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Browse the failure-mode index

19,875 real negative results, null findings, and replication failures · Negative / Null Result Report. Search the index →

WASTE indexes published research — it does not host or republish full papers. Each entry is a metadata record compiled from open scholarly databases; the abstract is shown in full only where the paper is openly licensed, otherwise a short excerpt under fair use. Classifications are automated and approximate.

Negative / Null Result Report

The Olfactory Stress Test does not replicate in a larger sample

Peter W. Schofield, John Attia, Houman Ebrahimi · 2023 · Alzheimer's & Dementia

Abstract Background We previously published data showing that olfactory test performance changes on the University of Pennsylvania Smell Identification Test (UPSIT) following an intranasal ‘challenge’ with the anticholinergic atropine (an…

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Negative / Null Result Report

Abstract 13428: The Long-term Therapy With Low-dose Aspirin Did Not Reduce Cardiovascular Events in Patients With Type 2 Diabetes in Primary Prevention Setting: 10-year Follow-up of a Randomized Controlled Trial

Yoshihiko Saito, Sadanori Okada, Hisao Ogawa et al. · 2016 · Circulation

Introduction: It takes long time to conclude efficacy and safety of an intervention on primary prevention of cardiovascular events. Up to date, there is no study to investigate the effect of the low-dose aspirin therapy on primary…

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Negative / Null Result ReportOpen accessComputer Science

Toward Reliable Machine Unlearning: Theory, Algorithms, and Evaluation

Ali Ebrahimpour-Boroojeny · 2025 · arXiv

We propose new methodologies for both unlearning random set of samples and class unlearning and show that they outperform existing methods. The main driver of our unlearning methods is the similarity of predictions to a retrained model on both the forget and remain samples. We introduce Adversarial Machine UNlearning (AMUN), which surpasses prior state-of-the-art methods for image classification based on SOTA MIA scores. AMUN lowers the model's confidence on forget samples by fine-tuning on their corresponding adversarial examples. Through theoretical analysis, we identify factors governing AM

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Negative / Null Result ReportOpen accessPhysics

Comparison of magnetic diffusion and reconnection in ideal and resistive relativistic magnetohydrodynamics, ideal magnetodynamics, and resistive force-free electrodynamics

Michael P. Grehan, Tanisha Ghosal, James R. Beattie et al. · 2025 · arXiv

High-energy astrophysical systems and compact objects are frequently modeled using ideal relativistic magnetohydrodynamic (MHD) or force-free electrodynamic (FFE) simulations, with the underlying assumption that the discretisation from the numerical scheme introduces an effective (numerical) magnetic resistivity that adequately resembles an explicit resistivity. However, it is crucial to note that numerical resistivity can fail to replicate essential features of explicit resistivity. In this study, we compare the 1D resistive decay and 2D reconnection properties of four commonly used physical

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Negative / Null Result Report

Myonuclei Can Replicate DNA

Agnieszka K. Borowik, Frederick F. Peelor, Esther E. Dupont‐Versteegden et al. · 2022 · The FASEB Journal

Skeletal muscle is a heterogenous tissue composed of multinucleated muscle fibers and accompanying supporting cells like satellite cells (SCs). Myonuclei are believed to be post‐mitotic, arrested in G0 phase of the cell cycle and thus…

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Negative / Null Result ReportOpen accessComputer Science

AnxietyFaceTrack: A Smartphone-Based Non-Intrusive Approach for Detecting Social Anxiety Using Facial Features

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

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Negative / Null Result ReportOpen accessComputer Science

A Concurrent CNN-RNN Approach for Multi-Step Wind Power Forecasting

Syed Kazmi, Berk Gorgulu, Mucahit Cevik et al. · 2023 · arXiv

Wind power forecasting helps with the planning for the power systems by contributing to having a higher level of certainty in decision-making. Due to the randomness inherent to meteorological events (e.g., wind speeds), making highly accurate long-term predictions for wind power can be extremely difficult. One approach to remedy this challenge is to utilize weather information from multiple points across a geographical grid to obtain a holistic view of the wind patterns, along with temporal information from the previous power outputs of the wind farms. Our proposed CNN-RNN architecture combine

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Negative / Null Result ReportMedicine

Amuc_1100 alleviates HFD-induced hepatic lipid accumulation via gut microbiota in zebrafish: insights from the role of intestinal 14-3-3β/α-A.

