MechIR: A Mechanistic Interpretability Framework for Information Retrieval

Mechanistic interpretability is an emerging diagnostic approach for neural models that has gained traction in broader natural language processing domains. This paradigm aims to provide attribution to components of neural systems where causal relationships between hidden layers and output were previously uninterpretable. As the use of neural models in IR for retrieval and evaluation becomes ubiquitous, we need to ensure that we can interpret why a model produces a given output for both transparency and the betterment of systems. This work comprises a flexible framework for diagnostic analysis and intervention within these highly parametric neural systems specifically tailored for IR tasks and architectures. In providing such a framework, we look to facilitate further research in interpretable IR with a broader scope for practical interventions derived from mechanistic interpretability. We provide preliminary analysis and look to demonstrate our framework through an axiomatic lens to show its applications and ease of use for those IR practitioners inexperienced in this emerging paradigm

Empowering Faculty Through Leadership: a Collaborative Approach to Blended Learning Adoption in Higher Education

The integration of blended learning into higher education presents significant challenges for faculty, requiring new pedagogical strategies, digital competencies, and institutional support to effectively engage students in hybrid environments. While blended learning has the potential to enhance flexibility, interactivity, and learning outcomes, its successful implementation demands faculty training, resource accessibility, and leadership-driven change. This study examines the impact and influence of a faculty development initiative—Online Sharing Sessions of Teaching Innovation for Blended Learning—designed to enhance Course Content Mapping (CCM) and multimedia content creation skills among academic staff. The initiative was structured around transformational, distributed, and lateral leadership models, fostering a culture of sustainable faculty engagement, peer mentorship, and collaborative learning across disciplines. Drawing on survey data from 21 faculty members across interdisciplines in the University of Glasgow Singapore, this study highlights the effectiveness of peer-led training, interdisciplinary knowledge-sharing, and structured mentorship in fostering institutional change and pedagogical innovation. Findings indicate that 80% of participants reported increased confidence in CCM, while 58% felt comfortable creating digital content, reinforcing the long-term impact of professional development programs in digital education. The results also emphasize the importance of distributed leadership structures, where faculty take ownership of their learning through peer collaboration and mentorship rather than relying on hierarchical directives. By linking these findings to leadership theories (Bass & Avolio, 1994; Bolden et al., 2009; Cohen & Bradford, 2011), this study argues that effective faculty development is not solely dependent on administrative initiatives but flourishes in a collaborative, faculty-driven learning culture. The study concludes by proposing a scalable faculty mentorship model that leverages interdisciplinary expertise, peer-led learning, and leadership development pathways to sustain blended learning adoption and digital pedagogy innovations in higher education. Keywords

PICASO: Cyclic Loading of Wind Turbine Monopiles

The PICASO project is a multi-faceted collaborative project between the University of Oxford and Ørsted that aims to develop the next generation of cyclic loading models for offshore wind turbine monopiles. The project focuses on a wide range of activities including (a) detailed development of PISA type 1D finite element models, including the application of machine learning techniques for model calibration, (b) completion of laboratory element and laboratory model testing in a wide range of soils, including monotonic and cyclic loading, (c) development of new theoretical methods to capture cyclic loading within a plasticity framework (HARM), such that it can be adapted to 0D, 1D and 3D type modelling of foundations, (d) field scale testing at a clay and sand site, adopting realistic loading patterns for cyclic loading, and exploring rate effects, at two different pile diameters (1.22 m and 2.5 m), (e) translation of the research findings to design applications. This paper provides an overview of the research completed and the important findings for design practice

Joint inference for gravitational wave signals and glitches using a data-informed glitch model

Gravitational wave data are often contaminated by non-Gaussian noise transients, “glitches,” which can bias the inference of astrophysical signal parameters. Traditional approaches either subtract glitches in a preprocessing step, or a glitch model can be included from an agnostic wavelet basis (e.g., BayesWave). In this work, we introduce a machine-learning-based approach to build a parametrized model of glitches. We train a normalizing flow on known glitches from the Gravity Spy catalog, constructing an informative prior on the glitch model. By incorporating this model into the Bayesian inference analysis with bilby, we estimate glitch and signal parameters simultaneously. We demonstrate the performance of our method through bias reduction, glitch identification, and Bayesian model selection on real glitches. Our results show that this approach effectively removes glitches from the data, significantly improving source parameter estimation and reducing bias

