Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Consensus Control for a Network of Incommensurate Fractional-Order Systems

This letter presents a distributed solution for consensus control of a network of single-integrator incommensurate fractional-order systems with nonlinear and uncertain dynamics. To consider a broader class of fractional-order systems, compared to existing results, the fractional derivative orders of agents’ dynamics are assumed non-identical, which makes the distributed control design more challenging. To cope with non-identical fractional derivative orders, the Mittag-Leffler function method is adopted to develop a novel distributed control scheme that guarantees consensus under mild assumptions. To deal with agents’ dynamic uncertainties, the proposed approach is integrated with adaptive neural networks to make the distributed tracking errors for all follower agents converge to a small neighborhood of origin. A numerical example is provided to demonstrate the effectiveness of the proposed method.
Unusual area-law violation in random inhomogeneous systems

The discovery of novel entanglement patterns in quantum many-body systems is a prominent research direction in contemporary physics. Here we provide the example of a spin chain with random and inhomogeneous couplings that in the ground state exhibits a very unusual area-law violation. In the clean limit, i.e. without disorder, the model is the rainbow chain and has volume law entanglement. We show that, in the presence of disorder, the entanglement entropy exhibits a power-law growth with the subsystem size, with an exponent 1/2. By employing the strong disorder renormalization group (SDRG) framework, we show that this exponent is related to the survival probability of certain random walks. The ground state of the model exhibits extended regions of short-range singlets (that we term ‘bubble’ regions) as well as rare long range singlet (‘rainbow’ regions). Crucially, while the probability of extended rainbow regions decays exponentially with their size, that of the bubble regions is power law. We provide strong numerical evidence for the correctness of SDRG results by exploiting the free-fermion solution of the model. Finally, we investigate the role of interactions by considering the random inhomogeneous XXZ spin chain. Within the SDRG framework and in the strong inhomogeneous limit, we show that the above area-law violation takes place only at the free-fermion point of phase diagram. This point divides two extended regions, which exhibit volume-law and area-law entanglement, respectively.
Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network

Most companies utilize demographic information to develop their strategy in a market. However, such information is not available to most retail companies. Several studies have been conducted to predict the demographic attributes of users from their transaction histories, but they have some limitations. First, they focused on parameter sharing to predict all attributes but capturing task-specific features is also important in multi-task learning. Second, they assumed that all transactions are equally important in predicting demographic attributes. However, some transactions are more useful than others for predicting a certain attribute. Furthermore, decision making process of models cannot be interpreted as they work in a black-box manner. To address the limitations, we propose an Embedding Transformation Network with Attention (ETNA) model which shares representations at the bottom of the model structure and transforms them to task-specific representations using a simple linear transformation method. In addition, we can obtain more informative transactions for predicting certain attributes using the attention mechanism. The experimental results show that our model outperforms the previous models on all tasks. In our qualitative analysis, we show the visualization of attention weights, which provides business managers with some useful insights.
Design of Wideband Endfire Antenna With H-shaped Resonator Structures

A wideband endfire antenna is presented in this paper. The gain enhancement is achieved by loading with an H-shaped resonator (HSR)structure in the endfire direction. Broad bandwidth is realized by using a microstrip-to-coplanar balun and bowtie dipole elements while gain enhancement is achieved by loading H-shaped resonator (HSR) structure in the endfire direction. The measurements show the HSRs-loaded antenna presents gain of about 4dB-8dB in the whole working band (4.5GHz-9.5 GHz), which is about 2 dB more than the unloaded one. The advantages of broad bandwidth and high gain make this antenna valuable in wireless communication systems.
Extraction of Periodicity for Quasi-Periodic Electromagnetic Surfaces Using Equivalence Principle Algorithm

Quasi-periodic electromagnetic surfaces have similar planar elements located on periodic lattices. Because the elements are different, the periodic boundary condition is not accurate for full wave modeling of quasi-periodic electromagnetic surfaces. In order to utilize the periodicity of elements to accelerate the full wave simulation, this work applies the idea of equivalence principle algorithm and projects the current of different elements onto periodic equivalence surfaces. The projection process can be written in matrix operators. For unknowns on the equivalence surfaces, the reduced basis method can be applied to reduced the number of unknowns. After extracting the periodicity or the translational invariance of the element alignment, the fast Fourier transform can be used to accelerate matrix vector production in iterative solvers for method of moments. It can reduce the computational complexity of matrix vector production from $O(N^{2})$ to $O(N\log\, N)$, where $N$ is the number of elements. Numerical examples show the algorithm can accelerate iterative solvers and has potential to solve large quasi-periodic electromagnetic surfaces.
TTR-Based Rewards for Reinforcement Learning with Implicit Model Priors

