Modern power systems are becoming increasingly dynamic, with changing topologies and time-varying loads driven by renewable energy variability, electric vehicle adoption, and active grid reconfiguration. Despite these changes, publicly available test cases remain scarce, due to security concerns and the significant effort required to anonymize real systems. Such limitations call for generative tools that can jointly synthesize grid structure and nodal dynamics. However, modeling the joint distribution of network topology, branch attributes, bus properties, and dynamic load profiles remains a major challenge, while preserving physical feasibility and avoiding prohibitive computational costs. We present PowerGrow, a co-generative framework that significantly reduces computational overhead while maintaining operational validity. The core idea is dependence decomposition: the complex joint distribution is factorized into a chain of conditional distributions over feasible grid topologies, time-series bus loads, and other system attributes, leveraging their mutual dependencies. By constraining the generation process at each stage, we implement a hierarchical graph beta-diffusion process for structural synthesis, paired with a temporal autoencoder that embeds time-series data into a compact latent space, improving both training stability and sample fidelity. Experiments across benchmark settings show that PowerGrow not only outperforms prior diffusion models in fidelity and diversity but also achieves a 98.9% power flow convergence rate and improved N-1 contingency resilience. This demonstrates its ability to generate operationally valid and realistic power grid scenarios.

Deep sequence models have achieved notable success in time-series analysis, such as interpolation and forecasting. Recent advances move beyond discrete-time architectures like Recurrent Neural Networks (RNNs) toward continuous-time formulations such as the family of Neural Ordinary Differential Equations (Neural ODEs). Generally, they have shown that capturing the underlying dynamics is beneficial for generic tasks like interpolation, extrapolation, and classification. However, existing methods approximate the dynamics using unconstrained neural networks, which struggle to adapt reliably under distributional shifts. In this paper, we recast time-series problems as the continuous ODE-based optimal control problem. Rather than learning dynamics solely from data, we optimize control actions that steer ODE trajectories toward task objectives, bringing control-theoretical performance guarantees. To achieve this goal, we need to (1) design the appropriate control actions and (2) apply effective optimal control algorithms. As the actions should contain rich context information, we propose to employ the discrete-time model to process past sequences and generate actions, leading to a coordinate model to extract long-term temporal features to modulate short-term continuous dynamics. During training, we apply model predictive control to plan multi-step future trajectories, minimize a task-specific cost, and greedily select the optimal current action. We show that, under mild assumptions, this multi-horizon optimization leads to exponential convergence to infinite-horizon solutions, indicating that the coordinate model can gain robust and generalizable performance. Extensive experiments on diverse time-series datasets validate our method's superior generalization and adaptability compared to state-of-the-art baselines.

The establishment of high-speed optical links between marine platforms for underwater internet of things (UIoT) applications is a rapidly evolving research area. This study investigates UIoT connectivity between mobile marine platforms, focusing on high-definition data retrieval from the ocean. We propose a virtual tethered autonomous underwater vehicle (VTAUV) system, which integrates the advantages of both tethered and untethered architectures to enhance real-time underwater communication. The core of our approach is an acousto-optic tethering method, which enables stable, high-speed optical links between mobile platforms. By leveraging acoustic ranging and a reinforcement learning-based tracking controller, the VTAUV effectively follows an autonomous surface vehicle (ASV). A multi-beam sonar system and a real-time processing algorithm accurately estimate pointing errors, ensuring robust optical link establishment for continuous data transmission. We validate our method through sea trials, demonstrating that the VTAUV can successfully maintain acousto-optic tethering while significantly reducing pointing errors with a moving ASV. These findings mark an important step toward reliable, high-bandwidth underwater communications, paving the way for future scalable UIoT deployments.

Offline reinforcement learning (RL) seeks to learn decision-making policies from historical datasets when online interaction is costly and risky. Static logs are common with suboptimal or noisy transitions and incomplete data coverage. Previous behavior-cloning methods regularize policies to shrink updates, yet they repeatedly sample low-quality transitions. Such uniform treatment propagates biases/errors in approximating Qfunction. Resampling strategies (e.g., PER and DisCor) attempt to identify the most valuable transitions but rely on online feedback or heuristic reward as utility estimates, inapplicable in purely offline contexts. We tackle this limitation with$S^{2} D C$, a selfsupervised distribution correction framework to reweight offline samples without any external supervision.$\mathbf{S}^{\mathbf{2}}$DC quantifies the information gain via how much each transition sharpens the current value estimate. It then adaptively reweights transitions to amplify high-utility transitions while downweighting redundant or suboptimal samples. Experiments on standard D4RL benchmarks and critical power system management show that$\mathbf{S}^{2}$DC significantly improves policy performance, convergence speed, and stability. This demonstrates that$\mathrm{S}^{2} \text{DC}$converts biased logs, which were previously intractable, into a learning asset to boost fully offline RL.

