techniques in computer vision and natural language processing in recent years has allowed more information related to natural disasters to be extracted from social media, such as the severity of building damage and flood water levels. Meanwhile, many recent studies have integrated information extracted from social media with that from other sources, such as remote sensing and sensor networks, to provide comprehensive and detailed information on natural disasters. Therefore, it is of great significance to review the existing work, given the rapid development of this field. In this review, we summarized eight common tasks and their solutions in social media content analysis for natural disasters. We also grouped and analyzed studies that make further use of this extracted information, either standalone or in combination with other sources. Based on the review, we identified and discussed challenges and opportunities.
NOVA, a Noncommutative-ring Based Unbalanced Oil and Vinegar Signature Scheme with Key-randomness Alignment

In this paper, we propose a noncommutative-ring based unbalanced oil and vinegar signature scheme with key-randomness alignment: NOVA (Noncommu-tative Oil and Vinegar with Alignment). Instead of fields or even commutative rings, we show that noncommutative rings can be used for algebraic cryptosystems. At the same or better level of security requirement, NOVA has a much smaller public key than UOV (Unbalanced Oil and Vinegar), which makes NOVA practical in most situations. We use Magma to actually implement and give a detailed security analysis against known major attacks. 1
Breaking and Fixing Speculative Load Hardening

In this paper we revisit the Spectre v1 vulnerability and software-only countermeasures. Speciﬁcally, we systematically investigate the performance penalty and security properties of multiple variants of speculative load hardening (SLH). As part of this investigation we implement the “strong SLH” variant by Patrignani and Guarnieri (CCS 2021) as a compiler extension to LLVM. We show that none of the existing variants, including strong SLH, is able to protect against all Spectre v1 attacks in practice. We do this by demonstrating, for the ﬁrst time, that variable-time arithmetic instructions leak secret information even if they are executed only speculatively. We extend strong SLH to include protections also against this kind of leakage, implement the resulting full protection in LLVM, and use the SPEC2017 benchmarks to compare its performance to the existing variants of SLH and to code that uses fencing instructions to completely prevent speculative execution. We show that our proposed countermeasure is able to offer full protection against Spectre v1 attacks at much better performance than code using fences. In fact, for several benchmarks our approach is more than twice as fast.
Snowball: Another View on Side-Channel Key Recovery Tools

The performance of Side-Channel Attacks (SCAs) decays rapidly when considering more sub-keys, making the full-key recovery a very challenging problem. Limited to independent collision information utilization, collision attacks establish the relationship among sub-keys but do not signiﬁcantly slow down this trend. To solve it, we ﬁrst exploit the samples from the previously attacked S-boxes to assist attacks on the targeted S-box under an assumption that similar leakage occurs in program loop or code reuse scenarios. The later considered S-boxes are easier to be recovered since more samples participate in this assist attack, which results in the “snowball” eﬀect. We name this scheme as Snowball, which signiﬁcantly slows down the attenuation rate of attack performance. We further introduce confusion coeﬃcient into the collision attack to construct collision confusion coeﬃcient, and deduce its relationship with correlation coeﬃcient. Based on this relationship, we give two optimizations on our Snowball exploiting the “values” information and “rankings” information of collision correlation coeﬃcients named Least Deviation from Pearson correlation coeﬃcient (PLD) and Least Deviation from confusion coeﬃcient (CLD). Experiments show that the above optimizations signiﬁcantly improve the performance of our Snowball.
On optimal interconnections

Many applications require algorithms for determining optimal interconnections. This dissertation centers on new geometric formulations and approximation algorithms for optimizing interconnection objectives which are of particular interest in the design of high-performance VLSI systems. These formulations include Steiner trees, pathlength-balanced trees, bounded-radius trees, and prescribed-width paths; we also address the closely related question of efficiently testing physical interconnections. For most cases, we have new, best-known results, and in all cases we have empirically demonstrated significant improvements over the best previous methods. We give the best-performing rectilinear Steiner tree heuristic to date: the algorithm has worst-case performance ratio strictly less than 3/2 times optimal, settling a long-standing open problem. We also give a class of instances which are pathological for virtually all existing Steiner tree heuristics in the literature, thus disproving several conjectures and claimed performance bounds. We propose a matching-based method for pathlength-balanced trees: the construction yields near-zero average pathlength skew while maintaining small total tree cost. To address a separate objective, we also offer the first general formulation of performance-driven routing, allowing a smooth tradeoff of tree cost for tree radius. Our algorithm melds the two classical constructions of the minimum spanning tree and the shortest paths tree, and has worst-case performance bounded by a constant times optimal with respect to both tree cost and tree radius. Motivated by recent circuit testing applications, we formulate connectivity testing as a problem of tree verification via k-probes. We present linear-time algorithms which compute a minimum probe set achieving complete coverage of both edge and node fault classes. Actual testing demands the efficient scheduling of probe operations: we show that this entails a special type of metric traveling salesman optimization, and we give provably good heuristics. Finally, we address a fundamental problem in routing and path planning: determining a minimum-cost path of prescribed width which connects a given source-destination pair in an arbitrarily costed region. We give the first known polynomial-time algorithm for this problem, and extend our approach to solve a discrete version of the classical Plateau problem on minimal surfaces.
A generating tree approach to k-nonnesting arc diagrams

