CSGNet: Neural Shape Parser for Constructive Solid Geometry

We present a neural architecture that takes as input a 2D or 3D shape and outputs a program that generates the shape. The instructions in our program are based on constructive solid geometry principles, i.e., a set of boolean operations on shape primitives defined recursively. Bottom-up techniques for this shape parsing task rely on primitive detection and are inherently slow since the search space over possible primitive combinations is large. In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions. Our model is also more effective as a shape detector compared to existing state-of-the-art detection techniques. We finally demonstrate that our network can be trained on novel datasets without ground-truth program annotations through policy gradient techniques.Comment: Accepted at CVPR-201

The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene representation and translates sentences into executable, symbolic programs. To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to. Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences. We use curriculum learning to guide the searching over the large compositional space of images and language. Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences. Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains. It also empowers applications including visual question answering and bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu

Augev Method and an Innovative Use of Vocal Spectroscopy in Evaluating and Monitoring the Rehabilitation Path of Subjects Showing Severe Communication Pathologies

A strongly connotative element of developmental disorders (DS) is the total or partial impairment of verbal communication and, more generally, of social interaction. The method of Vocal-verb self-management (Augev) is a systemic organicistic method able to intervene in problems regarding verbal, spoken and written language development successfully. This study intends to demonstrate that it is possible to objectify these progresses through a spectrographic examination of vocal signals, which detects voice phonetic-acoustic parameters. This survey allows an objective evaluation of how effective an educational-rehabilitation intervention is. This study was performed on a population of 40 subjects (34 males and 6 females) diagnosed with developmental disorders (DS), specifically with a diagnosis of the autism spectrum disorders according to the DSM-5. The 40 subjects were treated in “la Comunicazione” centers, whose headquarters are near Bari, Brindisi and Rome. The results demonstrate a statistical significance in a correlation among the observed variables: supervisory status, attention, general dynamic coordination, understanding and execution of orders, performing simple unshielded rhythmic beats, word rhythm, oral praxies, phono-articulatory praxies, pronunciation of vowels, execution of graphemes, visual perception, acoustic perception, proprioceptive sensitivity, selective attention, short-term memory, segmental coordination, performance of simple rhythmic beatings, word rhythm, voice setting, intonation of sounds within a fifth, vowel pronunciation, consonant pronunciation, graphematic decoding, syllabic decoding, pronunciation of caudate syllables, coding of final syllable consonant, lexical decoding, phoneme-grapheme conversion, homographic grapheme decoding, homogeneous grapheme decoding, graphic stroke