Miracle-dz/UniFAR-FTUs huggingface dataset:
Topology Optimization of Coupled Photonic Crystal Cavities for Flat-top Drop Filter Functionality

Topology Optimization of Coupled Photonic Crystal Cavities for Flat-top Drop Filter Functionality. Abstract Wavelength-division multiplexing (WDM) filters desire a flat-top and unity pass-band bandwidth to ensure signal fidelity and to increase the tolerance to signal wavelength shifts [1, 2]. In silicon photonics such performance is typically obtained by utilizing e.g. a binary tree of cascaded Mach-Zehnder-like lattice filters or coupled optical micro-ring resonators [2]. Moving away from conventional design solutions, it has been demonstrated that flat-top filter responses can be obtained by carefully designing the coupling and phase matching conditions between ultra-compact cavities in coupled photonic crystal (PhC) cavity systems However, designing PhC flat-top filters is not straight forward and may not give optimal solutions due to a limited design parameter space. a finite-difference time-domain (FDTD) nm spatial an nm spectral a
Theory of Mind and Trust in Human-Robot Navigation

In human-robot interaction, trust is affected by human, robot, and environmental factors. In the proposed research, we consider each of these factors, by focusing on the contribution of robot theory of mind (ToM), human visual perspective taking (a concept related to ToM), and environmental complexity, to the development and maintenance of human-robot trust. To do so, our experiment combines a psychological assessment of visual perspective taking (the Director Task), with a trust-based robot navigation task. Using the AREA model of Responsible Research and Innovation (RRI), we also highlight the implications of our experiment, in the context of trustworthy robotics and human-robot collaboration. We round off the article with a theoretical and research development synopses related to robot ToM and trust, and future work to be conducted in this area.
Digital twins for human-assistive robot teams in ambient assisted living

Digital twins are virtual replicas of physical systems that simulate real-world scenarios to optimize system performance, reduce physical losses, and ensure user safety. Although digital twins have been widely adopted in industrial settings, there is a lack of research on digital twins in everyday life scenarios. This report presents research aimed at developing a human-assistive robot interaction digital twin system. Our objective is to construct and utilize human biomechanical models of people using assistive devices and apply machine learning for recognition of impaired mobility, simulating edge scenarios to ensure the safety of human-assistive robot interaction prior to actual deployment. This research contributes to the advancement of digital twin technology to enhance the safety of assistive robots in the real-world.
TAME Pain: Trustworthy AssessMEnt of Pain from Speech and Audio for the Empowerment of Patients

Precise pain assessment is crucial for medical professionals to provide appropriate treatment. However, not every patient can verbalise the experienced pain for various reasons (e.g., speech disorders or language barriers). In these cases, medical practitioners must provide treatment based on non-verbal signs. The TAME Pain project aims to provide medical professionals with an additional pain assessment tool using audio analysis. We seek to develop an algorithm that functions as an autonomous system and evaluate its trustworthiness. This proof-of-concept study will investigate whether the acoustic signal extracted from healthy individuals subjected to pain can predict pain accurately. We will assess the system’s trustworthiness by surveying medical professionals. By comparing their assessments with and without our algorithm against pain levels reported by the participants, we evaluate the trustworthiness and feasibility of clinical implementation. Our project aims at empowering patients, including non-verbal and second-language speakers. Our work also supports healthcare professionals with an accessible decision support tool, ultimately reducing the risk of potential physical harm and improving patient experience. This paper describes the planned study to collect data for the system that assesses bioacoustic pain markers in the speech signal and outlines our project plan for achieving this.
Ethics of Trust/worthiness in Autonomous Systems: a scoping review.

The development of adaptive autonomous systems with evolving functionality (AASEFs) differs from their technological predecessors due to their changing, rather than static, architectures and processes; subsequently, their development, deployment, and implementation creates novel ethical issues. Our scoping review surveys the literature to identify the problematic nature of AASEFs, the ethical worries that they generate, and the ethical principles affected. Our literature examination also ascertains stakeholder needs and solutions regarding the trust and trustworthiness of ASSEFs. The characteristics of non-explicability and fluctuating behaviour are predominantly problematic of AASEFs. The literature advocates for AASEF development incorporating ‘ethics by design’ with enforceable standards supported via regulatory and legal structures.
A Privacy-Preserving Observatory of Misinformation using Linguistic Markers - A Work in Progress

