Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating environmental and ecological monitoring with seaweed farming

We explore the biodiversity impacts of seaweed and shellfish farms in Pembrokeshire, UK, with a focus on using monitoring methods that are affordable and can be integrated into existing seaweed farming operations. Monitoring methods used include Baited Remote Underwater Video Systems (BRUVs), Passive Acoustic Monitoring (PAM) and visual surveys of cultivation lines and natural settlement on farm infrastructure. BRUVs detected 13 motile fauna taxa, influenced mainly by site conditions, while PAM observed distinct patterns in dolphin and porpoise activity that changes between seasons. Visual surveys revealed colonisation of algal, hydroid, and bryozoan species on both the surface and seabed infrastructure, indicating that the farming structures serve as stable substrates for biodiversity accumulation. Apart from the natural settlement on infrastructure, our data does not show a conclusive link between biodiversity and farming due to the highly dynamic environment, small scale of operations, and relatively short monitoring timeframe. Despite these limitations, the data sets a crucial baseline for future studies and showed no negative impacts even as farming activities intensified over the monitoring period. The study aligns with what’s feasible for time-constrained farm operators and forms a foundation for routine, integrated environmental and biodiversity monitoring.
Aerobic exercise promotes PDAC vascular normalization through S1PR1 signaling in tumor endothelial cells

Pancreatic Ductal Adenocarcinoma (PDAC) is characterized by high resistance to anti-cancer therapies. This resistance is caused in part by hypo-vascularization and dysfunctional vessels which inefficiently deliver chemotherapy. Here, we define the mechanism of aerobic exercise-induced tumor vascular remodeling and improved chemotherapy delivery and efficacy. We found that aerobic exercise was able to improve tumor vascular function and increase the number of lymphatic vessels, and these effects were associated with increased gemcitabine delivery to and efficacy against PDAC in mice. Further, exercise increased sphingosine-1-phosphate (S1P) in plasma and the activation of sphingosine-1-phosphate receptor 1 (S1PR1) in tumor endothelial cells. S1PR1 endothelial cell-specific deletion blunted the exercise-induced improvements in tumor vascular function, gemcitabine efficacy and drug concentration. Preclinical findings were validated in a patient cohort in which we found that exercise during neoadjuvant chemotherapy remodeled PDAC vasculature and improved tumor vascular function. These findings provide direct evidence that exercise increases chemotherapy delivery and efficacy by improving vascular function, defining S1PR1 as a necessary mediator of exercise-induced vascular remodeling.
Mapping Spatial Patterns to Energetic Benefits in Groups of Flow-coupled Swimmers

The coordinated motion of animal groups through fluids is thought to reduce the cost of locomotion to individuals in the group. However, the connection between the spatial patterns observed in collectively moving animals and the energetic benefits at each position within the group remains unclear. To address this knowledge gap, we study the spontaneous emergence of cohesive formations in groups of fish, modeled as flapping foils, all heading in the same direction. We show in pairwise formations and with increasing group size that (1) in side-by-side arrangements, the reciprocal nature of flow coupling results in an equal distribution of energy requirements among all members, with reduction in cost of locomotion for swimmers flapping inphase but an increase in cost for swimmers flapping antiphase, and (2) in inline arrangements, flow coupling is non-reciprocal for all flapping phase, with energetic savings in favor of trailing swimmers, but only up to a finite number of swimmers, beyond which school cohesion and energetic benefits are lost at once. We explain these findings mechanistically and we provide efficient diagnostic tools for identifying locations in the wake of single (and multiple) swimmers that offer opportunities for hydrodynamic benefits to aspiring followers. Our results imply a connection between the resources generated by flow physics and social traits that influence greedy and cooperative group behavior.
Discovery and characterization of chromosomal inversions in the arboviral vector mosquito Aedes aegypti

Chromosomal inversions play a fundamental role in evolution and have been shown to regulate epidemiologically important traits in malaria mosquitoes. However, they have never been characterized in Aedes aegypti, the major vector of arboviruses, because of the poor structure of its polytene chromosomes. In this study, we applied a Hi-C proximity ligation approach to identify chromosomal inversions in 25 strains of Ae. aegypti, acquired from its worldwide distribution, as well as in one strain of Ae. mascarensis. The study identified 21 multi-megabase inversions with uneven distributions along the three chromosomes. All chromosomal inversions, including one specific for Ae. mascarensis, were polymorphic. Nevertheless, geographic origin separated the strains into two clusters carrying African and non-African inversions suggesting their potential association with Ae. aegypti subspecies. Some of the inversions colocalized with chemoreceptor genes and quantitative trait loci associated with pathogen infection, implicating the potential role of inversions in host choice and disease transmission.
Large Neural Networks at a Fraction

