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Dealing with segmentation errors in needle reconstruction for MRI-guided brachytherapy
No full textBrachytherapy involves bringing a radioactive source near tumor tissue using implanted needles. Image-guided brachytherapy planning requires amongst others, the reconstruction of the needles. Manually annotating these needles on patient images can be a challenging and time-consuming task for medical professionals. For automatic needle reconstruction, a two-stage pipeline is commonly adopted, comprising a segmentation stage followed by a post-processing stage. While deep learning models are effective for segmentation, their results often contain errors. No currently existing post-processing technique is robust to all possible segmentation errors. We therefore propose adaptations to existing post-processing techniques mainly aimed at dealing with segmentation errors and thereby improving the reconstruction accuracy. Experiments on a prostate cancer dataset, based on MRI scans annotated by medical professionals, demonstrate that our proposed adaptations can help to effectively manage segmentation errors, with the best adapted post-processing technique achieving median needle-tip and needle-bottom point localization errors of 1.07 (IQR ±1.04) mm and 0.43 (IQR ±0.46) mm, respectively, and median shaft error of 0.75 (IQR ±0.69) mm with 0 false positive and 0 false negative needles on a test set of 261 needles
ALL-IN meta-analysis: breathing life into living systematic reviews and prospective meta-analyses
Full text linkScience is justly admired as a cumulative process (“standing on the shoulders of giants”), yet scientific knowledge is typically built on a patchwork of research contributions without much coordination. This lack of efficiency has specifically been addressed in clinical research by recommendations against avoidable research waste and for living systematic reviews and prospective meta-analysis. We propose to further those recommendations with ALL-IN meta-analysis: Anytime Live and Leading INterim meta-analysis. ALL-IN provides meta-analysis based on e-values and anytime-valid confidence intervals that can be updated at any time—reanalyzing after each new observation while retaining type-I error and coverage guarantees, live—no need to prespecify the looks, and leading—in the decisions on whether individual studies should be initiated, stopped or expanded, the meta-analysis can be the leading source of information without losing validity to accumulation bias. The analysis design requires no information about the trial sample sizes or the number of trials eventually included. So ALL-IN meta-analysis can be applied retrospectively as well as prospectively, to evaluate the evidence once or sequentially. Because the intention of the analysis does not change the validity of the results, the results of the analysis can change the intentions (‘optional stopping’ and ‘optional continuation’ based on the results so far). On the one hand: any analysis can be turned into a living one, or even become prospective and real-time by updating with new trial data and including interim data from trials that are still ongoing — without any changes in the cut-offs for testing or the method for interval estimation. On the other hand: no stopping rule needs to be enforced for the analysis to remain valid, so participating in a prospective meta-analysis does not require outside control over data collection. Hence ALL-IN meta-analysis breathes life into living systematic reviews, and offers better and simpler statistics, efficiency, collaboration and communication
AC-IND: Sparse CT reconstruction based on attenuation coefficient estimation and implicit neural distribution
No full textComputed tomography (CT) reconstruction plays a crucial role in industrial nondestructive testing and medical diagnosis. Sparse view CT reconstruction aims to reconstruct high-quality CT images while only using a small number of projections, which helps to improve the detection speed of industrial assembly lines and is also meaningful for reducing radiation in medical scenarios. Sparse CT reconstruction methods based on implicit neural representations (INRs) have recently shown promising performance, but still produce artifacts because of the difficulty of obtaining useful prior information. In this work, we incorporate a powerful prior: the total number of material categories of objects. To utilize the prior, we design AC-IND, a self-supervised method based on Attenuation Coefficient Estimation and Implicit Neural Distribution. Specifically, our method first transforms the traditional INR from scalar mapping to probability distribution mapping. Then we design a compact attenuation coefficient estimator initialized with values from a rough reconstruction and fast segmentation. Finally, our algorithm finishes the CT reconstruction by jointly optimizing the estimator and the generated distribution. Through experiments, we find that our method not only outperforms the comparative methods in sparse CT reconstruction but also can automatically generate semantic segmentation maps. Code is available at https://github.com/AIIAAI/AC-IND
