Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Composite Convex Minimization with Affine Constraints

This paper presents the basic ingredients of a novel method, the stochastic Fejér-monotone hybrid steepest descent method S-FM-HSDM), designed to solve affinely constrained and composite convex minimization tasks. The minimization task is not known exactly; noise contaminates the information about the composite loss function and the affine constraints. S-FM-HSDM generates sequences of random variables that, under certain conditions and with respect to a probability space, converge pointwise to solutions of the noiseless minimization task. S-FM-HSDM enjoys desirable attributes of state-of-the-art stochastic-approximation techniques such as splitting of variables and constant step size (learning rate). Furthermore, it provides a novel way of exploiting the information about the affine constraints via fixed-point sets of appropriate mappings. Among the offsprings of S-FM-HSDM, the hierarchical recursive least squares (HRLS) takes advantage of S-FM-HSDM’s versatility toward affine constraints and offers a novel twist to LS by generating sequences of estimates that converge to solutions of a hierarchical optimization task: Minimize a convex loss over the set of minimizers of the ensemble least-squares loss. Numerical tests on a synthetic $\ell_{1}$-norm regularized LS task show that HRLS compares favorably to several state-of-the-art convex, as well as non-convex, stochastic-approximation and online-learning counterparts.
Accurate Performance Modeling of Uplink Transmission in NB-IoT

With the development of LPWA (Low Power Wide Area)technology, the emerging NB-IoT (Narrowband Internet of Things)has attracted much attention and enabled a wide range of applications. An uplink of NB-IoT is a link from a user equipment (UE)to a base station (BS). Uplink transmission is a key component of NB-IoT, accomplishing the sensor data collection task for many applications. However, the performance of uplink transmission has not been rigorously analyzed in the current literature, while uplink performance degradation like long latency could be harmful to many applications with strict uplink performance requirements. In this work, we show a way of mathematically analyzing the performance of uplink transmission for NB-IoT systems, concerning the transmission latency and transmission reliability. Our model is accurate with consideration of the protocol details and the new features of NB-IoT, including link quality, packet size, channel access contention, and etc. We validate the analytical results through detailed simulations. Results show that our analytical model can achieve 83% accuracy for latency calculation and 96% accuracy for reliability calculation. Moreover, we demonstrate that the analytical results can be used to aid protocol design for performance optimization, e.g., repetition number tuning for reducing the transmission latency.
Towards Robust End-to-End Alignment

Robust alignment is arguably both critical and extremely challenging. Loosely, it is the problem of designing algorithmic systems with strong guarantees of always being beneficial for mankind. In this paper, we propose a preliminary research program to address it in a reinforcement learning framework. This roadmap aims at decomposing the end-toend alignment problem into numerous more tractable subproblems. We hope that each subproblem is sufficiently orthogonal to others to be tackled independently, and that combining the solutions to all such subproblems may yield a solution to alignment.
The 24th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2018, Hotel Pullman, Redwood City, San Francisco Bay, USA, June 29 to 30, 2018

The ability of diagrams to convey information effectively comes, in part, from their ability to make facts explicit that would otherwise need to be inferred. This type of advantage has often been referred to as a free ride and was deemed to occur only when a diagram was obtained by translating a symbolic representation of information. Recent work generalised free rides, introducing the idea of an observational advantage, where the existence of such a translation is not required. In this talk, I will provide an overview of the theory of observation. Using observability, a formal characterisation of observational advantages can be explored. The talk will proceed to demonstrate the theory of observation and observational advantages by applying the concepts to set theory and Euler diagrams without existential import. It has been shown that Euler diagrams without existential import have significant observational advantages over set theory: they are observationally complete. The talk will then explore to what extent Euler diagrams with existential import are observationally complete with respect to set-theoretic sentences. In particular, it will be shown that existential import significantly limits the cases when observational completeness arises, due to the potential for overspecificity. These two results formally support Larkin and Simon's claim that “a diagram is (sometimes) worth ten thousand words”. (Note: This is joint research with Atsushi Shimojima and Mateja Jamnik).
A Distributed Control Plane for Path Computation Scalability in Software-Defined Networks

Given the shortcomings of traditional networks, Software-Defined Networking (SDN) is considered as the best solution to deal with the constant growth of mobile data traffic. SDN separates the data plane from the control plane, enabling network scalability and programmability. Initial SDN deployments promoted a centralized architecture with a single controller managing the entire network. This design has proven to be unsuited for nowadays large-scale networks. Though multi-controller architectures are becoming more popular, they bring new concerns. One critical challenge is how to efficiently perform path computation in large networks considering the substantial computational resources needed. In this paper, we propose DiSC, a distributed high-performance control plane for path computation in large SDNs. It endorses a hierarchical structure to distribute the load of path computation among different controllers, reducing therefore the transmission overhead. In addition, it uses node parallelism to accelerate the performance of path computation without generating high control overhead. Simulation results show that DiSC outperforms existing schemes, including the most recent ones, in terms of path computation time, path setup latency and end-to-end delay.
Interference Hypergraph-Based Resource Allocation (IHG-RA) for NOMA-Integrated V2X Networks

