Enhancing gravitational-wave detection: a machine learning pipeline combination approach with robust uncertainty quantification

Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artifacts, and rare astrophysical signals. Multiple search algorithms exist to detect the signals from compact binary coalescences, but their varying performance complicates interpretation. We present a machine-learning-driven approach that combines results from individual pipelines and utilizes conformal prediction to provide robust, calibrated uncertainty quantification. Using simulations, we demonstrate improved detection efficiency and apply our model to GWTC-3, enhancing confidence in multipipeline detections, such as the subthreshold binary neutron star candidate GW200311_103121.</p

Avelumab with axitinib for untreated advanced renal cell carcinoma (MA review of TA645)

This report is a critique of the company’s submission (CS) to NICE from Merck on the clinical effectiveness and cost effectiveness of avelumab (Bavencio) in combination with axitinib (Inlyta) for treating adults with untreated advanced renal cell carcinoma (aRCC). It identifies the strengths and weakness of the CS

International refugee norm sabotage: an analysis of anti-migration groups in the UK

The 2015 Refugee Crisis and 2016 Brexit referendum created a window of opportunity for anti-migration groups to strategically advance restrictive immigration agenda in the United Kingdom (UK). In recent years, the UK has introduced immigration policy and legislation restricting numerous basic rights for refugee protection. Considering the explicit aim to create barriers for asylum claimants and reduce refugee rights, this article investigates how UK anti-migration groups contest established international refugee norms. A norm sabotage framework of discursive persuasion, refuting, resisting, and obfuscating mechanisms is applied to analyse how refugee norms of non-refoulement, non-penalisation, non-criminalisation, and non-detention are undermined by UK anti-migration groups. The analysis demonstrates UK anti-migration groups using a norm sabotage strategy to undermine international refugee norms and identifies radical right populist (RRP) ideology as a tool used as moral and political leverage to defend restricting refugee rights. The findings reflect on the Nationality and Borders Act, the Rwanda Partnership, and the Illegal Migration Act which challenge international obligations regarding refugee rights in the UK. IR literature often focuses on how norm contestation legitimises norms, this case study contributes a deeper understanding of how norm saboteur strategies are used by domestic actors to challenge and undermine the legitimacy of established international norms within the context of anti-migration activism targeting refugees and asylum seekers in the UK

Ableist institutions and party selection processes: exploring the political recruitment of disabled candidates

Political parties in the UK and elsewhere have, to varying degrees, tried to diversify the pool of candidates from which they can select. Attempts to eradicate the range of institutional and cultural barriers experienced by candidates from under-represented groups, such as women and racially minoritized communities, are beginning to bear fruit. However, less attention has been paid to the specific processes and norms which might make it harder for disabled people to get selected as candidates for elected office. Accordingly, this study takes the UK as its case study to address two inter related questions: 1) what are the political parties doing to make candidate selection more accessible for disabled people?; and 2) what are the experiences of disabled people who participate in the candidate selection process? Drawing upon qualitative analysis of formal party rules and processes, alongside interviews undertaken with over 80 disabled candidates, politicians, and party activists from across the political spectrum, we find a great deal of variation in party approaches. We also identify gaps between formal rules adopted to ensure accessibility and the experiences of disabled candidates. Along the way we also note some of the methodological and empirical challenges of studying candidate selection processes in relation to disability

Observation of σ–π coupling and mode selection in optically trapped artificial polariton molecules

Microcavity exciton–polariton condensates under additional transverse confinement constitute a flexible optical platform to study the coupling and hybridization between neighboring nonlinear states of light and matter. Driven farfrom equilibrium, networks of polariton condensates can display spontaneous synchronization, pattern formation,and instabilities depending on the excitation and material parameters. Here, we investigate this coupling mechanismbetween polariton condensates populating the first excited p-state manifold in optical traps and show a rich structure ofpatterns based on excitation parameters. Spontaneous symmetry breaking in the p-orbital manifold upon condensationresults in an ordered arrangement of dipole-shaped condensates between coupled traps reminiscent of σ and π molecular bonding mechanisms but restricted to the plane. A salient advantage is offered by the optical reconfigurability of thelaser excitation patterns, which determine the parameters of the polariton trapping potential and coupling strength withneighboring condensates. Our results underpin the potential role of polariton condensates in exploring the conditionsof spontaneous order in the relative orientation of anisotropic nonlinear states of light and matte