cocydimo: Conducting Cylinder Discharge Model
No full textThis code implements a reduced discharge model for streamer discharges, in which the channels are represented by a collection of conducting cylindrical segments. The conductivity and electric potential together evolve on a numerical mesh
Bayesian reinforcement learning to optimize paid ancillary revenue in the airline industry
No full textTo optimize the pricing of paid ancillary seats, we adopt a revenue management approach that optimizes over the capacity of these seats while accounting for unknown underlying model parameters. We test various models against a simulation model to assess the performance against wide-ranging input parameters. We demonstrate that using a Bayesian exponential demand model to describe the relationship between price and seats sold, combined with a Bayesian reinforcement learning approach to estimate its parameters, outperforms other approaches. By using a relatively simple demand model with a limited number of parameters, updating in a Bayesian manner, and in one step estimating demand parameters to directly use for price optimization, the model is quickly able to perform well across a wide range of demand scenarios
Transporting household waste over water can reduce costs and emissions: A case study in the Netherlands
Full text linkInland waterways can be an attractive under-utilized alternative to road transport. In the current situation, heavy trucks transport residual household waste from municipalities to incineration plants around the Netherlands. Logistics research groups have suggested using barge pushing ships for household waste transport to reduce emissions and costs. We analyze this suggestion for a case study involving the residual household waste of 55 municipalities across three provinces in the Netherlands. A Mixed Integer Linear Programming formulation is used to find the optimal combination of trucks and barge pushing ships in this waste network. The results demonstrate that adopting electric pusher ships can achieve significant reductions in costs (19%), emissions (41%), and waste carrying truck traffic (48%), compared to truck-only solutions. Diesel cargo ships are also shown to outperform truck-only approaches but are less effective than electric alternatives in most metrics. Sensitivity analysis shows that the solutions are fairly robust to parameter variations
News, with a twist: Using contrastive learning to improve user embeddings for diverse news recommendations
Full text linkNews recommender systems (NRS) play a key role in delivering personalised content in fast-paced, high-volume environments. However, models optimised solely for accuracy often overlook important societal objectives such as fairness and diversity, leading to over-personalisation, biased exposure, and narrow content consumption. In this paper, we propose a contrastive learning framework for improving user representations in neural news recommendation. We build upon a bi‑encoder architecture and introduce self-supervised objectives that group semantically related news items by theme, encouraging the model to bring similar items closer in the embedding space while pushing dissimilar ones apart. This strategy mitigates embedding collapse and guides the model toward producing recommendations with broader topical coverage.
We evaluate our approach on the MIND dataset, comparing against state-of-the-art neural models, including LSTUR and NAML. Our results show that the proposed method achieves competitive accuracy and yields measurable improvements in beyond-accuracy objectives, particularly in content diversity and exposure fairness. Our results demonstrate the potential of contrastive learning to support more balanced and responsible news recommendations
Quantum PCPs: on adaptivity, multiple provers and reductions to local Hamiltonians
Full text linkWe define a general formulation of quantum PCPs, which captures adaptivity and multiple unentangled provers, and give a detailed construction of the quantum reduction to a local Hamiltonian with a constant promise gap. This reduction turns out to be a versatile subroutine to prove properties of quantum PCPs, allowing us to show: (i) Non-adaptive quantum PCPs can simulate adaptive quantum PCPs when the number of proof queries is constant. In fact, this can even be shown to hold when the non-adaptive quantum PCP picks the proof indices uniformly at random from a subset of all possible index combinations, answering an open question by Aharonov, Arad, Landau and Vazirani (STOC’09). (ii) If the q-local Hamiltonian problem with constant promise gap can be solved in QCMA, then QPCP[q] ⊆ QCMA for any q ∈ O(1). (iii) If QMA[k] has a quantum PCP for any 2 ≤ k ≤ poly(n), then QMA[2] = QMA, connecting two of the longest-standing open problems in quantum complexity theory. Moreover, we also show that there exist (quantum) oracles relative to which certain quantum PCP statements are false. Hence, any attempt to prove the quantum PCP conjecture requires, just as was the case for the classical PCP theorem, (quantumly) non-relativizing techniques
