Has family engagement finally gained foothold in forensic mental healthcare?

Despite advancements in promoting family engagement in mental health settings, limited involvement of family members persists in forensic mental healthcare. Forensic mental healthcare professionals face various barriers in engaging families, including a patient-centered approach and resource constraints. However, limited understanding exists of professionals' experiences with family engagement, which is crucial for improving care practices in this setting. Consequently, this study investigates the evolution of professionals' experiences with family engagement from 2015 to 2021 in Flanders, Belgium. Qualitative methods were employed, including focus group interviews in 2015 and individual interviews in 2021 with 23 forensic mental healthcare professionals. Thematic analysis is employed to identify patterns and changes over time. The Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist was utilised to report the study. The analysis revealed that while initial steps towards family engagement in forensic mental healthcare have been taken, full integration in organisational structures is still lacking. Future efforts should focus on involving family organisations and caregivers, addressing barriers like time and resource constraints, and fostering a cultural shift towards family engagement. Further research involving a broader range of stakeholders is needed to enhance family engagement initiatives in forensic mental healthcare settings

High-throughput adaptive co-channel interference cancellation for edge devices using depthwise separable convolutions, quantization, and pruning

Co-channel interference cancellation (CCI) is the process used to reduce interference from other signals using the same frequency channel, thereby enhancing the performance of wireless communication systems. An improvement to this approach is adaptive CCI, which reduces interference without relying on prior knowledge of the interfering signal characteristics. Recent work suggested using machine learning (ML) models for this purpose, but high-throughput ML solutions are still lacking, especially for edge devices with limited resources. This work explores the adaptation of U-Net Convolutional Neural Network models for high-throughput adaptive source separation. Our approach is established on architectural modifications, notably through quantization and the incorporation of depthwise separable convolution, to achieve a balance between computational efficiency and performance. Our results demonstrate that the proposed models achieve superior MSE scores when removing unknown interference sources from the signals while maintaining significantly lower computational complexity compared to baseline models. One of our proposed models is deeper and fully convolutional, while the other is shallower with a convolutional structure incorporating an LSTM. Depthwise separable convolution and quantization further reduce the memory footprint and computational demands, albeit with some performance tradeoffs. Specifically, applying depthwise separable convolutions to the model with the LSTM results in only a 0.72% degradation in MSE score while reducing MACs by 58.66%. For the fully convolutional model, we observe a 0.63% improvement in MSE score with even 61.10% fewer MACs. Additionally, the models exhibit excellent scalability on GPUs, with the fully convolutional model achieving the highest symbol rates (up to 800 $\times$ 103 symbol per second) at larger batch sizes. Overall, our findings underscore the feasibility of using optimized machine-learning models for interference cancellation in devices with limited resources

Pulmonary vascular complications of cirrhosis : hepatopulmonary syndrome and portopulmonary hypertension

Hepatopulmonary syndrome (HPS) and portopulmonary hypertension (POPH) are two distinct pulmonary vascular complications seen in patients with liver disease and/or portal hypertension. HPS is characterized by disturbed gas exchange and hypoxemia because of intrapulmonary vascular dilatations. POPH is defined by pulmonary arterial hypertension, which might lead to right heart failure. HPS affects up to 30% of patients with end-stage liver disease requiring liver transplantation. POPH is rarer and affects 1-5% of this patient population. If not recognized and left untreated, these disorders result in significant mortality. This review provides an update on HPS and POPH and discusses their clinical characteristics, screening and diagnostic modalities, and management, including the place of liver transplantation

Broadband, high resolution, sensitive spectrometer using an integrated optical phased array in silicon nitride and Fourier imaging

We present a novel hybrid configuration of a spectrometer consisting of an integrated Si3N4 optical phased array and a free-space Fourier-space imaging system. It combines broadband and high resolution performance in a small on-chip footprint. We achieve 0.5 nm resolution in a spectral range from 750 nm to 850 nm using an on-chip footprint of 0.56 × 0.22 mm2. As a proof of concept, we retrieve the optical spectrum of a single frequency titanium sapphire laser. Using an image sensor cooled down to −20 ∘C, the low detection limit is validated by measuring the optical spectrum of the Raman background generated by a laser pump propagating in a Si3N4 waveguide

Embedding-based pair generation for contrastive representation learning in audio-visual surveillance data

Smart cities deploy various sensors such as microphones and RGB cameras to collect data to improve the safety and comfort of the citizens. As data annotation is expensive, self-supervised methods such as contrastive learning are used to learn audio-visual representations for downstream tasks. Focusing on surveillance data, we investigate two common limitations of audio-visual contrastive learning: false negatives and the minimal sufficient information bottleneck. Irregular, yet frequently recurring events can lead to a considerable number of false-negative pairs and disrupt the model's training. To tackle this challenge, we propose a novel method for generating contrastive pairs based on the distance between embeddings of different modalities, rather than relying solely on temporal cues. The semantically synchronized pairs can then be used to ease the minimal sufficient information bottleneck along with the new loss function for multiple positives. We experimentally validate our approach on real-world data and show how the learnt representations can be used for different downstream tasks, including audio-visual event localization, anomaly detection, and event search. Our approach reaches similar performance as state-of-the-art modality- and task-specific approaches

Transitional justice and the struggle for reparations for slavery and its ongoing legacies in the United States : Joyce Hope Scott in conversation with Cira Pallí-Asperó and Tine Destrooper

As global uprisings for racial justice put questions about historical and ongoing racial injustice higher on academic, public and political agendas, transitional justice (TJ) scholars increasingly started to explore which dynamics of racial injustice might be supported by, or reflected in, the practice of TJ. These questions have mostly been addressed by critical TJ scholars, who formulate an encompassing critique regarding pitfalls of formalised and standardised TJ. Despite these critiques, however, some grassroots actors across the globe are mobilising, re-appropriating and re-signifying TJ practices and rhetoric as part of their struggle for redress. For example, this can be observed among certain activists seeking reparations for African people for enslavement and its associated legacies of institutional racism in the United States. In this interview between Joyce Hope Scott, Cira Pall & iacute;-Asper & oacute; and Tine Destrooper we explore what the implications are of this mobilisation of TJ rhetoric and tools by racial justice activists, both for this specific struggle for reparative justice, as well as for the broader domain of TJ. The piece underscores the nexus between TJ and protest foregrounding the revolutionary potential of TJ rhetoric and initiatives when these are used in non-scripted and innovative ways by social movements and activists who seek to disrupt a harmful status quo

Leaving terrorism behind? The role of terrorist attacks in shaping migration intentions around the world

Terrorism globally yields severe consequences for individuals and societies, potentially driving migration within and across borders. Yet, empirical evidence on its causal impact remains limited. The contribution of our paper is twofold. First, we construct various indicators of terrorist activity at a fine level of spatial and temporal granularity, which allow to accurately identify individuals' exposure to terrorist threat. Second, we use these geo-localised indicators to empirically analyse the role of terrorist attacks in shaping internal and international migration intentions for 133 countries between 2007 and 2015. Our results indicate that terrorist attacks spur both internal and international migration intentions, though the effect is stronger for the latter. The effect on international migration intentions is linked to the intensity of attacks, rather than their frequency. Furthermore, the impact varies based on individual and country characteristics

Clinical reasoning over tabular data and text with bayesian networks

Bayesian networks are well-suited for clinical reasoning on tabular data, but are less compatible with natural language data, for which neural networks provide a successful framework. This paper compares and discusses strategies to augment Bayesian networks with neural text representations, both in a generative and discriminative manner. This is illustrated with simulation results for a primary care use case (diagnosis of pneumonia) and discussed in a broader clinical context