A searchable index of real negative results, null findings, and replication failures from the published literature — so you can learn what didn't work before repeating it.
WASTE indexes published research — it does not host or republish full papers. Each entry is a metadata record (title, authors, DOI) compiled from open scholarly databases, with the abstract shown in full only where the paper is openly licensed (e.g. Creative Commons); otherwise a short excerpt is shown for reference under fair use. WASTE classifies each work by failure type; classifications are automated and approximate.
741 results for "aging" · page 20 of 25
Nikhil N. Verma · 2016 · Arthroscopy
Abstract There is considerable current interest in the role of the anterolateral ligament in persistent instability after anterior cruciate ligament reconstruction. The normal ligament may be identified using magnetic resonance imaging or…
Nikhil N. Verma · 2016 · Arthroscopy
Abstract Identification of symptomatic long head biceps pathology continues to be a clinical challenge. Magnetic resonance imaging may fail to identify symptomatic lesions when present. Clinicians must maintain a high index of suspicion,…
W Reay · 2025 · International Journal of Neuropsychopharmacology
Abstract Background One mechanism that holds great promise to drive drug development or drug repurposing in schizophrenia is through leveraging human genetics. Recent retrospective analyses of decades of clinical trial data have…
William Zhang, Wan Shen Lim, Andrew Pavlo · 2026 · Proceedings of the ACM on Management of Data
Tuning database management systems (DBMSs) is challenging due to trillions of possible configurations and evolving workloads. Recent advances in tuning have led to breakthroughs in optimizing over the possible configurations. However, due…
Anna Prucnal · 2025 · International Journal of Business & Management Studies
This study examines the effectiveness of the monetary policy of the European Central Bank (ECB) in managing inflation in the Euro Area compared to the independent monetary policy of seven European Union Member States outside the Economic…
Daniel I. Rhon, Tina A. Greenlee, Rachel Mayhew et al. · 2022 · Journal of the American Academy of Orthopaedic Surgeons
Introduction: After elective orthopaedic surgery, many individuals go on to become long-term opioid users. Mitigating this risk has become a priority for surgeons, other members of the medical care team, and healthcare systems. The purpose…
Bill Cheuk Long Chan, Billy Lee · 2024 · Studia sportiva
Managing the balance of academic and athletic responsibilities at university is a serious challenge for student athletes. This phenomenological case study illuminates how one individual successfully managed his study-sport balance at…
Xin Ding, Yongwei Wang, Zuheng Xu · 2023 · arXiv
Continuous Conditional Generative Adversarial Networks (CcGANs) enable generative modeling conditional on continuous scalar variables (termed regression labels). However, they can produce subpar fake images due to limited training data. Although Negative Data Augmentation (NDA) effectively enhances unconditional and class-conditional GANs by introducing anomalies into real training images, guiding the GANs away from low-quality outputs, its impact on CcGANs is limited, as it fails to replicate negative samples that may occur during the CcGAN sampling. We present a novel NDA approach called Dua
Nilesh Kumar Sahu, Snehil Gupta, Haroon R Lone · 2025 · arXiv
Social Anxiety Disorder (SAD) is a widespread mental health condition, yet its lack of objective markers hinders timely detection and intervention. While previous research has focused on behavioral and non-verbal markers of SAD in structured activities (e.g., speeches or interviews), these settings fail to replicate real-world, unstructured social interactions fully. Identifying non-verbal markers in naturalistic, unstaged environments is essential for developing ubiquitous and non-intrusive monitoring solutions. To address this gap, we present AnxietyFaceTrack, a study leveraging facial video
David M. Hernandez, Eric Agol, Matthew J. Holman et al. · 2021 · arXiv
Symplectic integrators are widely used for the study of planetary dynamics and other $N$-body problems. In a study of the outer Solar system, we demonstrate that individual symplectic integrations can yield biased errors in the semi-major axes and possibly other orbital elements. The bias is resolved by studying an ensemble of initial conditions of the outer Solar system. Such statistical sampling could significantly improve measurement of planetary system properties like their secular frequencies. We also compared the distributions of action-like variables between high and low accuracy integr
