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

Bootstrapping Parallel Anchors for Relative Representations

The use of relative representations for latent embeddings has shown potential in enabling latent space communication and zero-shot model stitching across a wide range of applications. Nevertheless, relative representations rely on a certain amount of parallel anchors to be given as input, which can be impractical to obtain in certain scenarios. To overcome this limitation, we propose an optimization-based method to discover new parallel anchors from a limited known set (seed). Our approach can be used to find semantic correspondence between different domains, align their relative spaces, and achieve competitive results in several tasks.Comment: 9 pages, 7 table

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

Different pH requirements are associated with divergent inhibitory effects of chloroquine on human and avian influenza A viruses

Chloroquine is a 4-aminoquinoline previously used in malaria therapy and now becoming an emerging investigational antiviral drug due to its broad spectrum of antiviral activities. To explore whether the low pH-dependency of influenza A viruses might affect the antiviral effects of chloroquine at clinically achievable concentrations, we tested the antiviral effects of this drug on selected human and avian viruses belonging to different subtypes and displaying different pH requirements. Results showed a correlation between the responses to chloroquine and NH4Cl, a lysosomotropic agent known to increase the pH of intracellular vesicles. Time-of-addition experiments showed that the inhibitory effect of chloroquine was maximal when the drug had been added at the time of infection and was lost after 2 h post-infection. This timing approximately corresponds to that of virus/cell fusion. Moreover, there was a clear correlation between the EC50 of chloroquine in vitro and the electrostatic potential of the HA subunit (HA2) mediating the virus/cell fusion process. Overall, the present study highlights the critical importance of a host cell factor such as intravesicular pH in determining the anti-influenza activity of chloroquine and other lysosomotropic agents

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

Proof of concept of a new plasma complement Factor H from waste plasma fraction

IntroductionComplement factor H (FH) is a major regulator of the complement alternative pathway, its mutations predispose to an uncontrolled activation in the kidney and on blood cells and to secondary C3 deficiency. Plasma exchange has been used to correct for FH deficiency and although the therapeutic potential of purified FH has been suggested by in vivo experiments in animal models, a clinical approved FH concentrate is not yet available. We aimed to develop a purification process of FH from a waste fraction rather than whole plasma allowing a more efficient and ethical use of blood and plasma donations.MethodsWaste fractions from industrial plasma fractionation (pooled human plasma) were analyzed for FH content by ELISA. FH was purified from unused fraction III and its decay acceleration, cofactor, and C3 binding capacity were characterized in vitro. Biodistribution was assessed by high-resolution dynamic PET imaging. Finally, the efficacy of the purified FH preparation was tested in the mouse model of C3 glomerulopathy (Cfh−/− mice).ResultsOur purification method resulted in a high yield of highly purified (92,07%), pathogen-safe FH. FH concentrate is intact and fully functional as demonstrated by in vitro functional assays. The biodistribution revealed lower renal and liver clearance of human FH in Cfh-/- mice than in wt mice. Treatment of Cfh-/- mice documented its efficacy in limiting C3 activation and promoting the clearance of C3 glomerular deposits.ConclusionWe developed an efficient and economical system for purifying intact and functional FH, starting from waste material of industrial plasma fractionation. The FH concentrate could therefore constitute possible treatments options of patients with C3 glomerulopathy, particularly for those with FH deficiency, but also for patients with other diseases associated with alternative pathway activation

Semantics and Ideology During the Renaissance: Confessional Translations of the Greek Word ἐπίσκοπος

During the sixteenth century the disputes between Catholics and Protestants became the battleground to determine and shape authentic Christianity and the Church. Humanism played a key role in this process conditioned by cultural and theological diversity, justifying doctrinal positions and legitimizing the existence of respective institutions with an appeal to history. Translations from church historical sources illustrate how they often derived from theological preconceptions. Starting with the ‘episcopacy issue’ opened initially by Luther and Calvin inter al., this article analyzes the translations of the Greek word episkopos in the Magdeburg Centuries, Cesare Baronio’s Ecclesiastical Annals, in contemporary vernacular versions of Eusebius’s Ecclesiastical History, in J. C. Dietrich’s Lexicon and in some English Bibles. The material gathered and also compared with the position of the Council of Trent shows how these confessionally conditioned translations impacted on the scholarly world, and how they influenced church law with religio-political consequences, thereby having a striking significance

OLIVAW: Mastering Othello without Human Knowledge, nor a Penny

We introduce OLIVAW, an AI Othello player adopting the design principles of the famous AlphaGo programs. The main motivation behind OLIVAW was to attain exceptional competence in a non-trivial board game at a tiny fraction of the cost of its illustrious predecessors. In this paper, we show how the AlphaGo Zero's paradigm can be successfully applied to the popular game of Othello using only commodity hardware and free cloud services. While being simpler than Chess or Go, Othello maintains a considerable search space and difficulty in evaluating board positions. To achieve this result, OLIVAW implements some improvements inspired by recent works to accelerate the standard AlphaGo Zero learning process. The main modification implies doubling the positions collected per game during the training phase, by including also positions not played but largely explored by the agent. We tested the strength of OLIVAW in three different ways: by pitting it against Edax, the strongest open-source Othello engine, by playing anonymous games on the web platform OthelloQuest, and finally in two in-person matches against top-notch human players: a national champion and a former world champion

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