Model-free reinforcement learning (RL) is a powerful approach for learning control policies directly from high-dimensional state and observation. However, it tends to be data-inefficient, which is especially costly in robotic learning tasks. On the other hand, optimal control does not require data if the system model is known, but cannot scale to models with high-dimensional states and observations. To exploit benefits of both model-free RL and optimal control, we propose time-to-reach-based (TTR-based) reward shaping, an optimal control-inspired technique to alleviate data inefficiency while retaining advantages of model-free RL. This is achieved by summarizing key system model information using a TTR function to greatly speed up the RL process, as shown in our simulation results. The TTR function is defined as the minimum time required to move from any state to the goal under assumed system dynamics constraints. Since the TTR function is computationally intractable for systems with high-dimensional states, we compute it for approximate, lower-dimensional system models that still captures key dynamic behaviors. Our approach can be flexibly and easily incorporated into any model-free RL algorithm without altering the original algorithm structure, and is compatible with any other techniques that may facilitate the RL process. We evaluate our approach on two representative robotic learning tasks and three well-known model-free RL algorithms, and show significant improvements in data efficiency and performance.
Partial Packet Forwarding to Improve Performance in Fully Adaptive Routing for Cache-Coherent NoCs

In the contest of cache-coherent Networks-on-Chip (NoCs), fully adaptive routing algorithms guarantee maximum flexibility to implement power-performance, fault tolerant, thermal and Quality of Service (QoS) management policies. However, to get rid of deadlock at both protocol and network level, their implementation imposes a relevant resource increase. Moreover, their performance are inferior to the one of deterministic and partially adaptive schemes mainly due to the additional constraints imposed to the virtual channel (VC) re-use policy. This work proposes a novel flow control scheme to improve the performance of fully adaptive routing algorithms by allowing an aggressive reuse of VCs in presence of both long and short packets. Our proposal works by splitting long packets in multiple chunks and by reallocating the VCs to the chunks rather that to the entire packet. By carefully sizing each chunk to fit the available space in the reallocated, eventually not empty, VC, we are avoiding deadlocks while increasing the NoC utilization and performance. Experimental results show that our solution offers a 23.8% increase, on average, in the saturation point when compared to the best state of the art flow control scheme for fully adaptive routing algorithms. Moreover, our flow control scheme offers similar or better performance than the XY routing algorithm with the same number of resources, and we also ensure superior flexibility in the definition of the routing function.
Factors influencing Fruit and Vegetable Intake in Adolescents

Fruit and vegetable intake (FVI) is an important factor in the preservation of health and the prevention of disease. Many dietary habits established during adolescence continue into adulthood, such as FVI. According to the World Health Organization, the daily FVI of adolescents was below the recommended values worldwide, despite the long-term health benefits associated with FVI. In this chapter, we updated and expanded previous research about factors influencing FVI during adolescence. Due to inductive thematic analysis based on Social Ecological Theory and Social Cognitive Theory, we identified three key factors that influence FVI: (a) individual factors (e.g., gender, age, knowledge, self-efficacy, taste preference and liking of FV, outcome expectations/expectancies, skill in preparing fruit and vegetable); (b) social factors (e.g., parents intake and modeling, parents and family support, family meals, peers influence); and (c) environmental factors (e.g., income, parents occupational status, parents education, household availability, school availability, neighborhood, television viewing). Development strategies and effective intervention programs aimed to increase FVI and to promote adolescents‘ healthy dietary behaviors could be achieved by understanding the relationship between FVI and above factors.
MWPoW - Multi-Winner Proof of Work Consensus Protocol: An Immediate Block-Confirm Solution and an Incentive for Common Devices to Join Blockchain

In this article, we present designs for a Multi Winner Proof of Work (MWPoW) consensus primitive. MWPoW is a consensus protocol that places the group mining idea directly into a protocol to prevent power centralization, higher general reward expectation among participants and shorten the interval time of block generation. It makes every miner a component of one of three groups assigned by the network, not as an individual competitor; all users in the winner group receive compensation. The hash difficulty is higher than ordinary blockchain because there are only three parties in the system. MWPoW powered block-chain is more secure from tampering. The blocks can be quickly final accepted without later block confirms, which makes it possible for block interval time to be minuteness. The experiment suggests the hash rate of MWPoW is around 1500 times larger than Nakamoto blockchain while it only requires about 1 second to finally accept a block in the lab environment (with 3-second block interval). MWPoW is universal profitable (the mining remuneration is much more distributed than Nakamoto block-chain).
Millimeter Wave Microstrip Antenna Array for Automotive Collision Avoidance Radar