Invertible neural networks (INNs) have gained attention for solving inverse problems where learning a bijective mapping is essential. However, despite their popularity, little guidance exists on how to select, adapt, or deploy different INN architectures, such as i-ResNet, NICE, or DipDNN, across diverse application domains. In this paper, we provide a unified theoretical and empirical analysis of two major INN families: iterative-contraction networks (e.g., i-ResNet) and triangular coupling networks (e.g., NICE, DipDNN). We show that these models exhibit fundamental tradeoffs in invertibility, expressivity, and compatibility with domain constraints. For example, contractive models fail to recover mappings with large gains or sign flips, whereas triangular schemes inherit rigid channel splits that can restrict feature richness. Building on these insights, we introduce a general-purpose customization framework. It decouples physics-informed constraints from measurement-driven inversion through a parallel INN design. We benchmark representative INNs on cross-domain tasks: edge monitoring, PDE dynamics, and robotic control. Our findings offer first comprehensive design guideline for choosing and adapting INN architectures based on problem structure, shedding light on when and how invertibility-based bidirectional inferences can be reliably applied in practice.

Shapley value-based methods have become foundational in explainable artificial intelligence (XAI), offering theoretically grounded feature attributions through cooperative game theory. However, in practice, particularly in vision tasks, the assumption of feature independence breaks down, as features (i.e., pixels) often exhibit strong spatial and semantic dependencies. To address this, modern SHAP implementations now include the Owen value, a hierarchical generalization of the Shapley value that supports group attributions. While the Owen value preserves the foundations of Shapley values, its effectiveness critically depends on how feature groups are defined. We show that commonly used segmentations (e.g., axis-aligned or SLIC) violate key consistency properties, and propose a new segmentation approach that satisfies the$\mathcal{T}$-property to ensure semantic alignment across hierarchy levels. This hierarchy enables computational pruning while improving attribution accuracy and interpretability. Experiments on image and tabular datasets demonstrate that O-Shap outperforms baseline SHAP variants in attribution precision, semantic coherence, and runtime efficiency, especially when structure matters.

Neural ordinary differential equations (NODE) have garnered significant attention for their design of continuous-depth neural networks and the ability to learn data/feature dynamics. However, for high-dimensional systems, estimating dynamics requires extensive calculations and suffers from high truncation errors for the ODE solvers. To address the issue, one intuitive approach is to consider the non-trivial topological space of the data distribution, i.e., a low-dimensional manifold. Existing methods often rely on knowledge of the manifold for projection or implicit transformation, restricting the ODE solutions on the manifold. Nevertheless, such knowledge is usually unknown in realistic scenarios. Therefore, we propose a novel approach to explore the underlying manifold to restrict the ODE process. Specifically, we employ a structure-preserved encoder to process data and find the underlying graph to approximate the manifold. Moreover, we propose novel methods to combine the NODE learning with the manifold, resulting in significant gains in computational speed and accuracy. Our experimental evaluations encompass multiple datasets, where we compare the accuracy, number of function evaluations (NFEs), and convergence speed of our model against existing baselines. Our results demonstrate superior performance, underscoring the effectiveness of our approach in addressing the challenges of high-dimensional datasets.

Learning dynamics is essential for model-based control and Reinforcement Learning in systems operating in changing environments, such as robotics, autonomous vehicles, and power systems. However, limited system measurements, such as those from low-resolution meters, demand sample-efficient learning. Symmetry provides a powerful inductive bias by characterizing equivalent relations in system behavior to improve sample efficiency. While recent methods attempt to discover symmetries from data, they typically assume a single global symmetry group and treat symmetry discovery and dynamic learning as separate tasks, leading to limited expressiveness and error accumulation. In this paper, we propose the Latent Mixture of Symmetries (Latent MoS), an expressive model that captures symmetry-governed latent factors from complex dynamical measurements. Latent MoS focuses on dynamic learning while locally preserving the underlying symmetric transformations. To further capture long-range temporal equivalence, we introduce a hierarchical architecture that stacks Latent MoS blocks across multiple time scales. Numerical experiments across diverse physical systems demonstrate that Latent MoS significantly outperforms state-of-the-art baselines in interpolation and extrapolation tasks while offering interpretable latent representations suitable for future geometric and safety-critical analysis.

