A Unified Bregman Alternating Minimization Algorithm for Generalized DC Programming with Application to Imaging Data

In this paper, we consider a class of nonconvex (not necessarily differentiable) optimization problems called generalized DC (Difference-of-Convex functions) programming, which is minimizing the sum of two separable DC parts and one two-block-variable coupling function. To circumvent the nonconvexity and nonseparability of the problem under consideration, we accordingly introduce a Unified Bregman Alternating Minimization Algorithm (UBAMA) by maximally exploiting the favorable DC structure of the objective. Specifically, we first follow the spirit of alternating minimization to update each block variable in a sequential order, which can efficiently tackle the nonseparablitity caused by the coupling function. Then, we employ the Fenchel-Young inequality to approximate the second DC components (i.e., concave parts) so that each subproblem reduces to a convex optimization problem, thereby alleviating the computational burden of the nonconvex DC parts. Moreover, each subproblem absorbs a Bregman proximal regularization term, which is usually beneficial for inducing closed-form solutions of subproblems for many cases via choosing appropriate Bregman kernel functions. It is remarkable that our algorithm not only provides an algorithmic framework to understand the iterative schemes of some novel existing algorithms, but also enjoys implementable schemes with easier subproblems than some state-of-the-art first-order algorithms developed for generic nonconvex and nonsmooth optimization problems. Theoretically, we prove that the sequence generated by our algorithm globally converges to a critical point under the Kurdyka-{\L}ojasiewicz (K{\L}) condition. Besides, we estimate the local convergence rates of our algorithm when we further know the prior information of the K{\L} exponent.Comment: 44 pages, 7figures, 5 tables. Any comments are welcom

Efficient Data Learning for Open Information Extraction with Pre-trained Language Models

Open Information Extraction (OpenIE) is a fundamental yet challenging task in Natural Language Processing, which involves extracting all triples (subject, predicate, object) from a given sentence. While labeling-based methods have their merits, generation-based techniques offer unique advantages, such as the ability to generate tokens not present in the original sentence. However, these generation-based methods often require a significant amount of training data to learn the task form of OpenIE and substantial training time to overcome slow model convergence due to the order penalty. In this paper, we introduce a novel framework, OK-IE, that ingeniously transforms the task form of OpenIE into the pre-training task form of the T5 model, thereby reducing the need for extensive training data. Furthermore, we introduce an innovative concept of Anchor to control the sequence of model outputs, effectively eliminating the impact of order penalty on model convergence and significantly reducing training time. Experimental results indicate that, compared to previous SOTA methods, OK-IE requires only 1/100 of the training data (900 instances) and 1/120 of the training time (3 minutes) to achieve comparable results

TSNet-SAC: Leveraging Transformers for Efficient Task Scheduling

In future 6G Mobile Edge Computing (MEC), autopilot systems require the capability of processing multimodal data with strong interdependencies. However, traditional heuristic algorithms are inadequate for real-time scheduling due to their requirement for multiple iterations to derive the optimal scheme. We propose a novel TSNet-SAC based on Transformer, that utilizes heuristic algorithms solely to guide the training of TSNet. Additionally, a Sliding Augment Component (SAC) is introduced to enhance the robustness and resolve algorithm defects. Furthermore, the Extender component is designed to handle multi-scale training data and provide network scalability, enabling TSNet to adapt to different access scenarios. Simulation demonstrates that TSNet-SAC outperforms existing networks in accuracy and robustness, achieving superior scheduling-making latency compared to heuristic algorithms

White Organic Light-Emitting Diodes with Thermally Activated Delayed Fluorescence Emitters

Recently, thermally activated delayed fluorescence (TADF) organic light-emitting diodes (OLEDs) have attracted both academic and industrial interest due to their extraordinary characteristics, such as high efficiency, low driving voltage, bright luminance, lower power consumption, and potentially long lifetime. In this chapter, various approaches to realize white OLEDs (WOLEDs) with TADF emitters have been introduced. The recent development of WOLEDs based on all TADF emitters, WOLEDs based on TADF and conventional fluorescence emitters, and WOLEDs based on TADF and phosphorescence emitters is highlighted. Particularly, the device structures, design strategies, working mechanisms, and electroluminescent processes of the representative high-performance WOLEDs with TADF emitters are reviewed. Moreover, challenges and opportunities for further enhancement of the performance of WOLEDs with TADF emitters are presented

Mahalanobis Distance Map Approach for Anomaly Detection

Web servers and web-based applications are commonly used as attack targets. The main issues are how to prevent unauthorised access and to protect web servers from the attack. Intrusion Detection Systems (IDSs) are widely used security tools to detect cyber-attacks and malicious activities in computer systems and networks. In this paper, we focus on the detection of various web-based attacks using Geometrical Structure Anomaly Detection (GSAD) model and we also propose a novel algorithm for the selection of most discriminating features to improve the computational complexity of payload-based GSAD model. Linear Discriminant method (LDA) is used for the feature reduction and classification of the incoming network traffic. GSAD model is based on a pattern recognition technique used in image processing. It analyses the correlations between various payload features and uses Mahalanobis Distance Map (MDM) to calculate the difference between normal and abnormal network traffic. We focus on the detection of generic attacks, shell code attacks, polymorphic attacks and polymorphic blending attacks. We evaluate accuracy of GSAD model experimentally on the real-world attacks dataset created at Georgia Institute of Technology. We conducted preliminary experiments on the DARPA 99 dataset to evaluate the accuracy of feature reduction

Different cDNA microarray patterns of gene expression reflecting changes during metastatic progression in adenoid cystic carcinoma

BACKGROUND: The metastatic ability of tumor cells is determined by level of expression of specific genes that may be identified with the aid of cDNA microarray containing thousands of genes and can be used to establish the expression profile of disease related genes in complex biological system. MATERIALS AND METHODS: Salivary adenoid cystic carcinoma cell line and its high metastases adenoid cystic carcinoma clone were used as model systems to reveal the gene expression alteration related to metastasis mechanism by cDNA microarray analysis. The correlation of metastatic phenotypic changes and expression levels of 4 selected genes (encoding CD98, L6, RPL29, and TSH) were further validated by using RT-PCR analysis of human tumor specimens from primary adenoid cystic carcinoma and corresponding metastasis lymph nodes. RESULTS: Of the 7,675 clones of known genes and expressed sequence tags (ESTs) that were analyzed, 30 showed significantly different (minimum 3 fold) expression levels in two cell lines. Out of 30 genes found differentially expressed, 18 were up regulated (with ratio more than 3) and 12 down regulated (with ratio less than 1/3). CONCLUSION: Some of these genes are known to be involved in human tumor antigen, immune surveillance, adhesion, cell signaling pathway and growth control. It is suggested that the microarray in combination with a relevant analysis facilitates rapid and simultaneous identification of multiple genes of interests and in this study it provided a profound clue to screen candidate targets for early diagnosis and intervention