ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training

Aligning the visual and language spaces requires to train deep neural networks from scratch on giant multimodal datasets; CLIP trains both an image and a text encoder, while LiT manages to train just the latter by taking advantage of a pretrained vision network. In this paper, we show that sparse relative representations are sufficient to align text and images without training any network. Our method relies on readily available single-domain encoders (trained with or without supervision) and a modest (in comparison) number of image-text pairs. ASIF redefines what constitutes a multimodal model by explicitly disentangling memory from processing: here the model is defined by the embedded pairs of all the entries in the multimodal dataset, in addition to the parameters of the two encoders. Experiments on standard zero-shot visual benchmarks demonstrate the typical transfer ability of image-text models. Overall, our method represents a simple yet surprisingly strong baseline for foundation multimodal models, raising important questions on their data efficiency and on the role of retrieval in machine learning.Comment: 13 pages, 5 figure

Relative representations enable zero-shot latent space communication

Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. Ideally, the distribution of the data points in the latent space should depend only on the task, the data, the loss, and other architecture-specific constraints. However, factors such as the random weights initialization, training hyperparameters, or other sources of randomness in the training phase may induce incoherent latent spaces that hinder any form of reuse. Nevertheless, we empirically observe that, under the same data and modeling choices, distinct latent spaces typically differ by an unknown quasi-isometric transformation: that is, in each space, the distances between the encodings do not change. In this work, we propose to adopt pairwise similarities as an alternative data representation, that can be used to enforce the desired invariance without any additional training. We show how neural architectures can leverage these relative representations to guarantee, in practice, latent isometry invariance, effectively enabling latent space communication: from zero-shot model stitching to latent space comparison between diverse settings. We extensively validate the generalization capability of our approach on different datasets, spanning various modalities (images, text, graphs), tasks (e.g., classification, reconstruction) and architectures (e.g., CNNs, GCNs, transformers).Comment: 20 pages, 8 figures, 16 table

A novel glyco-conjugate vaccine against fungal pathogens

To generate a vaccine to protect against a variety of human pathogenic fungi, we conjugated laminarin (Lam), a well-characterized but poorly immunogenic β-glucan preparation from the brown alga Laminaria digitata, with the diphtheria toxoid CRM197, a carrier protein used in some glyco-conjugate bacterial vaccines. This Lam-CRM conjugate proved to be immunogenic and protective as immunoprophylactic vaccine against both systemic and mucosal (vaginal) infections by Candida albicans. Protection probably was mediated by anti-β-glucan antibodies as demonstrated by passive transfer of protection to naive mice by the whole immune serum, the immune vaginal fluid, and the affinity-purified anti-β-glucan IgG fractions, as well as by administration of a β-glucan–directed IgG2b mAb. Passive protection was prevented by adsorption of antibodies on Candida cells or β-glucan particles before transfer. Anti-β-glucan antibodies bound to C. albicans hyphae and inhibited their growth in vitro in the absence of immune-effector cells. Remarkably, Lam-CRM–vaccinated mice also were protected from a lethal challenge with conidia of Aspergillus fumigatus, and their serum also bound to and markedly inhibited the growth of A. fumigatus hyphae. Thus, this novel conjugate vaccine can efficiently immunize and protect against two major fungal pathogens by mechanisms that may include direct antifungal properties of anti-β-glucan antibodies

Dental Ritual Mutilations and Forensic Odontologist Practice: a Review of the Literature