Keith Harrigian, Tina Tang, Anthony Gonzales et al. · 2023 · arXiv
Diabetic eye disease is a major cause of blindness worldwide. The ability to monitor relevant clinical trajectories and detect lapses in care is critical to managing the disease and preventing blindness. Alas, much of the information necessary to support these goals is found only in the free text of the electronic medical record. To fill this information gap, we introduce a system for extracting evidence from clinical text of 19 clinical concepts related to diabetic eye disease and inferring relevant attributes for each. In developing this ophthalmology phenotyping system, we are also afforded
Michael A. Cole, Aristeidis Lamprianidis, Ilya V. Shadrivov et al. · 2018 · arXiv
Metasurfaces are an enabling technology for complex wave manipulation functions, including in the terahertz frequency range, where they are expected to advance security, imaging, sensing, and communications technology. For operation in transmission, Huygens' metasurfaces are commonly used, since their good impedance match to the surrounding media minimizes reflections and maximizes transmission. Recent theoretical work has shown that Huygens' metasurfaces are non-optimal, particularly for large angles of refraction, and that to eliminate reflections and spurious diffracted beams it is necessar
Tristan Piater, Niklas Penzel, Gideon Stein et al. · 2024 · arXiv
A substantial body of research has focused on developing systems that assist medical professionals during labor-intensive early screening processes, many based on convolutional deep-learning architectures. Recently, multiple studies explored the application of so-called self-attention mechanisms in the vision domain. These studies often report empirical improvements over fully convolutional approaches on various datasets and tasks. To evaluate this trend for medical imaging, we extend two widely adopted convolutional architectures with different self-attention variants on two different medical
Amir Yunus, Peng Rend Gay, Oon Teng Lee · 2025 · arXiv
This study examines the development and deployment of a Generative AI proof-of-concept (POC) designed to support lecturers in a vocational education setting in Singapore. Employing a user-centred, mixed-methods design process, we co-developed an AI chatbot with lecturers to address recurring instructional challenges during exam preparation, specifically managing repetitive questions and scaling feedback delivery. The POC achieved its primary operational goals: lecturers reported streamlined workflows, reduced cognitive load, and observed improved student confidence in navigating course content
Nima Akhlaghi, Ananya Dhawan, Amir A. Khan et al. · 2018 · arXiv
Objective: The objectives of this paper are to determine the optimal location for ultrasound transducer placement on the anterior forearm for imaging maximum muscle deformations during different hand motions and to investigate the effect of using a sparse set of ultrasound scanlines for motion classification for ultrasound-based muscle computer interfaces (MCIs). Methods: The optimal placement of the ultrasound transducer along the forearm is identified using freehand 3D reconstructions of the muscle thickness during rest and motion completion. From the ultrasound images acquired from the opti
Denis Boyer, Peter D. Walsh · 2010 · arXiv
Thanks to recent technological advances, it is now possible to track with an unprecedented precision and for long periods of time the movement patterns of many living organisms in their habitat. The increasing amount of data available on single trajectories offers the possibility of understanding how animals move and of testing basic movement models. Random walks have long represented the main description for micro-organisms and have also been useful to understand the foraging behaviour of large animals. Nevertheless, most vertebrates, in particular humans and other primates, rely on sophistic
Weronika Ormaniec, Michael Vollenweider, Elisa Hoskovec · 2025 · arXiv
In this paper, we explore the idea of combining GCNs into one model. To that end, we align the weights of different models layer-wise using optimal transport (OT). We present and evaluate three types of transportation costs and show that the studied fusion method consistently outperforms the performance of vanilla averaging. Finally, we present results suggesting that model fusion using OT is harder in the case of GCNs than MLPs and that incorporating the graph structure into the process does not improve the performance of the method.
