Semiotic Dynamics Solves the Symbol Grounding Problem

Language requires the capacity to link symbols (words, sentences) through the intermediary of internal representations to the physical world, a process known as symbol grounding. One of the biggest debates in the cognitive sciences concerns the question how human brains are able to do this. Do we need a material explanation or a system explanation? John Searle's well known Chinese Room thought experiment, which continues to generate a vast polemic literature of arguments and counter-arguments, has argued that autonomously establishing internal representations of the world (called 'intentionality' in philosophical parlance) is based on special properties of human neural tissue and that consequently an artificial system, such as an autonomous physical robot, can never achieve this. Here we study the Grounded Naming Game as a particular example of symbolic interaction and investigate a dynamical system that autonomously builds up and uses the semiotic networks necessary for performance in the game. We demonstrate in real experiments with physical robots that such a dynamical system indeed leads to a successful emergent communication system and hence that symbol grounding and intentionality can be explained in terms of a particular kind of system dynamics. The human brain has obviously the right mechanisms to participate in this kind of dynamics but the same dynamics can also be embodied in other types of physical systems

Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning

Current advances in Artificial Intelligence and machine learning in general, and deep learning in particular have reached unprecedented impact not only across research communities, but also over popular media channels. However, concerns about interpretability and accountability of AI have been raised by influential thinkers. In spite of the recent impact of AI, several works have identified the need for principled knowledge representation and reasoning mechanisms integrated with deep learning-based systems to provide sound and explainable models for such systems. Neural-symbolic computing aims at integrating, as foreseen by Valiant, two most fundamental cognitive abilities: the ability to learn from the environment, and the ability to reason from what has been learned. Neural-symbolic computing has been an active topic of research for many years, reconciling the advantages of robust learning in neural networks and reasoning and interpretability of symbolic representation. In this paper, we survey recent accomplishments of neural-symbolic computing as a principled methodology for integrated machine learning and reasoning. We illustrate the effectiveness of the approach by outlining the main characteristics of the methodology: principled integration of neural learning with symbolic knowledge representation and reasoning allowing for the construction of explainable AI systems. The insights provided by neural-symbolic computing shed new light on the increasingly prominent need for interpretable and accountable AI systems

Global assessment of nitrogen deposition effects on terrestrial plant diversity : a synthesis

Atmospheric nitrogen (N) deposition is it recognized threat to plant diversity ill temperate and northern parts of Europe and North America. This paper assesses evidence from field experiments for N deposition effects and thresholds for terrestrial plant diversity protection across a latitudinal range of main categories of ecosystems. from arctic and boreal systems to tropical forests. Current thinking on the mechanisms of N deposition effects on plant diversity, the global distribution of G200 ecoregions, and current and future (2030) estimates of atmospheric N-deposition rates are then used to identify the risks to plant diversity in all major ecosystem types now and in the future. This synthesis paper clearly shows that N accumulation is the main driver of changes to species composition across the whole range of different ecosystem types by driving the competitive interactions that lead to composition change and/or making conditions unfavorable for some species. Other effects such its direct toxicity of nitrogen gases and aerosols long-term negative effects of increased ammonium and ammonia availability, soil-mediated effects of acidification, and secondary stress and disturbance are more ecosystem, and site-specific and often play a supporting role. N deposition effects in mediterranean ecosystems have now been identified, leading to a first estimate of an effect threshold. Importantly, ecosystems thought of as not N limited, such as tropical and subtropical systems, may be more vulnerable in the regeneration phase. in situations where heterogeneity in N availability is reduced by atmospheric N deposition, on sandy soils, or in montane areas. Critical loads are effect thresholds for N deposition. and the critical load concept has helped European governments make progress toward reducing N loads on sensitive ecosystems. More needs to be done in Europe and North America. especially for the more sensitive ecosystem types. including several ecosystems of high conservation importance. The results of this assessment Show that the Vulnerable regions outside Europe and North America which have not received enough attention are ecoregions in eastern and Southern Asia (China, India), an important part of the mediterranean ecoregion (California, southern Europe). and in the coming decades several subtropical and tropical parts of Latin America and Africa. Reductions in plant diversity by increased atmospheric N deposition may be more widespread than first thought, and more targeted Studies are required in low background areas, especially in the G200 ecoregions

Computational approaches to predicting treatment response to obesity using neuroimaging

