• This work proposes a practical initial layout method that utilizes matrix formulation to determine enhanced qubit assignments, without iterative optimization techniques and combinatorial search. • The proposed method computes the gradient of the objective function through a single matrix differentiation to obtain assignment suitability information between logical and physical qubits. • The gradient matrix encodes the interaction between physical-qubit centrality and logical-qubit connectivity, enabling direct mapping decisions that minimize error costs while satisfying hardware constraints. • Simulation results show that the proposed method improves computational efficiency while maintaining comparable execution reliability compared to existing qubit mapping methods. Qubit mapping is a crucial compilation process that assigns logical qubits from quantum circuits to physical qubits on quantum hardware, ensuring efficient and reliable execution on noisy intermediate-scale quantum (NISQ) computers. However, as the scale of quantum computers and circuit complexity increase, existing mapping approaches face significant computational complexity and suboptimal mapping quality. This work formulates the logic-to-physical qubit mapping problem as a matrix-form optimization problem to address scalability and computational efficiency challenges. The proposed method computes the gradient of the matrix-formulated problem through a single matrix differentiation and uses it as guidance to determine the enhanced qubit assignment, without the need for iterative combinatorial exploration and optimization solvers. Simulation results show that the proposed method achieves comparable execution reliability while significantly reducing the compilation time, even for multi-programming scenarios and large-scale quantum computers.

This paper presents a novel approach to addressing the control challenges of underactuated systems, focusing on the swing-up and stabilisation tasks on the double pendulum system. We propose the Average-Reward Entropy Advantage Policy Optimisation (AR-EAPO), a model-free reinforcement learning (RL) algorithm that integrates the strengths of the average-reward RL and the maximum entropy RL (MaxEnt RL). The average reward criterion allows the use of a simple reward function by naturally promoting the longterm goals, at the same time MaxEnt RL encourages the robustness of the policy. We validate our approach through simulations, consistently outperforming standard RL baselines and traditional control methods. Also, we provide preliminary test results on real double pendulum hardware. Additional experiments on MuJoCo environments further demonstrate AR-EAPO's efficacy on general continuous control tasks. This work underscores the potential of the average-reward criterion in simplifying control design while achieving superior results.

Entropy Regularisation is a widely adopted technique that enhances policy optimisation performance and stability. Maximum entropy reinforcement learning (MaxEnt RL) regularises policy evaluation by augmenting the objective with an entropy term, showing theoretical benefits in policy optimisation. However, its practical application in straightforward direct policy gradient settings remains surprisingly underexplored. We hypothesise that this is due to the difficulty of managing the entropy reward in practice. This paper proposes Entropy Advantage Policy Optimisation (EAPO), a simple method that facilitates MaxEnt RL implementation by separately estimating task and entropy objectives. Our empirical evaluations demonstrate that extending Proximal Policy Optimisation (PPO) and Trust Region Policy Optimisation (TRPO) within the MaxEnt framework improves optimisation performance, generalisation, and exploration in various environments. Moreover, our method provides a stable and performant MaxEnt RL algorithm for discrete action spaces.

Quantum multi-programming, which is a method to run multiple quantum programs concurrently, has the potential to significantly improve the overall throughput and resource utilization. However, previous approaches do not always guarantee consistent performance (e.g., circuit complexity and reliability) over all possible hardware platforms due to qubit connectivity and qubit error rates in individual hardware platforms. This paper proposes hardware-Agnostic QUbit Allocation, named$A Q U A$, that allows multiple quantum programs to run simultaneously while ensuring high reliability regardless of hardware platforms. The proposed AQUA optimizes quantum programs at the logicallevel, without physical qubit mappings and allocations, to ensure that they consist of fewer gates and shorter depths. By attempting to optimize quantum programs at the logical-level, regardless of hardware connectivity, the AQUA can ensure consistent circuit complexity and reliability. Then, the AQUA dynamically allocates quantum programs to more reliable physical qubits depending on circuit depths and qubit error rates. The effectiveness of the AQUA is demonstrated using several quantum hardware platforms, showing that AQUA ensures high reliability and consistent circuit complexity.