Existing attack prevention strategies in smart grid including firewalls, encryption, and access controls, face the challenge of static configurations prone to exploitation. Unlike traditional mechanisms, moving-target defense (MTD), by dynamically altering system parameters, introduces a layer of unpredictability, making it a potent tool against cyber-attacks, including false data injection attacks (FDIA) and zero-day exploits. While MTD has demonstrated efficacy in dc systems by complicating attackers' efforts, its application to ac systems introduces new complexities. AC systems' nonlinearity and vulnerability to topology changes challenge traditional MTD methods, especially in ensuring convergence and adapting to dynamic topologies during emergencies. Such methods, though innovative, fall short in real-world applications where topology changes can render such methods ineffective. Recognizing these limitations, our work introduces a data-driven moving-target defense (DD-MTD) strategy that employs Kullback–Leibler divergence and the Kolmogorov–Smirnov test. Our method quantifies the impact of system perturbations to improve FDIA detection while ensuring changes adhere to operational and cost constraints, a critical factor during contingencies. Our approach, leveraging piecewise linear approximation and mixed-integer linear programming, addresses convergence and adaptability issues, offering a robust defense for ac systems. Simulations on IEEE 14 and 118-bus systems demonstrate that our DD-MTD method enhances detection rates and efficiency, outperforming existing state-of-the-art MTD strategies.

Consistent bi-directional inferences are the key for many machine learning applications. Without consistency, inverse learning-based inferences can cause fuzzy images, erroneous control signals, and cascading failure in SCADA systems. Since standard deep neural networks (DNNs) are not inherently invertible to offer consistency, some past methods reconstruct DNN architecture analytically for one-to-one correspondence but compromise key features such as universal approximation. Other work maintains the capability of universal approximation in DNNs via iterative numerical approximation. However, these methods limit their applications significantly due to Lipschitz conditions and issues of numerical convergence. The dilemma of the analytical and numerical methods is the incompatibility between nonlinear layer compositions and bijective function construction for inverse modeling. Based on the observation, we propose decomposed-invertible-pathway DNNs (DipDNN). It relaxes the redundant reconstruction of nested DNN in the former methods and eases the Lipschitz constraint. As a result, we strictly guarantee the consistency of global inverse modeling without harming DNN's capability for universal approximation. As numerical stability and generalizability are keys for controlling critical infrastructures, we integrate contractive property with a parallel structure for inductive biases, leading to stable performance. Numerical results show that DipDNN performs significantly better than past methods, thanks to its enforcement of inverse consistency, numerical stability, and physical regularization.

Worldwide urbanization requires control systems to accommodate uncertain sources, e.g., wind and solar generations in the energy sector. This uncertainty poses significant challenges to optimal system operations. One solution is the data-driven approach, requiring great data qualities. To produce high-fidelity data, recent studies combine Generative Adversarial Networks (GANs) with physics to explore the stochastic physical data generation. Such an idea belongs to Physics-Informed Neural Networks (PINNs). However, PINN is not implementable when the parameters of system equations are unknown. While parameters of equations can be estimated via measurements, the estimation is inaccurate due to insufficient and/or missing data (e.g., derivatives of system states). We tackle the problem with an intuitive design: enhance the estimation by leveraging highfidelity fake data from GANs. Therefore, we propose to Enhance Physics-Informed Estimation via GAN (PIX-GAN). Specifically, PIX-GAN is based on the stochastic State Space Model (SSM) of physical systems. Then, we design a stochastic PINN to generate fake data. For example, the generator of PIX-GAN contains a probabilistic boundary-condition loss, quantified via a distribution difference. To measure the difference, a discriminator is utilized. Additionally, we design a parameter estimator to estimate the parameters of the SSM, bringing a function-form loss to PIX-GAN to better fit the underlying physics. Finally, we propose an iterative algorithm to train PIX-GAN efficiently. Extensive experiments demonstrate the high performance of PIXGAN using diversified physical systems.

