Decomposition of Sexual Orientation Wage Gap in Massachusetts and Alabama from 2001 to 2015

Sexual orientation wage gap has been an emerging topic in economic analysis. In United States, most of the research is focused on national level. However, given the deeply divided political ideologies on Lesbian, Gay, Bisexual and Transgender (LGBT) rights issues such as same-sex marriage legislation among different states, one might wonder whether the wage gap would be also different. In this paper, we take the first step to present a systematical comparison of the sexual orientation wage gap from the past 15 years between Massachusetts and Alabama, who are opposite sides on almost every issue regarding LGBT rights. We employed Ordinary Least Squared regression and Oaxaca decomposition to analyze the wage gap and found that there is a smaller sexual orientation wage gap in Massachusetts than in Alabama, and the wage is also closing faster in Massachusetts

BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures

We introduce BriskStream, an in-memory data stream processing system (DSPSs) specifically designed for modern shared-memory multicore architectures. BriskStream's key contribution is an execution plan optimization paradigm, namely RLAS, which takes relative-location (i.e., NUMA distance) of each pair of producer-consumer operators into consideration. We propose a branch and bound based approach with three heuristics to resolve the resulting nontrivial optimization problem. The experimental evaluations demonstrate that BriskStream yields much higher throughput and better scalability than existing DSPSs on multi-core architectures when processing different types of workloads.Comment: To appear in SIGMOD'1

Benne: A Modular and Self-Optimizing Algorithm for Data Stream Clustering

In various real-world applications, ranging from the Internet of Things (IoT) to social media and financial systems, data stream clustering is a critical operation. This paper introduces Benne, a modular and highly configurable data stream clustering algorithm designed to offer a nuanced balance between clustering accuracy and computational efficiency. Benne distinguishes itself by clearly demarcating four pivotal design dimensions: the summarizing data structure, the window model for handling data temporality, the outlier detection mechanism, and the refinement strategy for improving cluster quality. This clear separation not only facilitates a granular understanding of the impact of each design choice on the algorithm's performance but also enhances the algorithm's adaptability to a wide array of application contexts. We provide a comprehensive analysis of these design dimensions, elucidating the challenges and opportunities inherent to each. Furthermore, we conduct a rigorous performance evaluation of Benne, employing diverse configurations and benchmarking it against existing state-of-the-art data stream clustering algorithms. Our empirical results substantiate that Benne either matches or surpasses competing algorithms in terms of clustering accuracy, processing throughput, and adaptability to varying data stream characteristics. This establishes Benne as a valuable asset for both practitioners and researchers in the field of data stream mining

CTransNet: Convolutional Neural Network Combined with Transformer for Medical Image Segmentation

The Transformer has been widely used for many tasks in NLP before, but there is still much room to explore the application of the Transformer to the image domain. In this paper, we propose a simple and efficient hybrid Transformer framework, CTransNet, which combines self-attention and CNN to improve medical image segmentation performance. Capturing long-range dependencies at different scales. To this end, this paper proposes an effective self-attention mechanism incorporating relative position information encoding, which can reduce the time complexity of self-attention from O(n2) to O(n), and a new self-attention decoder that can recover fine-grained features in encoder from skip connection. This paper aims to address the current dilemma of Transformer applications: i.e., the need to learn induction bias from large amounts of training data. The hybrid layer in CTransNet allows the Transformer to be initialized as a CNN without pre-training. We have evaluated the performance of CTransNet on several medical segmentation datasets. CTransNet shows superior segmentation performance, robustness, and great promise for generalization to other medical image segmentation tasks

RGN-Net: A Global Contextual and Multiscale Information Association Network for Medical Image Segmentation

Segmentation of medical images is a necessity for the development of healthcare systems, particularly for illness diagnosis and treatment planning. Recently, convolutional neural networks (CNNs) have gained amazing success in automatically segmenting medical images to identify organs or lesions. However, the majority of these approaches are incapable of segmenting objects of varying sizes and training on tiny, skewed datasets, both of which are typical in biomedical applications. Existing solutions use multi-scale fusion strategies to handle the difficulties posed by varying sizes, but they often employ complicated models more suited to broad semantic segmentation computer vision issues. In this research, we present an end-to-end dual-branch split architecture RGN-Net that takes the benefits of the two networks into greater account. Our technique may successfully create long-term functional relationships and collect global context data. Experiments on Lung, MoNuSeg, and DRIVE reveal that our technique reaches state-of-the-art benchmarks in order to evaluate the performance of RGN-Net