Vehicular communication network is a core application scenario in the fifth generation (5G) mobile communication system which requires ultrahigh data rate and ultralow latency. Most recently, nonorthogonal multiple access (NOMA) has been regarded as a promising technique for future 5G systems due to its capability in significantly improving the spectral efficiency and reducing the data transmission latency. In this paper, we propose to introduce NOMA in device-to-device-enhanced vehicle-to-everything (V2X) networks, where resource sharing based on spatial reuse for different V2X communications are permitted through centralized resource management. Considering the complicated interference scenario caused by NOMA and spatial reuse-based resource sharing in the investigated NOMA-integrated V2X (NOMA-V2X) networks, we construct an interference hypergraph (IHG) to model the interference relationships among different communication groups. In addition, based on the constructed IHG, we further propose an IHG-based resource allocation (IHG-RA) scheme with cluster coloring algorithm, which can lead to both effective and efficient resource block assignment with low computational complexity. Simulation results verify the efficiency of our proposed IHG-RA scheme for NOMA-V2X communications in improving the network sum rate.
Securing Small Cell Networks Under Interference Constraint: A Quasi-Variational Inequality Approach

Small cell networks are envisioned as one of the critical enabling technologies for the next-generation wireless systems. However, due to the limited capability of small cell base stations as compared with the macro-cell base stations, the secure wireless communication faces significant challenges. Towards this issue, we target at enhancing the wireless security for small cell networks by employing the physical layer security techniques. Specifically, we maximize the secrecy rate for each individual small cell in a distributed manner, while protecting the transmissions in the macro-cell by imposing the aggregate interference constraint over the small cell transmissions. The distributed secrecy competition is formulated as a generalized Nash equilibrium problem, for which we adopt its equivalence in the form of the quasi-variational inequality to analyze the existence and uniqueness of the Nash equilibrium. Furthermore, we tackle the interference constraint as the penalty over the secrecy rate of the small cells and introduce the Nash equilibrium problem formulation. Then, the distributed algorithm is proposed based on the best-response strategy to solve for the Nash equilibrium of the secrecy competition game. Finally, simulation results are provided to corroborate our theoretical findings.
Phenomenology of Foliage Effect on 5G Millimeter-Wave V2V Communications

Autonomous driving system and intelligent transportation systems (ITS) become increasingly popular, which requires reliable and high-speed Vehicle-to-Vehicle (V2V) communications. In realistic road traffic environment, foliage is commonly seen and blocks the line-of-sight link. In this paper, semi-exact semi-closed-form models for the far-field as well as near-field scattering from a tree trunk are developed at 5.9 and 60 GHz. Extensive numerical evaluations on the scattering fields are presented. To make the results accessible to other users, curve-fitting functions are invoked to characterize the scattered field, and are attempted to extract a macro-model for the path loss, mainly as a function of the distance and azimuth angle. This work is promisingly useful for V2V millimeter-wave communications and radar sensing.
On The Limits of Finite-Time Distributed Consensus Through Successive Local Linear Operations

In this work, we explore the limits of finite-time distributed consensus through the intersection of graph filters and matrix function theory. We focus on algorithms capable to compute the consensus exactly through filtering operations over a graph, and that have been proven to converge in finite time. In this context, we show that there exists an algebraic algorithm that can minimize the minimum polynomial of a matrix whose support is known. Different from previous works, we leverage the structure of matrices that share the same support and are diagonalizable by the eigenbasis of the graph shift operator to prove a theoretical result with respect to the minimum number of diffusion steps required to reach consensus. We show that the previously known bound on the number of consensus iterations can be further reduced in accordance to the algebraic properties of the matrix representation of the network. Finally, insights with respect to the relation between the graph topology and the algebraic properties of such matrices are provided in order to encourage further discussion on the role of eigenvalues and eigenvectors in the network topology.
Effects of Biopreservatives Combined with Modified Atmosphere Packaging on the Quality of Apples and Tomatoes

