This invited paper introduces the concept of "proof-carrying neuro-symbolic code" and explains its meaning and value, from both the "neural" and the "symbolic" perspectives. The talk outlines the first successes and challenges that this new area of research faces. Keywords: Neural Networks Cyber-Physical System Verification Programming Languages Neuro-Symbolic Programs. 1 Neuro-Symbolic Proofs and Programs Proof Carrying Code is a long tradition within programming language research, broadly referring to methods that interleave verification with executable code, thus avoiding the inevitable discrepancies that arise when the code and the proofs are handled in different languages. Although the term was coined by Necula [50] almost three decades ago, with time, it grew to encompass any languages that are powerful enough to handle both the coding and the proving. Examples are dependently-typed (Agda, Idris, Coq/Rocq) and refinement-typed (F*, Liquid Haskell) languages.

Neural network verification is a new and rapidly developing field of research. So far, the main priority has been establishing efficient verification algorithms and tools, while proper support from the programming language perspective has been considered secondary or unimportant. Yet, there is mounting evidence that insights from the programming language community may make a difference in the future development of this domain. In this paper, we formulate neural network verification challenges as programming language challenges and suggest possible future solutions.

Deep neural networks have exhibited substantial success in the field of Natural Language Processing and ensuring their safety and reliability is crucial: there are safety critical contexts where such models must be robust to variability or attack, and give guarantees over their output. Unlike Computer Vision, NLP lacks a unified verification methodology and, despite recent advancements in literature, they are often light on the pragmatical issues of NLP verification. In this paper, we attempt to distil and evaluate general components of an NLP verification pipeline, that emerges from the progress in the field to date. Our contributions are two-fold. Firstly, we give a general (i.e. algorithm-independent) characterisation of verifiable subspaces that result from embedding sentences into continuous spaces. We identify, and give an effective method to deal with, the technical challenge of semantic generalisability of verified subspaces; and propose it as a standard metric in the NLP verification pipelines (alongside with the standard metrics of model accuracy and model verifiability). Secondly, we propose a general methodology to analyse the effect of the embedding gap -- a problem that refers to the discrepancy between verification of geometric subspaces, and the semantic meaning of sentences which the geometric subspaces are supposed to represent. In extreme cases, poor choices in embedding of sentences may invalidate verification results. We propose a number of practical NLP methods that can help to quantify the effects of the embedding gap; and in particular we propose the metric of falsifiability of semantic subspaces as another fundamental metric to be reported as part of the NLP verification pipeline. We believe that together these general principles pave the way towards a more consolidated and effective development of this new domain.

Ethical frameworks for the use of natural language processing (NLP) are urgently needed to shape how large language models (LLMs) and similar tools are used for healthcare applications. Healthcare faces existing challenges including the balance of power in clinician-patient relationships, systemic health disparities, historical injustices, and economic constraints. Drawing directly from the voices of those most affected, and focusing on a case study of a specific healthcare setting, we propose a set of guiding principles for the use of NLP in maternal healthcare. We led an interactive session centered on an LLM-based chatbot demonstration during a full-day workshop with 39 participants, and additionally surveyed 30 healthcare workers and 30 birthing people about their values, needs, and perceptions of NLP tools in the context of maternal health. We conducted quantitative and qualitative analyses of the survey results and interactive discussions to consolidate our findings into a set of guiding principles. We propose nine principles for ethical use of NLP for maternal healthcare, grouped into three themes: (i) recognizing contextual significance (ii) holistic measurements, and (iii) who/what is valued. For each principle, we describe its underlying rationale and provide practical advice. This set of principles can provide a methodological pattern for other researchers and serve as a resource to practitioners working on maternal health and other healthcare fields to emphasize the importance of technical nuance, historical context, and inclusive design when developing NLP technologies for clinical use.

