Miracle-dz/UniFAR-FTUs huggingface dataset:
The past, present and future genetic improvement of indigenous chicken of Kenya

Summary Genetic improvement of farm animals encompasses both mating and selection for desired traits and indigenous chicken genetic resources are no exception. In Kenya, previous attempts to genetically improve indigenous chicken involved cross-breeding scheme by utilizing cockerels and pullets of exotic breeds with the local indigenous chicken. This scheme was complimented with farmer training on good management practices and vaccination for disease control. The scheme was partially successful with improved performance in the crossbreds that declined with subsequent generations. Failure of the programme to meet stakeholder’s expectation led to its’ termination. The current attempt through the Smallholder Indigenous Chicken Improvement Programme has initiated an holistic and comprehensive approach to analysing the entire indigenous chicken actors and avert the causes of previous failures. The programme has genetically and phenotypically characterized the chicken; established reference/base population collected from different ecotypes/counties, established the breeding goals and designed breeding programmes that best suit the Kenyan stakeholders. The on-station research has reported variation on production traits, determined heritability estimate on growth. Current and ongoing research is focused on molecular characterization, selection for improved immune response, carcass quality, eggs production, growth and adaptation traits. The research is also concerned with conservation of these genetic resources.
Analysing Protest-related Tweets: An Evaluation of Techniques by the Open Source Intelligence Team

The police’s Open Source Intelligence (OSINT) team is constantly looking for better ways to analyze large corpora with high precision. This paper presents the evaluation by the OSINT team of more advanced methods than currently used to extract information from tweets related to an upcoming large-scale demonstration. An initial interview revealed the current way of working. Next, more advanced machine learning techniques such as sentiment analysis and network analysis are used to create visualizations that would better suit OSINT’s needs. Finally, our proposed visualizations are evaluated by two OSINT analysts who state that the results are clear, actionable, and relevant, whereas completeness of information and privacy pose additional challenges.
ConPR: Ongoing Construction Site Dataset for Place Recognition

—Place recognition, an essential challenge in computer vision and robotics, involves identifying previously visited locations. Despite algorithmic progress, challenges related to appearance change persist, with existing datasets often focusing on seasonal and weather variations but overlooking terrain changes. Understanding terrain alterations becomes critical for effective place recognition, given the aging infrastructure and ongoing city repairs. For real-world applicability, the comprehensive evaluation of algorithms must consider spatial dynamics. To address existing limitations, we present a novel multi-session place recognition dataset acquired from an active construction site. Our dataset captures ongoing construction progress through multiple data collections, facilitating evaluation in dynamic environments. It includes camera
Towards Discourse Representation via Pregroup Grammars

Every pregroup grammar is shown to be strongly equivalent to one which uses basic types and left and right adjoints of basic types only. Therefore a semantical interpretation is independent of the order of the associated logic. Lexical entries are read as expressions in a two sorted predicate logic with ∈ and functional symbols. The parsing of a sentence deﬁnes a substitution that combines the expressions associated to the individual words. The resulting variable free formula is the translation of the sentence. It can be computed in time proportional to the parsing structure. Non-logical axioms are associated to certain words (relative pronouns, indeﬁnite article, comparative determiners). Sample sentences are used to derive the characterizing formula of the DRS corresponding to the translation.
PRAT: PRofiling Adversarial aTtacks (Supplementary Material)

One principal application of PRAT is to provide information about the attacker which can be used to develop a defense beyond the abilities of the attacker, this will also nullify the possibility of an adaptive attack, hence making the target system more robust. For example, any existing largescale vision model can be analyzed under PRAT problem to identify attackers and devise a defense technique(possibly by including more of those attacked samples in the adversarial training) to improve it. In most real-world scenarios the deep model would be presented with an image without any knowledge of the attacker. We agree that additional details like targeted/untargeted, black box/white box could possibly help the ML practitioner to build a better defense system, but for this work, we take a step to identify a new problem, provide extensive research analysis, and develop GLOF based model to address it. As part of future work, we expect to release a much larger version of AID with a much wider problem statement while the current version of AID still remains the baseline.
[Re] CrossWalk: Fairness-enhanced Node Representation Learning

Scope of Reproducibility — This work aims to reproduce the findings of the paper “Cross‐ Walk: Fairness‐enhanced Node Representation Learning” [1] by investigating the two main claims made by the authors about CrossWalk, which suggest that (i) CrossWalk enhances fairness in three graph algorithms, while only suffering from small decreases in performance, and that (ii) CrossWalk preserves the necessary structural properties of the graph while reducing disparity.
FIGO good practice recommendations on surgical techniques to improve safety and reduce complications during cesarean delivery

