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With the rapid advancement of technology, the recognition of underwater acoustic signals in complex environments has become increasingly crucial. Currently, mainstream underwater acoustic signal recognition relies primarily on time-frequency analysis to extract spectral features, finding widespread applications in the field. However, existing recognition methods heavily depend on expert systems, facing limitations such as restricted knowledge bases and challenges in handling complex relationships. These limitations stem from the complexity and maintenance difficulties associated with rules or inference engines. Recognizing the potential advantages of deep learning in handling intricate relationships, this paper proposes a method utilizing neural networks for underwater acoustic signal recognition. The proposed approach involves continual learning of features extracted from spectra for the classification of underwater acoustic signals. Deep learning models can automatically learn abstract features from data and continually adjust weights during training to enhance classification performance.

In recent years, formation control of unmanned vehicles has received considerable interest, driven by the progress in autonomous systems and the imperative for multiple vehicles to carry out diverse missions. In this paper, we address the problem of behavior-based formation control of mobile robots, where we use safe multi-agent reinforcement learning~(MARL) to ensure the safety of the robots by eliminating all collisions during training and execution. To ensure safety, we implemented distributed model predictive control safety filters to override unsafe actions. We focus on achieving behavior-based formation without having individual reference targets for the robots, and instead use targets for the centroid of the formation. This formulation facilitates the deployment of formation control on real robots and improves the scalability of our approach to more robots. The task cannot be addressed through optimization-based controllers without specific individual reference targets for the robots and information about the relative locations of each robot to the others. That is why, for our formulation we use MARL to train the robots. Moreover, in order to account for the interactions between the agents, we use attention-based critics to improve the training process. We train the agents in simulation and later on demonstrate the resulting behavior of our approach on real Turtlebot robots. We show that despite the agents having very limited information, we can still safely achieve the desired behavior.

Computer Vision (CV) labelling algorithms play a pivotal role in the domain of low-level vision. For decades, it has been known that these problems can be elegantly formulated as discrete energy minimization problems derived from probabilistic graphical models (such as Markov Random Fields). Despite recent advances in inference algorithms (such as graph-cut and message-passing algorithms), the resulting energy minimization problems are generally viewed as intractable. The emergence of quantum computations, which offer the potential for faster solutions to certain problems than classical methods, has led to an increased interest in utilizing quantum properties to overcome intractable problems. Recently, there has also been a growing interest in Quantum Computer Vision (QCV), with the hope of providing a credible alternative or assistant to deep learning solutions in the field. This study investigates a new Quantum Annealing based inference algorithm for CV discrete energy minimization problems. Our contribution is focused on Stereo Matching as a significant CV labeling problem. As a proof of concept, we also use a hybrid quantum-classical solver provided by D-Wave System to compare our results with the best classical inference algorithms in the literature.

The evolution of autonomous driving has made remarkable advancements in recent years, evolving into a tangible reality. However, a human-centric large-scale adoption hinges on meeting a variety of multifaceted requirements. To ensure that the autonomous system meets the user's intent, it is essential to accurately discern and interpret user commands, especially in complex or emergency situations. To this end, we propose to leverage the reasoning capabilities of Large Language Models (LLMs) to infer system requirements from in-cabin users' commands. Through a series of experiments that include different LLM models and prompt designs, we explore the few-shot multivariate binary classification accuracy of system requirements from natural language textual commands. We confirm the general ability of LLMs to understand and reason about prompts but underline that their effectiveness is conditioned on the quality of both the LLM model and the design of appropriate sequential prompts. Code and models are public with the link \url{//github.com/KTH-RPL/DriveCmd_LLM}.

Fuzzing is a popular bug detection technique achieved by testing software executables with random inputs. This technique can also be extended to libraries by constructing executables that call library APIs, known as fuzz drivers. Automated fuzz driver synthesis has been an important research topic in recent years since it can facilitate the library fuzzing process. Nevertheless, existing approaches generally ignore generic APIs or simply treat them as normal APIs. As a result, they cannot generate effective fuzz drivers for generic APIs. This paper studies the automated fuzz driver synthesis problem for Rust libraries with generic APIs. The problem is essential because Rust emphasizes security, and generic APIs are widely employed in Rust crates. Each generic API can have numerous monomorphic versions as long as the type constraints are satisfied. The critical challenge to this problem lies in prioritizing these monomorphic versions and providing valid inputs for them. To address the problem, we extend existing API-dependency graphs to support generic APIs. By solving such dependencies and type constraints, we can generate a collection of candidate monomorphic APIs. Further, we apply a similarity-based filter to prune redundant versions, particularly if multiple monomorphic APIs adopt the identical trait implementation. Experimental results with 29 popular open-source libraries show that our approach can achieve promising generic API coverage with a low rate of invalid fuzz drivers. Besides, we find 23 bugs previously unknown in these libraries, with 18 bugs related to generic APIs.