Uvod: Etnička sakaćenja imaju antropološko značenje, kako u suvremenom tako i u nekadašnjem ljudskom ponašanju, ovisno o geografskim, religioznim i kulturnim čimbenicima koji znatno mogu pomoći forenzičnom stomatologu u postupku izrade dentalnog profila. Sakaćenje zuba i ukrasi na njima bili su, i još uvijek jesu, uobičajeni među mnogim etničkim skupinama i kulturama. Kroz povijest ljudskoga roda zdravlje zuba bilo je simbol mladosti, ljepote i snage, ali može imati i druga značenja. Sakaćenje zuba obilježje je mnogih nestalih kultura i prakticiralo se uglavnom tijekom religijskih rituala, u estetske svrhe i kao simbol pripadnosti određenoj socijalnoj skupini. No slični običaji i danas su uobičajeni u nekim područjima diljem svijeta. Materijali i metode: Članak je zapravo sustavni pregled literature o ritualnom sakaćenju zuba iz ranih 1960-ih, a uključeni su i podatci s PubMeda, Scopusa i Google Scolara. Istaraživanje je namjerno ograničeno na ritualno sakaćenje koje se može definirati kao bilo koje nepovratno narušavanje integriteta ljudskoga organizma učinjeno u ritualne svrhe i bez namjere liječenja. Zato su isključeni svi slučajevi pojedinačnih ili višestrukih samovađenja zuba učinjenih iz psihotičnih razloga te oralno sakaćenje djece jer se takva praksa u nekim etničkim skupinama smatra terapijskom. Zaključak: Spoznaje o promjenama na zubima nakon oralnoga sakaćenja važne su pri identifikaciji živih ili umrlih osoba, ili čak ljudskih ostataka jer odaju odgovarajuće informacije o etničkom i kulturološkom podrijetlu subjekta. U ovom članku navedena su i neka medicinskopravna stajališta o sakaćenju zuba, a namijenjena su doktorima dentalne medicine.Background: Ethnic mutilations have a social and anthropological significance both in contemporary and past human behavior, influenced by geographic, religious and cultural factors which can greatly help forensic odontologist’s practice in dental profiling process. ental ritual mutilations and dental decorations were - and still are - practiced among many ethnic groups and cultures. Throughout the history of humanity, having healthy teeth has a symbolic meaning of youth, beauty and strength, but it can also have other meanings. Dental ritual mutilations were documented in many cultures in the past and were practiced mainly for religious rituals purposes, for esthetic reasons and because they represented a symbol of status or of belonging to a particular social group. Similar rituals are still performed. Material and Method: The present paper is a systematic review of the literature reporting on dental ritual mutilations from the early 1960s and is included in Pubmed, Scopus and Googlescholar. The research was deliberately limited only to the ritual mutilations, which can be defined as “any irreversible impairment of the integrity of the human organism, made with a ritual purpose and without any curative aim”. Therefore all the articles dealing with single or multiple dental self extractions of psychotic origins were excluded, as well as the infant oral mutilations, since the practice is deemed to have therapeutical effects among ethnic groups dedited to this practice. Conclusions: The knowledge of dental alteration due to oral mutilations can be a powerful tool for the identification procedures of living or dead persons or even in human remains especially providing relevant information about the ethnic origins and the cultural background of a subject. Some medical legal issues for the odontologist about dental mutilation are also addressed in the paper

La solidita' delle incertezze. Appunti sulla conoscenza scientifica e i suoi problemi

Consiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7 Rome / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Latent space translation via semantic alignment

While different neural models often exhibit latent spaces that are alike when exposed to semantically related data, this intrinsic similarity is not always immediately discernible. Towards a better understanding of this phenomenon, our work shows how representations learned from these neural modules can be translated between different pre-trained networks via simpler transformations than previously thought. An advantage of this approach is the ability to estimate these transformations using standard, well-understood algebraic procedures that have closed-form solutions. Our method directly estimates a transformation between two given latent spaces, thereby enabling effective stitching of encoders and decoders without additional training. We extensively validate the adaptability of this translation procedure in different experimental settings: across various trainings, domains, architectures (e.g., ResNet, CNN, ViT), and in multiple downstream tasks (classification, reconstruction). Notably, we show how it is possible to zero-shot stitch text encoders and vision decoders, or vice-versa, yielding surprisingly good classification performance in this multimodal setting