Variational quantum algorithms (VQAs) are the leading algorithm for achieving quantum advantage using near-term quantum computers. VQAs use parameterized quantum circuits for inference, and the variational parameters in quantum circuits can be trained using a classical optimizer. The parameters are trained to guide how the quantum bits evolve and make the final measurements closely match the ground truth. However, this way of learning from raw data makes it difficult to capture the underlying dynamic information in the data, especially for time series data. To address this limitation, we proposed continuous variational quantum algorithms (CVQAs) for time series in this paper. CVQAs use quantum variational circuits to parameterize the dynamics of time series, thus they can learn the dynamic information behind the data. After the dynamics are trained, the prediction results will be obtained by a differential equation solver working on the dynamics. Since we aim to model the dynamics of data instead of the data itself, the quantum circuit in our approach will need fewer qubits and variational gates. To evaluate our proposed approach, we compare our model with baseline models on several weather time series. Experimental results prove that our approach has better or equivalent results but with fewer qubits and variational gates compared to baseline models.

Physical systems are extending their monitoring capacities to edge areas with low-cost, low-power sensors and advanced data mining and machine learning techniques. However, new systems often have limited data for training the model, calling for effective knowledge transfer from other relevant grids. Specifically, Domain Adaptation (DA) seeks domain-invariant features to boost the model performance in the target domain. Nonetheless, existing DA techniques face significant challenges due to the unique characteristics of physical datasets: (1) complex spatial-temporal correlations, (2) diverse data sources including node/edge measurements and labels, and (3) large-scale data sizes. In this paper, we propose a novel cross-graph DA based on two core designs of graph kernels and graph coarsening. The former design handles spatial-temporal correlations and can incorporate networked measurements and labels conveniently. The spatial structures, temporal trends, measurement similarity, and label information together determine the similarity of two graphs, guiding the DA to find domain-invariant features. Mathematically, we construct a Graph kerNel-based distribution Adaptation (GNA) with a specifically-designed graph kernel. Then, we prove the proposed kernel is positive definite and universal, which strictly guarantees the feasibility of the used DA measure. However, the computation cost of the kernel is prohibitive for large systems. In response, we propose a novel coarsening process to obtain much smaller graphs for GNA. Finally, we report the superiority of GNA in diversified systems, including power systems, mass-damper systems, and human-activity sensing systems.

In modern complex physical systems, advanced sensing technologies extend the sensor coverage but also increase the difficulties of improving system monitoring capabilities based on real-time data availability. Traditional model-based methods of sensor management are limited to specific systems/settings, which can be challenged when system knowledge is intractable. Fortunately, the large amount of data collected in real-time allows machine learning methods to be a complement. Especially, reinforcement learning-based control is recognized for its capability to dynamically interact with systems. However, the direct implementation of learning methods easily overfits and results in inaccurate physics modeling for sensor management. Although physical regularization is a popular direction to bridge the gap, learning-based sensor control still suffers from convergence failure under highly complex and uncertain scenarios. This paper develops physics-embedded and self-supervised reinforcement learning for sensor management using an intrinsic reward. Specifically, the intrinsic-motivated sensor management (IMSM) constructs the local surprise information from the physical latent features, which captures hidden states in observations, and thus intrinsically motivates the agent to speed-up exploration. We show that the designs can not only relieve the lack of consistency with underlying physics/physical dynamics, but also adapt the global objective of maximizing monitoring capabilities to local environment changes. We demonstrate its effectiveness by experiments on physical system sensor control. The proposed model is implemented for the sensor management of unmanned vehicles and sensor rescheduling in complex/settled power systems, with or without observability constraints. Numerical results show that our model provides consistently higher threat detection accuracy and better observability recovery, as compared to existing methods.

Learning the underlying equation from data is a fundamental problem in many disciplines. Recent advances rely on Neural Networks (NNs) but do not provide theoretical guarantees in obtaining the exact equations owing to the non-convexity of NNs. In this paper, we propose Convex Neural Symbolic Learning (CoNSoLe) to seek convexity under mild conditions. The main idea is to decompose the recovering process into two steps and convexify each step. In the first step of searching for right symbols, we convexify the deep Q-learning. The key is to maintain double convexity for both the negative Q-function and the negative reward function in each iteration, leading to provable convexity of the negative optimal Q function to learn the true symbol connections. Conditioned on the exact searching result, we construct a Locally Convex equation Learning (LoCaL) neural network to convexify the estimation of symbol coefficients. With such a design, we quantify a large region with strict convexity in the loss surface of LoCaL for commonly used physical functions. Finally, we demonstrate the superior performance of the CoNSoLe framework over the state-of-the-art on a diverse set of datasets.

