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Failure-mode index

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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.

1894 results for "Mpro" · page 18 of 64

Negative / Null Result ReportOpen accessComputer Science

Learning Robust State Abstractions for Hidden-Parameter Block MDPs

Amy Zhang, Shagun Sodhani, Khimya Khetarpal et al. · 2020 · arXiv

Many control tasks exhibit similar dynamics that can be modeled as having common latent structure. Hidden-Parameter Markov Decision Processes (HiP-MDPs) explicitly model this structure to improve sample efficiency in multi-task settings. However, this setting makes strong assumptions on the observability of the state that limit its application in real-world scenarios with rich observation spaces. In this work, we leverage ideas of common structure from the HiP-MDP setting, and extend it to enable robust state abstractions inspired by Block MDPs. We derive instantiations of this new framework f

Negative / Null Result ReportOpen accessComputer Science

Fully Dynamic Algorithms for Minimum Weight Cycle and Related Problems

Adam Karczmarz · 2021 · arXiv

We consider the directed minimum weight cycle problem in the fully dynamic setting. To the best of our knowledge, so far no fully dynamic algorithms have been designed specifically for the minimum weight cycle problem in general digraphs. One can achieve $\tilde{O}(n^2)$ amortized update time by simply invoking the fully dynamic APSP algorithm of Demetrescu and Italiano [J. ACM'04]. This bound, however, yields no improvement over the trivial recompute-from-scratch algorithm for sparse graphs. Our first contribution is a very simple deterministic $(1+ε)$-approximate algorithm supporting vertex

Negative / Null Result ReportOpen accessComputer Science

ArchAgent: Agentic AI-driven Computer Architecture Discovery

Raghav Gupta, Akanksha Jain, Abraham Gonzalez et al. · 2026 · arXiv

Agile hardware design flows are a critically needed force multiplier to meet the exploding demand for compute. Recently, agentic generative AI systems have demonstrated significant advances in algorithm design, improving code efficiency, and enabling discovery across scientific domains. Bridging these worlds, we present ArchAgent, an automated computer architecture discovery system built on AlphaEvolve. We show ArchAgent's ability to automatically design/implement state-of-the-art (SoTA) cache replacement policies (architecting new mechanisms/logic, not only changing parameters), broadly withi

Negative / Null Result ReportOpen accessComputer Science

On Ray Shooting for Triangles in 3-Space and Related Problems

Esther Ezra, Micha Sharir · 2021 · arXiv

We consider several problems that involve lines in three dimensions, and present improved algorithms for solving them. The problems include (i) ray shooting amid triangles in $R^3$, (ii) reporting intersections between query lines (segments, or rays) and input triangles, as well as approximately counting the number of such intersections, (iii) computing the intersection of two nonconvex polyhedra, (iv) detecting, counting, or reporting intersections in a set of lines in $R^3$, and (v) output-sensitive construction of an arrangement of triangles in three dimensions. Our approach is based on the

Negative / Null Result ReportOpen accessPhysics

Beyond the RPA on the cheap: improved correlation energies with the efficient "Radial Exchange Hole" kernel

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

Negative / Null Result ReportOpen accessComputer Science

On the Design and Optimization of a Quantum Polynomial-Time Attack on Elliptic Curve Cryptography

Donny Cheung, Dmitri Maslov, Jimson Mathew et al. · 2007 · arXiv

We consider a quantum polynomial-time algorithm which solves the discrete logarithm problem for points on elliptic curves over $GF(2^m)$. We improve over earlier algorithms by constructing an efficient circuit for multiplying elements of binary finite fields and by representing elliptic curve points using a technique based on projective coordinates. The depth of our proposed implementation, executable in the Linear Nearest Neighbor (LNN) architecture, is $O(m^2)$, which is an improvement over the previous bound of $O(m^3)$ derived assuming no architectural restrictions.

Negative / Null Result ReportOpen accessPhysics

Path-Extrema Upper Bounds on Mean Entropy Production

Surachate Limkumnerd · 2026 · arXiv

Fluctuation relations imply the second-law inequality $\langleΣ_T\rangle\ge0$, but path extrema can also constrain how large the mean entropy production can be. For steady-state processes with entropy-production martingale $M_t=e^{-Σ_t}$, we show that knowing only the positive running maximum of $Σ_t$ gives no improvement over the trivial endpoint bound: rare negative entropy-production excursions can still carry the exponential weight required by the fluctuation relation. Using the running extrema $L_T=\inf M_t$ and $H_T=\sup M_t$, we derive a path-extrema upper envelope $\mathcal{U}_{\rm ext

