This paper proposes a unidirectionally connected fully actuated system (UC-FAS) approach for the sub-stabilization and tracking control of 6-DOF quadrotors, tackling limitations both in state-space and FAS framework to some extent. The framework systematically converts underactuated quadrotor dynamics into a UC-FAS model, unifying the existing different FAS transformation ways. By eliminating estimation of the high-order derivatives of control inputs, a drawback of current methods, the UC-FAS model simplifies controller design and enables direct eigenstructure assignment for closed-loop dynamics. Simulations demonstrate precise 6-DOF tracking performance. This work bridges theoretical FAS approach advancements with practical implementation needs, offering a standardized paradigm for nonlinear quadrotor control.

A canonical problem in computer science is to find the shortest route to every point in a network. A new approach beats the classic algorithm taught in textbooks. If you want to solve a tricky problem, it often helps to get organized. You might, for example, break the problem into pieces and tackle the easiest pieces first. But this kind of sorting has a cost.

Recent advances have increasingly applied large language models (LLMs) to electrocardiogram (ECG) interpretation, giving rise to Electrocardiogram-Language Models (ELMs). Conditioned on an ECG and a textual query, an ELM autoregressively generates a free-form textual response. Unlike traditional classification-based systems, ELMs emulate expert cardiac electrophysiologists by issuing diagnoses, analyzing waveform morphology, identifying contributing factors, and proposing patient-specific action plans. To realize this potential, researchers are curating instruction-tuning datasets that pair ECGs with textual dialogues and are training ELMs on these resources. Yet before scaling ELMs further, there is a fundamental question yet to be explored: What is the most effective ECG input representation? In recent works, three candidate representations have emerged-raw time-series signals, rendered images, and discretized symbolic sequences. We present the first comprehensive benchmark of these modalities across 6 public datasets and 5 evaluation metrics. We find symbolic representations achieve the greatest number of statistically significant wins over both signal and image inputs. We further ablate the LLM backbone, ECG duration, and token budget, and we evaluate robustness to signal perturbations. We hope that our findings offer clear guidance for selecting input representations when developing the next generation of ELMs.

The recent explosion in energy-intensive artificial intelligence projects presents (many) problems, including how to keep them cool. The data centers needed to run them produce massive amounts of heat, and require substantial A/C systems to maintain them at functional temperatures. Vapor compression refrigeration--the method still most often found in cars, buildings, and factories--is commonly used to meet these cooling demands. But vapor compression still frequently relies on environmentally harmful chemical refrigerants. While there are a number of promising, recent advancements in refrigeration, thermogalvanic cells have not really been seriously considered.

Since the seminal work of Black and Scholes (1973) and Merton (1973), the Black-Scholes model has remained the most fundamental model for option pricing. However, its restrictive assumptions, such as constant volatility or Geometric Brownian Motion (GBM), have been criticized for not reflecting the empirical characteristics of financial markets. Many subsequent models have since been proposed to relax the assumptions of the Black-Scholes model. One successful approach is employing stochastic volatility under the generalized autoregressive conditional heteroskedastic (GARCH) framework. The early attempt was introduced by Engle and Mustafa (1992) focusing on implied conditional volatilities. Subsequently, Duan (1995) developed a more rigorous framework of the GARCH option pricing model using the locally risk-neutral valuation relationship that one-period ahead conditional variance remains constant under both the risk-neutral measure and the physical measure.

Researchers at the University of Rochester Hajim School of Engineering & Applied Sciences have developed a web-based system called BachDuet that allows users to improvise duets with an artificial intelligence (AI) counterpart in real time. By visiting the BachDuet website, users can play duets with the AI agent using a computer keyboard, mouse, touchscreen, or MIDI keyboard. To play a duet with German composer Johann Sebastian Bach, you don't have to travel back to the 18th century; thanks to a new program developed by researchers at the University of Rochester, you only need a computer. The web-based program, called BachDuet, was developed by Zhiyao Duan, an associate professor of electrical and computer engineering and of computer science, and members of his lab, including Yongi Zang '23 and PhD student Christodoulos Benetatos. BachDuet allows a person to improvise duets in the style of Bach with an artificial intelligence (AI) counterpoint in real time.

Most multi-label domains lack an authoritative taxonomy. Therefore, different taxonomies are commonly used in the same domain, which results in complications. Although this situation occurs frequently, there has been little study of it using a principled statistical approach. Given that (1) different taxonomies used in the same domain are generally founded on the same latent semantic space, where each possible label set in a taxonomy denotes a single semantic concept, and that (2) crowdsourcing is beneficial in identifying relationships between semantic concepts and instances at low cost, we proposed a novel probabilistic cascaded method for establishing a semantic matching function in a crowdsourcing setting that maps label sets in one (source) taxonomy to label sets in another (target) taxonomy in terms of the semantic distances between them. The established function can be used to detect the associated label set in the target taxonomy for an instance directly from its associated label set in the source taxonomy without any extra effort. Experimental results on real-world data (emotion annotations for narrative sentences) demonstrated that the proposed method can robustly establish semantic matching functions exhibiting satisfactory performance from a limited number of crowdsourced annotations.

From this point of view, dynamical modeling requires a deep understanding of the process to be analyzed. The essence of model abstraction is an approximation to the observed reality, which is usually represented by a system composed of ordinary or partial differential equations, deterministic or stochastic differential equations, and control equations. Although mathematical models are accurate for many processes, it is particularly difficult to develop such models for some of the most challenging systems, including climate dynamics, brain dynamics, biological systems and financial markets. Fortunately, more and more data are observed or measured in recent years with the development of scientific tools and simulation capabilities. Therefore, a large number of data-driven methods has been proposed to discover governing laws of systems from data. For instance, several researchers designed the Sparse Identification of Nonlinear Dynamics approach to extract deterministic ordinary [5] or partial [15, 29, 31] differential equations from available path data.

Advances in data science are leading to new progresses in the analysis and understanding of complex dynamics for systems with experimental and observational data. With numerous physical phenomena exhibiting bursting, flights, hopping, and intermittent features, stochastic differential equations with non-Gaussian L\'evy noise are suitable to model these systems. Thus it is desirable and essential to infer such equations from available data to reasonably predict dynamical behaviors. In this work, we consider a data-driven method to extract stochastic dynamical systems with non-Gaussian asymmetric (rather than the symmetric) L\'evy process, as well as Gaussian Brownian motion. We establish a theoretical framework and design a numerical algorithm to compute the asymmetric L\'evy jump measure, drift and diffusion (i.e., nonlocal Kramers-Moyal formulas), hence obtaining the stochastic governing law, from noisy data. Numerical experiments on several prototypical examples confirm the efficacy and accuracy of this method. This method will become an effective tool in discovering the governing laws from available data sets and in understanding the mechanisms underlying complex random phenomena.

This is where one needs to be careful. Our instinct might be to simply exponentiate the log-scale predictions back to raw-scale y. But our instinct would be wrong. Let's see why that is. If you like, you can skip the little bit of math that follows and scroll down to the section called Duan's smearing estimator.