Cyber-Physical Systems (CPS) plays a critical role in today’s social life, especially with occasional pandemic events. With more reliance on the cyber operation of infrastructures, it is important to understand attacking mechanisms in CPS for potential solutions and defenses, where False Data Injection Attack (FDIA) is an important class. FDIA methods in the literature require the mathematical CPS model and state variable values to create an efficient attack vector, unrealistic for many attackers in the real world. Also, they do not have performance guarantee. This paper shows that it is possible to deploy a FDIA without having the CPS model and state variables information. Additionally, we prove a theoretic bound for the proposed method. Specifically, we design a scheme that learns an implicit CPS model to create tampered sensor measurements to deploy an attack based only on historical data. The proposed framework utilizes a Wasserstein generative adversarial network with two regularization terms to create such tampered measurements also known as adversarial examples. To build an attack with confidence, we present a proof based on convergence in distribution and Lipschitz norm to show that our method captures the real observed measurement distribution. This means that our model learns the complex underlying processes from the CPSs. We demonstrate the robustness and universality of our proposed framework based on two diversified adversarial examples with different systems, domains, and datasets.

The large amount of data collected in complex physical systems allows machine learning models to solve a variety of prediction problems. However, the directly applied learning approaches, especially deep neural networks (DNN), are difficult to balance between universal approximation to minimize error and the interpretability to reveal underlying physical law. Their performance drops even faster with system unobservability (of measurements) issues due to limited measurements. In this paper, we construct the novel physics interpretable shallow-deep neural networks to integrate exact physical interpretation and universal approximation to address the concerns in previous methods. We show that not only the shallow layer of the structural DNN extracts interpretable physical features but also the designed physical-input convex property of the DNN guarantees the true physical function recovery. While input convexity conditions are strict, the proposed model retains the representation capability to universally approximate for the unobservable system regions. We demonstrate its effectiveness by experiments on physical systems. In particular, we implement the proposed model on the forward kinematics and complex power flow reproduction tasks, with or without observability issues. We show that, besides the physical interpretability, our model provides consistently smaller or similar prediction error for system identification, compared to the state-of-art learning methods.

Relation extraction is an important NLP task to extract the semantic relationship between two entities. Recently, large-scale pre-training language models have achieved excellent performance in many NLP applications. Most of the existing relation extraction models mainly rely on context information, but entity information is also very important for relation extraction, especially domain knowledge of entity and the direction between entity pairs. In this paper, based on the pre-trained BERT model, we propose a multi-task joint relation extraction model incorporating knowledge representation learning(KRL). The experimental results on the SemEval 2010 task 8 dataset and the KBP37 dataset show that our proposed model outperforms most of state-of-the-art methods. The results on the larger dataset FewRel80 refined from FewRel also indicate that increasing the knowledge representation learning as an auxiliary objective is helpful for the relation extraction task.

Deep neural networks (DNNs) have been extensively applied to various fields, including physical-system monitoring and control. However, the requirement of a high confidence level in physical systems made system operators hard to trust black-box type DNNs. For example, while DNN can perform well at both training data and testing data, but when the physical system changes its operation points at a completely different range, never appeared in the history records, DNN can fail. To open the black box as much as possible, we propose a Physics-Consistent Neural Network (PCNN) for physical systems with the following properties: (1) PCNN can be shrunk to physical equations for sub-areas with full observability, (2) PCNN reduces unobservable areas into some virtual nodes, leading to a reduced network. Thus, for such a network, PCNN can also represent its underlying physical equation via a specifically designed deep-shallow hierarchy, and (3) PCNN is theoretically proved that the shallow NN in the PCNN is convex with respect to physical variables, leading to a set of convex optimizations to seek for the physics-consistent initial guess for the PCNN. We also develop a physical rule-based approach for initial guesses, significantly shortening the searching time for large systems. Comprehensive experiments on diversified systems are implemented to illustrate the outstanding performance of our PCNN.

Machine Learning (ML) is gaining increasing popularity to tackle uncertainty in physical systems, such as modern power systems. However, ML models can be hardly trained for newly-built power grids with limited data, especially when different power grids have different dimensionalities and distributions for measurement data. To tackle this problem, we propose a novel Heterogeneous Transfer Learning (HTL)-based method to boost the data volume of the target grid. Specifically, we propose a Merged Multi-Modal Gaussian Graphical Model ( M3G2M) with a physical data merging process for knowledge transfer. To solve the maximum likelihood estimation of M3G2M with imbalanced data from two grids, we propose a novel Expectation-Maximization algorithm. Finally, we quantify the negative transfer via the KL-Divergence to measure the distribution similarity between the source grid and the target grid for the transferring confidence. We demonstrate the advantages and the generalizability of our proposed models in diversified data sets for power systems and human action-sensing systems.

