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Browse the failure-mode index

21,309 real negative results, null findings, and replication failures. 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 ReportOpen accessComputer Science

Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free

Syomantak Chaudhuri, Thomas A. Courtade · 2023 · arXiv

Differential Privacy (DP) is a well-established framework to quantify privacy loss incurred by any algorithm. Traditional DP formulations impose a uniform privacy requirement for all users, which is often inconsistent with real-world scenarios in which users dictate their privacy preferences individually. This work considers the problem of mean estimation under heterogeneous DP constraints, where each user can impose their own distinct privacy level. The algorithm we propose is shown to be minimax optimal when there are two groups of users with distinct privacy levels. Our results elicit an in

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Negative / Null Result ReportOpen accessComputer Science

Determination of the Source Flavor Ratio of Ultrahigh Energy Neutrinos

T. C. Liu, M. A. Huang, Guey-Lin Lin · 2010 · arXiv

We discuss the reconstruction of neutrino flavor neutrino at a distant source in the very high en- ergy regime. This reconstruction procedure is relevant to the confirmation of detecting cosmogenic neutrinos, for example. To facilitate such a reconstruction, it is imperative to achieve effective flavor discriminations in terrestrial neutrino telescopes. We note that, for energies beyond few tens of PeV, a tau-lepton behaves like a track similar to a muon. Hence, while it is rather challenging to separate νμ from ντ in this case, one can expect to isolate νe from the rest by a distinctive showe

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Negative / Null Result ReportOpen accessComputer Science

Multiple-shot labeling of quantum observables

Seyed Arash Ghoreishi, Nidhin Sudarsanan Ragini, Sk Sazim et al. · 2024 · arXiv

Quantum labeling tasks ask one to recover the missing associations between classical outcome labels and the effects forming the POVM. We study labeling in the multiple-shot regime, allowing a finite number of uses of the device and the most general tester-based strategies, including adaptivity. For binary observables, we show that if perfect labeling is impossible in a single shot, then it remains impossible with any finite number of shots. In particular, we derive the formula for minimum-error performance and highlight its ``even-odd" behavior. For non-binary observables, we derive the optima

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Negative / Null Result ReportOpen accessPhysics

Good and Proper: Self-similarity of N-body Simulations with Proper Force Softening

Lehman H. Garrison, Michael Joyce, Daniel J. Eisenstein · 2021 · arXiv

Analysis of self-similarity in scale-free $N$-body simulations reveals the spatial and temporal scales for which statistics measured in cosmological simulations are converged to the physical continuum limit. We examine how the range of scales in which the two-point correlation function is converged depends on the force softening length and whether it is held constant in comoving or proper coordinates. We find that a proper softening that reaches roughly 1/30th of the inter-particle spacing by the end of the simulation resolves the same spatial and temporal scales as a comoving softening of the

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Negative / Null Result ReportOpen accessComputer Science

ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge

Michele Filannino, Gavin Brown, Goran Nenadic · 2013 · arXiv

This paper describes a temporal expression identification and normalization system, ManTIME, developed for the TempEval-3 challenge. The identification phase combines the use of conditional random fields along with a post-processing identification pipeline, whereas the normalization phase is carried out using NorMA, an open-source rule-based temporal normalizer. We investigate the performance variation with respect to different feature types. Specifically, we show that the use of WordNet-based features in the identification task negatively affects the overall performance, and that there is no

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Negative / Null Result ReportOpen accessComputer Science

LLaMa-SciQ: An Educational Chatbot for Answering Science MCQ

Marc-Antoine Allard, Matin Ansaripour, Maria Yuffa et al. · 2024 · arXiv

Large Language Models (LLMs) often struggle with tasks requiring mathematical reasoning, particularly multiple-choice questions (MCQs). To address this issue, we developed LLaMa-SciQ, an educational chatbot designed to assist college students in solving and understanding MCQs in STEM fields. We begin by fine-tuning and aligning the models to human preferences. After comparing the performance of Mistral-7B and LLaMa-8B, we selected the latter as the base model due to its higher evaluation accuracy. To further enhance accuracy, we implement Retrieval-Augmented Generation (RAG) and apply quantiza

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Negative / Null Result ReportOpen accessComputer Science

Maximizing Prefix-Confidence at Test-Time Efficiently Improves Mathematical Reasoning

Matthias Otth, Jonas Hübotter, Ido Hakimi et al. · 2025 · arXiv

Recent work has shown that language models can self-improve by maximizing their own confidence in their predictions, without relying on external verifiers or reward signals. In this work, we study the test-time scaling of language models for mathematical reasoning tasks, where the model's own confidence is used to select the most promising attempts. Surprisingly, we find that we can achieve significant performance gains by continuing only the most promising attempt, selected by the model's prefix-confidence. We systematically evaluate prefix-confidence scaling on five mathematical reasoning da

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Negative / Null Result ReportOpen accessComputer Science

Long-Tail Crisis in Nearest Neighbor Language Models

Yuto Nishida, Makoto Morishita, Hiroyuki Deguchi et al. · 2025 · arXiv

The $k$-nearest-neighbor language model ($k$NN-LM), one of the retrieval-augmented language models, improves the perplexity for given text by directly accessing a large datastore built from any text data during inference. A widely held hypothesis for the success of $k$NN-LM is that its explicit memory, i.e., the datastore, enhances predictions for long-tail phenomena. However, prior works have primarily shown its ability to retrieve long-tail contexts, leaving the model's performance remain underexplored in estimating the probabilities of long-tail target tokens during inference. In this paper

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Negative / Null Result ReportOpen accessComputer Science

Perfect quantum excitation energy transport via single edge perturbation in a complete network