A 24GHz millimeter wave series-fed microstrip antenna array is presented in this paper. The simulation results of the proposed antenna show that the VSWR is lower than 1.5 and the gain is higher than 24dBi over the operating bandwidth from 24GHz to 24.25GHz. The power radio in both E-plane and H-plane follows the Chebyshev distribution for lower sidelobe level(SLL). The half power beamwidth(HPBW) is about 13° in the E-plane and 5.5° in the H-plane. And the SLL is lower than 16dB in the E-plane and 22dB in the H-plane, which makes it a good candidate for automotive collision avoidance radar.
Dietary nitrate intake is associated with muscle function in older women

In younger individuals, dietary nitrate supplementation has been shown to improve short‐term vascular and muscle function. The role of higher habitual nitrate intake as part of a typical diet on muscle function in ageing has not been investigated. A cross‐sectional study of relationships between dietary nitrate and measures of muscle function in older community‐dwelling Australian women (n = 1420, ≥70 years) was undertaken.
Fog-Assisted Caching Employing Solar Renewable Energy for Delivering Video on Demand Service

This paper examines the reduction in brown power consumption of transport networks and data centres achieved by caching Video-on-Demand (VoD) contents in solar-powered fog data centers with Energy Storage Devices (ESDs). A Mixed Integer Linear Programming (MILP) model was utilized to optimize the delivery from cloud or fog data centres. The results reveal that for brown-powered cloud and fog data centres with same Power Usage Effectiveness (PUE), a saving by up to 77% in transport network power consumption can be achieved by delivering VoD demands from fog data centres. With fully renewable-powered cloud data centres and partially solar-powered fog data centres, savings of up to 26% can be achieved when considering 250 m2 solar cells. Additional saving by up to 14% can be achieved with ESDs of 50 kWh capacity.
Core Access Hybridization in 5G

The fifth generation (5G) cellular communication system will have several advanced features, majority of which will come into existence for the first time. According to the ITU recommendations, 5G will provide minimum 3 GB/s end to end data rates under the static conditions and in the mobile condition the minimum data rate is 100 Mb/s. The latency will be brought down to 1 ms and the device densities will increase by many folds. These features cannot be provided in the pure wireless domain. Certainly, the support of the optical networks is essential to provide these advanced features in 5G communication. These features need special architectures. In this article, we provide advanced optical wireless hybrid architectures for 5G networks. We explain the importance of this architecture and also show the nodal configuration of these hybrid networks.
An Analytical Model for Performance and Lifetime Estimation of Hybrid DRAM-NVM Main Memories

Emerging Non-Volatile Memories (NVMs) have promising advantages (e.g., lower idle power, higher density, and non-volatility) over the existing predominant main memory technology, DRAM. Yet, NVMs also have disadvantages (e.g., longer latencies, higher active power, and limited endurance). System architects are therefore examining hybrid DRAM-NVM main memories to enable the advantages of NVMs while avoiding the disadvantages as much as possible. Unfortunately, the hybrid memory design space is very large and complex due to the existence of very different types of NVMs and their rapidly-changing characteristics. Therefore, optimization of performance and lifetime of hybrid memory based computing platforms and their experimental evaluation using traditional simulation methods can be very time-consuming and sometimes even impractical. As such, it is necessary to develop a fast and flexible analytical model to estimate the performance and lifetime of hybrid memories on various workloads. This paper presents an analytical model for hybrid memories based on Markov decision processes. The proposed model estimates the hit ratio and lifetime for various configurations of DRAM-NVM hybrid main memories. Our model also provides accurate estimation of the effect of data migration policies on the hybrid memory hit ratio (i.e., percentage of accesses supplied by either DRAM or NVM), one of the most important factors in hybrid memory performance and lifetime. Such an analytical model can aid designers to tune hybrid memory configurations to improve performance and/or lifetime. We present several optimizations that make our model more efficient while maintaining its accuracy. Our experimental evaluations conducted using the PARSEC benchmark suite show that the proposed model (a) accurately predicts the hybrid memory hit ratio compared to the state-of-the-art hybrid memory simulators with an average (maximum) error of 4.61 percent (13.6 percent) on a commodity server (equipped with 192 GB main memory and quad-core Xeon processor), (b) accurately estimates the NVM lifetime with an average (maximum) error of 2.93 percent (8.8 percent), and (c) is on average (up to) 4x (10x) faster than conventional state-of-the-art simulation platforms for hybrid memories.
Improved Path Planning by Tightly Combining Lattice-based Path Planning and Numerical Optimal Control