FIGO is actively contributing to the global effort to reduce maternal morbidity, mortality, and disability worldwide. Cesarean delivery rates are increasing globally, without signs of slowing down. Bleeding associated with cesarean delivery has become an important cause of hemorrhage‐related maternal deaths in many low‐ and middle‐income countries. Correct surgical techniques to improve safety and reduce complications of cesarean delivery is of the utmost importance. This article presents FIGO's good practice recommendations for effective surgical techniques to reduce cesarean complications. Evidence‐based information is included where data are available. An expanded WHO Surgical Safety Checklist for maternity cases is suggested. Different incision techniques through the layers of the abdominal wall with appropriate indications are discussed. Hysterotomy through a transverse incision is described, as are indications for low vertical and classical incisions. Important precautions when extracting the fetus are explained. Uterine closure includes a safe method ensuring adequate reapproximation of the upper segment if a vertical incision is made. The paper concludes with the management of two common bleeding problems following delivery of the placenta.
Patient prioritisation of impact items to develop the patient‐reported impact of dermatological diseases (PRIDD) measure: European Delphi data

The Global Research on the Impact of Dermatological Diseases (GRIDD) project is developing a patient‐reported measure of the impact of dermatological disease on the patient's life called Patient Reported Impact of Dermatological Diseases (PRIDD). We developed a list of 263 potential impact items through a global qualitative interview study with 68 patients. We next conducted a Delphi study to seek consensus on which of these items to prioritize for inclusion in PRIDD. This study aims to explore patterns in demographic (e.g. country) and clinical variables (e.g. disease group) across the impacts ranked as most important to European dermatology patients.
Mapping active magma chambers by b values beneath the off‐Ito volcano, Japan

The b value of the frequency-magnitude relation, and thus the mean magnitude, in the off-Ito volcanic area is not uniform based on detailed b value tomography using about 10,000 events in an area of 10 km radius and in the upper 15 km of the crust. A high b value anomaly (b=15) below 7 km depth and with a radius of about 2 km, located below the coast southeast of Ito, contrasts with lower values of typically b=0.7 north of it and at shallower depths. On the basis of surface deformations, a tensile crack dislocation source was located above 7 km depth. Thus we surmise that the magma chamber in the off-Ito area was located below 7 km depth. The correlation of a high b value anomaly with this model supports our hypothesis that active magma chambers may be mapped by high b value anomalies. The magma body may be described as consisting of two parts: a lower, well-established chamber below 11 km depth and a shallower part (11–7 km depth). The b value in the volume surrounding the magma chamber increased from 0.7 during the 1980s to 1.5 during the earthquake swarms of the 1990s. This may reflect a trend of increasing crack density due to the intrusion activity.
Pointing Gesture Recognition via Self-supervised Regularization for ASD Screening

The ability to point to objects for sharing social purpose or attention is known as one of the key indicators in distinguishing children with typical development (TD) from those with autism spectrum disorder (ASD). However, there is a lack of datasets specifically tailored for children’s pointing gestures. This lack of training data from the target domain becomes a major factor in the performance degradation of conventional supervised CNNs due to domain shift. Toward an effective and practical solution, we propose an end-to-end learning scheme for domain generalized pointing gesture recognition adopting self-supervised regularization (SSR). To prove the effectiveness of our method in real-world situations, we designed a Social Interaction-Inducing Content (SIIC)-based ASD diagnostic system and collected an ASD-Pointing dataset consisting of 40 TD and ASD children. Through extensive experiments on our collected datasets, we achieved an ASD screening accuracy of 72.5%, showing that pointing ability can play a vital role as an indicator in distinguishing between ASD and TD.
Estimating shallow water sound power levels and mitigation radii for the R/V Marcus G. Langseth using an 8 km long MCS streamer

For seismic surveys in shallow‐water environments, the complexity of local geology and seafloor topography can make it difficult to accurately predict associated sound levels and establish appropriate mitigation radii required to ensure the safety of local marine protected species. This is primarily because necessary detailed information regarding the local seafloor topography and subseafloor geology is often unavailable before a survey begins. One potential solution to this problem is to measure received levels using the ship's multichannel seismic (MCS) streamer, which could allow for the dynamic real‐time determination of sound levels and mitigation radii while a survey is underway. We analyze R/V Langseth streamer data collected on the shelf and slope near the Washington coast during the Cascadia Open‐Access Seismic Transects (COAST) and Ridge2Trench projects to measure received levels up to a distance of approximately 8 km from the sound source array. We establish methods to filter, clean, and process streamer data to accurately determine received power levels and confidently establish mitigation radii. We show that in shallow water measured power levels can fluctuate due to the influence of seafloor topographic features, but that the use of the streamer for the establishment of dynamic mitigation radii is feasible and should be further pursued. The establishment of mitigation radii based on local conditions may help to maximize the safety of marine protected species while also maximizing the ability of researchers to conduct seismic studies.
Origin of lunar high‐titanium ultramafic glasses: Constraints from phase relations and dissolution kinetics of clinopyroxene‐ilmenite cumulates