Detecting fake news has received a lot of attention. Many previous methods concatenate independently encoded unimodal data, ignoring the benefits of integrated multimodal information. Also, the absence of specialized feature extraction for text and images further limits these methods. This paper introduces an end-to-end model called TT-BLIP that applies the bootstrapping language-image pretraining for unified visionlanguage understanding and generation (BLIP) for three types for images, and bidirectional BLIP encoders for multimodal information. The Multimodal Tri-Transformer fuses tri-modal features using three types of multi-head attention mechanisms, ensuring integrated modalities for enhanced representations and improved multimodal data analysis. The experiments are performed using two fake news datasets, Weibo and Gossipcop. The results indicate TT-BLIP outperforms the state-of-the-art models.

Recent DNN model serving systems have begun to use multi-GPUs and distributed systems to serve a variety of inference models as services to users. However, modern GPU-based model serving systems cannot execute multiple inference models beyond the GPU memory size. This is because inference models occupy and execute on their own pre-allocated GPU memory that cannot be shared with other inference models. As a result, more GPUs or large systems are required as the demand for more inference models increases. This paper proposes an efficient GPU Memory Management-based model serving system, called GMM, to serve multiple inference models beyond the GPU memory limit. The GMM initializes the GPU memory space as a large tensor to allow all models to cache anywhere in the GPU memory without any constraints. Then to ensure that inference models can execute on the GPU without any conflicts, the GMM finds unused GPU memory space and uses the memory overwriting method to cache model’s parameters for execution. The proposed system allows for more inference models to be executed parallel on a single GPU than previous systems, resulting in higher throughput and in some cases, shorter inference time.

In this paper, we propose a Gaussian Random Trajectory guided Hierarchical Reinforcement Learning (GRT-HL) method for autonomous furniture assembly. The furniture assembly problem is formulated as a comprehensive human-like long-horizon manipulation task that requires a long-term planning and a sophisticated control. Our proposed model, GRT-HL, draws inspirations from the semi-supervised adversarial autoencoders, and learns latent representations of the position trajectories of the end-effector. The high-level policy generates an optimal trajectory for furniture assembly, considering the structural limitations of the robotic agents. Given the trajectory drawn from the high-level policy, the low-level policy makes a plan and controls the end-effector. We first evaluate the performance of GRT-HL compared to the state-of-the-art reinforcement learning methods in furniture assembly tasks. We demonstrate that GRT-HL successfully solves the long-horizon problem with extremely sparse rewards by generating the trajectory for planning.

Generating images from sound is a challenging task. This paper proposes a novel deep learning model that generates bird images from their corresponding sound information. Our proposed model includes a sound encoder in order to extract suitable feature representations from audio recordings, and then it generates bird images that corresponds to its calls using conditional generative adversarial networks (cGANs) with auxiliary classifiers. We demonstrate that our model produces better image generation results which outperforms other state-of-the-art methods in a similar context.

Hearthstone is a popular online collectible card game (CCG). Hearthstone imposes interesting challenges in developing a search algorithm for the game AI. As a CCG, it has a considerable amount of hidden information from each player’s private hand and deck. Moreover, the action space is full of stochastic actions compared to other similar games. That is, instead of a single move, each player is allowed to build a move sequence via various combinations of atomic actions. Therefore, when applying any heuristic search algorithm, the branching factor of the search space is extremely large. In this paper, we explore the use of Monte Carlo Tree Search (MCTS) with approaches to reduce the complexity of the search space and decide on the best strategy. First, we utilise state abstraction to present the search space as a Directed Acyclic Graph (DAG) and introduce a variant of Upper Confidence Bound for Trees (UCT) algorithm for the DAG. Next, we apply the sparse sampling algorithm to handle imperfect information and randomness and reduce the stochastic branching factor. This paper presents empirical evaluations of the proposed framework for Hearthstone and the experimental results suggest that our approach is well suited for developing a better AI agent.

