Comment on “early efficacy of intra-articular HYADD® 4 (Hymovis®) injections for symptomatic knee osteoarthritis”

We read with great interest the study by Priano titled“Early efficacy of intra-articular HYADD® 4 (Hymovis®) injections for symptomatic knee osteoarthritis.” 1 The author would like to explore the efficacy of intra-articular HYADD 4 (Hymovis) injections for symptomatic knee osteoarthritis. Results from this study are very interesting and promising from a clinical aspect; however, we believe that studying patient’sclinical status with visual analog scale and Western Ontario and McMaster Universities Arthritis Index scale should be supported by biomechanical information. From this point of view, to have more data that could influence the clinical practice, it is important to note the possible action that intraarticular injections of different kinds of hyaluronic acid could have on walking biomechanics using an objective measurement tool as gait analysis. In our opinion, the work by Priano1 is promising because it investigates the efficacy of a new formulation of hyaluronic acid. Nowadays, many hyaluronic acid formulations are approved for clinical use in Europe and the United States. Furthermore, hyaluronic acid injections’ efficacy has been demonstrated also in hip osteoarthritis. 2 However, even if these formulations differ in their chemical– physical properties, joint space half-life, rheological properties, and clinical efficacy, there are few studies that investigate hyaluronic acid’s possible action from a biomechanical point of view. 3,4 From this point of view, we believe that osteoarthritis management and rehabilitation should be prescribed after an objective analysis of functional walking alterations using gait analysis instrumentations. The use of gait analysis should be desirable during diagnosis and follow-up. In fact, it is capable to identify different walking patterns in patient with osteoarthritis of the lower limbs, whereas the radiology can evaluate the status of the joint’s structures

Secure rendezvous and static containment in multi-agent systems with adversarial intruders

In this paper we propose a novel distributed local interaction protocol for networks of multi-agent systems (MASs) in a multi-dimensional space under directed time-varying graph with the objective to achieve secure rendezvous or static containment within the convex hull of a set of leader agents. We consider the scenario where a set of anonymous adversarial agents may intrude the network (or may be hijacked by a cyber-attack) and show that the proposed strategy guarantees the achievement of the global objective despite the continued influence of the adversaries which cannot be detected nor identified by the collaborative agents. We characterize the convergence properties of the proposed protocol in terms of the characteristics of the underlying network topology of the multi-agent system. Numerical simulations and examples corroborate the theoretical results

Assessing the cervical range of motion in infants with positional plagiocephaly

Purpose: To determine if infants with positional plagiocephaly have limitations of active and passive cervical range of motion measured with simple and reliable methods. Methods: The examiners assessed bilateral active and passive cervical rotations and passive cervical lateral flexion. Cervical assessment was performed twice by 2 different physicians to assess intertester reliability. To assess intratester reliability the first investigator performed a second examination 48 hours after the first one. Results: One-hundred nine subjects were analyzed; 70.7% of the sample had head positional preference on the right, while 29.3% had head positional preference on the left (x2 35.52, P <0.001). Cervical rotations and lateral flexion showed reliable levels of agreement for intra and intertester reliability. Conclusions: The most limited range of motion in infants with positional plagiocephaly was cervical active rotation which affected more than 90% of patients. Passive cervical rotations and lateral flexion were limited in more than 60% of patient

Matrix Models and Holography: Mass Deformations of Long Quiver Theories in 5d and 3d

We enlarge the dictionary between matrix models for long linear quivers preserving eight supercharges in $d=5$ and $d=3$ and type IIB supergravity backgrounds with AdS$_{d+1}$ factors. We introduce mass deformations of the field theory that break the quiver into a collection of interacting linear quivers, which are decoupled at the end of the RG flow. We find and solve a Laplace problem in supergravity which realises these deformations holographically. The free energy and expectation values of antisymmetric Wilson loops are calculated on both sides of the proposed duality, finding agreement. Our matching procedure sheds light on the F-theorem in five dimensions.Comment: 46 pages plus appendices. Various figures. Some improvements and references added. SciPost Physics versio

Multimodal Neural Databases

The rise in loosely-structured data available through text, images, and other modalities has called for new ways of querying them. Multimedia Information Retrieval has filled this gap and has witnessed exciting progress in recent years. Tasks such as search and retrieval of extensive multimedia archives have undergone massive performance improvements, driven to a large extent by recent developments in multimodal deep learning. However, methods in this field remain limited in the kinds of queries they support and, in particular, their inability to answer database-like queries. For this reason, inspired by recent work on neural databases, we propose a new framework, which we name Multimodal Neural Databases (MMNDBs). MMNDBs can answer complex database-like queries that involve reasoning over different input modalities, such as text and images, at scale. In this paper, we present the first architecture able to fulfill this set of requirements and test it with several baselines, showing the limitations of currently available models. The results show the potential of these new techniques to process unstructured data coming from different modalities, paving the way for future research in the area. Code to replicate the experiments will be released at https://github.com/GiovanniTRA/MultimodalNeuralDatabase

syntnn at semeval 2018 task 2 is syntax useful for emoji prediction embedding syntactic trees in multi layer perceptrons

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Latent Autoregressive Source Separation

Autoregressive models have achieved impressive results over a wide range of domains in terms of generation quality and downstream task performance. In the continuous domain, a key factor behind this success is the usage of quantized latent spaces (e.g., obtained via VQ-VAE autoencoders), which allow for dimensionality reduction and faster inference times. However, using existing pre-trained models to perform new non-trivial tasks is difficult since it requires additional fine-tuning or extensive training to elicit prompting. This paper introduces LASS as a way to perform vector-quantized Latent Autoregressive Source Separation (i.e., de-mixing an input signal into its constituent sources) without requiring additional gradient-based optimization or modifications of existing models. Our separation method relies on the Bayesian formulation in which the autoregressive models are the priors, and a discrete (non-parametric) likelihood function is constructed by performing frequency counts over latent sums of addend tokens. We test our method on images and audio with several sampling strategies (e.g., ancestral, beam search) showing competitive results with existing approaches in terms of separation quality while offering at the same time significant speedups in terms of inference time and scalability to higher dimensional data.Comment: Accepted to AAAI 202

Accelerating Transformer Inference for Translation via Parallel Decoding

Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT). The community proposed specific network architectures and learning-based methods to solve this issue, which are expensive and require changes to the MT model, trading inference speed at the cost of the translation quality. In this paper, we propose to address the problem from the point of view of decoding algorithms, as a less explored but rather compelling direction. We propose to reframe the standard greedy autoregressive decoding of MT with a parallel formulation leveraging Jacobi and Gauss-Seidel fixed-point iteration methods for fast inference. This formulation allows to speed up existing models without training or modifications while retaining translation quality. We present three parallel decoding algorithms and test them on different languages and models showing how the parallelization introduces a speedup up to 38% w.r.t. the standard autoregressive decoding and nearly 2x when scaling the method on parallel resources. Finally, we introduce a decoding dependency graph visualizer (DDGviz) that let us see how the model has learned the conditional dependence between tokens and inspect the decoding procedure.Comment: Accepted at ACL 2023 main conferenc

A Case of Coinfection with SARS-COV-2 and Cytomegalovirus in the Era of COVID-19

The World Health Organization has declared novel coronavirus disease 2019 (COVID-19) an international public health emergency. We describe the case of a 92-year-old woman who was admitted to our unit with fever and chills with laboratory evidence of coinfection with SARS-CoV-2 and cytomegalovirus