Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient resource allocation for all-optical multicasting over spectrum-sliced elastic optical networks

Recently, optical orthogonal frequency-division multiplexing technology has attracted intensive research interest because spectrum-sliced elastic optical networks (EONs) can be constructed based on it. In this paper, we investigate how to serve multicast requests over EONs with multicast-capable routing, modulation level, and spectrum assignment (RMSA). Both EON planning with static multicast traffic and EON provisioning with dynamic traffic are studied. For static EON planning, we formulate two integer linear programming (ILP) models, i.e., the joint ILP and the separate ILP. The joint ILP optimizes all multicast requests together, while the separate ILP optimizes one request each time in a sequential way. We also propose a highly efficient heuristic that is based on an adaptive genetic algorithm (GA) with minimum solution revisits. The simulation results indicate that the ILPs and the GA provide more efficient EON planning than the existing multicast-capable RMSA algorithms that use the shortest path tree (SPT) and the minimal spanning tree (MST). The results also show that the GA obtains more efficient EON planning results than the separate ILP with much less running time, as it can optimize all multicast requests together in a highly efficient manner. For the dynamic EON provisioning, we demonstrate that the GA is also applicable, and it achieves lower request blocking probabilities than the benchmark algorithms using SPTand MST.
Communication Sequence Graphs for Mutation-Oriented Integration Testing

Integration testing (IT) plays an important role for validation of the communication between different software components to ensure their optimal cooperation. In this paper we (i) introduce communication sequence graphs (CSG) for integration testing representing the communication between software components on a meta-level, (ii) define coverage criteria based on these graphs, and (iii) give hints how to extend CSG notion by Boolean algebra in order to represent complex data structures of data exchanged between components to be integrated. A case study borrowed from a robot controlling system illustrates CSG as a new integration testing approach.
CoP-Man -- Perception for Mobile Pick-and-Place in Human Living Environments

While many specific perception tasks have been addressed in the context of robot manipulation, the problem of how to design and realize comprehensive and integrated robot perception systems for manipulation tasks has received little attention so far. In this paper, we describe and discuss the design and realization of COP-MAN, a perception system that is tailored for personal robots performing pick-and-place tasks, such as setting the table, loading the dishwasher, and cleaning up, in human living environments. We describe our approach to decomposing and structuring the perception tasks into subtasks in order to make the overall perception system effective, reliable, and fast. Distinctive characteristics and features of COP-MAN include semantic perception capabilities, passive perception and a knowledge processing interface to perception. The semantic perception capabilities enable the robot to perceive the environment in terms of objects of given categories, to infer functional and affordance based information about objects and the geometric and part-based reconstruction of objects for grasping. Passive perception allows for real-time coarse-grained perception of the dynamic aspects, and the knowledge processing interface to perception enables the robot to query the information it needs, which is then automatically acquired through active perception routines.
Novel P 300 BCI Interfaces to Directly Select Physical and Virtual Objects

We discuss two novel integrations of a brain-computer interface (BCI) to inform the wider BCI community of the possibilities presented by these applications. BCIs based on the P300 paradigm often use a flashing character or picture visual stimulus to elicit an event-related potential in the brain’s EEG signal. Traditionally, P300-based BCI paradigms use a grid layout of visual targets (commonly an alphabet) and allow users to select representations of objects using their thoughts. First, we present a P300 BCI application that allows users to directly select 3D objects in a fully immersive virtual environment. Second, we discuss an application that allows users to select physical objects in the real world directly. This is done by using a multi-touch table that senses and highlights objects placed upon its surface by flashing an area of light around them. Both of these systems allows us to construct a P300-based BCI that uses a collection of objects as targets, rather than a pre-determined grid layout of representations of targets. Results show that our new paradigm works just as well as the traditional paradigm, thus highlighting the potential for BCIs to be integrated in a broader range of situations. This opens up the field of possibilities for the future of novel and integrated real-world P300 BCIs.
Nonparametrically Guided Autoencoder with Laplace Approximation for dimensionality reduction

Unsupervised learning aims to discovery latent representation embedded in the observation, which is useful for data visualization, dimensionality reduction, and density modeling. Autoencoders have been successfully used to learn the latent variations in data, especially with the recent reintroduction by deep learning. For some specific tasks, there are supervised information or labels that can be used to further guide the unsupervised autoencoder model for finding latent representation. The Non-Parametrically Guided Autoencoder (NPGA) has been proved to be an effective model. It tries to utilize Gaussian Process Regression (GPR) to model the unknown mapping from unknown latent representation to extra supervised information. However for the discrete label information in classification tasks, using GPR could be unwise and inefficient. In this paper, we propose the Non-Parametrically Guided Autoencoder with Laplace Approximation (NPGA-LA) to effectively handle discrete labels. The idea of NPGA-LA is to make use of Gaussian Process Classification (GPC) rather than GPR to model the transformation between the latent space and the discrete label space. The experimental results verify the excellent performance of the newly developed method.
A higher baseline plasma uric acid level is an independent predictor of arterial stiffness