This paper presents a unified optimization-based path planning approach to efficiently compute locally optimal solutions to advanced path planning problems. The approach is motivated by first showing that a lattice-based path planner can be cast and analyzed as a bilevel optimization problem. This information is then used to tightly integrate a lattice-based path planner and numerical optimal control in a novel way. The lattice-based path planner is applied to the problem in a first step using a discretized search space, where system dynamics and objective function are chosen to coincide with those used in a second numerical optimal control step. As a consequence, the lattice planner provides the numerical optimal control step with a resolution optimal solution to the problem, which is highly suitable as a warm-start to the second step. This novel tight combination of a sampling-based path planner and numerical optimal control makes, in a structured way, benefit of the former method's ability to solve combinatorial parts of the problem and the latter method's ability to obtain locally optimal solutions not constrained to a discretized search space. Compared to previously presented combinations of sampling-based path planners and optimization, the proposed approach is shown in several path planning experiments to provide significant improvements in terms of computation time, numerical reliability, and objective function value.
Offloading Cryptographic Services to the SIM Card

Mobile OS security relies heavily on cryptography. Features like file or device encryption and secure keystores are essential to protect user data from intrusions. As smartphones increasingly gain popularity, mobile platforms keep evolving their hardware and software modules that work as cryptographic providers for the rest of the system. These solutions are usually designed to resist software attacks by introducing a Trusted Execution Environment (TEE). This kind of protection is not enough when physical control over the device is lost. Secure elements such as smart cards, on the other hand, include a set of protections that make them physically tamper resistant devices. In this paper we propose the use of the SIM card, the only universally present secure element in mobile phones, as a cryptographic provider and put forward a proof-of-concept prototype developed under Android OS. We also present the results of a performance evaluation that was conducted and study the impact on battery consumption, comparing our solution to the default implementation of an Android mobile phone. Despite some performance limitations our approach proves to be a valid alternative to provide enhanced security features on any smartphone.
A Generalized Meta-loss function for regression and classification using privileged information

Learning using privileged information (LUPI) is a powerful heterogenous feature space machine learning framework that allows a machine learning model to learn from highly informative or privileged features which are available during training only to generate test predictions using input space features which are available both during training and testing. LUPI can significantly improve prediction performance in a variety of machine learning problems. However, existing large margin and neural network implementations of learning using privileged information are mostly designed for classification tasks. In this work, we have proposed a simple yet effective formulation that allows us to perform regression using privileged information through a custom loss function. Apart from regression, our formulation allows general application of LUPI to classification and other related problems as well. We have verified the correctness, applicability and effectiveness of our method on regression and classification problems over different synthetic and real-world problems. To test the usefulness of the proposed model in real-world problems, we have evaluated our method on the problem of protein binding affinity prediction. The proposed LUPI regression-based model has shown to outperform the current state-of-the-art predictor.
Designing and implementing mathematical tasks: the T-shirt task

The research reported here offers insights from the collaboration between university researchers and in-service teachers. It focuses on the processes behind the design of a specific task (the T-shirt task) by researchers, and its implementation by a lower secondary school teacher. The T-shirt task was designed as a phone call where one person has to explain to another the design of a logo to be reproduced on a T-shirt. This research is situated within an ongoing project at the University of Agder called Teaching Better Mathematics. The project aims to promote better understanding and competency in mathematics for pupils in schools, and to explore better teaching approaches. Our collaboration with teachers is organised around workshops and school visits. Central to this research is the recognition of researchers and teachers as co-learners (Wagner 1997) and the importance of inquiry (Jaworski 2006). In our project, we used Activity Theory (Engeström 1999) as a means to characterise the participants’ development. Here, the researchers’ motive is to engage collaboratively with teachers in inquiry about teaching and learning of mathematics, in order to improve pupils’ achievement in mathematics, and their actions and operations consist of organising workshops and designing suitable tasks. The teachers’ motive is to create opportunities for pupils to engage with mathematics, and their actions and operations consist of preparing and developing their own teaching while choosing suitable tasks. In order to deepen the theoretical ideas of actions and operations, I propose to introduce the theoretical constructs of ‘didactical aim’ and ‘pedagogical means’ which I developed previously in my doctoral thesis (Berg 2009). Didactical aim relates to which mathematical goal the researchers/ teachers plan to address during the workshops/teaching. Pedagogical means refers to the process of choosing, transforming, and adapting a particular task for a specific social setting in order to address a chosen didactical aim. The rationale for focusing on the T-shirt task in my research is that two teachers, one from a primary school and the other from a lower secondary school, invited me to observe the way they implemented the T-shirt task in their respective classes. In this report I present results from implementation in the lower secondary school. Data collection consisted of audio-recording the researchers’ meeting (26.11.08) during which the T-shirt task was designed, and video-recording both the workshop where this task was presented to the teachers (03.12.08) and its implementation in school (05.05.09). In addition, I conducted interviews (video-recorded) with the teacher