In this paper, we present a novel architecture of a block interleaver in MB-OFDM systems based on Mixed Radix System (MRS). We prove mathematically that the proposed architecture can support bit permutations in the interleaving process. The hierarchical property of our proposed MRS-based design methodology allows the proposed architecture to support all the required data rates in the MB-OFDM systems with simple modular design. Furthermore, the same design to be used for the interleaver can also be used for the operation of de-interleaving, which reduces the implementation complexity significantly. The latency of our architecture is as low as 6 MB-OFDM symbols. In addition, when comparing our proposed architecture with the conventional approach, we are able to reduce the implementation complexity by 85.5%, 69.4%, and 40.3% for 80, 200, and 480 Mb/s data rates, respectively, while improving our operating maximum clock frequency by more than 3.3 times over the conventional design. We also show that the power consumption is reduced by 87.4%, 73.6%, and 39.8% for 80, 200, and 480 Mb/s, respectively.

As the Internet became a popular source of information, more and more people are using the Internet in their everyday life. Therefore, for the servers that maintain web page sites get a lot of transactions and to maintain a stable environment, a lot of web sites have more than one server for these transaction requests. A common architecture to balance the transactional load is by using a L7 switch to distribute the requests. The current L7 switch utilizes methods that cannot sustain the web site for long if there are dynamic interactive pages. There have been researches to load balance web sites for static web pages. However, as more and more web pages become dynamic and interactive, there needed to be a method that prolongs the web site crashes without buying large number of servers. Our methods use explicit CPU utilization information to dynamically decide which transactional request is mapped where. Using our methods, the longevity (the length of the web site remains open) of a website is higher than other researched/utilized methods.

A signal space diversity (SSD) scheme is one of techniques to achieve diversity gain in fading channels. This method consists of two key operations: constellation rotation and component-wise interleaving. Because of these operations, the decision boundaries for the SSD are no longer perpendicular, and thus, different coordination approaches are required for the analysis of error rates compared to conventional rectangular coordinates. In this letter, we derive an exact expression of the symbol error rate for the SSD scheme in Rician fading channels with M-QAM and M-PSK. By defining the ratio of the standard deviation of the inphase and quadrature components, we introduce a new signal model for the SSD. Based on this signal model, we can compute the exact symbol error rate using polar coordinates. The computer simulation results confirm the accuracy of our analysis for fading channels.

One of major design concerns about wireless headphone is power consumption. In this paper, we propose a novel design for extreme low power headphone implementation by extending the EPC Class-1 Generation 2 RFID protocol for delivering stream data. We prototyped a reader as a stream generator, and a passive tag as an audio receiver to consume less power than any other protocols for wireless headphones.

An ad hoc grid is a wireless heterogeneous computing environment without a fixed infrastructure. This study considers wireless devices that have different capabilities, have limited battery capacity, support dynamic voltage scaling, and are expected to be used for eight hours at a time and then recharged. To maximize the performance of the system, it is essential to assign resources to tasks (match) and order the execution of tasks on each resource (schedule) in a manner that exploits the heterogeneity of the resources and tasks while considering the energy constraints of the devices. In the single-hop ad hoc grid heterogeneous environment considered in this study, tasks arrive unpredictably, are independent (i.e., no precedent constraints for tasks), and have priorities and deadlines. The problem is to map (match and schedule) tasks onto devices such that the number of highest priority tasks completed by their deadlines during eight hours is maximized while efficiently utilizing the overall system energy. A model for dynamically mapping tasks onto wireless devices is introduced. Seven dynamic mapping heuristics for this environment are designed and compared to each other and to a mathematical bound.

This research investigates two distinct issues related to a resource allocation: its robustness and the failure rate of the heuristic used to determine the allocation. The target system consists of a number of sensors feeding a set of heterogeneous applications continuously executing on a set of heterogeneous machines connected together by high-speed heterogeneous links. There are number of quality of service (QoS) constraints that must be satisfied. A heuristic failure occurs if the heuristic cannot find an allocation that allows the system to meet its QoS constraints. The system is expected to operate in an uncertain environment where the workload, i.e., the load presented by the set of sensors, is likely to change unpredictably, possibly invalidating a resource allocation that was based on the initial workload estimate. The focus of this paper is the design of a static heuristic that: (a) determines a robust resource allocation, i.e., a resource allocation that maximizes the allowable increase in workload until a run-time reallocation of resources is required to avoid a QoS violation, and (b) has a very low failure rate. This study proposes a heuristic that performs well with respect to the failure rates and robustness to unpredictable workload increases. This heuristic is, therefore, very desirable for systems where low failure rates can be a critical requirement and where unpredictable circumstances can lead to unknown increases in the system workload.