Abstract Hyperuricemia has been demonstrated to be a risk factor for cardiovascular diseases. Though the association between uric acid (UA) and arterial stiffness has been investigated previously in patients with basic diseases, the predictive value of baseline UA level for arterial stiffness has not been conducted. We aimed at identifying the predictive role of UA for arterial stiffness prospectively. A longitudinal follow-up study in a routine health check-up population was performed with an average follow-up of 4.8 years. The demographic information, baseline and follow-up anthropometric parameters, arterial stiffness (pulse-wave velocity, PWV), and biomarker variables including UA have been measured and analyzed. A total of 1447 valid follow-ups were available for the final analysis. Both of the baseline and follow-up UA levels were significantly higher in the arterial stiffness groups than that in the nonarterial stiffness groups (all P values <0.001). The follow-up carotid-femoral PWV [(cf-PWV), r = 0.161, P < 0.001] was strongly correlated with baseline UA. At the follow-up cross-section, cf-PWV was also closely associated with UA (r = 0.101, P < 0.001). Logistic regressions revealed that a higher baseline UA level was an independent predictor of follow-up arterial stiffness assessed by cf-PWV [odds ratio (OR): 1.824; P = 0.046]. A higher baseline level of UA is closely related to arterial stiffness and is an independent predictor of arterial stiffening.
Reinforcement Learning in Non-Stationary Environments

Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary environment. However, the stationary assumption on the environment is very restrictive. In many real world problems like traffic signal control, robotic applications, one often encounters situations with non-stationary environments and in these scenarios, RL methods yield sub-optimal decisions. In this paper, we thus consider the problem of developing RL methods that obtain optimal decisions in a non-stationary environment. The goal of this problem is to maximize the long-term discounted reward achieved when the underlying model of the environment changes over time. To achieve this, we first adapt a change point algorithm to detect change in the statistics of the environment and then develop an RL algorithm that maximizes the long-run reward accrued. We illustrate that our change point method detects change in the model of the environment effectively and thus facilitates the RL algorithm in maximizing the long-run reward. We further validate the effectiveness of the proposed solution on non-stationary random Markov decision processes, a sensor energy management problem and a traffic signal control problem.
Macroscopic Noisy Bounded Confidence Models With Distributed Radical Opinions

In this article, we study the nonlinear Fokker–Planck (FP) equation that arises as a mean-field (macroscopic) approximation of bounded confidence opinion dynamics, where opinions are influenced by environmental noises and opinions of radicals (stubborn individuals). The distribution of radical opinions serves as an infinite-dimensional exogenous input to the FP equation, visibly influencing the steady opinion profile. We establish mathematical properties of the FP equation. In particular, we, first, show the well-posedness of the dynamic equation, second, provide existence result accompanied by a quantitative global estimate for the corresponding stationary solution, and, third, establish an explicit lower bound on the noise level that guarantees exponential convergence of the dynamics to stationary state. Combining the results in second and third readily yields the input–output stability of the system for sufficiently large noises. Next, using Fourier analysis, the structure of opinion clusters under the uniform initial distribution is examined. The results of analysis are validated through several numerical simulations of the continuum-agent model (partial differential equation) and the corresponding discrete-agent model (interacting stochastic differential equations) for a particular distribution of radicals.
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition

We study the problem of top-$k$ selection over a large domain universe subject to user-level differential privacy. Typically, the exponential mechanism or report noisy max are the algorithms used to solve this problem. However, these algorithms require querying the database for the count of each domain element. We focus on the setting where the data domain is unknown, which is different than the setting of frequent itemsets where an apriori type algorithm can help prune the space of domain elements to query. We design algorithms that ensures (approximate) $(\epsilon,\delta>0)$-differential privacy and only needs access to the true top-$\bar{k}$ elements from the data for any chosen $\bar{k} \geq k$. This is a highly desirable feature for making differential privacy practical, since the algorithms require no knowledge of the domain. We consider both the setting where a user's data can modify an arbitrary number of counts by at most 1, i.e. unrestricted sensitivity, and the setting where a user's data can modify at most some small, fixed number of counts by at most 1, i.e. restricted sensitivity. Additionally, we provide a pay-what-you-get privacy composition bound for our algorithms. That is, our algorithms might return fewer than $k$ elements when the top-$k$ elements are queried, but the overall privacy budget only decreases by the size of the outcome set.
Annual runoff and evapotranspiration of forestlands and non‐forestlands in selected basins of the Loess Plateau of China