Assessing atmospheric CO2 capture with legacy paper mill waste in Scotland

Global warming over the past 70 years has been driven by rising atmospheric CO2 levels, largely resulting from industrialization. During this period, large quantities of alkaline waste materials were generated, many of which have the potential to capture atmospheric CO2 through mineral carbonation, hence offsetting some of these industrial emissions. One such material is paper mill sludge (PMS), a by-product of paper production. Significant volumes of legacy PMS exist worldwide, offering an untapped resource for carbon sequestration. To assess its carbon capture potential, this study maps and quantifies legacy PMS deposits in Scotland, a region with a long history of paper-making. Using historical records and GIS-based spatial analysis, 23 PMS deposits were identified across Scotland, primarily concentrated in the central and northeastern regions. The total volume of these deposits was estimated at 1,450,745 m3. X-ray diffraction (XRD) analysis revealed that PMS samples are composed predominantly of calcite (∼95%), indicating near-complete carbonation. This equates to the sequestration of approximately 1.72 million tonnes of atmospheric CO2 since deposition. Spatial analysis examined the co-location of PMS deposits with designated ecological and cultural protection zones, revealing minimal overlap. This underscores the need for targeted management strategies to safeguard these carbon sinks f

Being a first generation university graduate, the impact on a career in science

Being in the first generation to access Higher Education (First Gen) is a barrier to academic success. First Gens face difficulties transitioning into, completing, and attaining competitive grades in undergraduate studies despite intervention strategies. Triangulating data across studies, we reveal the unique challenges resulting from being First Gen in science and show how these persist at every stage of a career in academia. We propose that invitation practices, i.e. behaviors that encourage, guide, and/or affirm others, need to be intentionally directed towards First Gens throughout their career journey to successfully support their retention and progression in science. As First Gens are overrepresented in other intersectionally marginalised groups, such actions will contribute to building a more inclusive and diverse scientific community

Waymo-3DSkelMo: A Multi-Agent 3D Skeletal Motion Dataset for Pedestrian Interaction Modeling in Autonomous Driving

Large-scale high-quality 3D motion datasets with multi-person interactions are crucial for data-driven models in autonomous driving to achieve fine-grained pedestrian interaction understanding in dynamic urban environments. However, existing datasets mostly rely on estimating 3D poses from monocular RGB video frames, which suffer from occlusion and lack of temporal continuity, thus resulting in unrealistic and low-quality human motion. In this paper, we introduce Waymo-3DSkelMo, the first large-scale dataset providing high-quality, temporally coherent 3D skeletal motions with explicit interaction semantics, derived from the Waymo Perception dataset. Our key insight is to utilize 3D human body shape and motion priors to enhance the quality of the 3D pose sequences extracted from the raw LiDAR point clouds. The dataset covers over 14,000 seconds across more than 800 real driving scenarios, including rich interactions among an average of 27 agents per scene (with up to 250 agents in the largest scene). Furthermore, we establish 3D pose forecasting benchmarks under varying pedestrian densities, and the results demonstrate its value as a foundational resource for future research on fine-grained human behavior understanding in complex urban environments

Does the impact of pupil absences on achievement depend on their timing?

Using linked data from the Millennium Cohort Study and National Pupil Database (N = 8,139), this study examined how the timing of school absences (years 1 to 11 between 2006 and 2017) affects achievement at the end of compulsory schooling in England. Absences during any school year are harmful to student achievement. However, absences in years 1 and 6 (the final year of primary school), and between years 6 to 10 (the penultimate year of compulsory secondary schooling) are more detrimental to academic performance than in other years. Authorized absences hurt academic performance as much as unauthorized absences. To test the external validity of our findings, we used comparable data and analytic methods for Wales and reached the same conclusions

Photoproduction of two charged pions off protons in the resonance region

Photoproduction of charged pions pairs off protons is studied within the invariant masses of the final state hadrons from 1.6 to 2.4 GeV at the Thomas Jefferson National Accelerator Facility with the CLAS detector. The total and differential cross sections and spin-density matrix elements are presented for the isobars pρ0 (770), Δ (1232)++ π−, and Δ (1232)0π+. The data are included in the Bonn-Gatchina coupled-channel analysis and provide the information necessary to determine the branching fractions of Nρ (770) decays for most known N* and Δ* resonances. For the first time, the Nρ branching ratios are obtained here from an event-based likelihood to yp→π+π−p

What is the LTS Job Role? Pursuing Careers in Learning and Teaching

The Innovative Pedagogy Pub warmly hosts a PGR/GTA/Tutor event on 'What is the LTS job role? Pursuing careers in Learning and Teaching'. This session has been designed for those considering pursuing a Learning and Teaching position in higher education within the Social Sciences. In this session we will consider, What is the LTS career track? How might your training and experience be relevant in the job market? We will also discuss experiences of the LTS track from Pedagogy Hub representatives and hear insight into the role from L and T specialists. This session is designed to be interactive and get you thinking about career opportunities after the PhD