Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages

Microblogging platforms, of which Twitter is a representative example, are valuable information sources for market screening and financial models. In them, users voluntarily provide relevant information, including educated knowledge on investments, reacting to the state of the stock markets in real-time and, often, influencing this state. We are interested in the user forecasts in financial, social media messages expressing opportunities and precautions about assets. We propose a novel Targeted Aspect-Based Emotion Analysis (tabea) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet (instead of making an overall guess about that whole tweet). It is based on Natural Language Processing (nlp) techniques and Machine Learning streaming algorithms. The system comprises a constituency parsing module for parsing the tweets and splitting them into simpler declarative clauses; an offline data processing module to engineer textual, numerical and categorical features and analyse and select them based on their relevance; and a stream classification module to continuously process tweets on-the-fly. Experimental results on a labelled data set endorse our solution. It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter. To the best of our knowledge, no prior work in the literature has addressed this problem despite its practical interest in decision-making, and we are not aware of any previous nlp nor online Machine Learning approaches to tabea.Xunta de Galicia | Ref. ED481B-2021-118Xunta de Galicia | Ref. ED481B-2022-093Financiado para publicación en acceso aberto: Universidade de Vigo/CISU

Property and Precaution

Property in land suffers from an unacknowledged precautionary deficit. Ownership is dispensed in standardized blocks of monopoly control that are routinely retained in their entirety until someone raises an issue regarding an actual or potential incompatible land use. This arrangement, which encourages owners to take sustained, unpriced draws against a limited stock of future flexibility, sets the stage for future impasse as inconsistent plans develop. It also makes property an unnecessarily accident-prone institution, given the role that bargaining failure plays in producing costly land use conflicts. Expanding the slate of potential precautions beyond owners\u27 locational and operational choices to include their choices about the strength and content of their own entitlements offers new traction on land use disputes. It also presents interesting institutional and theoretical challenges. In this essay, I propose using a local option exchange to confront owners with the opportunity costs of maintaining veto power over unused, low-valued rights. Enabling owners to relinquish property-rule protection of such rights before conflicts arise could make property more flexible and communicative, and hence reduce the costs of incompatible land uses. This approach requires rethinking the limits of customization in property bundles and the potential for owner participation in entitlement definition

Property and Precaution

Property in land suffers from an unacknowledged precautionary deficit. Ownership is dispensed in standardized blocks of monopoly control that are routinely retained in their entirety until someone raises an issue regarding an actual or potential incompatible land use. This arrangement, which encourages owners to take sustained, unpriced draws against a limited stock of future flexibility, sets the stage for future impasse as inconsistent plans develop. It also makes property an unnecessarily accident-prone institution, given the role that bargaining failure plays in producing costly land use conflicts. Expanding the slate of potential precautions beyond owners\u27 locational and operational choices to include their choices about the strength and content of their own entitlements offers new traction on land use disputes. It also presents interesting institutional and theoretical challenges. In this essay, I propose using a local option exchange to confront owners with the opportunity costs of maintaining veto power over unused, low-valued rights. Enabling owners to relinquish property-rule protection of such rights before conflicts arise could make property more flexible and communicative, and hence reduce the costs of incompatible land uses. This approach requires rethinking the limits of customization in property bundles and the potential for owner participation in entitlement definition