Online misinformation is a serious problem that can have a negative impact on individuals, societies, and democracies. It can lead to the spread of false information, the erosion of trust in institutions, and the polarisation of political discourse. The spread of online misinformation can have a number of negative consequences, including the erosion of trust in institutions, the polarisation of political discourse, and the spread of violence. It can also have a significant impact on individuals, leading to them making decisions based on false information or becoming more isolated from others, leading to wider societal issues. Usual ways of detecting misinformation involve the large scale crawling of public data from public-facing websites (such as social media websites and online news outlets), which is ethically dubious as it violates the right to consent from the online users who generated this data. We present a lightweight, responsible and ethical way of monitoring online misinformation through the use of a distributed approach using a set of linguistic markers extracted from the literature in order to allow users of our browser extension to detect potential misinformation and, with their consent, submit a censored version of that data to our distributed corpus.
The Need for Unsupervised Outlier Model Selection: A Review and Evaluation of Internal Evaluation Strategies

Given an unsupervised outlier detection task, how should one select i) a detection algorithm, and ii) associated hyperparameter values (jointly called a model)? E ective outlier model selection is essential as di erent algorithms may work well for varying detection tasks, and moreover their performance can be quite sensitive to the values of the hyperparameters (HPs). On the other hand, unsupervised model selection is notoriously difficult, in the absence of hold-out validation data with ground-truth labels. Therefore, the problem is vastly understudied in the outlier mining literature. There exists a body of work that propose internal model evaluation strate- gies for selecting a model. These so-called internal strategies solely rely on the input data (without labels) and the output (outlier scores) of the candidate models. In this paper, we rst survey internal model evaluation strategies including both those proposed speci cally for outlier detection, as well as those that can be adapted from the unsupervised deep representation learning literature. Then, we investigate their e ectiveness empirically in comparison to simple baselines such as random selection and the popular state-of-the-art detector Isolation Forest (iForest) with default HPs. To this end, we set up (and open-source) a large testbed with 39 detection tasks and 297 candidate models comprised of 8 different detectors and various HP con gurations. We evaluate internal strategies from 7 di erent families on their ability to discriminate between models w.r. t. detection performance, without using any labels. Our study reports a striking nding, that none of the existing and adapted strategies would be practically useful: stand-alone ones are not signi cantly di erent from random, and consensus-based ones do not outperform iForest (w/ default HPs) while being more expensive (as all candidate models need to be trained for evaluation). Our survey stresses the importance of and the standing need for e ective unsupervised outlier model selection, and acts as a call for future work on the problem.
Intersectional Analysis of the Challenges and Opportunities of Equitable Remote Operation in the UK Maritime Sector

The maritime sector is increasingly implementing Maritime Autonomous Systems (MAS), such as remote operation of boats. This paper examines the potential for remote operations to address equity issues in the maritime sector, particularly for historically excluded and underrepresented groups. The study adopts an intersectional Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to explore the social inclusion benefits and challenges of remote operations. Increased safety and flexibility are identified as key strengths, offering opportunities for inclusion of individuals who may have faced barriers in traditional seafaring roles. However, skepticism and a lack of influence among decision-makers are identified as weaknesses, hindering the recognition of intersectional realities and the allocation of resources for accommodating diverse perspectives in design and deployment of MAS. This research contributes a holistic understanding of promoting social inclusion amidst technological transformation within the maritime sector. By centering the voices of underrepresented groups, it aims to inform the development of strategies and policies that address equity issues and enhance diversity in the sector. The findings emphasize the importance of considering intersectionality and adopting responsible research and innovation principles in the design and implementation of autonomous systems.
Belonolaimus longicaudatus management using metam potassium and fluensulfone in potato

Abstract Belonolaimus longicaudatus (sting nematode) is an important pest in Florida potato production and is managed primarily by fumigation using 1,3-dichloropropene (1,3-D). Other effective nematicides are needed for more flexibility in managing this pest. The objective of this study was to evaluate fluensulfone, metam potassium, and mixtures of the two products, relative to 1,3-D and untreated control, for efficacy at managing sting nematode, and for non-target effects on free-living nematodes in potato. To test this objective, a small-plot field experiment was conducted in northeast Florida in 2020 and repeated in 2021. Metam potassium fumigation (390 kg a.i./treated ha)—with or without fluensulfone—managed sting nematode soil abundances but was phytotoxic to potato. Strategies that mitigate metam potassium phytotoxicity, such as reduced application rates, are needed before efficacy of metam potassium in this system can be determined. As a preplant soil spray, fluensulfone alone (403 g a.i./treated ha) did not manage sting nematode abundances and had an inconsistent effect on yield. Fumigation with 1,3-D (88.3 kg a.i./treated ha) was the only treatment that consistently managed sting nematode and increased potato yield. Nematicides did not consistently affect free-living nematodes.
Multi-stage attack: concepts, detection and defence

