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Pull-tabbing is an evaluation technique for functional logic programs which computes all non-deterministic results in a single graph structure. Pull-tab steps are local graph transformations to move non-deterministic choices towards the…

Programming Languages · Computer Science 2020-08-28 Michael Hanus , Finn Teegen

Over the past thirty years, there has been considerable progress in the design of natural language interfaces to databases. Most of this work has concerned snapshot databases, in which there are only limited facilities for manipulating…

cmp-lg · Computer Science 2008-02-03 I. Androutsopoulos , G. D. Ritchie , P. Thanisch

Recent advances in vision-language-action (VLA) models for robotics have highlighted the importance of reliable uncertainty quantification in sequential tasks. However, assessing and improving calibration in such settings remains mostly…

Robotics · Computer Science 2026-04-23 Shelly Francis-Meretzki , Mirco Mutti , Yaniv Romano , Aviv Tamar

As industrial systems become more complex and monitoring sensors for everything from surveillance to our health become more ubiquitous, multivariate time series prediction is taking an important place in the smooth-running of our society. A…

Machine Learning · Computer Science 2022-03-03 Fan Jin , Ke Zhang , Yipan Huang , Yifei Zhu , Baiping Chen

Most existing natural language interfaces to databases (NLIDBs) were designed to be used with ``snapshot'' database systems, that provide very limited facilities for manipulating time-dependent data. Consequently, most NLIDBs also provide…

cmp-lg · Computer Science 2016-08-31 Ion Androutsopoulos

Sequential tabular data is one of the most commonly used data types in real-world applications. Different from conventional tabular data, where rows in a table are independent, sequential tabular data contains rich contextual and sequential…

This paper introduces a scheme for data stream processing which is robust to batch duration. Streaming frameworks process streams in batches retrieved at fixed time intervals. In a common setting a pattern recognition algorithm is applied…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-02-20 David Tolpin

Linearizability is the commonly accepted notion of correctness for concurrent data structures. It requires that any execution of the data structure is justified by a linearization --- a linear order on operations satisfying the data…

Programming Languages · Computer Science 2017-07-07 Artem Khyzha , Mike Dodds , Alexey Gotsman , Matthew Parkinson

Probabilistic Logic Programming (PLP) under the Distribution Semantics is a leading approach to practical reasoning under uncertainty. An advantage of the Distribution Semantics is its suitability for implementation as a Prolog or Python…

Logic in Computer Science · Computer Science 2026-01-14 Damiano Azzolini , Fabrizio Riguzzi , Theresa Swift

Wearable human activity recognition (WHAR) models often suffer from performance degradation under real-world cross-user distribution shifts. Test-time adaptation (TTA) mitigates this degradation by adapting models online using unlabeled…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Zishu Zhou , Zaipeng Xie , Xuanyao Jie

While tabular machine learning has achieved remarkable success, temporal distribution shifts pose significant challenges in real-world deployment, as the relationships between features and labels continuously evolve. Static models assume…

Machine Learning · Computer Science 2025-12-04 Hao-Run Cai , Han-Jia Ye

Our goal is to recover time-delayed latent causal variables and identify their relations from measured temporal data. Estimating causally-related latent variables from observations is particularly challenging as the latent variables are not…

Machine Learning · Statistics 2022-02-10 Weiran Yao , Yuewen Sun , Alex Ho , Changyin Sun , Kun Zhang

Timeline-based planning is an approach originally developed in the context of space mission planning and scheduling, where problem domains are modelled as systems made of a number of independent but interacting components, whose behaviour…

Artificial Intelligence · Computer Science 2021-07-26 Nicola Gigante

In this paper we combine Answer Set Programming (ASP) with Dynamic Linear Time Temporal Logic (DLTL) to define a temporal logic programming language for reasoning about complex actions and infinite computations. DLTL extends propositional…

Artificial Intelligence · Computer Science 2011-10-18 Laura Giordano , Alberto Martelli , Daniele Theseider Dupré

Temporal link prediction, aiming at predicting future interactions among entities based on historical interactions, is crucial for a series of real-world applications. Although previous methods have demonstrated the importance of relative…

Machine Learning · Computer Science 2024-10-08 Xiaodong Lu , Leilei Sun , Tongyu Zhu , Weifeng Lv

Query evaluation on probabilistic databases is generally intractable (#P-hard). Existing dichotomy results have identified which queries are tractable (or safe), and connected them to tractable lineages. In our previous work, using…

Databases · Computer Science 2023-04-14 Antoine Amarilli , Pierre Bourhis , Pierre Senellart

Time is one of the most difficult aspects to handle in real world applications such as database systems. Relational database management systems proposed by Codd offer very little built-in query language support for temporal data management.…

Databases · Computer Science 2010-02-08 Nadeem Mahmood , Aqil Burney , Kamran Ahsan

When deploying LLMs in agentic architectures requiring real-time decisions under temporal constraints, we assume they reliably determine whether action windows remain open or have closed. This assumption is untested. We characterize…

Computation and Language · Computer Science 2025-11-17 Javier Marín

In this note we consider the problem of introducing variables in temporal logic programs under the formalism of "Temporal Equilibrium Logic" (TEL), an extension of Answer Set Programming (ASP) for dealing with linear-time modal operators.…

Artificial Intelligence · Computer Science 2016-09-20 Felicidad Aguado , Pedro Cabalar , Martín Diéguez , Gilberto Pérez , Concepción Vidal

Temporal Point Processes (TPP) play an important role in predicting or forecasting events. Although these problems have been studied extensively, predicting multiple simultaneously occurring events can be challenging. For instance, more…

Machine Learning · Computer Science 2023-10-02 Parag Dutta , Kawin Mayilvaghanan , Pratyaksha Sinha , Ambedkar Dukkipati