Regulating the Risks of AI

Companies and governments now use Artificial Intelligence (“AI”) in a wide range of settings. But using AI leads to well-known risks that arguably present challenges for a traditional liability model. It is thus unsurprising that lawmakers in both the United States and the European Union (“EU”) have turned to the tools of risk regulation in governing AI systems. This Article describes the growing convergence around risk regulation in AI governance. It then addresses the question: what does it mean to use risk regulation to govern AI systems? The primary contribution of this Article is to offer an analytic framework for understanding the use of risk regulation as AI governance. It aims to surface the shortcomings of risk regulation as a legal approach, and to enable readers to identify which type of risk regulation is at play in a given law. The theoretical contribution of this Article is to encourage researchers to think about what is gained and what is lost by choosing a particular legal tool for constructing the meaning of AI systems in the law. Whatever the value of using risk regulation, constructing AI harms as risks is a choice with consequences. Risk regulation comes with its own policy baggage: a set of tools and troubles that have emerged in other fields. Risk regulation tends to try to fix problems with the technology so it may be used, rather than contemplating that it might sometimes not be appropriate to use it at all. Risk regulation works best on quantifiable problems and struggles with hard-toquantify harms. It can cloak what are really policy decisions as technical decisions. Risk regulation typically is not structured to make injured people whole. And the version of risk regulation typically deployed to govern AI systems lacks the feedback loops of tort liability. Thus the choice to use risk regulation in the first place channels the law towards a particular approach to AI governance that makes implicit tradeoffs and carries predictable shortcomings. The second, more granular observation this Article makes is that not all risk regulation is the same. That is, once regulators choose to deploy risk regulation, there are still significant variations in what type of risk regulation they might use. Risk regulation is a legal transplant with multiple possible origins. This Article identifies at least four models for AI risk regulation that meaningfully diverge in how they address accountability

Safety Analysis of Battery-Powered Adaptive Ride-on Toys for Children with Disabilities

Modified battery-powered ride-on toy cars, or adaptive ride-on toys, represent novel rehabilitation tools and developmental aids for children with disabilities. Studies have shown that children are benefiting socially and developmentally from their use. However, the use of these toys by children with disabilities potentially poses a risk of injury and it is vitally important to ensure the safe use of these toys, particularly for the benefit of those with developmental challenges. Within this context, the purpose of the first study was to determine whether modifications to ride-on toys are sufficient to prevent common modes of injury such as falls, passenger excursion, and impact with the interior of the vehicle using an average six-year-old test dummy. Because the population of children with disabilities who are receiving adaptive ride-on toys ha a wide range of mobility impairments and may suffer from a wide range of musculoskeletal disorders, those with both decreased and increased muscle stiffness were considered in the second study. In both studies, safety modifications sufficiently reduced risk of primary injury mechanisms with little-to-no added risk. These studies are significant due to lack of research in the field of safety of pediatric rehabilitative devices, specifically adaptive ride-on toys. The proven success of these rehabilitative programs further shows these studies are a valuable tool intended to better equip pediatric care providers with knowledge on the safety of car modifications. Furthermore, the findings of these studies support the growth of adaptive ride-on toy programs to increase rehabilitation opportunities for children with disabilities

Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context Learning

Following the success of Large Language Models (LLMs), Large Multimodal Models (LMMs), such as the Flamingo model and its subsequent competitors, have started to emerge as natural steps towards generalist agents. However, interacting with recent LMMs reveals major limitations that are hardly captured by the current evaluation benchmarks. Indeed, task performances (e.g., VQA accuracy) alone do not provide enough clues to understand their real capabilities, limitations, and to which extent such models are aligned to human expectations. To refine our understanding of those flaws, we deviate from the current evaluation paradigm, and (1) evaluate 10 recent open-source LMMs from 3B up to 80B parameter scale, on 5 different axes; hallucinations, abstention, compositionality, explainability and instruction following. Our evaluation on these axes reveals major flaws in LMMs. While the current go-to solution to align these models is based on training, such as instruction tuning or RLHF, we rather (2) explore the training-free in-context learning (ICL) as a solution, and study how it affects these limitations. Based on our ICL study, (3) we push ICL further and propose new multimodal ICL variants such as; Multitask-ICL, Chain-of-Hindsight-ICL, and Self-Correcting-ICL. Our findings are as follows. (1) Despite their success, LMMs have flaws that remain unsolved with scaling alone. (2) The effect of ICL on LMMs flaws is nuanced; despite its effectiveness for improved explainability, answer abstention, ICL only slightly improves instruction following, does not improve compositional abilities, and actually even amplifies hallucinations. (3) The proposed ICL variants are promising as post-hoc approaches to efficiently tackle some of those flaws. The code is available here: https://github.com/mshukor/EvALign-ICL.Comment: ICLR 2024. Project Page: https://evalign-icl.github.io