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Parameter inference for dynamical models of (bio)physical systems remains a challenging problem. Intractable gradients, high-dimensional spaces, and non-linear model functions are typically problematic without large computational budgets. A recent body of work in that area has focused on Bayesian inference methods, which consider parameters under their statistical distributions and therefore, do not derive point estimates of optimal parameter values. Here we propose a new metaheuristic that drives dimensionality reductions from feature-informed transformations (DR-FFIT) to address these bottlenecks. DR-FFIT implements an efficient sampling strategy that facilitates a gradient-free parameter search in high-dimensional spaces. We use artificial neural networks to obtain differentiable proxies for the model's features of interest. The resulting gradients enable the estimation of a local active subspace of the model within a defined sampling region. This approach enables efficient dimensionality reductions of highly non-linear search spaces at a low computational cost. Our test data show that DR-FFIT boosts the performances of random-search and simulated-annealing against well-established metaheuristics, and improves the goodness-of-fit of the model, all within contained run-time costs.

Current reinforcement learning algorithms struggle in sparse and complex environments, most notably in long-horizon manipulation tasks entailing a plethora of different sequences. In this work, we propose the Intrinsically Guided Exploration from Large Language Models (IGE-LLMs) framework. By leveraging LLMs as an assistive intrinsic reward, IGE-LLMs guides the exploratory process in reinforcement learning to address intricate long-horizon with sparse rewards robotic manipulation tasks. We evaluate our framework and related intrinsic learning methods in an environment challenged with exploration, and a complex robotic manipulation task challenged by both exploration and long-horizons. Results show IGE-LLMs (i) exhibit notably higher performance over related intrinsic methods and the direct use of LLMs in decision-making, (ii) can be combined and complement existing learning methods highlighting its modularity, (iii) are fairly insensitive to different intrinsic scaling parameters, and (iv) maintain robustness against increased levels of uncertainty and horizons.

The proper design of automated market makers (AMMs) is crucial to enable the continuous trading of assets represented as digital tokens on markets of cryptoeconomic systems. Improperly designed AMMs can make such markets suffer from the thin market problem (TMP), which can cause cryptoeconomic systems to fail their purposes. We developed an AMM taxonomy that showcases AMM design characteristics. Based on the AMM taxonomy, we devised AMM archetypes implementing principal solution approaches for the TMP. The main purpose of this article is to support practitioners and researchers in tackling the TMP through proper AMM designs.

Artificial intelligence (AI) algorithms based on neural networks have been designed for decades with the goal of maximising some measure of accuracy. This has led to two undesired effects. First, model complexity has risen exponentially when measured in terms of computation and memory requirements. Second, state-of-the-art AI models are largely incapable of providing trustworthy measures of their uncertainty, possibly `hallucinating' their answers and discouraging their adoption for decision-making in sensitive applications. With the goal of realising efficient and trustworthy AI, in this paper we highlight research directions at the intersection of hardware and software design that integrate physical insights into computational substrates, neuroscientific principles concerning efficient information processing, information-theoretic results on optimal uncertainty quantification, and communication-theoretic guidelines for distributed processing. Overall, the paper advocates for novel design methodologies that target not only accuracy but also uncertainty quantification, while leveraging emerging computing hardware architectures that move beyond the traditional von Neumann digital computing paradigm to embrace in-memory, neuromorphic, and quantum computing technologies. An important overarching principle of the proposed approach is to view the stochasticity inherent in the computational substrate and in the communication channels between processors as a resource to be leveraged for the purpose of representing and processing classical and quantum uncertainty.

This paper implements and analyses multiple nets to determine their suitability for edge devices to solve the problem of detecting Threat Objects from X-ray security imaging data. There has been ongoing research on applying Deep Learning techniques to solve this problem automatedly. We utilize an alternative activation function calculated to have zero expected conversion error with the activation of a spiking activation function, in the our tiny YOLOv7 model. This QCFS version of the tiny YOLO replicates the activation of ultra-low latency and high-efficiency SNN architecture and achieves state-of-the-art performance on CLCXray which is another open-source XRay Threat Detection dataset, hence making improvements in the field of using spiking for object detection. We also analyze the performance of a Spiking YOLO network by converting our QCFS network into a Spiking Network.

