Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language

We present Le-RNR-Map, a Language-enhanced Renderable Neural Radiance map for Visual Navigation with natural language query prompts. The recently proposed RNR-Map employs a grid structure comprising latent codes positioned at each pixel. These latent codes, which are derived from image observation, enable: i) image rendering given a camera pose, since they are converted to Neural Radiance Field; ii) image navigation and localization with astonishing accuracy. On top of this, we enhance RNR-Map with CLIP-based embedding latent codes, allowing natural language search without additional label data. We evaluate the effectiveness of this map in single and multi-object searches. We also investigate its compatibility with a Large Language Model as an "affordance query resolver". Code and videos are available at https://intelligolabs.github.io/Le-RNR-Map/Comment: Accepted at ICCVW23 VLA

Demo: gesture based interaction with the Hololens 2

Gesture recognition is one of the default interaction modalities in many XR applications, although the gesture types recognized by many applications is typically limited to few static poses. In this demo we show that a recent network-based solution for online, sliding window, gesture classification from hand pose streams (On-Off deep Multi-View Multi-Task) can be used for the simultaneous detection and recognition of heterogeneous gestures, including dynamic coarse and fine grained ones, enabling interaction designers to create novel ways to perform interactive tasks that can be applied to different domains

Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.Comment: Accepted at the 26th Forum on specification and Design Languages (FDL 2023

Toward Smart Doors: A Position Paper

Conventional automatic doors cannot distinguish between people wishing to pass through the door and people passing by the door, so they often open unnecessarily. This leads to the need to adopt new systems in both commercial and non-commercial environments: smart doors. In particular, a smart door system predicts the intention of people near the door based on the social context of the surrounding environment and then makes rational decisions about whether or not to open the door. This work proposes the first position paper related to smart doors, without bells and whistles. We first point out that the problem not only concerns reliability, climate control, safety, and mode of operation. Indeed, a system to predict the intention of people near the door also involves a deeper understanding of the social context of the scene through a complex combined analysis of proxemics and scene reasoning. Furthermore, we conduct an exhaustive literature review about automatic doors, providing a novel system formulation. Also, we present an analysis of the possible future application of smart doors, a description of the ethical shortcomings, and legislative issues

An Experimental and Theoretical Investigation of Loperamide Hydrochloride-Glutaric Acid Co-Crystals.

Co-crystallization is a powerful method to improve the physico-chemical properties of drugs. Loperamide hydrochloride is a topical analgesic for the gastro intestinal tract showing low and pH-dependent solubility, for this reason an enhancement of its solubility and/or dissolution rate, particularly at the pH of the intestinal tract, could improve its local efficacy. Here, we prepared co-crystals of this active principle with glutaric acid, so obtaining a new crystalline solid representing a viable alternative to improve the physico-chemical properties and thus the pharmaceutical behavior of the drug. Differential scanning calorimetry, X-ray powder diffraction, Fourier infrared spectroscopy, solid-state NMR and scanning electron microscopy coupled with the energy dispersive X-ray spectrometry were used to investigate the new solid phase formation. DFT calculations at B3LYP/6-31G(d) level of theory, in the gas phase, including frequencies computation, provided a rationale for the interaction between loperamide hydrochloride and glutaric acid. The co-crystals showed improved water solubility in comparison to loperamide HCl, and the pharmaceutical formulation proposed was able to release the drug more rapidly in comparison to three reference commercial products, when tested at neutral pHs

Selective alterations of endocannabinoid system genes expression in obsessive compulsive disorder

Abstract Obsessive Compulsive Disorder (OCD) is listed as one of the top 10 most disabling neuropsychiatric conditions in the world. The neurobiology of OCD has not been completely understood and efforts are needed in order to develop new treatments. Beside the classical neurotransmitter systems and signalling pathways implicated in OCD, the possible involvement of the endocannabinoid system (ECS) has emerged in pathophysiology of OCD. We report here selective downregulation of the genes coding for enzymes allowing the synthesis of the endocannabinoids. We found reduced DAGLα and NAPE-PLD in blood samples of individuals with OCD (when compared to healthy controls) as well as in the amygdala complex and prefrontal cortex of dopamine transporter (DAT) heterozygous rats, manifesting compulsive behaviours. Also mRNA levels of the genes coding for cannabinoid receptors type 1 and type 2 resulted downregulated, respectively in the rat amygdala and in human blood. Moreover, NAPE-PLD changes in gene expression resulted to be associated with an increase in DNA methylation at gene promoter, and the modulation of this gene in OCD appears to be correlated to the progression of the disease. Finally, the alterations observed in ECS genes expression appears to be correlated with the modulation in oxytocin receptor gene expression, consistently with what recently reported. Overall, we confirm here a role for ECS in OCD at both preclinical and clinical level. Many potential biomarkers are suggested among its components, in particular NAPE-PLD, that might be of help for a prompt and clear diagnosis

Neuro-Symbolic Empowered Denoising Diffusion Probabilistic Models for Real-Time Anomaly Detection in Industry 4.0

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before

Alcune considerazioni su codificazione e decodificazione

La recentissima approvazione della legge 103/2017 (cd. Riforma del processo penale) è solo l’ultimo dei tentativi, alcuni di successo, altri abortiti, di riforma di parti del codice di procedura penale. Le riforme dei grandi codici, però, quando riuscite, vanno di pari passo con la presenza di una pletora di leggi speciali ad essi esterne che rendono sempre più difficoltoso per l’operatore del diritto riuscire a districarsi nella giungla di norme allo stato esistenti. Semplificazioni normative anche numericamente imponenti possono porsi solo quale palliativo per il passato e non quale modus operandi per il futuro. L’articolo si propone una veloce panoramica sulla problematica e alcuni spunti di riflessione per soluzioni future