Obesity is a worldwide disease associated with multiple severe adverse consequences and comorbid conditions. While an increased body weight is the defining feature in obesity, etiologies, clinical phenotypes and treatment responses vary between patients. These variations can be observed within individual treatment options which comprise lifestyle interventions, pharmacological treatment, and bariatric surgery. Bariatric surgery can be regarded as the most effective treatment method. However, long-term weight regain is comparably frequent even for this treatment and its application is not without risk. A prognostic tool that would help predict the effectivity of the individual treatment methods in the long term would be essential in a personalized medicine approach. In line with this objective, an increasing number of studies have combined neuroimaging and computational modeling to predict treatment outcome in obesity. In our review, we begin by outlining the central nervous mechanisms measured with neuroimaging in these studies. The mechanisms are primarily related to reward-processing and include "incentive salience" and psychobehavioral control. We then present the diverse neuroimaging methods and computational prediction techniques applied. The studies included in this review provide consistent support for the importance of incentive salience and psychobehavioral control for treatment outcome in obesity. Nevertheless, further studies comprising larger sample sizes and rigorous validation processes are necessary to answer the question of whether or not the approach is sufficiently accurate for clinical real-world application

THE EFFECT OF A NOVEL REHABILITATION PROGRAM ON WALKING PERFORMANCE IN PERSONS WITH MULTIPLE SCLEROSIS

The current study examined the effects of the NewGait™ device on walking performance in persons with multiple sclerosis (MS). Eight MS patients participated in this study. Pre- and post-testing assessed kinematic gait variables (step width, length, and speed), ankle range of motion, and rating of perceived exertion (RPE). Participants completed an 8-week physical therapy (PT) protocol aimed to improve gait and balance, with the experimental group wearing the NewGait™ device. Repeated measures mixed ANOVA showed no main effects between the gait variables or between groups. Post-hoc paired t-tests indicated that the NewGait™ device elicited meaningful change in left and right step length and speed. The NewGait™ device may be a promising rehabilitation device to help induce positive walking performance changes in persons with MS

THE EFFECT OF A NOVEL REHABILITATION DEVICE ON MUSCLE ACTIVATION DURING GAIT IN PERSONS WITH MULTIPLE SCLEROSIS

This study examined the acute effect of a novel rehabilitation device, NewGait™, on muscle activation in persons with Multiple Sclerosis. Through electromyography, muscle activation of the vastus medialis (VM), gastrocnemius lateralis (GL) and tibialis anterior (TA) was measured in seventeen patients (n=17). Three trials were conducted in each condition: a 10-meter control walk and 10-meter NewGait™ walk. Results showed a non-significant change in muscle activity with moderate effect sizes in the right VM (increase of 39.72% MVC, p=0.082, d=0.626) and right TA (decrease of 12.71% MVC, p=0.069, d=0.427). In general, no change in muscle activation was noted when wearing the NewGait™ device. Future research should include a larger sample size and differentiation between the stance phases to accurately measure the outcomes of the NewGait™ device on muscle activation

CHANGES IN GAIT AND COORDINATION VARIABILITY IN PERSONS WITH MULTIPLE SCLEROSIS FOLLOWING A REHABILITATION PROGRAM

This study investigated changes in gait and coordination variability in persons with Multiple Sclerosis (MS) after an 8-week rehabilitation intervention. Data for eight participants (Control: 4, Intervention: 4) were analyzed via Cortex Motion Analysis software and Visual 3D to calculate knee and ankle joint angles as well as discrete spatiotemporal parameters. The knee and ankle joint angles were further analyzed using a vector coding technique to quantify coordination between these joints and how they produce a functional gait pattern. No significant changes in gait or coordination variability were found after rehabilitation, but some meaningful changes with large and moderate effect sizes were present. This study demonstrated a comprehensive overview of the relationship between process and outcome variability in a clinical population

Renal function is independently associated with circulating betatrophin

OBJECTIVE: Betatrophin has been identified as a marker linking liver with beta cell function and lipid metabolism in murine models. Until now, the regulation of circulating betatrophin in humans is not entirely clear. We here analyzed the relation of betatrophin levels to phenotypes of the metabolic syndrome and speculated that renal function might influence circulating betatrophin levels and explain age-dependent changes of betatrophin. SUBJECTS: We analyzed blood samples from 535 individuals participating in the Metabolic Syndrome Berlin Potsdam study. RESULTS: In a crude analysis we found a positive correlation between betatrophin levels and HbA1c (r = 0.24; p < 0.001), fasting glucose (r = 0.20; p < 0.001) and triglycerides (r = 0.12; p = 0.007). Furthermore betatrophin was positively correlated with age (r = 0.47; p <0.001), systolic blood pressure (r = 0.17; p < 0.001), intima media thickness (r = 0.26; p < 0.001) and negatively correlated with CKD-EPI eGFR (r = -0.33; p < 0.001) as an estimate of renal function. Notably, eGFR remained highly associated with betatrophin after adjustment for age, waist circumference, gender, HbA1c and lipid parameters in a multivariate linear regression model ({beta} = -0.197, p< 0.001). CONCLUSIONS: Our data suggest that circulating levels of betatrophin depend on age, gender, waist circumference, total/HDL cholesterol ratio and renal function. Especially the association to eGFR highlights the importance for future studies to address renal function as possible influence on betatrophin regulation and consider eGFR as potential confounder when analyzing the role of betatrophin in humans