Conversational aspect-based sentiment quadruple analysis (DiaASQ) aims to extract the quadruple of target-aspect-opinion-sentiment within a dialogue. In DiaASQ, a quadruple's elements often cross multiple utterances. This situation complicates the extraction process, emphasizing the need for an adequate understanding of conversational context and interactions. However, existing work independently encodes each utterance, thereby struggling to capture long-range conversational context and overlooking the deep inter-utterance dependencies. In this work, we propose a novel Dynamic Multi-scale Context Aggregation network (DMCA) to address the challenges. Specifically, we first utilize dialogue structure to generate multi-scale utterance windows for capturing rich contextual information. After that, we design a Dynamic Hierarchical Aggregation module (DHA) to integrate progressive cues between them. In addition, we form a multi-stage loss strategy to improve model performance and generalization ability. Extensive experimental results show that the DMCA model outperforms baselines significantly and achieves state-of-the-art performance.

This scientific paper explores two distinct approaches for identifying and approximating the simulation model, particularly in the context of the snap process crucial to medical device assembly. Simulation models play a pivotal role in providing engineers with insights into industrial processes, enabling experimentation and troubleshooting before physical assembly. However, their complexity often results in time-consuming computations. To mitigate this complexity, we present two distinct methods for identifying simulation models: one utilizing Spline functions and the other harnessing Machine Learning (ML) models. Our goal is to create adaptable models that accurately represent the snap process and can accommodate diverse scenarios. Such models hold promise for enhancing process understanding and aiding in decision-making, especially when data availability is limited.

This paper studies the design of energy-efficient artificial noise (AN) schemes in the context of physical layer security in visible light communications (VLC). Two different transmission schemes termed $\textit{selective AN-aided single-input single-output (SISO)}$ and $\textit{AN-aided multiple-input single-output (MISO)}$ are examined and compared in terms of secrecy energy efficiency (SEE). In the former, the closest LED luminaire to the legitimate user (Bob) is the information-bearing signal's transmitter. At the same time, the rest of the luminaries act as jammers transmitting AN to degrade the channels of eavesdroppers (Eves). In the latter, the information-bearing signal and AN are combined and transmitted by all luminaries. When Eves' CSI is unknown, an indirect design to improve the SEE is formulated by maximizing Bob's channel's energy efficiency. A low-complexity design based on the zero-forcing criterion is also proposed. In the case of known Eves' CSI, we study the design that maximizes the minimum SEE among those corresponding to all eavesdroppers. At their respective optimal SEEs, simulation results reveal that when Eves' CSI is unknown, the selective AN-aided SISO transmission can archive twice better SEE as the AN-aided MISO does. In contrast, when Eves' CSI is known, the AN-aided MISO outperforms by 30%.

In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the trained model cannot produce a highly discriminative feature representation for the target domain because the training data is dominated by labeled samples from the source domain. This could lead to disconnection between the labeled and unlabeled target samples as well as misalignment between unlabeled target samples and the source domain. In this paper, we propose a novel approach called Cross-domain Adaptive Clustering to address this problem. To achieve both inter-domain and intra-domain adaptation, we first introduce an adversarial adaptive clustering loss to group features of unlabeled target data into clusters and perform cluster-wise feature alignment across the source and target domains. We further apply pseudo labeling to unlabeled samples in the target domain and retain pseudo-labels with high confidence. Pseudo labeling expands the number of ``labeled" samples in each class in the target domain, and thus produces a more robust and powerful cluster core for each class to facilitate adversarial learning. Extensive experiments on benchmark datasets, including DomainNet, Office-Home and Office, demonstrate that our proposed approach achieves the state-of-the-art performance in semi-supervised domain adaptation.

Multi-relation Question Answering is a challenging task, due to the requirement of elaborated analysis on questions and reasoning over multiple fact triples in knowledge base. In this paper, we present a novel model called Interpretable Reasoning Network that employs an interpretable, hop-by-hop reasoning process for question answering. The model dynamically decides which part of an input question should be analyzed at each hop; predicts a relation that corresponds to the current parsed results; utilizes the predicted relation to update the question representation and the state of the reasoning process; and then drives the next-hop reasoning. Experiments show that our model yields state-of-the-art results on two datasets. More interestingly, the model can offer traceable and observable intermediate predictions for reasoning analysis and failure diagnosis, thereby allowing manual manipulation in predicting the final answer.