A physical layer network coding based modify-and-forward with opportunistic secure cooperative transmission protocol

This paper investigates a new secure relaying scheme, namely physical layer network coding based modify-and-forward (PMF), in which a relay node linearly combines the decoded data sent by a source node with an encrypted key before conveying the mixed data to a destination node. We first derive the general expression for the generalized secrecy outage probability (GSOP) of the PMF scheme and then use it to analyse the GSOP performance of various relaying and direct transmission strategies. The GSOP performance comparison indicates that these transmission strategies offer different advantages depending on the channel conditions and target secrecy rates, and relaying is not always desirable in terms of secrecy. Subsequently, we develop an opportunistic secure transmission protocol for cooperative wireless relay networks and formulate an optimisation problem to determine secrecy rate thresholds (SRTs) to dynamically select the optimal transmission strategy for achieving the lowest GSOP. The conditions for the existence of the SRTs are derived for various channel scenarios

Pareto-optimal pilot design for cellular massive MIMO systems

We introduce a non-orthogonal pilot design scheme that simultaneously minimizes two contradicting targets of channel estimation errors of all base stations (BSs) and the total pilot power consumptions of all users in a multi-cell massive MIMO system, subject to the transmit power constraints of the users in the network. We formulate a multi-objective optimization problem (MOP) with two objective functions capturing the contradicting targets and find the Pareto optimal solutions for the pilot signals. Using weighted-sum-scalarization technique, we first convert the MOP to an equivalent single-objective optimization problem (SOP), which is not convex. Assuming that each BS is provided with the most recent knowledge of the pilot signals of the other BSs, we then decompose the SOP into a set of distributed non-convex optimization problems to be solved at individual BSs. Finally, we introduce an alternating optimization approach to cast each one of the resulting distributed optimization problems into a convex linear matrix inequality (LMI) form. We provide a mathematical proof for the convergence of the proposed alternating approach and a complexity analysis for the LMI optimization problem. Simulation results confirm that the proposed approach significantly reduces pilot power, whilst maintaining the same level of channel estimation error as in [1]

High-rate groupwise STBC using low-complexity SIC based receiver

In this paper, using diagonal signal repetition with Alamouti code employed as building blocks, we propose a high- rate groupwise space-time block code (GSTBC) which can be effectively decoded by a low-complexity successive interference cancellation (SIC) based receiver. The proposed GSTBC and SIC based receiver are jointly designed such that the diversity repetition in a GSTBC can induce the dimension expansion to suppress interfering signals as well as to obtain diversity gain. Our proposed scheme can be easily applied to the case of large number of antennas while keeping a reasonably low complexity at the receiver. It is found that the required minimum number of receive antennas is only two for the SIC based receiver to avoid the error floor in performance. The simulation results show that the proposed GSTBC with SIC based receiver obtains a near maximum likelihood (ML) performance while having a significant performance gain over other codes equipped with linear decoders

Power allocation in wireless multi-user relay networks

In this paper, we consider an amplify-and-forward wireless relay system where multiple source nodes communicate with their corresponding destination nodes with the help of relay nodes. Conventionally, each relay equally distributes the available resources to its relayed sources. This approach is clearly sub-optimal since each user experiences dissimilar channel conditions, and thus, demands different amount of allocated resources to meet its quality-of-service (QoS) request. Therefore, this paper presents novel power allocation schemes to i) maximize the minimum signal-to-noise ratio among all users; ii) minimize the maximum transmit power over all sources; iii) maximize the network throughput. Moreover, due to limited power, it may be impossible to satisfy the QoS requirement for every user. Consequently, an admission control algorithm should first be carried out to maximize the number of users possibly served. Then, optimal power allocation is performed. Although the joint optimal admission control and power allocation problem is combinatorially hard, we develop an effective heuristic algorithm with significantly reduced complexity. Even though theoretically sub-optimal, it performs remarkably well. The proposed power allocation problems are formulated using geometric programming (GP), a well-studied class of nonlinear and nonconvex optimization. Since a GP problem is readily transformed into an equivalent convex optimization problem, optimal solution can be obtained efficiently. Numerical results demonstrate the effectiveness of our proposed approach

A metric learning-based method for biomedical entity linking

Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept or entity in a knowledge base, e.g., the Unified Medical Language System (UMLS). One of the most challenging aspects of the entity linking task is the ambiguity of mentions, i.e., (1) mentions whose surface forms are very similar, but which map to different entities in different contexts, and (2) entities that can be expressed using diverse types of mentions. Recent studies have used BERT-based encoders to encode mentions and entities into distinguishable representations such that their similarity can be measured using distance metrics. However, most real-world biomedical datasets suffer from severe imbalance, i.e., some classes have many instances while others appear only once or are completely absent from the training data. A common way to address this issue is to down-sample the dataset, i.e., to reduce the number instances of the majority classes to make the dataset more balanced. In the context of entity linking, down-sampling reduces the ability of the model to comprehensively learn the representations of mentions in different contexts, which is very important. To tackle this issue, we propose a metric-based learning method that treats a given entity and its mentions as a whole, regardless of the number of mentions in the training set. Specifically, our method uses a triplet loss-based function in conjunction with a clustering technique to learn the representation of mentions and entities. Through evaluations on two challenging biomedical datasets, i.e., MedMentions and BC5CDR, we show that our proposed method is able to address the issue of imbalanced data and to perform competitively with other state-of-the-art models. Moreover, our method significantly reduces computational cost in both training and inference steps. Our source code is publicly available here