Evaluating counterfactual statements is a fundamental problem for many approaches to causal reasoning [40]. Such reasoning can for instance be used to explain erroneous system behavior with a counterfactual statement such as'If the input i at the first position of the observed computation π had not been enabled then the system would not have reached an error e.' which can be formalized using the counterfactual operator and the temporal operator F: π ( i) ( Fe). Since the foundational work by Lewis[38] on the formal semantics of counterfactual conditionals, many applications for counterfactuals [28, 5, 34, 46, 3, 15] and some theoretical results on the decidability of the original theory [37] and related notions [20, 2] have been discovered. Still, certain domains have proven elusive for a long time, for instance, theories involving higher-order reasoning and an infinite number of variables. In this paper, we consider a domain that combines both of these aspects: temporal reasoning over infinite sequences. In particular, we consider counterfactual conditionals that relate two properties expressed in temporal logics, such as the temporal property F e from the introductory example. Temporal logics are used ubiquitously as high-level specifications for verification [21, 4] and synthesis [22, 41], and recently have also found use in specifying reinforcement learning tasks [32, 39]. Our work lifts the language of counterfactual reasoning to similar high-level expressions.

Financial technology services are increasingly large and diverse, not only representing a change for users, but also for banks that have had to adapt as new developments allow greater knowledge of the market and customers. Faced with this situation, they have launched in Colombia a platform that will use advanced artificial intelligence functions to generate a credit score for each person and allow financial institutions to identify potential clients. The new system is developed by the fintech Yabx which specializes in enabling credit for unbanked sectors, so thanks to an alliance it will base its data on Telecom's Telecommunications system in association with Claro, therefore It will allow the identification of new clients not recognized by the criteria of traditional banking. The platform will use machine-learning algorithms (artificial intelligence machine learning) to provide a credit score and other products that can be offered to banks or other fintech companies that want to improve their abilities to acquire and qualify customers whose applications to banks traditional are rejected. Thanks to the association with Claro, one of the largest telecommunications networks in the country, the new system will be able to cover around 67% of Colombian adults, in addition, it will allow credit institutions to reduce their rejection rates by up to 40% by take into account factors that are not normally observed.

Classification in the dissimilarity space has become a very active research area since it provides a possibility to learn from data given in the form of pairwise non-metric dissimilarities, which otherwise would be difficult to cope with. The selection of prototypes is a key step for the further creation of the space. However, despite previous efforts to find good prototypes, how to select the best representation set remains an open issue. In this paper we proposed scalable methods to select the set of prototypes out of very large datasets. The methods are based on genetic algorithms, dissimilarity-based hashing, and two different unsupervised and supervised scalable criteria. The unsupervised criterion is based on the Minimum Spanning Tree of the graph created by the prototypes as nodes and the dissimilarities as edges. The supervised criterion is based on counting matching labels of objects and their closest prototypes. The suitability of these type of algorithms is analyzed for the specific case of dissimilarity representations. The experimental results showed that the methods select good prototypes taking advantage of the large datasets, and they do so at low runtimes. Preprint submitted to Elsevier December 27, 2017 1. Introduction The vector space representation is a common option to represent the data for learning tasks since many statistical techniques are applicable for this kind of representation. However, there is an increasing number of real-world problems which are not vectorial. Instead, the data are given in terms of pairwise dissimilarities which may be non-Euclidean and even non-metric.

Traditional techniques for monitoring wildlife populations are temporally and spatially limited. Alternatively, in order to quickly and accurately extract information about the current state of the environment, tools for processing and recognition of acoustic signals can be used. In the past, a number of research studies on automatic classification of species through their vocalizations have been undertaken. In many of them, however, the segmentation applied in the preprocessing stage either implies human effort or is insufficiently described to be reproduced. Therefore, it might be unfeasible in real conditions. Particularly, this paper is focused on the extraction of local information as units --called instances-- from audio recordings. The methodology for instance extraction consists in the segmentation carried out using image processing techniques on spectrograms and the estimation of a needed threshold by the Otsu's method. The multiple instance classification (MIC) approach is used for the recognition of the sound units. A public data set was used for the experiments. The proposed unsupervised segmentation method has a practical advantage over the compared supervised method, which requires the training from manually segmented spectrograms. Results show that there is no significant difference between the proposed method and its baseline. Therefore, it is shown that the proposed approach is feasible to design an automatic recognition system of recordings which only requires, as training information, labeled examples of audio recordings.