An analytical lower bound for a class of minimizing quadratic integer optimization problems

Lower bounds for minimization problems are essential for convergence of both branching-based and iterative solution methods for optimization problems. They also serve an important role in evaluating the quality of feasible solutions by providing conservative optimality gaps. We derive a closed-form analytical lower bound for a class of quadratic optimization problems with binary decision variables. Unlike traditional lower bounds obtained by solving relaxed models, our bound is purely analytical and does not require numerically solving any optimization problem. This is particularly valuable for problem instances that are too large to even formulate or load into a solver due to memory limitations. Further, we propose a greedy heuristic for obtaining feasible solutions. Together, the analytical bound and heuristic provide a provable optimality gap without solving any optimization model. Numerical experiments demonstrate that we can solve real-world large-scale instances, that were previously unsolvable due to memory limitations, in under a minute with provable optimality gaps of under 7%. For smaller instances where the optimal solution is computable, our greedy solutions are about 1% away from the optimal. These results highlight the practical value and scalability of our approach when direct solution methods are computationally prohibitive.</p

Effect of hypertension on long-term adverse clinical outcomes and liver fibrosis progression in MASLD

Background &amp; aims: hypertension is common in metabolic dysfunction-associated steatotic liver disease (MASLD), but its impact on long-term clinical outcomes and disease progression remains unclear. This study investigated the association of hypertension and risk of adverse clinical outcomes and progression of liver stiffness/fibrosis in MASLD.Methods: three multicenter prospective cohorts were analyzed: the UK BioBank (UKBB) cohort to assess the risk of adverse clinical outcomes, the VCTE-Prognosis cohort to assess liver stiffness/fibrosis progression, and the Paired Liver Biopsy cohort to assess histologic liver fibrosis progression. Adverse clinical outcomes were defined as all-cause mortality, cardiovascular events, and/or liver-related events. Liver stiffness progression was defined as an increase in liver stiffness measurement (LSM) from &lt;10 kPa to ≥10 kPa or an increase of ≥20% for baseline LSM ≥10 kPa. Liver fibrosis progression was defined as a 1-stage fibrosis stage increase. Cox regression and Kaplan-Meier analyses were used to evaluate the impact of baseline hypertension on the outcomes.Results: 107,316 adults from the UKBB cohort, 8,169 from the VCTE-Prognosis cohort, and 1,670 from the Paired Liver Biopsy cohort were included. Hypertension rates were 37.1%, 33.4%, and 48.9%, respectively. In the UKBB cohort, hypertension was associated with long-term adverse clinical outcomes (adjusted HR=1.30, 95%CI 1.26-1.33, P&lt;0.001). In the VCTE-Prognosis cohort, hypertension was associated with a higher risk of liver stiffness progression (adjusted HR=1.57, 95%CI 1.30-1.90, P&lt;0.001), while in the Paired Liver Biopsy cohort, hypertension was associated with a greater risk of histologic liver fibrosis progression (adjusted HR=1.41, 95%CI 1.12-1.78, P=0.004). Subgroup and sensitivity analyses supported these findings.Conclusions: hypertension is a modifiable risk factor and increases risk of adverse clinical outcomes and progression of liver stiffness/fibrosis

Optimization models for cumulative prospect theory under incomplete preference information

Prospect stochastic dominance conditions can be used to compare pairs of uncertain decision alternatives when the decision makers' choice behavior is characterized by cumulative prospect theory, but their preferences are not precisely specified. This paper extends the use of prospect stochastic dominance conditions to decision settings in which the use of pairwise comparisons is not possible due to large or possibly infinite number of decision alternatives (e.g., financial portfolio optimization). In particular, we first establish equivalence results between these conditions and the existence of solutions to a specific system of linear inequalities. We then utilize these results to develop stochastic optimization models whose feasible solutions are guaranteed to dominate a pre-specified benchmark distribution. These models can be used to identify if there exists a decision alternative within a set that is preferred to a given benchmark by all decision makers with an S-shaped value function and a pair of inverse S-shaped probability weighting functions. Thus, the models offer a flexible tool to analyze choice behavior in decision settings that can be modeled as optimization problems. We demonstrate the use of the developed models with two empirical applications in financial portfolio diversification and procurement optimization