397 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Recent Advances in Research on Island Phenomena
In natural languages, filler-gap dependencies can straddle across an unbounded distance. Since the 1960s, the term “island” has been used to describe syntactic structures from which extraction is impossible or impeded. While examples from English are ubiquitous, attested counterexamples in the Mainland Scandinavian languages have continuously been dismissed as illusory and alternative accounts for the underlying structure of such cases have been proposed. However, since such extractions are pervasive in spoken Mainland Scandinavian, these languages may not have been given the attention that they deserve in the syntax literature. In addition, recent research suggests that extraction from certain types of island structures in English might not be as unacceptable as previously assumed either. These findings break new empirical ground, question perceived knowledge, and may indeed have substantial ramifications for syntactic theory. This volume provides an overview of state-of-the-art research on island phenomena primarily in English and the Scandinavian languages, focusing on how languages compare to English, with the aim to shed new light on the nature of island constraints from different theoretical perspectives
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)
This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
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Abstractions for Probabilistic Programming to Support Model Development
Probabilistic programming is a recent advancement in probabilistic modeling whereby we can express a model as a program with little concern for the details of probabilistic inference.
Probabilistic programming thereby provides a clean and powerful abstraction to its users, letting even non-experts develop clear and concise models that can leverage state-of-the-art computational inference algorithms. This model-as-program representation also presents a unique opportunity: we can apply methods from the study of programming languages directly onto probabilistic models. By developing techniques to analyze, transform, or extend the capabilities of probabilistic programs, we can immediately improve the workflow of probabilistic modeling and benefit all of its applications throughout science and industry.
The aim of this dissertation is to support an ideal probabilistic modeling workflow byaddressing two limitations of probabilistic programming: that a program can only represent one model; and that the structure of the model that it represents is often opaque to users and to the compiler. In particular, I make the following primary contributions:
(1) I introduce Multi-Model Probabilistic Programming: an extension of probabilistic programming whereby a program can represent a network of interrelated models. This new representation allows users to construct and leverage spaces of models in the same way that probabilistic programs do for individual models. Multi-Model Probabilistic Programming lets us visualize and navigate solution spaces, track and document model development paths, and audit modeler degrees of freedom to mitigate issues like p-hacking. It also provides an efficient computational foundation for the automation of model-space applications like model search, sensitivity analysis, and ensemble methods.
I give a formal language specification and semantics for Multi-Model Probabilistic Programming built on the Stan language, I provide algorithms for the fundamental model-space operations along with proofs of correctness and efficiency, and I present a prototype implementation, with which I demonstrate a variety of practical applications.
(2) I present a method for automatically transforming probabilistic programs into semantically related forms by using static analysis and constraint solving to recover the structure of their underlying models. In particular, I automate two general model transformations that are required for diagnostic checks which are important steps of a model-building workflow. Automating these transformations frees the user from manually rewriting their models, thereby avoiding potential correctness and efficiency issues.
(3) I present a probabilistic program analysis tool, “Pedantic Mode”, that automatically warns users about potential statistical issues with the model described by their program. “Pedantic Mode” uses specialized static analysis methods to decompose the structure of the underlying model. Lastly, I discuss future work in these areas, such as advanced model-space algorithms and other general-purpose model transformations. I also discuss how these ideas may fit into future modeling workflows as technologies
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
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