Large-scale deep learning models are known for their large amount of parameters, weighing down on the computational resources. The core of the Lottery Ticket Hypothesis showed us the potential of pruning to reduce such parameters without a significant drop in accuracy. Quaternion neural networks achieve comparable accuracy to equivalent real-valued networks on multi-dimensional prediction tasks. In our work, we implement pruning on real and quaternion-valued implementations of large-scale networks in the task of image recognition. For instance, our implementation of the ResNet-101 architecture on the CIFAR-100 and ImageNet64x64 datasets resulted in pruned quaternion models outperforming their real-valued counterparts by 4% and 7% in accuracy at sparsities of about 6% and 0.4%, respectively. We also got quaternion implementations of ResNet-101 and ResNet-152 on CIFAR-100 with steady Lottery tickets, whereas the Real counterpart failed to train at the same sparsity. Our experiments show that the pruned quaternion implementations perform better at higher sparsity than the corresponding real-valued counterpart, even in some larger neural networks.
Ready, Set, Plan! Planning to Goal Sets Using Generalized Bayesian Inference

: Many robotic tasks can have multiple and diverse solutions and, as such, are naturally expressed as goal sets. Examples include navigating to a room, finding a feasible placement location for an object, or opening a drawer enough to reach inside. Using a goal set as a planning objective requires that a model for the objective be explicitly given by the user. However, some goals are intractable to model, leading to uncertainty over the goal (e.g. stable grasping of an object). In this work, we propose a technique for planning directly to a set of sampled goal configurations. We formulate a planning as inference problem with a novel goal likelihood evaluated against the goal samples. To handle the intractable goal likelihood, we employ generalized Bayesian inference to approximate the trajectory distribution. The result is a fully differentiable cost which generalizes across a diverse range of goal set objectives for which samples can be obtained. We show that by considering all goal samples throughout the planning process, our method reliably finds plans for manipulation and navigation problems where heuristic approaches fail.
α-MDF: An Attention-based Multimodal Differentiable Filter for Robot State Estimation

: Differentiable Filters are recursive Bayesian estimators that derive the state transition and measurement models from data alone. Their data-driven nature eschews the need for explicit analytical models, while remaining algorithmic components of the filtering process intact. As a result, the gain mechanism – a critical component of the filtering process – remains non-differentiable and cannot be adjusted to the specific nature of the task or context. In this paper, we pro-pose an attention-based Multimodal Differentiable Filter ( α -MDF) which utilizes modern attention mechanisms to learn multimodal latent representations. Unlike previous differentiable filter frameworks, α -MDF substitutes the traditional gain, e.g., the Kalman gain, with a neural attention mechanism. The approach generates specialized, context-dependent gains that can effectively combine multiple input modalities and observed variables. We validate α -MDF on a diverse set of robot state estimation tasks in real world and simulation. Our results show α -MDF achieves significant reductions in state estimation errors, demonstrating nearly 4-fold improvements compared to state-of-the-art sensor fusion strategies for rigid body robots. Additionally, the α -MDF consistently outperforms differentiable filter baselines by up to 45% in soft robotics tasks. The codebase is available at https://github.com/ir-lab/alpha-MDF
Uncovering the Varied Impact of Behavioral Change Messages on Population Groups

Individuals such as medical interns who work in high-stress environments often face mental health challenges including depression and anxiety. These challenges are exacerbated by the limited access to traditional mental health services due to demanding work schedules. In this context, mobile health interventions such as push notifications targeting behavioral modification to improve mental health outcomes could deliver much needed support. In this work, we study the effectiveness of these interventions on subgroups, by studying the conditional average causal effect of these interventions. We design a two step approach for estimating the conditional average causal effect of interventions and identifying specific subgroups of the population who respond positively or negatively to the interventions. The first step of our approach follows existing causal effect estimation approaches, while the second step involves a novel tree-based approach to identify subgroups who respond to the treatment. The novelty in the second step stems from a pruning approach that deploys hypothesis testing to identify subgroups experiencing a statistically significant positive or negative causal effect. Using a semi-simulated dataset, we show that our approach retrieves affected subpopulations with a higher precision than alternatives while maintaining the same recall and accuracy. Using a real dataset with randomized push interventions among the medical intern population at a large hospital, we show how our approach can be used to identify subgroups who might benefit the most from interventions.
Spatial and temporal patterns of dissolved organic matter quantity and quality in the Mississippi River Basin, 1997–2013