— In recent years, cyber security has experienced several challenges due to the exponential growth of the internet and the impact of it worldwide. Nowadays, it is nearly impossible to live without being connected, and, even if this has its positive side, there are also many dangers that come along the use of the internet. Day after day we find about new cyber-attacks all around the world, some of these being harder to understand than others, becoming incredibly hard to control or even to prevent them. The following report will focus on one of the toughest types of attacks that exists, the multi-stage attack, which pretends to perpetrate a cyber-attack that affects the target in more than one stage. It will also be discussed the different detection approaches and types that exist, and how helpful these are to identify if the attack is a multi-stage one or not, so this way we can be sure about what we are facing. Finally, different prevention mechanisms will be discussed, and how these are helpful against multi-stage attacks.
LRRK2 G2019S promotes the development of colon cancer via modulating intestinal inflammation

LRRK2 G2019S is the most prevalent variant associated with Parkinson’s disease (PD), found in 1-3% of sporadic and 4-8% of familial PD cases. Intriguingly, emerging clinical studies have suggested that LRRK2 G2019S carriers have an increased risk of cancers including colorectal cancer. However, the underlying mechanisms of the positive correlation between LRRK2-G2019S and colorectal cancer remain unknown. Using a mouse model of colitis-associated cancer (CAC) and LRRK2 G2019S knockin (KI) mice, here we report that LRRK2 G2019S promotes the pathogenesis of colon cancer as evidenced by increased tumor number and tumor size in LRRK2 G2019S KI mice. LRRK2 G2019S promoted intestinal epithelial cell proliferation and inflammation within the tumor microenvironment. Mechanistically, we found that LRRK2 G2019S KI mice are more susceptible to dextran sulfate sodium (DSS)-induced colitis. Suppressing the kinase activity of LRRK2 ameliorated the severity of colitis in both LRRK2 G2019S KI and WT mice. At the molecular level, our investigation unveiled that LRRK2 G2019S promotes the production of reactive oxygen species, triggers inflammasome activation, and induces cell necrosis in the gut epithelium in a mouse model of colitis. Collectively, our data provide direct evidence that gain-of-kinase activity in LRRK2 promotes colorectal tumorigenesis, implicating LRRK2 as a potential target in colon cancer patients with hyper LRRK2 kinase activity.
Wound microbiota-mediated correction of matrix metalloproteinase expression promotes re-epithelialization of diabetic wounds

Chronic wounds are a common and costly complication of diabetes, where multifactorial defects contribute to dysregulated skin repair, inflammation, tissue damage, and infection. We previously showed that aspects of the diabetic foot ulcer microbiota were correlated with poor healing outcomes, but many microbial species recovered remain uninvestigated with respect to wound healing. Here we focused on Alcaligenes faecalis, a Gram-negative bacterium that is frequently recovered from chronic wounds but rarely causes infection. Treatment of diabetic wounds with A. faecalis accelerated healing during early stages. We investigated the underlying mechanisms and found that A. faecalis treatment promotes re-epithelialization of diabetic keratinocytes, a process which is necessary for healing but deficient in chronic wounds. Overexpression of matrix metalloproteinases in diabetes contributes to failed epithelialization, and we found that A. faecalis treatment balances this overexpression to allow proper healing. This work uncovers a mechanism of bacterial-driven wound repair and provides a foundation for the development of microbiota-based wound interventions.
Human Th17- and IgG3-associated autoimmunity induced by a translocating gut pathobiont

Extraintestinal autoimmune diseases are multifactorial with translocating gut pathobionts implicated as instigators and perpetuators in mice. However, the microbial contributions to autoimmunity in humans remain largely unclear, including whether specific pathological human adaptive immune responses are triggered by such pathobionts. We show here that the translocating pathobiont Enterococcus gallinarum induces human IFNγ+ Th17 differentiation and IgG3 subclass switch of anti-E. gallinarum RNA and correlating anti-human RNA autoantibody responses in patients with systemic lupus erythematosus and autoimmune hepatitis. Human Th17 induction by E. gallinarum is cell-contact dependent and involves TLR8-mediated human monocyte activation. In murine gnotobiotic lupus models, E. gallinarum translocation triggers IgG3 anti-RNA autoantibody titers that correlate with renal autoimmune pathophysiology and with disease activity in patients. Overall, we define cellular mechanisms of how a translocating pathobiont induces human T- and B-cell-dependent autoimmune responses, providing a framework for developing host- and microbiota-derived biomarkers and targeted therapies in extraintestinal autoimmune diseases. One Sentence Summary Translocating pathobiont Enterococcus gallinarum promotes human Th17 and IgG3 autoantibody responses linked to disease activity in autoimmune patients.
Analysis of histone H3K4me3 modifications in bovine placenta derived from different calf-production methods

