We use coupled hidden Markov models to automatically annotate the 371 Bach chorales in the Riemenschneider edition, a corpus containing approximately 100,000 notes and 20,000 chords. We give three separate analyses that achieve progressively greater accuracy at the cost of making increasingly strong assumptions about musical syntax. Although our method makes almost no use of human input, we are able to identify both chords and keys with an accuracy of 85% or greater when compared to an expert human analysis, resulting in annotations accurate enough to be used for a range of music-theoretical purposes, while also being free of subjective human judgments. Our work bears on longstanding debates about the objective reality of the structures postulated by standard Western harmonic theory, as well as on specific questions about the nature of Western harmonic syntax.

eBay partners with external vendors, which allows customers to freely select a vendor to complete their eBay experiences. However, vendor outages can hinder customer experiences. Consequently, eBay can disable a problematic vendor to prevent customer loss. Disabling the vendor too late risks losing customers willing to switch to other vendors, while disabling it too early risks losing those unwilling to switch. In this paper, we propose a data-driven solution to answer whether eBay should disable a problematic vendor and when to disable it. Our solution involves forecasting customer behavior. First, we use a multiplicative seasonality model to represent behavior if all vendors are fully functioning. Next, we use a Monte Carlo simulation to represent behavior if the problematic vendor remains enabled. Finally, we use a linear model to represent behavior if the vendor is disabled. By comparing these forecasts, we determine the optimal time for eBay to disable the problematic vendor.

It can also facilitate the identification of and depression has attracted increased attention. Confidence estimation ambiguous and borderline cases, necessitating the input of clinical is crucial for a trust-worthy automatic diagnostic system expertise. While confidence estimation techniques have which informs the clinician about the confidence of model been applied in areas like speech recognition [23-26] and dialogue predictions and helps reduce the risk of misdiagnosis. This paper systems [27], their application in detecting mental illnesses investigates confidence estimation for automatic detection through speech analysis remains largely unexplored. of AD and depression based on clinical interviews. A novel Bayesian approach is proposed which uses a dynamic Dirichlet This paper investigates confidence estimation for automatic prior distribution to model the second-order probability of AD and depression detection based on speech recordings from the predictive distribution.

User mobility modeling serves a crucial role in analysis and optimization of contemporary wireless networks. Typical stochastic mobility models, e.g., random waypoint model and Gauss Markov model, can hardly capture the distribution characteristics of users within real-world areas. State-of-the-art trace-based mobility models and existing learning-based trajectory generation methods, however, are frequently constrained by the inaccessibility of substantial real trajectories due to privacy concerns. In this paper, we harness the intrinsic correlation between street maps and trajectories and develop a novel zero-shot trajectory generation method, named Map2Traj, by exploiting the diffusion model. We incorporate street maps as a condition to consistently pilot the denoising process and train our model on diverse sets of real trajectories from various regions in Xi'an, China, and their corresponding street maps. With solely the street map of an unobserved area, Map2Traj generates synthetic trajectories that not only closely resemble the real-world mobility pattern but also offer comparable efficacy. Extensive experiments validate the efficacy of our proposed method on zero-shot trajectory generation tasks in terms of both trajectory and distribution similarities. In addition, a case study of employing Map2Traj in wireless network optimization is presented to validate its efficacy for downstream applications.

RNA plays a crucial role in diverse life processes. In contrast to the rapid advancement of protein design methods, the work related to RNA is more demanding. Most current RNA design approaches concentrate on specified target attributes and rely on extensive experimental searches. However, these methods remain costly and inefficient due to practical limitations. In this paper, we characterize all sequence design issues as conditional generation tasks and offer parameterized representations for multiple problems. For these problems, we have developed a universal RNA sequence generation model based on flow matching, namely RNACG. RNACG can accommodate various conditional inputs and is portable, enabling users to customize the encoding network for conditional inputs as per their requirements and integrate it into the generation network. We evaluated RNACG in RNA 3D structure inverse folding, 2D structure inverse folding, family-specific sequence generation, and 5'UTR translation efficiency prediction. RNACG attains superior or competitive performance on these tasks compared with other methods. RNACG exhibits extensive applicability in sequence generation and property prediction tasks, providing a novel approach to RNA sequence design and potential methods for simulation experiments with large-scale RNA sequence data.