Ding, Lou, Liang et al. · 2026 · Journal of animal science and biotechnology

Amuc_1100, the most abundant outer membrane protein of Akkermansia muciniphila, alleviates high-fat diet (HFD)-induced hepatic lipid accumulation and modulates gut microbiota in fish; however, its mechanism and mediators remain unknown.…

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Negative / Null Result ReportOpen accessEngineering

Neuro-MoBRE: Exploring Multi-subject Multi-task Intracranial Decoding via Explicit Heterogeneity Resolving

Di Wu, Yifei Jia, Siyuan Li et al. · 2025 · arXiv

Neurophysiological decoding, fundamental to advancing brain-computer interface (BCI) technologies, has significantly benefited from recent advances in deep learning. However, existing decoding approaches largely remain constrained to single-task scenarios and individual subjects, limiting their broader applicability and generalizability. Efforts towards creating large-scale neurophysiological foundation models have shown promise, but continue to struggle with significant challenges due to pervasive data heterogeneity across subjects and decoding tasks. Simply increasing model parameters and da

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Negative / Null Result ReportOpen accessComputer Science

Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions

Emily L. Aiken, Andre T. Nguyen, Mauricio Santillana · 2019 · arXiv

We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Internet search data. We find that a GRU has lower prediction error than current state-of-the-art methods for data-driven influenza prediction at time horizons of over two weeks. In contrast with other machine learning approaches, the inclusion of real-time Internet search data does not improve GRU predictions.

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Negative / Null Result Report

Succession if the reorganization of corporation is failed

Arsenii Ivanovich Alekseev · 2024 · Право и политика

The article analyzes the consequences of the failed reorganization of the corporation and the recognition by the court of the reorganization of the corporation as invalid by referring to the nature and content of such categories as the…

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Negative / Null Result Report

Management of Failed Spinal Anesthesia

Wiji Hastuti · 2026 · Jurnal Komplikasi Anestesi

The management of failed spinal anesthesia is a significant challenge in surgical procedures. Spinal anesthesia failure can occur either partially or totally, often leading to conversion to general anesthesia, which adds clinical risks and…

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Negative / Null Result ReportOpen accessComputer Science

Hyperparameters in Continual Learning: A Reality Check

Sungmin Cha, Kyunghyun Cho · 2024 · arXiv

Continual learning (CL) aims to train a model on a sequence of tasks (i.e., a CL scenario) while balancing the trade-off between plasticity (learning new tasks) and stability (retaining prior knowledge). The dominantly adopted conventional evaluation protocol for CL algorithms selects the best hyperparameters (e.g., learning rate, mini-batch size, regularization strengths, etc.) within a given scenario and then evaluates the algorithms using these hyperparameters in the same scenario. However, this protocol has significant shortcomings: it overestimates the CL capacity of algorithms and relies

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Negative / Null Result ReportOpen accessComputer Science

Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing

Jiarui Xie, Mutahar Safdar, Andrei Mircea et al. · 2024 · arXiv

Machine learning (ML)-based cyber-physical systems (CPSs) have been extensively developed to improve the print quality of additive manufacturing (AM). However, the reproducibility of these systems, as presented in published research, has not been thoroughly investigated due to a lack of formal evaluation methods. Reproducibility, a critical component of trustworthy artificial intelligence, is achieved when an independent team can replicate the findings or artifacts of a study using a different experimental setup and achieve comparable performance. In many publications, critical information nec

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Negative / Null Result ReportOpen accessComputer Science

Dissecting Role Cognition in Medical LLMs via Neuronal Ablation

Xun Liang, Huayi Lai, Hanyu Wang et al. · 2025 · arXiv

Large language models (LLMs) have gained significant traction in medical decision support systems, particularly in the context of medical question answering and role-playing simulations. A common practice, Prompt-Based Role Playing (PBRP), instructs models to adopt different clinical roles (e.g., medical students, residents, attending physicians) to simulate varied professional behaviors. However, the impact of such role prompts on model reasoning capabilities remains unclear. This study introduces the RP-Neuron-Activated Evaluation Framework(RPNA) to evaluate whether role prompts induce disti

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Negative / Null Result ReportOpen accessPhysics

Dynamical Constraints on Mercury's Collisional Origin

Matthew S. Clement, Nathan A. Kaib, John E. Chambers · 2019 · arXiv

Of the solar system's four terrestrial planets, the origin of Mercury is perhaps the most mysterious. Modern numerical simulations designed to model the dynamics of terrestrial planet formation systematically fail to replicate Mercury; which possesses just 5% the mass of Earth and the highest orbital eccentricity and inclination among the planets. However, Mercury's large iron-rich core and low volatile inventory stand out among the inner planets, and seem to imply a violent collisional origin. Because most algorithms used for simulating terrestrial accretion do not consider the effects of col

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Negative / Null Result ReportOpen accessComputer Science

Human Preferences in Large Language Model Latent Space: A Technical Analysis on the Reliability of Synthetic Data in Voting Outcome Prediction

Sarah Ball, Simeon Allmendinger, Frauke Kreuter et al. · 2025 · arXiv

Generative AI (GenAI) is increasingly used in survey contexts to simulate human preferences. While many research endeavors evaluate the quality of synthetic GenAI data by comparing model-generated responses to gold-standard survey results, fundamental questions about the validity and reliability of using LLMs as substitutes for human respondents remain. Our study provides a technical analysis of how demographic attributes and prompt variations influence latent opinion mappings in large language models (LLMs) and evaluates their suitability for survey-based predictions. Using 14 different model

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