During the cultivation and harvesting of fruit and vegetables, a large number of microorganisms accumulate on their surface. Their active and excessive reproduction leads to spoilage of products. The purpose of the study was to assess the effect of combining various packaging technologies with different biopreservatives on the stability of physicochemical and microbiological characteristics of fresh vegetables and fruit during storage. Samples of fruit and vegetable products (apples, tomatoes) were subjected to the following procedures: packaging without treatment, treatment with a mixture of bacteriocin-like substances and packaging with or without modi ﬁ ed atmosphere. Packaged samples were stored in a refrigerator at a temperature of 4°C for 25 days. The bacteriocin-like substances in combination with modi ﬁ ed atmosphere reduced the contamination of samples by pathogenic microorganisms at least 4 times while maintaining the quality characteristics of the fruit during the storage period. A biopreservative in combination with modi ﬁ ed atmosphere can be used to control microbial spoilage and to keep fruit and vegetables fresh after harvest.
Monte-Carlo Tree Search Aided Contextual Online Learning Approach for Wireless Caching

Caching popular contents at the edge of wireless networks has recently emerged as a promising technique to offload mobile data traffic and improve the quality of service for users. In the big-data era, the size of the content space is essentially infinite. Moreover, users with common features typically share similar content preference. In order to address these issues, we model the wireless caching problem as a contextual multi-armed bandit (CMAB) problem that considers the infinitely arms, and propose a Monte-Carlo tree search aided contextual upper confidence bound (MCTS-CUCB) algorithm, to make accurate content caching with low complexity. Specifically, we introduce a tree-based search method to analyze the content subspace instead of a single content, thereby reducing the computing load. In the search process, a cover tree is built in an incremental and asymmetric manner, which can reflect the users' content preference. Besides, contextualization allows to learn content preferences for groups of users having similar contexts, which significantly accelerates the learning process and improve the cache hit rate. Our simulation results on a real-world data set (MovieLens 1M Dataset) demonstrate that the proposed MCTS-CUCB algorithm is capable of achieving a considerable reduction in complexity compared with the existing related algorithms with a superior cache hit rate performance.
Distributed Multiple Gaussian Filtering for Multiple Target Localization in Wireless Sensor Networks

Indoor target tracking appears in several engineering problems and is a key enabler to a myriad of new applications. Localization in such global navigation satellite system (GNSS)-denied environments typically relies on the use of existing infrastructures and already deployed technologies. In this paper, we are interested in received signal strength (RSS)-based multiple target tracking (MTT) in wireless sensor networks (WSN). From an estimation standpoint, two problems arise: i) standard Bayesian filtering techniques are not able to cope with high-dimensional systems, and ii) WSN are typically built with resource-constrained low-cost sensors, which implies the need for distributed algorithms. A possible solution is to use a multiple Bayesian filtering approach, where the state-space is partitioned in several lower dimensional sub-spaces, and then a set of parallel filters are used to characterize the marginal subspace posteriors. In this work, we propose a new distributed multiple Gaussian filtering (MGF) formulation, to solve both the curse-of-dimensionality in high-dimensional systems and the need of distributed algorithms in network localization applications.
Non-convex approach to binary compressed sensing

We propose a new approach for the recovery of binary signals in compressed sensing, based on the local minimization of a non-convex cost functional. The desired signal is proved to be a local minimum of the functional under mild conditions on the sensing matrix and on the number of measurements. We develop a procedure to achieve the desired local minimum, and, finally, we propose numerical experiments that show the improvement obtained by the proposed approach with respect to classical convex methods.
Delay-Optimal Computation Offloading for Computation-Constrained Mobile Edge Networks

Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we construct a Markov decision process (MDP) framework to optimize the delay performance in computation-constrained mobile edge networks. Different to most of the existing works, we consider the computation task queue at the MEC server due to its constrained computation capability. In this case, the task queue at the MT and that at the MEC server are mutually strongly coupled in a cascade manner, which creates complex interdependence and brings new technical challenges. To address the challenge, the infinite horizon average cost MDP is reformulated to a virtual continuous time system (VCTS) with reflections. We derive the closed-form approximate priority functions for the MDP in different system scenarios with dynamic rate estimation. Based on the approximate priority function, we propose a multi-level water filling solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI) on the computation offloading policy in a closed form. Finally, the simulation results show that the proposed computation offloading scheme outperforms the conventional schemes.
A Cross-Tier Cooperation Resource Allocation Approach Over Ultra-Dense Heterogeneous Networks

