PhysRFANet: Physics-Guided Neural Network for Real-Time Prediction of Thermal Effect During Radiofrequency Ablation Treatment

Radiofrequency ablation (RFA) is a widely used minimally invasive technique for ablating solid tumors. Achieving precise personalized treatment necessitates feedback information on in situ thermal effects induced by the RFA procedure. While computer simulation facilitates the prediction of electrical and thermal phenomena associated with RFA, its practical implementation in clinical settings is hindered by high computational demands. In this paper, we propose a physics-guided neural network model, named PhysRFANet, to enable real-time prediction of thermal effect during RFA treatment. The networks, designed for predicting temperature distribution and the corresponding ablation lesion, were trained using biophysical computational models that integrated electrostatics, bio-heat transfer, and cell necrosis, alongside magnetic resonance (MR) images of breast cancer patients. Validation of the computational model was performed through experiments on ex vivo bovine liver tissue. Our model demonstrated a 96% Dice score in predicting the lesion volume and an RMSE of 0.4854 for temperature distribution when tested with foreseen tumor images. Notably, even with unforeseen images, it achieved a 93% Dice score for the ablation lesion and an RMSE of 0.6783 for temperature distribution. All networks were capable of inferring results within 10 ms. The presented technique, applied to optimize the placement of the electrode for a specific target region, holds significant promise in enhancing the safety and efficacy of RFA treatments.

CLIP-guided Source-free Object Detection in Aerial Images

Domain adaptation is crucial in aerial imagery, as the visual representation of these images can significantly vary based on factors such as geographic location, time, and weather conditions. Additionally, high-resolution aerial images often require substantial storage space and may not be readily accessible to the public. To address these challenges, we propose a novel Source-Free Object Detection (SFOD) method. Specifically, our approach is built upon a self-training framework; however, self-training can lead to inaccurate learning in the absence of labeled training data. To address this issue, we further integrate Contrastive Language-Image Pre-training (CLIP) to guide the generation of pseudo-labels, termed CLIP-guided Aggregation. By leveraging CLIP's zero-shot classification capability, we use it to aggregate scores with the original predicted bounding boxes, enabling us to obtain refined scores for the pseudo-labels. To validate the effectiveness of our method, we constructed two new datasets from different domains based on the DIOR dataset, named DIOR-C and DIOR-Cloudy. Experiments demonstrate that our method outperforms other comparative algorithms.

Error estimation for physics-informed neural networks with implicit Runge-Kutta methods

The ability to accurately approximate trajectories of dynamical systems enables their analysis, prediction, and control. Neural network (NN)-based approximations have attracted significant interest due to fast evaluation with good accuracy over long integration time steps. In contrast to established numerical approximation schemes such as Runge-Kutta methods, the estimation of the error of the NN-based approximations proves to be difficult. In this work, we propose to use the NN's predictions in a high-order implicit Runge-Kutta (IRK) method. The residuals in the implicit system of equations can be related to the NN's prediction error, hence, we can provide an error estimate at several points along a trajectory. We find that this error estimate highly correlates with the NN's prediction error and that increasing the order of the IRK method improves this estimate. We demonstrate this estimation methodology for Physics-Informed Neural Network (PINNs) on the logistic equation as an illustrative example and then apply it to a four-state electric generator model that is regularly used in power system modelling.

Machine Teaching for Building Modular AI Agents based on Zero-shot Learners

The recent advances in large language models (LLMs) have led to the creation of many modular AI agents. These agents employ LLMs as zero-shot learners to perform sub-tasks in order to solve complex tasks set forth by human users. We propose an approach to enhance the robustness and performance of modular AI agents that utilize LLMs as zero-shot learners. Our iterative machine teaching method offers an efficient way to teach AI agents over time with limited human feedback, addressing the limit posed by the quality of zero-shot learning. We advocate leveraging the data traces from initial deployments and outputs or annotations from the zero-shot learners to train smaller and task-specific substitute models which can reduce both the monetary costs and environmental impact. Our machine teaching process avails human expertise to correct examples with a high likelihood of misannotations. Results on three tasks, common to conversational AI agents, show that close-to-oracle performance can be achieved with supervision on 20-70% of the dataset depending upon the complexity of the task and performance of zero-shot learners.

