Exploring the link among state of mind concerning childhood attachment, attachment in close relationships, parental bonding, and psychopathological symptoms in substance users

In the present study, we have explored the link among styles of attachment and psychopathology in drug users. We know that insecure attachment predisposes the individuals the development of drug-addiction and psychopathological symptoms. However, we do not know which attachment is more frequent in drug users and which is related to particular psychopathological symptoms. The aim of the present work is to explore the relationship between childhood attachment state of mind, attachment in close relationships, parental bonding and psychopathology in sample of Italian substance users

Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders

In this paper, we consider the problem of improving the inference latency of language model-based dense retrieval systems by introducing structural compression and model size asymmetry between the context and query encoders. First, we investigate the impact of pre and post-training compression on the MSMARCO, Natural Questions, TriviaQA, SQUAD, and SCIFACT, finding that asymmetry in the dual encoders in dense retrieval can lead to improved inference efficiency. Knowing this, we introduce Kullback Leibler Alignment of Embeddings (KALE), an efficient and accurate method for increasing the inference efficiency of dense retrieval methods by pruning and aligning the query encoder after training. Specifically, KALE extends traditional Knowledge Distillation after bi-encoder training, allowing for effective query encoder compression without full retraining or index generation. Using KALE and asymmetric training, we can generate models which exceed the performance of DistilBERT despite having 3x faster inference.Comment: 8 pages, 4 figures, 30 table

Noise-Robust Dense Retrieval via Contrastive Alignment Post Training

The success of contextual word representations and advances in neural information retrieval have made dense vector-based retrieval a standard approach for passage and document ranking. While effective and efficient, dual-encoders are brittle to variations in query distributions and noisy queries. Data augmentation can make models more robust but introduces overhead to training set generation and requires retraining and index regeneration. We present Contrastive Alignment POst Training (CAPOT), a highly efficient finetuning method that improves model robustness without requiring index regeneration, the training set optimization, or alteration. CAPOT enables robust retrieval by freezing the document encoder while the query encoder learns to align noisy queries with their unaltered root. We evaluate CAPOT noisy variants of MSMARCO, Natural Questions, and Trivia QA passage retrieval, finding CAPOT has a similar impact as data augmentation with none of its overhead.Comment: 8 pages, 6 figures, 30 table

Effective electrothermal analysis of electronic devices and systems with parameterized macromodeling

We propose a parameterized macromodeling methodology to effectively and accurately carry out dynamic electrothermal (ET) simulations of electronic components and systems, while taking into account the influence of key design parameters on the system behavior. In order to improve the accuracy and to reduce the number of computationally expensive thermal simulations needed for the macromodel generation, a decomposition of the frequency-domain data samples of the thermal impedance matrix is proposed. The approach is applied to study the impact of layout variations on the dynamic ET behavior of a state-of-the-art 8-finger AlGaN/GaN high-electron mobility transistor grown on a SiC substrate. The simulation results confirm the high accuracy and computational gain obtained using parameterized macromodels instead of a standard method based on iterative complete numerical analysis

Application of self-mixing interferometry for depth monitoring in the ablation of TiN coatings

Among possible monitoring techniques, self-mixing interferometry stands out as an appealing option for online ablation depth measurements. The method uses a simple laser diode, interference is detected inside the diode cavity and measured as the optical power fluctuation by the photodiode encased in the laser diode itself. This way, self-mixing interferometry combines the advantages of a high resolution point displacement measurement technique, with high compactness and easiness of operation. For a proper adaptation of self-mixing interferometry use in laser micromachining to monitor ablation depth, certain optical, electronical, and mechanical limitations need to be overcome. In laser surface texturing of thin ceramic coatings, the ablation depth control is critically important to avoid damage by substrate contamination. In this work, self-mixing interferometry was applied in the laser percussion drilling of TiN coatings. The ∼4 μm thick TiN coatings were drilled with a 1 ns green fiber laser, while the self-mixing monitoring was applied with a 785 nm laser diode. The limitations regarding the presence of process plasma are discussed. The design criteria for the monitoring device are explained. Finally, the self-mixing measurements were compared to a conventional optical measurement device. The concept was validated as the measurements were statistically the same

Preclinical developments of enzyme-loaded red blood cells

Therapeutic enzymes are currently used in the treatment of several diseases. In most cases, the benefits are limited due to poor in vivo stability, immunogenicity, and drug-induced inactivating antibodies. A partial solution to the problem is obtained by masking the therapeutic protein by chemical modifications. Unfortunately, this is not a satisfactory solution because frequent adverse events, including anaphylaxis, can arise

Overview of the TREC 2023 Product Product Search Track

This is the first year of the TREC Product search track. The focus this year was the creation of a reusable collection and evaluation of the impact of the use of metadata and multi-modal data on retrieval accuracy. This year we leverage the new product search corpus, which includes contextual metadata. Our analysis shows that in the product search domain, traditional retrieval systems are highly effective and commonly outperform general-purpose pretrained embedding models. Our analysis also evaluates the impact of using simplified and metadata-enhanced collections, finding no clear trend in the impact of the expanded collection. We also see some surprising outcomes; despite their widespread adoption and competitive performance on other tasks, we find single-stage dense retrieval runs can commonly be noncompetitive or generate low-quality results both in the zero-shot and fine-tuned domain.Comment: 14 pages, 4 figures, 11 tables - TREC 202

Exploring Machine Learning for Untargeted Metabolomics Using Molecular Fingerprints

Background Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways. Methods This study, inspired by well-established methods in drug discovery, employs machine learning on metabolite fingerprints to explore the relationship of their structure with responses in experimental conditions beyond known pathways, shedding light on metabolic processes. It evaluates fingerprinting effectiveness in representing metabolites, addressing challenges like class imbalance, data sparsity, high dimensionality, duplicate structural encoding, and interpretable features. Feature importance analysis is then applied to reveal key chemical configurations affecting classification, identifying related metabolite groups. Results The approach is tested on two datasets: one on Ataxia Telangiectasia and another on endothelial cells under low oxygen. Machine learning on molecular fingerprints predicts metabolite responses effectively, and feature importance analysis aligns with known metabolic pathways, unveiling new affected metabolite groups for further study. Conclusion In conclusion, the presented approach leverages the strengths of drug discovery to address critical issues in metabolomics research and aims to bridge the gap between these two disciplines. This work lays the foundation for future research in this direction, possibly exploring alternative structural encodings and machine learning models

Arretium or Arezzo? A Neural Approach to the Identification of Place Names in Historical Texts

This paper presents the application of a neural architecture to the identification of place names in English historical texts. We test the impact of different word embeddings and we compare the results to the ones obtained with the Stanford NER module of CoreNLP before and after the retraining using a novel corpus of manually annotated historical travel writings