Weakly Supervised Dense Video Captioning

This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences. The proposed method is trained without explicit annotation of fine-grained sentence to video region-sequence correspondence, but is only based on weak video-level sentence annotations. It differs from existing video captioning systems in three technical aspects. First, we propose lexical fully convolutional neural networks (Lexical-FCN) with weakly supervised multi-instance multi-label learning to weakly link video regions with lexical labels. Second, we introduce a novel submodular maximization scheme to generate multiple informative and diverse region-sequences based on the Lexical-FCN outputs. A winner-takes-all scheme is adopted to weakly associate sentences to region-sequences in the training phase. Third, a sequence-to-sequence learning based language model is trained with the weakly supervised information obtained through the association process. We show that the proposed method can not only produce informative and diverse dense captions, but also outperform state-of-the-art single video captioning methods by a large margin.Comment: To appear in CVPR 201

Genomic Biomarkers for Personalized Medicine in Breast Cancer

Breast cancer is the most common malignant disease in Western women. Historically, breast cancer was perceived as a single disease with various clinicalpathological features, and therefore, “one drug fits all” approaches drove the treatment regimens. The advent of genomics studies has led to a new paradigm in which breast cancer is heterogeneous consisting of different diseases from the same organ site. For example, gene expression profiling analysis revealed that estrogen receptor (ER)-positive and ER negative breast cancer are two distinct diseases with different risk factors, clinical presentations, outcomes, and responses to systemic therapies. Consequently, the new paradigm demands a personalized strategy in cancer medicine, in which the selection of treatment regimens for each cancer patient will largely rely on assessment by predictive biomarkers and study of the anatomical and pathological features of the cancer

Artificial Intelligence: an emerging tool for studying drug-induced liver injury.

https://openpolicyfinder.jisc.ac.uk/id/publication/11895Drug-induced liver injury (DILI) is a complex and potentially severe adverse reaction to drugs, herbal products or dietary supplements. DILI can mimic other liver diseases clinical presentation, and currently lacks specific diagnostic biomarkers, which hinders its diagnosis. In some cases, DILI may progress to acute liver failure. Given its public health risk, novel methodologies to enhance the understanding of DILI are crucial. Recently, the increasing availability of larger datasets has highlighted artificial intelligence (AI) as a powerful tool to construct complex models. In this review, we summarise the evidence about the use of AI in DILI research, explaining fundamental AI concepts and its subfields. We present findings from AI-based approaches in DILI investigations for risk stratification, prognostic evaluation and causality assessment and discuss the adoption of natural language processing (NLP) and large language models (LLM) in the clinical setting. Finally, we explore future perspectives and challenges in utilising AI for DILI research.This study was supported by grants from Instituto de Salud Carlos III, cofunded by Fondo Europeo de Desarrollo Regional - FEDER, cofunded by the European Union (grant number: PI21/01248; PID2022-140169OB-C21, PT23/00137) and by the Agencia Española de Medicamentos y Productos Sanitarios. CIBERehd and Plataforma de Investigación Clinica are funded by ISCIII. HN holds a postdoctoral research contract funded by Junta de Andalucía (POSTDOC_21_00780). Funding for open access charge: Universidad de Málaga/CBUA. The funding sources had no involvement in the writing of the report or in the decision to submit the manuscript for publication

On the Lab. II building site

425 special CDSs in PCN033 (XLSX 29 kb

An Efficient Source Model Selection Framework in Model Databases

With the explosive increase of big data, training a Machine Learning (ML) model becomes a computation-intensive workload, which would take days or even weeks. Thus, reusing an already trained model has received attention, which is called transfer learning. Transfer learning avoids training a new model from scratch by transferring knowledge from a source task to a target task. Existing transfer learning methods mostly focus on how to improve the performance of the target task through a specific source model, and assume that the source model is given. Although many source models are available, it is difficult for data scientists to select the best source model for the target task manually. Hence, how to efficiently select a suitable source model in a model database for model reuse is an interesting but unsolved problem. In this paper, we propose SMS, an effective, efficient, and flexible source model selection framework. SMS is effective even when the source and target datasets have significantly different data labels, and is flexible to support source models with any type of structure, and is efficient to avoid any training process. For each source model, SMS first vectorizes the samples in the target dataset into soft labels by directly applying this model to the target dataset, then uses Gaussian distributions to fit for clusters of soft labels, and finally measures the distinguishing ability of the source model using Gaussian mixture-based metric. Moreover, we present an improved SMS (I-SMS), which decreases the output number of the source model. I-SMS can significantly reduce the selection time while retaining the selection performance of SMS. Extensive experiments on a range of practical model reuse workloads demonstrate the effectiveness and efficiency of SMS

Microfluidic Fabrication of Monodisperse and Recyclable TiO₂-Poly(ethylene glycol) Diacrylate Hybrid Microgels for Removal of Methylene Blue from Aqueous Medium

Nearly monodisperse titanium oxide–polyethylene glycol diacrylate [TiO2–P(EGDA)] hybrid microbeads containing 0.5 wt % TiO2 nanoparticles entrapped within a P(EGDA) cross-linked polymeric network were synthesized using a modular Lego-inspired glass capillary microfluidic device. TiO2–P(EGDA) hybrid microgels were characterized by optical microscopy, scanning electron microscopy, X-ray diffraction, energy dispersive X-ray spectroscopy, and thermogravimetric analysis. The fabricated TiO2–P(EGDA) hybrid microgel system showed 100% removal efficiency of methylene blue (MB) from its 1–3 ppm aqueous solutions after 4 h of UV light irradiation at 0.2 mW/cm2 at the loading of 25 g/L photocatalyst beads in the reaction mixture, corresponding to the loading of naked TiO2 of just 0.025 g/L. No decrease in photocatalytic efficiency was observed in 10 repeated runs with recycled photocatalyst using a fresh 1 ppm MB solution in each cycle. The rate of photocatalytic degradation was controlled by the UV light irradiance, catalyst loading, and the initial dye concentration. Physical adsorption of MB onto the surface of composite microgel was also observed. The adsorption data was best fitted with the Langmuir adsorption isotherm and the Elovich kinetic model. TiO2–P(EGDA) microgel beads are biocompatible, can be prepared with a tunable size in the microfluidic device, and can easily be separated from the reaction mixture by gravity settling. The TiO2–P(EGDA) system can be used for the removal of other toxic dyes and micropollutants from industrial wastewater

SparDL: Distributed Deep Learning Training with Efficient Sparse Communication

Top-k sparsification has recently been widely used to reduce the communication volume in distributed deep learning. However, due to the Sparse Gradient Accumulation (SGA) dilemma, the performance of top-k sparsification still has limitations. Recently, a few methods have been put forward to handle the SGA dilemma. Regrettably, even the state-of-the-art method suffers from several drawbacks, e.g., it relies on an inefficient communication algorithm and requires extra transmission steps. Motivated by the limitations of existing methods, we propose a novel efficient sparse communication framework, called SparDL. Specifically, SparDL uses the Spar-Reduce-Scatter algorithm, which is based on an efficient Reduce-Scatter model, to handle the SGA dilemma without additional communication operations. Besides, to further reduce the latency cost and improve the efficiency of SparDL, we propose the Spar-All-Gather algorithm. Moreover, we propose the global residual collection algorithm to ensure fast convergence of model training. Finally, extensive experiments are conducted to validate the superiority of SparDL