Parallel and distributed systems may operate in an environment that undergoes unpredictable changes causing certain system performance features to degrade. Such systems need robustness to guarantee limited degradation despite fluctuations in the behavior of its component parts or environment. This research investigates the robustness of an allocation of resources to tasks in parallel and distributed systems. The main contributions are 1) a mathematical description of a metric for the robustness of a resource allocation with respect to desired system performance features against multiple perturbations in multiple system and environmental conditions, and 2) a procedure for deriving a robustness metric for an arbitrary system. For illustration, this procedure is employed to derive robustness metrics for three example distributed systems. Such a metric can help researchers evaluate a given resource allocation for robustness against uncertainties in specified perturbation parameters.

When user's tasks in a distributed heterogeneous computing environment are allocated resources, and the total demand placed on system resources by the tasks, for a given interval of time, exceeds the resources available, some tasks will receive degraded service, receive no service at all, or may be dropped from the system. One part of a measure to quantify the success of a resource management system (RMS) in such an environment is the collective value of the tasks completed during an interval of time, as perceived by the user, the application, or the policy maker. For the case where a task may be a data communication request, the collective value of data communication requests that are satisfied during an interval of time is measured. The Flexible Integrated System Capability (FISC) measure defined here is one way of obtaining a multi-dimensional measure for quantifying this collective value. While the FISC measure itself is not sufficient for scheduling purposes, it can be a critical part of a scheduler or a scheduling heuristic. The primary contribution of this work is providing a way to measure the collective value accrued by an RMS using a broad range of attributes and to construct a flexible framework that can be extended for particular problem domains.

Clustered microcalcifications on X-ray mammograms are an important sign for early detection of breast cancer. Texture-analysis methods can be applied to detect clustered microcalcifications in digitized mammograms. In this paper, a comparative study of texture-analysis methods is performed for the surrounding region-dependence method, which has been proposed by the authors, and conventional texture-analysis methods, such as the spatial gray-level dependence method, the gray-level run-length method, and the gray-level difference method. Textural features extracted by these methods are exploited to classify regions of interest (ROI's) into positive ROI's containing clustered microcalcifications and negative ROI's containing normal tissues. A three-layer backpropagation neural network is used as a classifier. The results of the neural network for the texture-analysis methods are evaluated by using a receiver operating-characteristics (ROC) analysis. The surrounding region-dependence method is shown to be superior to the conventional texture-analysis methods with respect to classification accuracy and computational complexity.

Clustered microcalcifications on X-ray mammograms are an important feature in the detection of breast cancer. For the detection of the clustered microcalcification on digitized mammograms, this paper proposes a texture analysis method called the surrounding region dependence method (SRDM), which is a statistical texture analysis based on the second-order histogram in two surrounding regions. Four textural features are extracted from the SRDM. These features are used to classify region of interests (ROIs) into positive ROIs containing clustered microcalcifications and negative ROIs of normal breast tissues. The three-layer backpropagation neural network is employed as a classifier with input data of four textural features. The classification performance of the proposed method is evaluated by using the round-robin method and the receiver operating-characteristics (ROC) analysis.

This paper proposes an adaptive image enhancement method for mammographic images, which is based on the first derivative and the local statistics. The adaptive enhancement method consists of three processing steps. The first step is to remove the film artifacts which may be misread as microcalcifications. The second step is to compute the gradient images by using the first derivative operators. The third step is to enhance the important features of the mammographic image by adding the adaptively weighted gradient images. Local statistics of the image are utilized for adaptive realization of the enhancement, so that image details can be enhanced and image noises can be suppressed. The objective performances of the proposed method were compared with those by the conventional image enhancement methods for a simulated image and the seven mammographic images containing real microcalcifications. The performance of the proposed method was also evaluated by means of the receiver operating-characteristics (ROC) analysis for 78 real mammographic images with and without microcalcifications.