The rapid development of nonvolatile memory technologies such as flash, PCM, and Memristor has made processing in storage (PIS) a viable approach. We present an FPGA module augmented to an SSD storage controller that provides wire-speed object deserialization, referred to as HODS for hardware object deserialization in SSD. A pipelined circuit structure was designed to tailor to high-speed data conversion specifically. HODS is capable of conducting deserialization while data is being transferred on I/O bus from the storage device to host. The FPGA module has been integrated with our newly designed NVM-e SSD. The working prototype demonstrated significant performance benefits. The FPGA module can process data in line speed at 100MHz on 16 Byte data stream. For integer benchmarks, HODS showed deserialization speedup of 8~12× as compared to the traditional deserialization on a high-end host CPU. The speedup can reach 17~21× for floating-point datasets. The measured object deserialization throughput is 1GB/s on average at a clock speed of 100MHz. The overall performance improvements at the application level range from 10% to a factor of 4.3× depending on the proportion of deserialization time over total application running time. Compared to traditional SSD on the same server, HODS showed visible differences regarding application execution time while running Matlab, 3D modeling, and scientific computations.

In the fact of growing number of cases, Chinese courts have gradually formed a trial mode to improve the efficiency of trials by conducting trials around the controversial issues. However, identifying the controversy issue in specific cases is not only affected by the uncertainty of facts and laws, but also by the discretion of the judges and extra-case factors, and cannot be expressed as a standard format, which lead to the controversial issues based case retrieval a challenge problem. In this paper, we propose a controversial issues merging algorithm based on K-means clustering for Chinese legal texts. The proposed algorithm can determine the number of clusters of the given cause of action automatically and merge the controversial issues semantically, which makes the case information retrieval more accurate and effective.

This paper considers the asymptotic behavior of the outage probability of a two-source block-fading single-antenna Gaussian interference channel in the high-SNR regime by means of the diversity-multiplexing tradeoff. A general setting where the user rates and the average channel gains are not restricted to be symmetric is investigated. This asymmetric scenario allows to analyze networks with “mixed” interference, i.e., when different sources are at different distance from their intended destination, that are not possible under the commonly used symmetric assumption. Inner and outer bounds for the diversity are derived. The outer bound is based on the recent “to within one bit” capacity result of Etkin for the unfaded Gaussian channel and is a re-derivation of a known bound for which an error is pointed out. The inner bound is based on the Han and Kobayashi achievable region both without rate splitting and with a rate spitting inspired by the “to within one bit” capacity result. An analytical comparison of the diversity upper and lower bounds for a general channel seems difficult; by numerical evaluations, the two bounds are shown to coincide for a fairly large set of channel parameters.

Deploying the sensor nodes at the best locations for random field reconstruction via sensor network is a fundamental task. One-dimensional random field is a stochastic process. In this paper, we first propose an optimal sensor deployment strategy for Wiener process estimation. The optimal locations for the deployed sensors are uniformly distributed in the field. In addition, we propose a suboptimal sensor deployment to estimate the Gaussian random field which is described as an Ornstein-Uhlenbeck process. We show that the suboptimal deployment strategy for Gaussian random field is also uniformly distributed. Several simulations show the performance of our proposed deployment strategies.

This paper considers the asymptotic behavior of two-source block-fading single-antenna Gaussian Interference Channels in the high-SNR regime by means of the diversity multiplexing tradeoff. We consider a general setting where the users and the average channel gains are not restricted to be symmetric. Our results are not just extensions of previous results for symmetric networks, as our setting covers scenarios that are not possible under the symmetric assumption, such as the case of "mixed" interference, i.e., when difference sources have different distances from their intended receivers. We derive lower bounds on the DMT by considering rate splitting. We show that, rate splitting improves the inner bound with respect to the previous results and coincides with the outer bound for a fairly large set of channel parameters.

This work analyzes a particular achievable region for Gaussian interference channels (IFC) derived from the general Han-Kobayashi region. By reformulating the Han-Kobayashi achievable region as the sum of two sets, we characterize the maximum achievable sum-rate with Gaussian inputs and without time-sharing in closed from for any channel parameter. We then show that the computed sum-rate meets the upper bound by Kramer for any IFC with mixed interference, and not only for IFC with strong interference. We then show that for a certain subclass of IFCs with mixed interference, the capacity region contains a line segment of slope -1 of which we characterize the extreme points in term of the power allocation among private and common messages.