With the wide application of machine translation, the testing of Machine Translation Systems (MTSs) has attracted much attention. Recent works apply Metamorphic Testing (MT) to address the oracle problem in MTS testing. Existing MT methods for MTS generally follow the workflow of input transformation and output relation comparison, which generates a follow-up input sentence by mutating the source input and compares the source and follow-up output translations to detect translation errors, respectively. These methods use various input transformations to generate test case pairs and have successfully triggered numerous translation errors. However, they have limitations in performing fine-grained and rigorous output relation comparison and thus may report false alarms and miss true errors. In this paper, we propose a word closure-based output comparison method to address the limitations of the existing MTS MT methods. Specifically, we first build a new comparison unit called word closure, where each closure includes a group of correlated input and output words in the test case pair. Word closures suggest the linkages between the appropriate fragment in the source output translation and its counterpart in the follow-up output for comparison. Next, we compare the semantics on the level of word closure to identify the translation errors. In this way, we perform a fine-grained and rigorous semantic comparison for the outputs and thus realize more effective violation identification. We evaluate our method with the test cases generated by five existing input transformations and translation outputs from three popular MTSs. Results show that our method significantly outperforms the existing works in violation identification by improving the precision and recall and achieving an average increase of 29.8% in F1 score. It also helps to increase the F1 score of translation error localization by 35.9%.

Clustering has been one of the most basic and essential problems in unsupervised learning due to various applications in many critical fields. The recently proposed sum-of-nums (SON) model by Pelckmans et al. (2005), Lindsten et al. (2011) and Hocking et al. (2011) has received a lot of attention. The advantage of the SON model is the theoretical guarantee in terms of perfect recovery, established by Sun et al. (2018). It also provides great opportunities for designing efficient algorithms for solving the SON model. The semismooth Newton based augmented Lagrangian method by Sun et al. (2018) has demonstrated its superior performance over the alternating direction method of multipliers (ADMM) and the alternating minimization algorithm (AMA). In this paper, we propose a Euclidean distance matrix model based on the SON model. An efficient majorization penalty algorithm is proposed to solve the resulting model. Extensive numerical experiments are conducted to demonstrate the efficiency of the proposed model and the majorization penalty algorithm.

The Adjusted Rand Index (ARI) is a widely used method for comparing hard clusterings, but requires a choice of random model that is often left implicit. Several recent works have extended the Rand Index to fuzzy clusterings, but the assumptions of the most common random model is difficult to justify in fuzzy settings. We propose a single framework for computing the ARI with three random models that are intuitive and explainable for both hard and fuzzy clusterings, along with the benefit of lower computational complexity. The theory and assumptions of the proposed models are contrasted with the existing permutation model. Computations on synthetic and benchmark data show that each model has distinct behaviour, meaning that accurate model selection is important for the reliability of results.

Despite the decomposition of convolutional kernels for lightweight CNNs being well studied, existing works that rely on tensor network diagrams or hyperdimensional abstraction lack geometry intuition. This work devises a new perspective by linking a 3D-reshaped kernel tensor to its various slice-wise and rank-1 decompositions, permitting a straightforward connection between various tensor approximations and efficient CNN modules. Specifically, it is discovered that a pointwise-depthwise-pointwise (PDP) configuration constitutes a viable construct for lightweight CNNs. Moreover, a novel link to the latest ShiftNet is established, inspiring a first-ever shift layer pruning that achieves nearly 50% compression with < 1% drop in accuracy for ShiftResNet.

Large Language Models (LLMs) have shown excellent generalization capabilities that have led to the development of numerous models. These models propose various new architectures, tweaking existing architectures with refined training strategies, increasing context length, using high-quality training data, and increasing training time to outperform baselines. Analyzing new developments is crucial for identifying changes that enhance training stability and improve generalization in LLMs. This survey paper comprehensively analyses the LLMs architectures and their categorization, training strategies, training datasets, and performance evaluations and discusses future research directions. Moreover, the paper also discusses the basic building blocks and concepts behind LLMs, followed by a complete overview of LLMs, including their important features and functions. Finally, the paper summarizes significant findings from LLM research and consolidates essential architectural and training strategies for developing advanced LLMs. Given the continuous advancements in LLMs, we intend to regularly update this paper by incorporating new sections and featuring the latest LLM models.