Information Flow Rate for Cross-Correlated Stochastic Processes

Causal inference seeks to identify cause-and-effect interactions in coupled systems. A recently proposed method by Liang detects causal relations by quantifying the direction and magnitude of information flow between time series. The theoretical formulation of information flow for stochastic dynamical systems provides a general expression and a data-driven statistic for the rate of entropy transfer between different system units. To advance understanding of information flow rate in terms of intuitive concepts and physically meaningful parameters, we investigate statistical properties of the data-driven information flow rate between coupled stochastic processes. We derive relations between the expectation of the information flow rate statistic and properties of the auto- and cross-correlation functions. Thus, we elucidate the dependence of the information flow rate on the analytical properties and characteristic times of the correlation functions. Our analysis provides insight into the influence of the sampling step, the strength of cross-correlations, and the temporal delay of correlations on information flow rate. We support the theoretical results with numerical simulations of correlated Gaussian processes.

Transportation Market Rate Forecast Using Signature Transform

Currently, Amazon relies on third parties for transportation marketplace rate forecasts, despite the poor quality and lack of interpretability of these forecasts. While transportation marketplace rates are typically very challenging to forecast accurately, we have developed a novel signature-based statistical technique to address these challenges and built a predictive and adaptive model to forecast marketplace rates. This novel technique is based on two key properties of the signature transform. The first is its universal nonlinearity which linearizes the feature space and hence translates the forecasting problem into a linear regression analysis; the second is the signature kernel which allows for comparing computationally efficiently similarities between time series data. Combined, these properties allow for efficient feature generation and more precise identification of seasonality and regime switching in the forecasting process. Preliminary result by the model shows that this new technique leads to far superior forecast accuracy versus commercially available industry models with better interpretability, even during the period of Covid-19 and with the sudden onset of the Ukraine war.

Agent Alignment in Evolving Social Norms

Agents based on Large Language Models (LLMs) are increasingly permeating various domains of human production and life, highlighting the importance of aligning them with human values. The current alignment of AI systems primarily focuses on passively aligning LLMs through human intervention. However, agents possess characteristics like receiving environmental feedback and self-evolution, rendering the LLM alignment methods inadequate. In response, we propose an evolutionary framework for agent evolution and alignment, named EvolutionaryAgent, which transforms agent alignment into a process of evolution and selection under the principle of survival of the fittest. In an environment where social norms continuously evolve, agents better adapted to the current social norms will have a higher probability of survival and proliferation, while those inadequately aligned dwindle over time. Experimental results assessing the agents from multiple perspectives in aligning with social norms demonstrate that EvolutionaryAgent possesses the capability to align progressively better with the evolving social norms while maintaining its proficiency in general tasks. Effectiveness tests conducted on various open and closed-source LLMs as the foundation for agents also prove the applicability of our approach.

Model-Driven Dataset Generation for Data-Driven Battery SOH Models

Estimating the State of Health (SOH) of batteries is crucial for ensuring the reliable operation of battery systems. Since there is no practical way to instantaneously measure it at run time, a model is required for its estimation. Recently, several data-driven SOH models have been proposed, whose accuracy heavily relies on the quality of the datasets used for their training. Since these datasets are obtained from measurements, they are limited in the variety of the charge/discharge profiles. To address this scarcity issue, we propose generating datasets by simulating a traditional battery model (e.g., a circuit-equivalent one). The primary advantage of this approach is the ability to use a simulatable battery model to evaluate a potentially infinite number of workload profiles for training the data-driven model. Furthermore, this general concept can be applied using any simulatable battery model, providing a fine spectrum of accuracy/complexity tradeoffs. Our results indicate that using simulated data achieves reasonable accuracy in SOH estimation, with a 7.2% error relative to the simulated model, in exchange for a 27X memory reduction and a =2000X speedup.

Greedy Algorithm for Inference of Decision Trees from Decision Rule Systems

Decision trees and decision rule systems play important roles as classifiers, knowledge representation tools, and algorithms. They are easily interpretable models for data analysis, making them widely used and studied in computer science. Understanding the relationships between these two models is an important task in this field. There are well-known methods for converting decision trees into systems of decision rules. In this paper, we consider the inverse transformation problem, which is not so simple. Instead of constructing an entire decision tree, our study focuses on a greedy polynomial time algorithm that simulates the operation of a decision tree on a given tuple of attribute values.

MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts

State Space Models (SSMs) have become serious contenders in the field of sequential modeling, challenging the dominance of Transformers. At the same time, Mixture of Experts (MoE) has significantly improved Transformer-based LLMs, including recent state-of-the-art open-source models. We propose that to unlock the potential of SSMs for scaling, they should be combined with MoE. We showcase this on Mamba, a recent SSM-based model that achieves remarkable, Transformer-like performance. Our model, MoE-Mamba, outperforms both Mamba and Transformer-MoE. In particular, MoE-Mamba reaches the same performance as Mamba in 2.2x less training steps while preserving the inference performance gains of Mamba against the Transformer.