This thesis describes a strategy for exhaustively generating series information and enumerating combinatorial classes that can be represented using arc diagrams. We focus on k-nonnesting set partitions, permutations, matchings and tangled diagrams. Results are new functional equations, counting sequences, bijections and asymptotic results for these classes. Our key innovation is a generalized arc diagram in which arcs may have left endpoints, but not right endpoints, and our main tool is generating trees.
Solving Games and All That

Efficient best-first search algorithms have been developed for deterministic two-player games with two-outcome.We present a formal framework to represent such best-first search algorithms.The framework is general enough to express popular algorithms such as Proof Number Search, Monte Carlo Tree Search, and the Product Propagation algorithm.We then show how a similar framework can be devised for two more general settings: two-player games with multiple outcomes, and the model checking problem in modal logic K.This gives rise to new Proof Number and Monte Carlo inspired search algorithms for these settings.Similarly, the alpha-beta pruning technique is known to be very important in games with sequential actions.We propose an extension of this technique for stacked-matrix games, a generalization of zero-sum perfect information two-player games that allows simultaneous moves
An analysis of the dynamical magnetic susceptibility in non-Fermi liquids

In recent years there has been growing interest in the existence of strongly correlated, non-Fermi-liquid states with (incipient) phase transitions at zero temperature. A unifying model of the dynamical magnetic susceptibility, χ({q,ω}), in the non-Fermi-liquid state at a finite temperature above such a quantum critical point is presented which addresses the analysis of both inelastic neutron scattering and thermodynamic experiments on a wide range of materials. The functional forms and symmetries of χ({q,ω}) derived from these analyses may serve to guide further experimental and theoretical efforts in elucidating the non-Fermi-liquid state, in addition to furnishing the basis for semi-microscopical calculations of thermodynamic and transport properties.
Visual orientation behavior of gypsy moth larvae to emissive colors

Gypsy moth larvae Lymantria dispar (L.) are phytophagous feeders and destroy millions of acres of forests each year. In addition to olfactory and gustatory cues, it has been thought that visual cues also play an important role in aiding these larvae to navigate their walk. In the present study, we demonstrated that these larvae exhibit positive phototactic behavior when stimulated with different wavelengths of light produced by light emitting diodes in a dual‐choice arena adapted to a servosphere. We found that larvae preferred to orient towards green (570 nm), blue (472 nm), white (420–775 nm), orange (590 nm), red (660 nm), and yellow (585 nm) over darkness when both alternatives were offered. Further analyses of paired light‐, as well as walking performance, are still ongoing. This research may provide a basis for the development of more user‐friendly biocontrol measures using light traps to control for this pest.
Theorie des systemes lineaires dans les dioides

On etudie les systemes lineaires sur certaines structures de dioides (demi-anneaux dont l'addition est idempotente). On etudie tout d'abord les systemes d'equations lineaires dans certains dioides, pour lesquels on retrouve l'analogue de la theorie de cramer a condition de se placer dans un dioide symetrise. On adapte les algorithmes iteratifs usuels (jacobi, gauss-seidel). On obtient des resultats de dualite. On etend par ailleurs aux matrices reductibles la theorie spectrale (max,+). On etudie ensuite les systemes dynamiques lineaires dans ces algebres (representation par des operateurs a noyaux ou operateurs de convolution, role de la transformee de fenchel). On obtient a l'aide de resultats de symetrisation une borne inferieure pour la dimension minimale de realisation. L'on donne ensuite des algorithmes de calcul dans un dioide de series rationnelles propre a representer les graphes d'evenements temporises. On presente max, le programme de calcul formel ecrit en maple dans lequel on a implemente ces algorithmes (fonction de transfert, calcul du taux de production). On considere enfin l'optimisation des ressources. Ce probleme conduit a une enumeration algebrique de contraintes modulo certaines congruences. Une seconde approche formule ces contraintes en termes de sous-vecteur propre
Cratering in the Jovian System: Intersatellite Comparisons

Cratering on the outer three Galilean satellites has been studied from Galileo prime mission images. The data are at a variety of spatial scales and do not survey the smaller scales at more than a select number of locations. Nevertheless, it is possible to address some of the broad questions concerning the nature of the cratering and of the exogenic and endogenic processes that erase craters and yield the observable crater populations.
Influence of the Cardiomyocyte Circadian Clock on Autophagy in the Heart

In addition to its roles during physiologic (e.g., fasting) and pathologic (e.g., ischemia) states, autophagy is critical for cellular ‘housekeeping’, playing an important role in the normal turnover of damaged proteins and organelles (e.g., mitochondria). Over the course of the day, cells/organs are subjected to profound fluctuations in energy supply/demand, as well as exposure to environmental stresses known to induce protein/organelle dysfunction. This is particularly true for the heart, whose workload and oxidative metabolism dramatically increase during the awake (versus sleep) period. Recent studies have shown that the cardiomyocyte circadian clock directly modulates myocardial processes in a time‐of‐day‐dependent fashion, including metabolism and contractile function. The importance of the cardiomyocyte circadian clock is highlighted by observations that genetic disruption of this mechanism in mice (i.e., cardiomyocyte‐specific BMAL1 knockout; CBK) results in age‐onset cardiomyopathy and premature death.