Hassan Bassereh, Vahid Salari, Farhad Shahbazi et al. · 2015 · arXiv

We consider quantum excitation energy transport (EET) in a network of two-state nodes in the Markovian approximation by employing the Lindblad formulation. We find that EET from an initial site, where the excitation is inserted to the sink, is generally inefficient due to the inhibition of transport by localization of the excitation wave packet in a symmetric, fully-connected network. We demonstrate that the EET efficiency can be significantly increased up to %100 by perturbing hopping transport between the initial node and the one connected directly to the sink, while the rate of energy trans

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Negative / Null Result ReportOpen accessComputer Science

DVB-S2 Spectrum Efficiency Improvement with Hierarchical Modulation

Hugo Meric, Jose Miguel Piquer · 2013 · arXiv

We study the design of a DVB-S2 system in order to maximise spectrum efficiency. This task is usually challenging due to channel variability. Modern satellite communications systems such as DVB-SH and DVB-S2 rely mainly on a time sharing strategy to optimise the spectrum efficiency. Recently, we showed that combining time sharing with hierarchical modulation can provide significant gains (in terms of spectrum efficiency) compared to the best time sharing strategy. However, our previous design does not improve the DVB-S2 performance when all the receivers experience low or large signal-to-noise

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Negative / Null Result ReportOpen accessComputer Science

Is Bottom-Up Attention Useful for Scene Recognition?

Samuel F. Dodge, Lina J. Karam · 2013 · arXiv

The human visual system employs a selective attention mechanism to understand the visual world in an eficient manner. In this paper, we show how computational models of this mechanism can be exploited for the computer vision application of scene recognition. First, we consider saliency weighting and saliency pruning, and provide a comparison of the performance of different attention models in these approaches in terms of classification accuracy. Pruning can achieve a high degree of computational savings without significantly sacrificing classification accuracy. In saliency weighting, however,

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Negative / Null Result ReportOpen accessComputer Science

Towards Deep Robot Learning with Optimizer applicable to Non-stationary Problems

Taisuke Kobayashi · 2020 · arXiv

This paper proposes a new optimizer for deep learning, named d-AmsGrad. In the real-world data, noise and outliers cannot be excluded from dataset to be used for learning robot skills. This problem is especially striking for robots that learn by collecting data in real time, which cannot be sorted manually. Several noise-robust optimizers have therefore been developed to resolve this problem, and one of them, named AmsGrad, which is a variant of Adam optimizer, has a proof of its convergence. However, in practice, it does not improve learning performance in robotics scenarios. This reason is h

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Negative / Null Result ReportOpen accessComputer Science

Background Knowledge Grounding for Readable, Relevant, and Factual Biomedical Lay Summaries

Domenic Rosati · 2023 · arXiv

Communication of scientific findings to the public is important for keeping non-experts informed of developments such as life-saving medical treatments. However, generating readable lay summaries from scientific documents is challenging, and currently, these summaries suffer from critical factual errors. One popular intervention for improving factuality is using additional external knowledge to provide factual grounding. However, it is unclear how these grounding sources should be retrieved, selected, or integrated, and how supplementary grounding documents might affect the readability or rele

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Negative / Null Result ReportOpen accessComputer Science

Non-unitary neutrino mixing in the NO$ν$A near detector data

Ushak Rahaman, Soebur Razzaque · 2021 · arXiv

The $ν_μ\to ν_e$ oscillation probability over short baseline ($\lesssim 1$~km) would be negligible in case the mixing matrix for three active neutrinos is unitary. However, in case of non-unitary mixing of three neutrinos, this probability would be non-negligible due to the so-called "zero distance" effect. Hence, the near detector of the accelerator experiments such as NO$ν$A can provide strong constraints on the parameters of the non-unitary mixing with very large statistics. By analyzing the NO$ν$A near detector data we find that the non-unitary mixing does not improve fits to the $ν_e$ or

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Negative / Null Result ReportOpen accessComputer Science

Joint-sparse recovery from multiple measurements

Ewout van den Berg, Michael P. Friedlander · 2009 · arXiv

The joint-sparse recovery problem aims to recover, from sets of compressed measurements, unknown sparse matrices with nonzero entries restricted to a subset of rows. This is an extension of the single-measurement-vector (SMV) problem widely studied in compressed sensing. We analyze the recovery properties for two types of recovery algorithms. First, we show that recovery using sum-of-norm minimization cannot exceed the uniform recovery rate of sequential SMV using $\ell_1$ minimization, and that there are problems that can be solved with one approach but not with the other. Second, we analyze

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Negative / Null Result Report

No significant effect of acupuncture on assisted pregnancy

L Rose · 2011 · Focus on Alternative and Complementary Therapies

Andersen D, Løssl K, Andersen AN, Fürbringer J, Bach H, Simonsen J, Larsen EC. Acupuncture on the day of embryo transfer: a randomized controlled trial of 635 patients. Reprod Biomed Online 2010; 21: 366–372.

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Negative / Null Result Report

Effect of Latitude As a Significant Element on the Results of Direct UTM Coordinates Transformation Method

Mohammed Anwer Jassim, Darin Mohammed Tofiq Mohammed · 2023 · Photogrammetric Engineering & Remote Sensing

The transformation of global navigation satellite systems coordi- nates between any two datums represents a vital procedure. Although the geocentric coordinates (X, Y, Z) of two datums can be trans- formed using the 7-parameters method,…

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Negative / Null Result Report

Effect of significant ventricular septal defect on the left atrial function and strain using three-dimensional speckle tracking echocardiography

H Nossier, E Ali, H Attia et al. · 2026 · European Heart Journal - Cardiovascular Imaging

Abstract Introduction Among the various types of congenital heart diseases (CHDs), significant left-to-right shunts such as ventricular septal defects (VSDs) alter normal cardiac physiology and hemodynamics, leading to increased volume…

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