A Survey on Transactional Stream Processing

Transactional stream processing (TSP) strives to create a cohesive model that merges the advantages of both transactional and stream-oriented guarantees. Over the past decade, numerous endeavors have contributed to the evolution of TSP solutions, uncovering similarities and distinctions among them. Despite these advances, a universally accepted standard approach for integrating transactional functionality with stream processing remains to be established. Existing TSP solutions predominantly concentrate on specific application characteristics and involve complex design trade-offs. This survey intends to introduce TSP and present our perspective on its future progression. Our primary goals are twofold: to provide insights into the diverse TSP requirements and methodologies, and to inspire the design and development of groundbreaking TSP systems

TransNFV: Integrating Transactional Semantics for Efficient State Management in Virtual Network Functions

Managing shared mutable states in high concurrency state access operations is a persistent challenge in Network Functions Virtualization (NFV). This is particularly true when striving to meet chain output equivalence (COE) requirements. This paper presents TransNFV, an innovative NFV framework that incorporates transactional semantics to optimize NFV state management. The TransNFV integrates VNF state access operations as transactions, resolves transaction dependencies, schedules transactions dynamically, and executes transactions efficiently. Initial findings suggest that TransNFV maintains shared VNF state consistency, meets COE requirements, and skillfully handles complex cross-flow states in dynamic network conditions. TransNFV thus provides a promising solution to enhance state management and overall performance in future NFV platforms

A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics

Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. And a physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).Comment: 17 pages, 8 Figure

Automated Formation Control Synthesis from Temporal Logic Specifications

In this paper, we propose a novel framework using formal methods to synthesize a navigation control strategy for a multi-robot swarm system with automated formation. The main objective of the problem is to navigate the robot swarm toward a goal position while passing a series of waypoints. The formation of the robot swarm should be changed according to the terrain restrictions around the corresponding waypoint. Also, the motion of the robots should always satisfy certain runtime safety requirements, such as avoiding collision with other robots and obstacles. We prescribe the desired waypoints and formation for the robot swarm using a temporal logic (TL) specification. Then, we formulate the transition of the waypoints and the formation as a deterministic finite transition system (DFTS) and synthesize a control strategy subject to the TL specification. Meanwhile, the runtime safety requirements are encoded using control barrier functions, and fixed-time control Lyapunov functions ensure fixed-time convergence. A quadratic program (QP) problem is solved to refine the DFTS control strategy to generate the control inputs for the robots, such that both TL specifications and runtime safety requirements are satisfied simultaneously. This work enlights a novel solution for multi-robot systems with complicated task specifications. The efficacy of the proposed framework is validated with a simulation study

Automated Formation Control Synthesis from Temporal Logic Specifications

In this paper, we propose a novel framework using formal methods to synthesize a navigation control strategy for a multi-robot swarm system with automated formation. The main objective of the problem is to navigate the robot swarm toward a goal position while passing a series of waypoints. The formation of the robot swarm should be changed according to the terrain restrictions around the corresponding waypoint. Also, the motion of the robots should always satisfy certain runtime safety requirements, such as avoiding collision with other robots and obstacles. We prescribe the desired waypoints and formation for the robot swarm using a temporal logic (TL) specification. Then, we formulate the transition of the waypoints and the formation as a deterministic finite transition system (DFTS) and synthesize a control strategy subject to the TL specification. Meanwhile, the runtime safety requirements are encoded using control barrier functions, and fixed-time control Lyapunov functions ensure fixed-time convergence. A quadratic program (QP) problem is solved to refine the DFTS control strategy to generate the control inputs for the robots, such that both TL specifications and runtime safety requirements are satisfied simultaneously. This work enlights a novel solution for multi-robot systems with complicated task specifications. The efficacy of the proposed framework is validated with a simulation study