Recent studies have found insignificant or decreasing trends in time‐series dissolved organic carbon (DOC) datasets, questioning the assumption that long‐term DOC concentrations in surface waters are increasing in response to anthropogenic forcing, including climate change, land use, and atmospheric acid deposition. We used the weighted regressions on time, discharge, and season (WRTDS) model to estimate annual flow‐normalized concentrations and fluxes to determine if changes in DOC quantity and quality signal anthropogenic forcing at 10 locations in the Mississippi River Basin. Despite increases in agriculture and urban development throughout the basin, net increases in DOC concentration and flux were significant at only 3 of 10 sites from 1997 to 2013 and ranged between −3.5% to +18% and −0.1 to 19%, respectively. Positive shifts in DOC quality, characterized by increasing specific ultraviolet absorbance at 254 nm, ranged between +8% and +45%, but only occurred at one of the sites with significant DOC quantity increases. Basinwide reductions in atmospheric sulfate deposition did not result in large increases in DOC either, likely because of the high buffering capacity of the soil. Hydroclimatic factors including annual discharge, precipitation, and temperature did not significantly change during the 17‐year timespan of this study, which contrasts with results from previous studies showing significant increases in precipitation and discharge over a century time scale. Our study also contrasts with those from smaller catchments, which have shown stronger DOC responses to climate, land use, and acidic deposition. This temporal and spatial analysis indicated that there was a potential change in DOC sources in the Mississippi River Basin between 1997 and 2013. However, the overall magnitude of DOC trends was not large, and the pattern in quantity and quality increases for the 10 study sites was not consistent throughout the basin.
Data and Network Science

The current study aimed to examine marketing cyber-security (DDoS Attacks, Cross-Site Scripting, SQL Attacks, and passwords attacks) in the digital age by presenting evidence from digital marketing platforms. Depending on the quantitative approach and utilizing a questionnaire as a tool, (133) marketing managers in digital marketing companies in Jordan responded to an online questionnaire. SPSS was used to screen and analyze the gathered data. Results of the study accepted the main hypothesis, and it appeared that marketing cyber-security has a statistically positive influence on marketing platforms, in addition to that, it appeared that the highest influence of sub-variables was for the benefit of Structured Query Language (SQL) Attacks explaining 35.8% of the variation. This result meant that SQL attacks-security does have a statistically positive influence on marketing platforms. This hypothesis could be tested through various methodologies, for example, surveys, interviews, focus groups, and/or experiments. The study recommended that marketers should use role-based access to limit the data employees can access and regularly review their permissions. Further recommendations were presented in the study.
A hybridizable discontinuous Galerkin method for the dual-porosity-Stokes problem

We introduce and analyze a hybridizable discontinuous Galerkin (HDG) method for the dual-porosity-Stokes problem. This coupled problem describes the interaction between free flow in macrofractures/conduits, governed by the Stokes equations, and flow in microfractures/matrix, governed by a dual-porosity model. We prove that the HDG method is strongly conservative, well-posed, and give an a priori error analysis showing dependence on the problem parameters. Our theoretical findings are corroborated by numerical examples
Region-based Saliency Explanations on the Recognition of Facial Genetic Syndromes

Deep neural networks in computer vision have shown remarkable progress in recognizing facial genetic syndromes. Many genetic syndromes are difficult to detect, even for experienced clinicians, and computer-aided phenotyping can accelerate clinical diagnosis. High-stakes clinical tasks using deep learning, as in clinical genetics, require human un-derstandable explanations for model decisions. Saliency methods are used to explain DNN predictions in various image analysis domains but have yet to be studied in facial genetics. The syndromic features of most genetic conditions are often localized to areas like the eyes, nose, and mouth. In this paper, to summarize the contribution of key facial regions to a specific disease prediction, we propose a face region relevance score that can be applied to any saliency method. We also investigate how prior knowledge, namely human phenotype ontology and DNN model explanations, align. Quantitative experiments are performed on a new database containing over 3,500 images of 11 rare facial syndromes, a healthy control group, and an additional test set of 171 facial images, whose respective facial phenotypes are labeled by clinicians. Current saliency methods are good at capturing dysmorphism in particular regions (parts of the face), but they may not completely capture all the relevant features in a given person or condition. Our study indicates which saliency explanations and face regions are more consistent with the phenotypes of specific genetic syndromes and could be used in large-scale clinical evaluations.
Note : Intercomparison of ILAS-II version 2 and 1 . 4 trace species with MIPAS-B measurements

The Improved Limb Atmospheric Spectrometer (ILAS)-II sensor aboard the Japanese ADEOS-II satellite was launched into its sun-synchronous orbit on 14 December 2002 and performed solar occultation measurements of trace species, aerosols, temperature, and pressure in the polar stratosphere until 25 October 2003. Vertical trace gas profiles obtained with the balloon version of the Michelson Interferometer for Passive Atmospheric Sounding (MIPASB) provide one of the sparse data sets for validating ILAS-II version 2 and 1.4 data. The MIPAS-B limb emission spectra were collected on 20 March 2003 over Kiruna (Sweden, 68 N) at virtually the same location that has been sounded by ILAS-II about 5.5 h prior to the sampling of MIPAS-B. The intercomparison of the new ILAS-II version 2 (Northern Hemispheric sunrise) data to MIPAS-B vertical trace gas profiles shows a good to excellent agreement within the combined error limits for the species O 3, N2O, CH4, H2O (above 21 km), HNO3, ClONO2, and CFC-11 (CCl 3F) in the compared altitude range between 16 and 31 km such that these data appear to be very useful for scientific analysis. With regard to the previous version 1.4 ILAS-II data, significant improvements in the consistency with MIPAS-B are obvious especially for the species CH 4 and H2O, but also for O3, HNO3, ClONO2, NO2, and N2O5. However, comparing gases like NO2, N2O5, and CFC-12 (CCl 2F2) exhibits only poor agreement with MIPAS-B such that these species cannot be assumed to be validated at the present time. Correspondence to: G. Wetzel (gerald.wetzel@imk.fzk.de)
interventions for anxiety management in choking-susceptible