Motivated by the increasing popularity of overparameterized Stochastic Differential Equations (SDEs) like Neural SDEs, Wang, Blanchet and Glynn recently introduced the generator gradient estimator, a novel unbiased stochastic gradient estimator for SDEs whose computation time remains stable in the number of parameters. In this note, we demonstrate that this estimator is in fact an adjoint state method, an approach which is known to scale with the number of states and not the number of parameters in the case of Ordinary Differential Equations (ODEs). In addition, we show that the generator gradient estimator is a close analogue to the exact Integral Path Algorithm (eIPA) estimator which was introduced by Gupta, Rathinam and Khammash for a class of Continuous-Time Markov Chains (CTMCs) known as stochastic chemical reactions networks (CRNs).

The performance of sensor arrays in sensing and wireless communications improves with more elements, but this comes at the cost of increased energy consumption and hardware expense. This work addresses the challenge of selecting $k$ sensor elements from a set of $m$ to optimize a generic Quality-of-Service metric. Evaluating all $\binom{m}{k}$ possible sensor subsets is impractical, leading to prior solutions using convex relaxations, greedy algorithms, and supervised learning approaches. The current paper proposes a new framework that employs deep generative modeling, treating sensor selection as a deterministic Markov Decision Process where sensor subsets of size $k$ arise as terminal states. Generative Flow Networks (GFlowNets) are employed to model an action distribution conditioned on the state. Sampling actions from the aforementioned distribution ensures that the probability of arriving at a terminal state is proportional to the performance of the corresponding subset. Applied to a standard sensor selection scenario, the developed approach outperforms popular methods which are based on convex optimization and greedy algorithms. Finally, a multiobjective formulation of the proposed approach is adopted and applied on the sparse antenna array design for Integrated Sensing and Communication (ISAC) systems. The multiobjective variation is shown to perform well in managing the trade-off between radar and communication performance.

Recent advances in artificial intelligence have seen Large Language Models (LLMs) demonstrate notable proficiency in causal discovery tasks. This study explores the factors influencing the performance of LLMs in causal discovery tasks. Utilizing open-source LLMs, we examine how the frequency of causal relations within their pre-training corpora affects their ability to accurately respond to causal discovery queries. Our findings reveal that a higher frequency of causal mentions correlates with better model performance, suggesting that extensive exposure to causal information during training enhances the models' causal discovery capabilities. Additionally, we investigate the impact of context on the validity of causal relations. Our results indicate that LLMs might exhibit divergent predictions for identical causal relations when presented in different contexts. This paper provides the first comprehensive analysis of how different factors contribute to LLM performance in causal discovery tasks.

Thus, word segmentation is important and is influential in many fields including developing text processing applications, such as Information Extraction, Document Summarization, Machine Translation (MT), Natural Language Processing, Information Retrieval, Language Modeling, and Speech Recognition.(15) Word segmentation is often a vital task of language processing. In addition, the reason why word segmentation is significant in the field of Natural Language Processing is because it is the initial step for most higher level natural language processing tasks, such as part-of-speech tagging and parsing. In addition, for languages that are space-delimited such as English or Russian, these languages are being segmented differently as opposed to those that don't have explicit word boundary delimiters, such as Chinese and Japanese. There is a common goal for this task, which is to have a near-perfect word segmentation system, which can still perform reasonably with no or minimum language-specific adaptations (9).

The presence of unobserved common causes and the presence of measurement error are two of the most limiting challenges in the task of causal structure learning. Ignoring either of the two challenges can lead to detecting spurious causal links among variables of interest. In this paper, we study the problem of causal discovery in systems where these two challenges can be present simultaneously. We consider linear models which include four types of variables: variables that are directly observed, variables that are not directly observed but are measured with error, the corresponding measurements, and variables that are neither observed nor measured. We characterize the extent of identifiability of such model under separability condition (i.e., the matrix indicating the independent exogenous noise terms pertaining to the observed variables is identifiable) together with two versions of faithfulness assumptions and propose a notion of observational equivalence. We provide graphical characterization of the models that are equivalent and present a recovery algorithm that could return models equivalent to the ground truth.