Detecting Low Probability of Intercept (LPI) radar signals in congested spectral environments remains a difficult challenge, as fixed-resolution spectrograms cannot simultaneously capture fine temporal and spectral details. We present TF-Attn-YOLO, a YOLO-based detector augmented with time–frequency attention blocks (TF-Attn) that emphasize informative spectro-temporal patterns while reducing complexity. To overcome the resolution trade-off, we leverage multi-resolution spectrograms processed jointly within the detector. Experiments on a large simulated LPI dataset show consistent gains in detection probability at low SNR, improved time–frequency localization, and more reliable waveform discrimination compared to single-resolution baselines1.
The string-matching problem has a broad variety of applications due to its pattern-matching ability. The circuit-level implementation of a quantum string-matching algorithm, which matches a search string (pattern) of length M inside a longer text of length N, has already been demonstrated in the literature to outperform its classical counterparts in terms of time complexity and space complexity. Higher-dimensional quantum computing is becoming more and more common as a result of its powerful storage and processing capabilities. In this article, we have shown an improved quantum circuit implementation for the string-matching problem with the help of higher-dimensional intermediate temporary qudits. It is also shown that with the help of intermediate qudits not only the complexity of depth can be reduced but also query complexity can be reduced for a quantum algorithm, for the first time to the best of our knowledge. Our algorithm has an improved query complexity of O(root N - M + 1) with overall time complexity O (root N - M + 1 ((log (N - M + 1) log N) + log(M))) as compared to the state-of-the-art work which has a query complexity of root O( N) with overall time complexity O(root N ((log N)(2) + log(M))), while the ancilla count also reduces to N/2 from N/2 + M. The cost of the state-of-theart quantum circuits for string-matching problem is colossal due to a huge number of Fredkin gates and multi-controlled Toffoli gates. We have exhibited an improved gate cost and depth over the circuit by applying a proposed Fredkin gate decomposition with intermediate qutrits (3-dimensional qudits or ternary systems) and already existing logarithmic-depth decomposition of n-qubit Toffoli or multi-controlled Toffoli gate (MCT) with intermediate ququarts (4-dimensional qudits or quaternary systems). We have also asserted that the quantum circuit cost is relevant instead of using higher dimensional qudits through error analysis.
Infrastructure as Code (IaC) uses versionable software code to define, deploy, and configure physical computational resources, software execution platforms, and applications. As a result, IaC enables the scalable management of complex computing environments while preventing environment drift. IaC frameworks typically offer specific languages such as the industrial Terraform, Ansible, Chef, or TOSCA-standing for Topology and Orchestration Specification for Cloud Applications-the OASIS (Organization for the Advancement of Structured Information Standards) open standard approach to IaC. Developing high-quality IaC for deploying and managing applications demands expertise and knowledge in specific IaC languages, infrastructure resources, resource providers, quality issues in IaC scripts, and so on. While several model-driven engineering (MDE) approaches have been proposed to simplify IaC development, they cannot capture and use expert knowledge to assist with modeling tasks and MDE processes by providing interactive recommendations. This paper presents a knowledge-based framework for guiding the model-driven development of IaC. We use TOSCA as the target IaC language as it is an open standard. We enable IaC and resource experts to share their IaC and resource-related knowledge with application operational experts to help simplify the development of application deployment models. We use an ontology to record the relevant deployment knowledge and ontology reasoning to implement modeling guidance capabilities such as TOSCA model auto-completion, code smell and error detection, and model element matchmaking. We show the flexibility of our methodology by applying it to three industrial applications, covering cloud, edge, and HPC (High-Performance Computing) domains. Moreover, we also assess the use acceptance of our approach and framework by conducting controlled experiments with expert and non-expert IaC users. The results indicate that our method can simplify IaC development by providing appropriate recommendations.
Verletzungen des vorderen Kreuzbands (VKB) im Kindes- und Jugendalter stellen eine schwerwiegende Verletzung des Kniegelenks dar. In den vergangenen 20 Jahren stiegen die Inzidenzen weltweit erheblich. Bei der Behandlung sind neben Begleitverletzungen unbedingt die Besonderheiten des noch wachsenden Bewegungsapparats für eine erfolgreiche Therapie zu berücksichtigen. Zahlreiche Behandlungsstrategien wurden über die Jahrzehnte angewandt. In Langzeitdaten zeigen sich nun signifikant bessere Ergebnisse nach operativer Stabilisierung. Dabei hat sich die transepiphysäre VKB-Plastik als Goldstandard etabliert hinsichtlich funktioneller Outcomes und (Leistungs-)Sportfähigkeit.
This research work proposes a LLM-powered chatbot aimed at supporting students and educators in databases and information systems in higher education. The chatbot leverages the capabilities of LLM to facilitate interactive learning, offering real-time assistance on various database-related topics, including query formulation, data modeling, and system design. The chatbot incorporates advanced pre-processing techniques such as text cleaning, tokenization, stop words removal, and stemming/lemmatization to prepare user inputs for effective analysis. Feature extraction methods such as Bag-of-Words (BOW), n-grams, and Improved Term Frequency-Inverse Document Frequency (ITF-IDF) convert textual data into numerical forms, enhancing model understanding and accuracy. Additionally, deep learning algorithms, Hybrid Convolutional Long Short-Term Memory (HCLSTM) networks, are integrated to enhance the chatbot's ability to generate contextually relevant responses. Optimization is achieved through the Improved Artificial Bee Colony (IABC) algorithm, which fine-tunes the chatbot's parameters for superior performance. Through an interactive user interface, the chatbot provides personalized learning experiences, helping students grasp complex concepts in databases and information systems. Initial testing indicates the system's effectiveness in delivering insightful, tailored responses, making it a valuable tool for enhancing the learning process in higher education.