In ruminants, overgrowth of offspring produced by in vitro fertilization (IVF) is a common problem. Abnormal epigenetic modifications caused by environmental factors during the early embryonic period are suspected as an etiology of overgrowth. In this study, we investigated the genome-wide histone H3K4me3 profiles of bovine placentae that play a pivotal role in fetal development and compared their characteristics between artificial insemination (AI)- and IVF-derived samples. Cotyledons were harvested from the placentae obtained at parturition of 5 AI- and 13 IVF-derived calves, and chromatin immunoprecipitation sequencing was performed for H3K4me3. We confirmed no significant maternal tissue contamination in the samples we used. The revealed H3K4me3 profiles reflected the general characteristics of the H3K4me3 modification, which is abundantly distributed in the promoter region of active genes. By extracting common modifications from multiple samples, the genes involved in placenta-specific biological processes could be enriched. Comparison with the H3K4me3 modifications of blastocyst samples was also effective for enriching the placenta-specific features. Principal component analysis suggested the presence of differential H3K4me3 modifications in AI- and IVF-derived samples. The genes contributing to the difference were related to the developmental biological processes. Imprinted genes such as BEGAIN, ZNF215, and DLX5 were among the extracted genes. Principal component and discriminant analyses using only male samples categorized the samples into three groups based on fetal weight and calf-production methods. To our knowledge, this is the first study to profile the genome-wide histone modifications of bovine fetal placentae and reveal their differential characteristics between different calf-production methods.
What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines?

The rapid development and open-source release of highly performant computer vision models offers new potential for examining how different inductive biases impact representation learning and emergent alignment with the high-level human ventral visual system. Here, we assess a diverse set of 224 models, curated to enable controlled comparison of different model properties, testing their brain predictivity using large-scale functional magnetic resonance imaging data. We find that models with qualitatively different architectures (e.g. CNNs versus Transformers) and markedly different task objectives (e.g. purely visual contrastive learning versus vision-language alignment) achieve near equivalent degrees of brain predictivity, when other factors are held constant. Instead, variation across model visual training diets yields the largest, most consistent effect on emergent brain predictivity. Overarching model properties commonly suspected to increase brain predictivity (e.g. greater effective dimensionality; learnable parameter count) were not robust indicators across this more extensive survey. We highlight that standard model-to-brain linear re-weighting methods may be too flexible, as most performant models have very similar brain-predictivity scores, despite significant variation in their underlying representations. Broadly, our findings point to the importance of visual diet, challenge common assumptions about the methods used to link models to brains, and more concretely outline future directions for leveraging the full diversity of existing open-source models as tools to probe the common computational principles underlying biological and artificial visual systems.
Comparative analysis of single-cell RNA sequencing methods with and without sample multiplexing

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10X Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10X data where samples are not multiplexed. Despite a relatively lower library and cell capture efficiencies, Parse can detect rare cell types (e.g. plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.
Autonomous Navigation, Mapping and Exploration with Gaussian Processes

—Navigating and exploring an unknown environment is a challenging task for autonomous robots, especially in complex and unstructured environments. We propose a new framework that can simultaneously accomplish multiple objectives that are essential to robot autonomy including identifying free space for navigation, building a metric-topological representation for mapping, and ensuring good spatial coverage for unknown space exploration. Different from existing work that model these critical objectives separately, we show that navigation, mapping, and exploration can be derived with the same foundation modeled with a sparse variant of a Gaussian process. Specifically, in our framework the robot navigates by following frontiers computed from a local Gaussian process perception model, and along the way builds a map in a metric-topological form where nodes are adaptively selected from important perception frontiers. The topology expands towards unexplored areas by assessing a low-cost global uncertainty map also computed from a sparse Gaussian process. Through evaluations in various cluttered and unstructured environments, we validate that the proposed framework can explore unknown environments faster and with a shorter distance travelled than the state-of-the-art frontier exploration approaches. Through field demonstration, we have begun to lay the groundwork for field robots to explore challenging environments such as forests that humans have yet to set foot in 1 .