On staying grounded and avoiding Quixotic dead ends

The 15 articles in this special issue on The Representation of Concepts illustrate the rich variety of theoretical positions and supporting research that characterize the area. Although much agreement exists among contributors, much disagreement exists as well, especially about the roles of grounding and abstraction in conceptual processing. I first review theoretical approaches raised in these articles that I believe are Quixotic dead ends, namely, approaches that are principled and inspired but likely to fail. In the process, I review various theories of amodal symbols, their distortions of grounded theories, and fallacies in the evidence used to support them. Incorporating further contributions across articles, I then sketch a theoretical approach that I believe is likely to be successful, which includes grounding, abstraction, flexibility, explaining classic conceptual phenomena, and making contact with real-world situations. This account further proposes that (1) a key element of grounding is neural reuse, (2) abstraction takes the forms of multimodal compression, distilled abstraction, and distributed linguistic representation (but not amodal symbols), and (3) flexible context-dependent representations are a hallmark of conceptual processing

HiSSNet: Sound Event Detection and Speaker Identification via Hierarchical Prototypical Networks for Low-Resource Headphones

Modern noise-cancelling headphones have significantly improved users' auditory experiences by removing unwanted background noise, but they can also block out sounds that matter to users. Machine learning (ML) models for sound event detection (SED) and speaker identification (SID) can enable headphones to selectively pass through important sounds; however, implementing these models for a user-centric experience presents several unique challenges. First, most people spend limited time customizing their headphones, so the sound detection should work reasonably well out of the box. Second, the models should be able to learn over time the specific sounds that are important to users based on their implicit and explicit interactions. Finally, such models should have a small memory footprint to run on low-power headphones with limited on-chip memory. In this paper, we propose addressing these challenges using HiSSNet (Hierarchical SED and SID Network). HiSSNet is an SEID (SED and SID) model that uses a hierarchical prototypical network to detect both general and specific sounds of interest and characterize both alarm-like and speech sounds. We show that HiSSNet outperforms an SEID model trained using non-hierarchical prototypical networks by 6.9 - 8.6 percent. When compared to state-of-the-art (SOTA) models trained specifically for SED or SID alone, HiSSNet achieves similar or better performance while reducing the memory footprint required to support multiple capabilities on-device

Gesture and naming therapy for people with severe aphasia: a group study

In this study, the authors (a) investigated whether a group of people with severe aphasia could learn a vocabulary of pantomime gestures through therapy and (b) compared their learning of gestures with their learning of words. The authors also examined whether gesture therapy cued word production and whether naming therapy cued gestures

Visual detail about the body modulates tactile localisation biases

The localisation of tactile stimuli requires the integration of visual and somatosensory inputs within an internal representation of the body surface, and is prone to consistent bias. Joints may play a role in segmenting such internal body representations, and may therefore influence tactile localisation biases, although the nature of this influence remains unclear. Here, we investigate the relationship between conceptual knowledge of joint locations and tactile localisation biases on the hand. In one task, participants localised tactile stimuli applied to the dorsum of their hand. A distal localisation bias was observed in all participants, consistent with previous results. We also manipulated the availability of visual information during this task, to determine whether the absence of this information could account for the distal bias observed here and by Mancini and colleagues (2011). The observed distal bias increased in magnitude when visual information was restricted, without a corresponding decrease in precision. In a separate task, the same participants indicated, from memory, knuckle locations on a silhouette image of their hand. Analogous distal biases were also seen in the knuckle localisation task. The accuracy of conceptual joint knowledge was not correlated with tactile localisation bias magnitude, although a similarity in observed bias direction suggests that both tasks may rely on a common, higher-order body representation. These results also suggest that distortions of conceptual body representation may be more common in healthy individuals than previously thought

Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU.

OBJECTIVES: To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs). DESIGN: Prospective cohort study. SETTING: A total of 306 ICUs from 24 European countries. PARTICIPANTS: Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81-87 y]; 51.8% male). MEASUREMENTS: Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30-day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves. RESULTS: The 30-day-mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was .80, and the Brier score was .18. At a cut point of 10 or higher (75% of all patients), the model predicts 30-day mortality in 91.1% of all patients who die. CONCLUSION: A predictive model of cumulative events predicts 30-day mortality in patients older than 80 years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision-making capacity

Methylation of unactivated alkenes with engineered methyltransferases to generate non‐natural terpenoids

Terpenoids are built from isoprene building blocks and have numerous biological functions. Selective late-stage modification of their carbon scaffold has the potential to optimize or transform their biological activities. However, the synthesis of terpenoids with a non-natural carbon scaffold is often a challenging endeavor because of the complexity of these molecules. Herein we report the identification and engineering of (S)-adenosyl-l-methionine-dependent sterol methyltransferases for selective C-methylation of linear terpenoids. The engineered enzyme catalyzes selective methylation of unactivated alkenes in mono-, sesqui- and diterpenoids to produce C11, C16 and C21 derivatives. Preparative conversion and product isolation reveals that this biocatalyst performs C-C bond formation with high chemo- and regioselectivity. The alkene methylation most likely proceeds via a carbocation intermediate and regioselective deprotonation. This method opens new avenues for modifying the carbon scaffold of alkenes in general and terpenoids in particular.Bundesministerium für Bildung und ForschungProjekt DEA