Abstract— Phase equilibrium and dissolution kinetics experiments on synthetic late‐stage magma ocean cumulates are used to place constraints on hypotheses for the origin of lunar high‐Ti ultramafic glasses. Models for the production of high‐Ti lunar magmas have called for either (1) assimilation of late‐stage clinopyroxene‐ilmenite cumulates at shallow levels or (2) sinking of clinopyroxene‐ilmenite cumulates to form a hybrid mantle source. To satisfy the constraints of our experiments, we propose an alternative model that involves shallow‐level reaction and mixing of cumulates, followed by sinking of hybrid high‐Ti materials. This model can fulfill compositional requirements imposed by the pristine lunar glass suite that are difficult to satisfy in assimilation models. It also avoids difficulties that arise in overturn models from the low solidus temperatures of clinopyroxene‐ilmenite cumulates. Partially molten clinopyroxene‐ilmenite cumulates become gravitationally unstable with respect to underlying mafic cumulates only when they have cooled to within ∼30°C of their solidus (∼1125°C at 100 km depth). At these temperatures, the viscosity of mafic cumulates is too high to allow for growth and descent of clinopyroxene‐ilmenite diapirs on the appropriate time scale. Reaction and mixing between late‐stage liquids and mafic cumulates at shallow levels would produce a refractory hybrid material that is negatively buoyant at higher temperatures and could sink more efficiently to the depths inferred for production of high‐Ti ultramafic glasses.
A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System

The value of lidar derives from its ability to map ecosystem vertical structure which can be used to estimate aboveground carbon storage. Spaceborne lidar sensors collect data along transects and gain value for the global change science community when combined with data sources that have complete horizontal coverage. Data sources and methods for this type of analysis require evaluation. In this work we use image segmentation of 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) data to produce a global map of 4.4 million forest patches. Where a Geoscience Laser Altimeter System (GLAS) transect intersects a patch, its height is calculated from the GLAS observations directly. Regression analysis is then used to estimate the heights of those patches without GLAS observations. Regression goodness‐of‐fit statistics indicate moderately strong relationships for predicting the 90th percentile patch height, with a mean RMSE of 5.9 m and mean correlation (R2) of 0.67.
[Re] Variational Neural Cellular Automata

Scope of Reproducibility — The main claim of the paper being reproduced is that the pro‐ posed Variational Neural Cellular Automata (VNCA) architecture, composed of a convo‐ lutional encoder and a Neural Cellular Automata (NCA)‐based decoder, is able to gen‐ erate high‐quality samples. The paper presents two variants of this VNCA decoder: the doubling VNCA variant that is claimed to have a simple latent space, and the non‐ doubling VNCA variant that is claimed to be optimized for damage recovery and stability over many steps.
Validation of Regional Climate Models for Hydrological Impact Studies at the Catchment-Scale

Traditionally, simulations of global climate models (GCMs) have been the basis of hydrological impact studies. Progress in regional climate models (RCMs) has recently made the use of these simulations as a basis for hydrological studies more attractive. RCMs allow transferring the large-scale information from GCMs to scales which are closer to the catchment scale. However, the use of RCM simulations for hydrological studies is often challenging. Although RCMs simulate hydrological variables in addition to climate variables, these simulations do often not agree with runoff observations and, thus, might not be directly useful to assess hydrological impacts. Therefore, usually RCM output data is used in off-line mode to force a hydrological model. Considerable biases in the RCM simulations often make this approach challenging.
The Impact of Virtual Reality (VR) Tour Experience on Tourists' Intention to Visit