artists individually in a coaching setting. Methods: We conducted a mixed-methods (QUANT + QUAL) collective case study with nine performing artists, who each received ﬁve individual coaching sessions. The tailored choking interventions comprised acclimatization training, goal setting, and pre-performance routines, including elements such as imagery, self-talk, and relaxation techniques. Before and after the 10-week intervention phase, they ﬁlled in questionnaires on trait performance anxiety, fear of negative evaluation, and self-efficacy, performed in front of a jury, and were interviewed about their experiences. Transcripts of interviews and coaching sessions were analyzed using thematic analysis. Heart rate measurements, weekly performance videos, and expert evaluations were also part of our comprehensive data. Results: Quantitative data showed reductions in performance anxiety and fear of negative evaluation, and increases in self-efficacy and performance quality, from before to after the intervention phase. Most participants also had a lower heart rate when performing for the jury. Themes from qualitative analysis comprised managing nervousness and feeling more relaxed, becoming more self-conﬁdent, satisfaction with artistic and mental performance, feeling good and enjoying performing, and general positive effects. Conclusion: Tailoring psychological interventions may provide several beneﬁts for choking-susceptible performing artists.
Wave attenuation over the Oesterdam tidal flat nourishment: Wind-wave transformation in the intertidal zone

The Oesterdam tidal flat nourishment is a hook-shaped nourishment that was constructed in November 2013 in the Kom area of the Eastern Scheldt. One of the objectives of the nourishment is to increase the design life of the Oesterdam in the face of increasing wave loads due to structural erosion of the intertidal flats. A study is conducted in order to examine the available hydrodynamic and morphological data, validate an existing hydrodynamic model, and estimate projections of design wave loads.
randomized controlled trial to evaluate the effectiveness and cost effectiveness of a multi-component intervention to reduce substance use and risk-taking behavior in adolescents involved

Background: Adolescence is a critical developmental stage when young people make lifestyle choices that have the potential to impact on their current and future health and social wellbeing. The relationship between substance use and criminal activity is complex but there is clear evidence that the prevalence of problematic substance use is far higher among adolescent offenders than the general adolescent population. Adolescent offenders are a marginalized and vulnerable population who are significantly more likely to experience health and social inequalities in later life than their non-offending peers. There is a paucity of evidence on effective interventions to address substance use and risk-taking behaviours in adolescent offender populations but it is clear that preventative or abstinence orientated interventions are not effective. RISKIT-CJS is an intervention developed in collaboration with young people taking account of the current best evidence. Feasibility and pilot studies have found the intervention addresses the needs of adolescents, is acceptable and has demonstrated potential in reducing substance use and risk-taking behavior. Methods: The study is a mixed method, two-armed, prospective, pragmatic randomized controlled trial with individual randomisation to either treatment as usual alone or the RISKIT-CJS intervention in addition to treatment as usual. Adolescents, aged 13 to 17 years inclusive, engaged with the criminal justice system who are identified as having problematic substance use are eligible to participate. The study will be conducted across three geographical areas; South and South East England, London and North East England between March 2017 and February 2019. Discussion: The study represents an ambitious programme of work to address an area of need for a marginalized and vulnerable population.
University of Birmingham Film, interrupted

Mobile devices are increasingly used while watching video, both as a secondary device and for dedicated viewing. However, devices frequently issue notiﬁcations that can interrupt viewing. This study investigated the effect of interruptions from notiﬁcations on viewer immersion. Participants watched 10 minutes of a movie without notiﬁca-tions, and 10 minutes while receiving message notiﬁcations. There were two participant groups: one watched video on a 30-inch monitor with messages sent to a separate smart-phone; while another watched on a smartphone while also receiving messages on it. Viewer immersion was assessed after each condition via questionnaire. We also considered message response times. Results showed that immersion scores were lower when the video was interrupted with no-tiﬁcations, regardless of viewing device. Message response times were fastest when using the phone as a secondary device. Our results suggest that device-driven interruptions should be minimised for an immersive ﬁlm experience.