In this paper, we proposed a cross-tier cooperation resource allocation (CCRA) scheme over the Ultra-dense heterogeneous network (UDHN) to improve the whole system throughput. First, we use the cooperation signal-to-interference-plus-noise ratio (SINR) threshold to classify three types of user equipments (UEs). And we use stochastic geometry to analyze the effect of the cooperation SINR threshold on the number of UEs. We focus on using the dedicated channel to decrease cross-tier interference. In order to enhance the total system throughput, an NP-hard problem is formulated. In order to reduce the computational complexity, we decompose this resource allocation problem into subcarrier allocation and power allocation and we use two algorithms to solve it. The simulation results show that the system throughput is significantly improved by using the proposed CCRA scheme in a dense environment.
A comprehensive review of geothermal energy evolution and development

ABSTRACT Global energy demand is increasing, driven by population rise, technological development, and a desire for a better lifestyle. However, because environmental issues such as fossil-fuel-sourced greenhouse gas (GHG) emissions are emerging as constraints on the nature of energy sources, using renewable and sustainable energy sources is the appropriate and applicable response. Geothermal energy is one form of renewable and sustainable energy, which has certain advantages such as consistency, a vast amount of untapped potential, availability, and a wide range of possible applications that make it an interesting and viable solution for helping meet the world’s energy needs while reducing GHG emissions (especially CO2). We provide a comprehensive review on the evolution of geothermal energy production from its obscure beginnings to the present time by reporting production data from individual countries and collective data of worldwide production. In addition, we provide an overview of relevant technologies at the industrial level, such as site identification, power production methods, and direct use. Finally, we discuss the geothermal power production prospects for 2050, the classification of production capacity on the technology side, and existing roadmaps for points of interest concerning technological development. We hope this review helps to identify existing gaps, future challenges, and areas needing further attention and investigation.
Two-Step Analog-Digital Multiuser Equalizer for Hybrid Precoded mmWave Communications

In this paper we propose a hybrid multi-user multi-stream equalizer combined with low complexity analog-digital precoders for uplink broadband millimeter wave massive multiple-input-multiple-output (MIMO) systems. Although hybrid analog/digital MIMO techniques were originally proposed for flat fading channels, there is a growing interest in using these techniques in frequency-selective channels such as the ones associated to broadband wireless systems. However, it is expected the development of wideband solutions, and then it is crucial to propose transmit and receive schemes for frequency selective channels. The proposed two-step iterative analog-digital equalizer is designed by minimizing the sum of the mean square error of all subcarriers. The analog part is constant over the subcarriers and the iterations due to hardware constraints, while the digital part is iterative and computed on per subcarrier basis. The results show that the proposed multiuser equalizer achieves a performance close to recently proposed fully iterative analog-digital equalizers, while being more suited for practical applications.
Minimizing the Negative Side Effects of Planning with Reduced Models

Reduced models of large Markov decision processes accelerate planning by considering a subset of outcomes for each state-action pair. This reduction in reachable states leads to replanning when the agent encounters states without a precomputed action during plan execution. However, not all states are suitable for replanning. In the worst case, the agent may not be able to reach the goal from the newly encountered state. Agents should be better prepared to handle such risky situations and avoid replanning in risky states. Hence, we consider replanning in states that are unsafe for deliberation as a negative side effect of planning with reduced models. While the negative side effects can be minimized by always using the full model, this defeats the purpose of using reduced models. The challenge is to plan with reduced models, but somehow account for the possibility of encountering risky situations. An agent should thus only replan in states that the user has approved as safe for replanning. To that end, we propose planning using a portfolio of reduced models, a planning paradigm that minimizes the negative side effects of planning using reduced models by alternating between different outcome selection approaches. We empirically demonstrate the effectiveness of our approach on three domains: an electric vehicle charging domain using real-world data from a university campus and two benchmark planning problems.
Demand Response Management Using Lattice-Based Cryptography in Smart Grids

The prolonged usage of non-renewable resources like petroleum and coal have adverse affect on the environment and has led to energy crisis in the world. In order to mitigate the situation, efficient strategies have been proposed for generation, distribution and consumption of energy obtained from renewable sources such as tidal, wind and solar power. With the advent of Smart Grids being developed world wide, the most widely accepted strategy is Demand-Response management. In this strategy, the customers or end-users are incentivized to change their energy-utility behavior with time in response to fluid price changes or to induce lower energy consumption during peak demand time. The system is controlled by a cloud of servers that monitor the demand- supply chain over a network all the time. This brings up the issue of security within the operations of the system. Current security mechanisms such as Rivest-Shamir-Alderman (RSA) public key encryption, Advanced Encryption Standard (AES) symmetric encryption, Elliptic Curve Cryptography (ECC) public-key cryptosytem and the recently proposed works are not future- proof in the world of post-quantum cryptography. This paper proposes a lattice based cryptographic scheme to ensure proper security in the system. The proposed scheme has been proven secure against major known attacks.