Drawing on media richness theory, this study investigates the effect of rich media, such as virtual reality (VR), on visit intentions for a specific destination. Specifically, this research employs a mixed-method approach, using abductive theorization to explore and confirm the dimensions of the VR visit experience, notably those related to telepresence, a key concept in tourism through VR. Furthermore, the study aims to elucidate how telepresence influences mental imagery, attitudes towards tourist destinations, and actual visit intentions. To do this, qualitative data were gathered between February and June 2022 from 34 semi-structured interviews with respondents who viewed a VR video of the destination. A second study collected quantitative data from 400 participants through face-to-face questionnaires after a VR video view between June and August 2022. The findings reveal that telepresence comprises three dimensions: realism of the virtual environment, immersion, and the sense of presence in the virtual environment. Telepresence, in turn, both directly and indirectly affects actual visit intentions, with mental imagery and attitude toward tourist destinations partially mediating those relationships. This study provides methodological, theoretical, and tourism management implications to enhance our comprehension of telepresence’s facets, its measurement, and the process by which VR influences real visit intentions.
Carbonate compensation dynamics

Carbonate saturation (zsat) and compensation (zcc) depths change with deep‐ocean acidification and basification. We present simple, explicit, mechanistic formulas for the positions of these two critical depths. In particular zcc is expressed as a function of the mean dissolved carbonate ion concentration of the deep ocean, [CO3]D, the supply of dissolvable CaCO3, Fc, and the dissolution rate constant at the sediment‐water interface, kc, which we show to be essentially mass‐transfer controlled. Calculations reveal that zsat and zcc are today some ∼0.9 km apart and will rise and separate by as much as 1.7 km with acidification; conversely, if [CO3]D increases, zsat and zcc will deepen, but their separation will asymptote to ∼0.7 km.
Regional innovation systems (RIS) in the U.S. *South: The role of *local characteristics of RIS in rural development

Regional innovation systems (RIS) and innovative activity are now recognized as having important roles to play in regional economic development policy. The goal of this study is to expand our understanding of the relationship between regional economic growth and the local characteristics of RIS. The research identified the existence and importance of sources of innovation, knowledge spillovers, and regional spillovers as the principal characteristics of RIS in the South. A knowledge production function approach was used to estimate the determinants of innovative activity in rural counties. A zero inflated negative binomial model was estimated to capture the influence of local characteristics of the county on the existence and volume of innovative activity in the county. The findings of this research indicate that local innovative activity and characteristics of RIS matter in regional economic growth. Patenting activities in metro areas had a positive and statistically significant association with patent totals for nearby rural areas. However, the results of the OLS models and the simultaneous system of equations for the extended Carlino-Mills model found a negative association between metro patenting activity and economic growth of neighboring rural areas, indicating “backwash” effects. Thus, the implication from these findings is that regional policymakers should be careful of investments in metro RIS if the goal is economic development in nearby rural areas.
Our validity looks like justice. Does yours?

Educational assessments, from kindergarden to 12th grade (K-12) to licensure, have a long, well-documented history of oppression and marginalization. In this paper, we (the authors) ask the field of educational assessment/measurement to actively disrupt the White supremacist and racist logics that fuel this marginalization and re-orient itself toward assessment justice. We describe how a justice-oriented, antiracist validity (JAV) approach to validation processes can support assessment justice efforts, specifically with respect to language assessment. Relying on antiracist principles and critical quantitative methodologies, a JAV approach proposes a set of critical questions to consider when gathering validity evidence, with potential utility for language testers.
Reformulating Reactivity Design for Data-Efficient Machine Learning

Machine learning (ML) can deliver rapid and accurate reaction barrier predictions for use in rational reactivity design. However, model training requires large data sets of typically thousands or tens of thousands of barriers that are very expensive to obtain computationally or experimentally. Furthermore, bespoke data sets are required for each region of interest in reaction space as models typically struggle to generalize. We have therefore reformulated the ML barrier prediction problem toward a much more data-efficient process: finding a reaction from a prespecified set with a desired target value. Our reformulation enables the rapid selection of reactions with purpose-specific activation barriers, for example, in the design of reactivity and selectivity in synthesis, catalyst design, toxicology, and covalent drug discovery, requiring just tens of accurately measured barriers. Importantly, our reformulation does not require generalization beyond the domain of the data set at hand, and we show excellent results for the highly toxicologically and synthetically relevant data sets of aza-Michael addition and transition-metal-catalyzed dihydrogen activation, typically requiring less than 20 accurately measured density functional theory (DFT) barriers. Even for incomplete data sets of E2 and SN2 reactions, with high numbers of missing barriers (74% and 56% respectively), our chosen ML search method still requires significantly fewer data points than the hundreds or thousands needed for more conventional uses of ML to predict activation barriers. Finally, we include a case study in which we use our process to guide the optimization of the dihydrogen activation catalyst. Our approach was able to identify a reaction within 1 kcal mol–1 of the target barrier by only having to run 12 DFT reaction barrier calculations, which illustrates the usage and real-world applicability of this reformulation for systems of high synthetic importance.