Large‐scale forestation has been undertaken over decades principally to control the serious soil erosion in the Loess Plateau of China. A quantitative assessment of the hydrological effects of forestation, especially on basin water yield, is critical for the sustainable forestry development within this dry region. In this study, we constructed the multi‐annual water balances to estimate the respective grand average of annual evapotranspiration (ET) and runoff for forestlands and non‐forestlands of 57 basins. The overall annual runoff and corresponding runoff/precipitation ratio were low, with a mean of 33 mm (7%) ranging from 10 (2%) to 56 mm (15%). Taking the grand average of annual precipitation of 463 mm for all basins, the corresponding grand averages of annual ET and runoff were 447 and 16 mm for forestlands, 424 and 39 mm for non‐forestlands, respectively. Thus, the corresponding ratios of annual ET and runoff to precipitation were 91·7 and 8·3% for non‐forestlands, 96·6 and 3·4% for forestlands, respectively. Although the absolute difference in grand average of annual runoff was only 23 mm, it represents a large difference in relative terms, as it equates up to 58% of annual runoff from non‐forestlands. We argue that the large‐scale forestation may have serious consequences for water management and sustainable development in the dry region of NW China because of a runoff reduction. This study highlights the importance of quantifying the ET of forests and other land uses and to examine how land cover change may affect the water balances in an arid environment. Copyright © 2011 John Wiley & Sons, Ltd.
Almost-sure exponential mixing of passive scalars by the stochastic Navier–Stokes equations

We deduce almost-sure exponentially fast mixing of passive scalars advected by solutions of the stochastically-forced 2D Navier-Stokes equations and 3D hyper-viscous Navier-Stokes equations in $\mathbb T^d$ subjected to non-denegenerate $H^\sigma$-regular noise for any $\sigma$ sufficiently large. That is, for all $s > 0$ there is a deterministic exponential decay rate such that all mean-zero $H^s$ passive scalars decay in $H^{-s}$ at this same rate with probability one. This is equivalent to what is known as \emph{quenched correlation decay} for the Lagrangian flow in the dynamical systems literature. This is a follow-up to our previous work, which establishes a positive Lyapunov exponent for the Lagrangian flow-- in general, almost-sure exponential mixing is much stronger than this. Our methods also apply to velocity fields evolving according to finite-dimensional fluid models, for example Galerkin truncations of Navier-Stokes or the Stokes equations with very degenerate forcing. For all $0 \leq k < \infty $ we exhibit many examples of $C^k_t C^\infty_x$ random velocity fields that are almost-sure exponentially fast mixers.
Minimum Throughput Maximization in LoRa Networks Powered by Ambient Energy Harvesting

In this paper, we investigate the uplink transmissions in low-power wide-area networks (LPWAN) where the users are self-powered by the energy harvested from the ambient environment. Demonstrating their potential in supporting diverse Internet-of-Things (IoT) applications, we focus on long range (LoRa) networks where the LoRa users are using the harvested energy to transmit data to a gateway via different spreading codes. Precisely, we study the throughput fairness optimization problem for LoRa users by jointly optimizing the spreading factor (SF) assignment, energy harvesting (EH) time duration, and the transmit power of LoRa users. First, through examination of the various permutations of collisions among users, we derive a general expression of the packet collision time between LoRa users, which depends on the SFs and EH duration requirements. Then, after reviewing prior SF allocation work, we develop two types of algorithms that either assure fair SF assignment indeed purposefully ‘unfair’ allocation schemes for the LoRa users. Our results unearth three new findings. Firstly, we demonstrate that, to maximize the minimum rate, the unfair SF allocation algorithm outperforms the other approaches. Secondly, considering the derived expression of packet collision between simultaneous users, we are now able to improve the performance of the minimum rate of LoRa users and show that it is protected from inter-SF interference which occurs between users with different SFs. That is, imperfect SF orthogonality has no impact on minimum rate performance. Finally, we have observed that co-SF interference is the main limitation in the throughput performance, and not the energy scarcity.
Power Efficient Visible Light Communication With Unmanned Aerial Vehicles

A novel approach that combines visible light communication (VLC) with unmanned aerial vehicles (UAVs) to simultaneously provide flexible communication and illumination is proposed. To minimize the power consumption, the locations of UAVs and the cell associations are optimized under illumination and communication constraints. An efficient sub-optimal solution that divides the original problem into two sub-problems is proposed. The first sub-problem is modeled as a classical smallest enclosing disk problem to obtain the optimal locations of UAVs, given the cell association. Then, assuming fixed UAV locations, the second sub-problem is modeled as a min-size clustering problem to obtain the optimized cell association. In addition, the obtained UAV locations and cell associations are iteratively optimized multiple times to reduce the power consumption. The numerical results show that the proposed approach can reduce the total transmit power consumption by at least 53.8% compared with two baseline algorithms with fixed UAV locations.
Assuring the Machine Learning Lifecycle

Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed machine learning (ML) at the top of research, economic, and political agendas. Such unprecedented interest is fuelled by a vision of ML applicability extending to healthcare, transportation, defence, and other domains of great societal importance. Achieving this vision requires the use of ML in safety-critical applications that demand levels of assurance beyond those needed for current ML applications. Our article provides a comprehensive survey of the state of the art in the assurance of ML, i.e., in the generation of evidence that ML is sufficiently safe for its intended use. The survey covers the methods capable of providing such evidence at different stages of the machine learning lifecycle, i.e., of the complex, iterative process that starts with the collection of the data used to train an ML component for a system, and ends with the deployment of that component within the system. The article begins with a systematic presentation of the ML lifecycle and its stages. We then define assurance desiderata for each stage, review existing methods that contribute to achieving these desiderata, and identify open challenges that require further research.
Second Order Value Iteration in Reinforcement Learning

Value iteration is a fixed point iteration technique utilized to obtain the optimal value function and policy in a discounted reward Markov Decision Process (MDP). Here, a contraction operator is constructed and applied repeatedly to arrive at the optimal solution. Value iteration is a first order method and therefore it may take a large number of iterations to converge to the optimal solution. In this work, we propose a novel second order value iteration procedure based on the Newton-Raphson method. We first construct a modified contraction operator and then apply Newton-Raphson method to arrive at our algorithm. We prove the global convergence of our algorithm to the optimal solution and show the second order convergence. Through experiments, we demonstrate the effectiveness of our proposed approach.
Future trends in software evolution metrics

Since the famous statement "What is not measurable make measurable" of Galileo Galilei (1564 - 1642) it has been a major goal in science to quantify observations as a way to understand and control the underlying causes. With the growing awareness that evolution is a key aspect of software, an increasing number of computer scientists is investigating how metrics can be applied to evolving software artifacts. This paper provides a classification of the various approaches that use metrics to analyse, understand, control and improve the software evolution process, and identifies topics that require further research. As such, we expect that this paper will stimulate this emerging research area.
Optimizing Sensing, Computing, and Communication for Energy Harvesting IoTs: A Survey

With businesses deploying a growing number of Internet of Things (IoTs), battery maintenance has become a limiting factor for realizing a sustainable IoT infrastructure. To overcome battery limitations, industry is now considering energy harvesting as a viable alternative to autonomously power IoT devices as much as possible, which has resulted in a number of batteryless energy harvesting IoTs (EH-IoTs) appearing in the market in recent years. Standards activities are also underway, which involve wireless protocol design suitable for EH-IoTs as well as testing procedures for various energy harvesting methods. Despite the early commercial and standards activities, IoT sensing, computing and communication under unpredictable power supply still faces significant research challenges and has become a topic of intense research. This paper surveys recent advances in EH-IoTs from several perspectives. First, it surveys the recent commercial developments for EH-IoT in terms of both products and services, followed by a review of initial standards activities in this space. Then it surveys methods that enable use of energy harvesting hardware as a proxy for conventional sensors to detect contexts more power-efficiently. The advancements in efficient checkpointing and timekeeping for intermittently powered IoT devices are reviewed next. We also survey recent research in novel wireless communication techniques for EH-IoTs, including applications of reinforcement learning to optimize power allocations on-the-fly under unpredictable energy productions as well as packet-less IoT communication and backscatter communication techniques for energy impoverished environments. We conclude the paper with a discussion of future research directions.
Towards an understanding of the resolution dependence of Core-Collapse Supernova simulations

Using our new state-of-the-art core-collapse supernova (CCSN) code Fornax, we explore the dependence upon spatial resolution of the outcome and character of three-dimensional (3D) supernova simulations. For the same 19 M⊙ progenitor star, energy and radial binning, neutrino microphysics, and nuclear equation of state, changing only the number of angular bins in the θ and ϕ directions, we witness that our lowest resolution 3D simulation does not explode. However, when jumping progressively up in resolution by factors of two in each angular direction on our spherical-polar grid, models then explode, and explode slightly more vigorously with increasing resolution. This suggests that there can be a qualitative dependence of the outcome of 3D CCSN simulations upon spatial resolution. The critical aspect of higher spatial resolution is the adequate capturing of the physics of neutrino-driven turbulence, in particular its Reynolds stress. The greater numerical viscosity of lower resolution simulations results in greater drag on the turbulent eddies that embody turbulent stress, and, hence, in a diminution of their vigor. Turbulent stress not only pushes the temporarily stalled shock further out, but bootstraps a concomitant increase in the deposited neutrino power. Both effects together lie at the core of the resolution dependence we observe.