Emotional Theory of Mind: Bridging Fast Visual Processing with Slow Linguistic Reasoning

The emotional theory of mind problem in images is an emotion recognition task, specifically asking "How does the person in the bounding box feel?" Facial expressions, body pose, contextual information and implicit commonsense knowledge all contribute to the difficulty of the task, making this task currently one of the hardest problems in affective computing. The goal of this work is to evaluate the emotional commonsense knowledge embedded in recent large vision language models (CLIP, LLaVA) and large language models (GPT-3.5) on the Emotions in Context (EMOTIC) dataset. In order to evaluate a purely text-based language model on images, we construct "narrative captions" relevant to emotion perception, using a set of 872 physical social signal descriptions related to 26 emotional categories, along with 224 labels for emotionally salient environmental contexts, sourced from writer's guides for character expressions and settings. We evaluate the use of the resulting captions in an image-to-language-to-emotion task. Experiments using zero-shot vision-language models on EMOTIC show that combining "fast" and "slow" reasoning is a promising way forward to improve emotion recognition systems. Nevertheless, a gap remains in the zero-shot emotional theory of mind task compared to prior work trained on the EMOTIC dataset.Comment: 16 pages(including references and appendix), 8 Tables, 3 figure

Designing AI Interfaces for Children with Special Needs in Educational Contexts

The IDC research community has a growing interest in designing AI interfaces for children with special educational needs. Nonetheless, little research has explored the research and design issues, rationale, challenges, and opportunities in this field. Therefore, we propose to host a half-day workshop to bring together researchers and practitioners from the Learning & Education, Accessibility, and Intelligent User Interfaces sub-fields to discuss and identify existing design issues, challenges, and collaboration barriers, to establish consensus on the design of a pragmatic framework, as well as explore future innovation and research opportunities. We aim to foster mutual unders

Adsorption of levodopa onto Amberlite resins: equilibrium studies and D-optimal modeling based on response surface methodology

This paper investigates the removal of the antiparkinsonism drug levodopa from aqueous solutions utilizing weakly basic Amberlite resins. In the first step of experimental investigation, the adsorption equilibration time (0-24 h) was determined. After that, the effects of properties such as initial levodopa concentration in the solution (20-100 ppm), adsorbent amount (10-30 mg), pH (2-10), and temperature (25-40 degrees C) on the adsorption of levodopa with Amberlite IRA-67 were investigated. Adsorption isotherm, kinetic and thermodynamic parameters were also determined. In the next step of experimental research, the impact of adsorbent type was examined by employing the experimental design. This design work was carried out in Design-Expert (R) Software using D-optimal design based on response surface methodology (RSM). The highest adsorption capacities were achieved as 33.09 mg g(-1)for Amberlite IRA-67 and 20.11 mg g(-1)for Amberlite IRA-400. These results showed that Amberlite resins were effective in removing levodopa from the water

Competitive Adsorption of Anti-Parkinson Drugs on Different Amberlite Resins from Water: Quantitative Analysis by Ultra Performance Liquid Chromatography (UPLC)

Anti-Parkinson drugs, levodopa and entacapone, are potential pollutants of concern that need to be removed from water. These drug compounds generally occur in the aqueous medium in the form of multicomponent mixtures. This work focuses on the competitive adsorption effects of multiple anti-Parkinson drugs in single and binary systems. Four effective Amberlite IRA resins (IRA-67, IRA-96, IRA-400, and IRA-958) have been utilized as adsorbents. The quantitative analysis of anti-Parkinson drugs was performed by a rapid and sensitive ultra performance liquid chromatography (UPLC) method. The structure and surface chemistry of Amberlite resins were characterized by Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), and scanning electron microscopy (SEM). The results of single and binary drug systems showed that the affinity sequence of Amberlite IRA resins toward the anti-Parkinson drugs changed as entacapone > levodopa. In most examined conditions, in the case of binary system, entacapone enhanced the levodopa adsorption. Conversely, levodopa suppressed the adsorption of entacapone in the binary system. The highest total adsorption capacity values were found as 35.59 mg.g(-1) and 73.01 mg.g(-1) for single levodopa and entacapone systems, respectively, and 83.44 mg.g(-1) for binary (levodopa + entacapone) system. Amberlite IRA-67 showed high performance in the adsorption of entacapone in aqueous solutions, presenting a higher adsorption capacity with 73.01 mg.g(-1) in single system and 48.33 mg.g(-1) in binary system. The experimental data were modeled using single-component adsorption isotherm models (Langmuir, Freundlich, and Temkin) and multi-component adsorption isotherm models (non-modified Langmuir, modified Langmuir, and extended Freundlich). The single adsorption of two drugs obeyed well the Langmuir isotherm model (R-2 >= 0.9). The binary drug adsorption data exhibited a good fit to the single-component and multi-component adsorption isotherm models studied