A reduced basis approach for variational problems with stochastic parameters: Application to heat conduction with variable Robin coefficient

In this work, a Reduced Basis (RB) approach is used to solve a large number of boundary value problems parametrized by a stochastic input – expressed as a Karhunen–Loève expansion – in order to compute outputs that are smooth functionals of the random solution fields. The RB method proposed here for variational problems parametrized by stochastic coefficients bears many similarities to the RB approach developed previously for deterministic systems. However, the stochastic framework requires the development of new a posteriori estimates for “statistical” outputs – such as the first two moments of integrals of the random solution fields; these error bounds, in turn, permit efficient sampling of the input stochastic parameters and fast reliable computation of the outputs in particular in the many-query context.United States. Air Force Office of Scientific Research (Grant FA9550-07-1-0425)Singapore-MIT Alliance for Research and TechnologyChaire d’excellence AC

Molecular markers reveal diversity in composition of Megastigmus (Hymenoptera: Megastigmidae) from eucalypt galls

Since outbreaks of the invasive blue gum chalcids Leptocybe spp. began, the genus Megastigmus (Hymenoptera: Megastigmidae) has been increasingly studied as containing potential biocontrol agents against these pests. Megastigmus species have been collected and described from Australia, the presumed origin of Leptocybe spp., with M. zvimendeli and M. lawsoni reported as Leptocybe spp. parasitoids established outside of Australia. Parasitic Megastigmus have been reported to occur locally in the Neotropics, Afrotropic, Palearctic, and Indomalaya biogeographic realms, and in many cases described as new to science. However, molecular tools have not been used in studying parasitic Megastigmus, and difficulties in morphological taxonomy have compromised further understanding of eucalypt-associated Megastigmus as well as the Megastigmus-Leptocybe association. In this study, we used molecular markers to study the species composition and phylogeny of Megastigmus collected from eucalypt galls in Australia and from Leptocybe spp. galls from South Africa, Kenya, Israel, China, and Vietnam. We record thirteen discrete species and a species complex associated with eucalypt galls. A summary of morphological characters is provided to assist morphological delimitation of the studied group. A phylogeny based on 28S rDNA identified species groups of importance to Leptocybe spp. biocontrol agents from four clades with nine species. Relationships between Megastigmus from eucalypt galls and their phytophagous congeners were unresolved. Further molecular work is needed to clarify the identity of many species

Learning evolving relations for multivariate time series forecasting

Multivariate time series forecasting is essential in various fields, including healthcare and traffic management, but it is a challenging task due to the strong dynamics in both intra-channel relations (temporal patterns within individual variables) and inter-channel relations (the relationships between variables), which can evolve over time with abrupt changes. This paper proposes ERAN (Evolving Relational Attention Network), a framework for multivariate time series forecasting, that is capable to capture such dynamics of these relations. On the one hand, ERAN represents inter-channel relations with a graph which evolves over time, modeled using a recurrent neural network. On the other hand, ERAN represents the intra-channel relations using a temporal attentional convolution, which captures the local temporal dependencies adaptively with the input data. The elvoving graph structure and the temporal attentional convolution are intergrated in a unified model to capture both types of relations. The model is experimented on a large number of real-life datasets including traffic flows, energy consumption, and COVID-19 transmission data. The experimental results show a significant improvement over the state-of-the-art methods in multivariate time series forecasting particularly for non-stationary data

Universality in odd-even harmonic generation and application in terahertz waveform sampling

Odd-even harmonics emitted from a laser-target system imprint rich, subtle information characterizing the system's dynamical asymmetry, which is desirable to decipher. In this Letter, we discover a simple universal relation between the odd-even harmonics and the asymmetry of the THz-assisted laser-atomic system -- atoms in a fundamental mid-IR laser pulse combined with a THz laser. First, we demonstrate numerically and then analytically formulize the harmonic even-to-odd ratio as a function of the THz electric field, the source of the system's asymmetry. Notably, we suggest a scaling that makes the obtained rule universal, independent of the parameters of both the fundamental pulse and atomic target. This universality facilitates us to propose a general pump-probe scheme for THz waveform sampling from the even-to-odd ratio, measurable within a conventional compact setup