Modifizierte Enzyme ermöglichen die selektive N‐Alkylierung von Pyrazolen unter Verwendung einfacher Halogenalkane

Die selektive Alkylierung von Pyrazolen ist eine Herausforderung in der Chemie und könnte die Synthese wichtiger Moleküle vereinfachen. In dieser Arbeit berichten wir über eine katalysatorgesteuerte Alkylierung von Pyrazolen durch eine cyclische Kaskadenreaktion mit zwei Enzymen. In diesem enzymatischen System nutzt ein promiskuitives Enzym Halogenalkane als Ausgangsstoffe, um nicht-natürliche Analoga des Cosubstrats S-Adenosyl-l-Methionin zu synthetisieren. Ein zweites engineertes Enzym überträgt die Alkylgruppen in einer hochselektiven C-N-Bindungsknüpfung auf das Pyrazol-Substrat. Das Cosubstrat wird regeneriert und nur in katalytischen Mengen eingesetzt. Für das Enzym-Engineering wurde eine computerbasierte Methode verwendet, um eine Mutantenbibliothek in silico zu entwickeln. In einer Runde von Mutagenese und Screening wurde somit eine promiskuitive Methyltransferase in eine kleine Pyrazol-alkylierende Enzymfamilie umgewandelt. Mit diesem bienzymatischen System konnte die Alkylierung von Pyrazolen (Methylierung, Ethylierung, Propylierung) mit bislang unerreichter Regioselektivität (>99 %), Regiodivergenz und in einem ersten Beispiel in präparativem Maßstab gezeigt werden.Bundesministerium für Bildung und ForschungDeutsche Forschungsgemeinschaf

Verification of Decision Making Software in an Autonomous Vehicle: An Industrial Case Study

Correctness of autonomous driving systems is crucial as\ua0incorrect behaviour may have catastrophic consequences. Many different\ua0hardware and software components (e.g. sensing, decision making, actuation,\ua0and control) interact to solve the autonomous driving task, leading to a level of complexity that brings new challenges for the formal verification\ua0community. Though formal verification has been used to prove\ua0correctness of software, there are significant challenges in transferring\ua0such techniques to an agile software development process and to ensure\ua0widespread industrial adoption. In the light of these challenges, the identification\ua0of appropriate formalisms, and consequently the right verification\ua0tools, has significant impact on addressing them. In this paper, we\ua0evaluate the application of different formal techniques from supervisory\ua0control theory, model checking, and deductive verification to verify existing\ua0decision and control software (in development) for an autonomous\ua0vehicle. We discuss how the verification objective differs with respect tothe choice of formalism and the level of formality that can be applied.\ua0Insights from the case study show a need for multiple formal methods to\ua0prove correctness, the difficulty to capture the right level of abstraction\ua0to model and specify the formal properties for the verification objectives

Tracking Down Abstract Linguistic Meaning: Neural Correlates of Spatial Frame of Reference Ambiguities in Language

This functional magnetic resonance imaging (fMRI) study investigates a crucial parameter in spatial description, namely variants in the frame of reference chosen. Two frames of reference are available in European languages for the description of small-scale assemblages, namely the intrinsic (or object-oriented) frame and the relative (or egocentric) frame. We showed participants a sentence such as “the ball is in front of the man”, ambiguous between the two frames, and then a picture of a scene with a ball and a man – participants had to respond by indicating whether the picture did or did not match the sentence. There were two blocks, in which we induced each frame of reference by feedback. Thus for the crucial test items, participants saw exactly the same sentence and the same picture but now from one perspective, now the other. Using this method, we were able to precisely pinpoint the pattern of neural activation associated with each linguistic interpretation of the ambiguity, while holding the perceptual stimuli constant. Increased brain activity in bilateral parahippocampal gyrus was associated with the intrinsic frame of reference whereas increased activity in the right superior frontal gyrus and in the parietal lobe was observed for the relative frame of reference. The study is among the few to show a distinctive pattern of neural activation for an abstract yet specific semantic parameter in language. It shows with special clarity the nature of the neural substrate supporting each frame of spatial reference

Interpreting Spatial Language in Image Captions

The map as a tool for accessing data has become very popular in recent years, but a lot of data do not have the necessary spatial meta-data to allow for that. Some data such as photographs however have spatial information in their captions and if this could be extracted, then they could be made available via map-based interfaces. Towards this goal, we introduce a model and spatio-linguistic reasoner for interpreting the spatial information in image captions that is based upon quantitative data about spatial language use acquired directly from people. Spatial language is inherently vague, and both the model and reasoner have been designed to incorporate this vagueness at the quantitative level and not only qualitatively