Felix Krejca, Tobias Kietreiber, Alexander Buchelt et al. · 2025 · arXiv
The increasing volume of online discussions requires advanced automatic content moderation to maintain responsible discourse. While hate speech detection on social media is well-studied, research on German-language newspaper forums remains limited. Existing studies often neglect platform-specific context, such as user history and article themes. This paper addresses this gap by developing and evaluating binary classification models for automatic content moderation in German newspaper forums, incorporating contextual information. Using LSTM, CNN, and ChatGPT-3.5 Turbo, and leveraging the One Mi
Orhan B, Nauta M · 2026 · Death studies
Meaning Management Theory suggests that engaging with meaning-related resources helps individuals constructively regulate consciously experienced death anxiety. Consistent with that theory, we tested whether a single-session, online…
Louis Jalouzot, Alexis Thual, Yair Lakretz et al. · 2025 · arXiv
We investigate optimal strategies for decoding perceived natural speech from fMRI data acquired from a limited number of participants. Leveraging Lebel et al. (2023)'s dataset of 8 participants, we first demonstrate the effectiveness of training deep neural networks to predict LLM-derived text representations from fMRI activity. Then, in this data regime, we observe that multi-subject training does not improve decoding accuracy compared to single-subject approach. Furthermore, training on similar or different stimuli across subjects has a negligible effect on decoding accuracy. Finally, we fin
Jose A. Rivera, Carmen E. Gonzalez, Tonita Bates · 2024 · JCO Oncology Practice
327 Background: Delayed assessment of diagnostic imaging (DI) significant actionable findings (AFs) at an oncological center prompted the creation of a safety net system which used technological advancements to improve communication…
Jan Tiemann, Matthew McGinity, Ulrik Günther · 2026 · arXiv
In contemporary biology and medicine, 3D microscopy is one of the most widely-used techniques for imaging and manipulation of various kinds of samples. Navigating such a micrometer-sized, 3-dimensional sample under the microscope -- e.g. to find relevant imaging regions -- can pose a tedious challenge for the experimenter. In this paper, we examine whether 2D desktop, 3D desktop, or Virtual Reality (VR) interfaces provide the best user experience and performance for the exploration of 3D samples. We invited 12 skilled microscope operators to perform two different exploration tasks in 2D, 3D an
Arjun D. Desai, Francesco Caliva, Claudia Iriondo et al. · 2020 · arXiv
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Simil
Zhen Xuen Brandon Low, Rory Zhang, Hang Min et al. · 2025 · arXiv
Enlarged perivascular spaces (PVS) are increasingly recognized as biomarkers of cerebral small vessel disease, Alzheimer's disease, stroke, and aging-related neurodegeneration. However, manual segmentation of PVS is time-consuming and subject to moderate inter-rater reliability, while existing automated deep learning models have moderate performance and typically fail to generalize across diverse clinical and research MRI datasets. We adapted MedNeXt-L-k5, a Transformer-inspired 3D encoder-decoder convolutional network, for automated PVS segmentation. Two models were trained: one using a homog
John T. Holodnak, Allan B. Wollaber · 2021 · arXiv
Multiple techniques for producing calibrated predictive probabilities using deep neural networks in supervised learning settings have emerged that leverage approaches to ensemble diverse solutions discovered during cyclic training or training from multiple random starting points (deep ensembles). However, only a limited amount of work has investigated the utility of exploring the local region around each diverse solution (posterior mode). Using three well-known deep architectures on the CIFAR-10 dataset, we evaluate several simple methods for exploring local regions of the weight space with re
Tim Gould · 2012 · arXiv
The "ACFD-RPA" correlation energy functional has been widely applied to a variety of systems to successfully predict energy differences, and less successfully predict absolute correlation energies. Here we present a parameter-free exchange-correlation kernel that systematically improves absolute correlation energies, while maintaining most of the good numerical properties that make the ACFD-RPA numerically tractable. The "RXH" kernel is constructed to approximate the true exchange kernel via a carefully weighted, easily computable radial averaging. Correlation energy errors of atoms with two t
Uchechi Vanessa Alintah-Abel, Francisca Nkachukwu Okeke, Eucharia Chika Enebe · 2025 · British Journal of Multidisciplinary and Advanced Studies
The goal of most countries is the desire to maintain a stable price level of goods and services. This however, appears to be an uphill task given the incidence of subsidy removal that is presently ravaging the developing economies of the…
· 2025 · European Journal of Clinical Pharmacy
Pediatric nephrolithiasis is an increasing concern and often requires complete stone clearance due to high recurrence risk. Managing large renal stones (>2 cm), lower calyceal stones (>1 cm), and stones refractory to Extracorporeal Shock…
Ariane Kovac · 2024 · AЯGOS
Divine healing is an emotionally and theologically conflictive field where actors communicate positions and draw boundaries by engaging in certain practices and renouncing others. In this article, I analyse how a progressive evangelical…
Valerie Keller · 2023 · Frontiers in Sociology
This article explored the ways in which living with dementia brings potentials to queer the concept of “successful aging” and associated notions of being human. Regarding the progressive development of dementia, it can be assumed that…