Negative / Null Result ReportOpen accessComputer Science

Monte-Carlo Planning: Theoretically Fast Convergence Meets Practical Efficiency

Zohar Feldman, Carmel Domshlak · 2013 · arXiv

Popular Monte-Carlo tree search (MCTS) algorithms for online planning, such as epsilon-greedy tree search and UCT, aim at rapidly identifying a reasonably good action, but provide rather poor worst-case guarantees on performance improvement over time. In contrast, a recently introduced MCTS algorithm BRUE guarantees exponential-rate improvement over time, yet it is not geared towards identifying reasonably good choices right at the go. We take a stand on the individual strengths of these two classes of algorithms, and show how they can be effectively connected. We then rationalize a principle

Negative / Null Result ReportOpen accessComputer Science

Scalable Approach for Normalizing E-commerce Text Attributes (SANTA)

Ravi Shankar Mishra, Kartik Mehta, Nikhil Rasiwasia · 2021 · arXiv

In this paper, we present SANTA, a scalable framework to automatically normalize E-commerce attribute values (e.g. "Win 10 Pro") to a fixed set of pre-defined canonical values (e.g. "Windows 10"). Earlier works on attribute normalization focused on fuzzy string matching (also referred as syntactic matching in this paper). In this work, we first perform an extensive study of nine syntactic matching algorithms and establish that 'cosine' similarity leads to best results, showing 2.7% improvement over commonly used Jaccard index. Next, we argue that string similarity alone is not sufficient for a

Negative / Null Result ReportOpen accessComputer Science

Bounded Memory Active Learning through Enriched Queries

Max Hopkins, Daniel Kane, Shachar Lovett et al. · 2021 · arXiv

The explosive growth of easily-accessible unlabeled data has lead to growing interest in active learning, a paradigm in which data-hungry learning algorithms adaptively select informative examples in order to lower prohibitively expensive labeling costs. Unfortunately, in standard worst-case models of learning, the active setting often provides no improvement over non-adaptive algorithms. To combat this, a series of recent works have considered a model in which the learner may ask enriched queries beyond labels. While such models have seen success in drastically lowering label costs, they tend

Negative / Null Result ReportOpen accessComputer Science

Towards Single Exponential Time for Temporal and Spatial Reasoning: A Study via Redundancy and Dynamic Programming

Victor Lagerkvist, Johanna Groven, Leif Eriksson · 2026 · arXiv

The region connection calculus ($RCC$) and Allen's interval algebra ($IA$) are two well-known NP-hard spatial-temporal qualitative reasoning problems. They are solvable in $2^{O(n \log n)}$ time, where $n$ is the number of variables, and $IA$ is additionally known to be solvable in $o(n)^n$ time. However, no improvement over exhaustive search is known for $RCC$, and if they are also solvable in single exponential time $2^{O(n)}$ is unknown. We investigate multiple avenues towards reaching such bounds. First, we show that branching is insufficient since there are too many non-redundant constrai

Negative / Null Result ReportOpen accessMathematics

Coherent-Classical Estimation for Linear Quantum Systems

Shibdas Roy, Ian R. Petersen, Elanor H. Huntington · 2015 · arXiv

We study a coherent-classical estimation scheme for a class of linear quantum systems, where the estimator is a mixed quantum-classical system that may or may not involve coherent feedback. We show that when the quantum plant or the quantum part of the estimator (coherent controller) is an annihilation operator only system, coherent-classical estimation without coherent feedback can provide no improvement over purely-classical estimation. Otherwise, coherent-classical estimation without feedback can be better than classical-only estimation for certain homodyne detector angles, although the for

Negative / Null Result ReportOpen accessPhysics

Modeling and Optimization of Two-Terminal Spin-Orbit-Torque MRAM

Md Nahid Haque Shazon, Piyush Kumar, Luqiao Liu et al. · 2025 · arXiv

This paper presents physical modeling and benchmarking for two-terminal spin-orbit torque magnetic random-access memory (2T-SOT-MRAM). The results indicate that the common SOT materials that provide only in-plane torque can provide little to no improvement over spin-transfer-torque (STT) MRAM in terms of write energy. However, emerging SOT materials that provide out-of-plane torques with efficiencies as small as 0.1 can result in significant improvements in the write energy for such 2-terminal devices, especially when the magnet lateral dimensions are scaled down to 30 or 20 nm. Additionally,

Negative / Null Result Report

Exploratory outcomes of the DHA WIN randomized controlled trial: Supplementing women with docosahexaenoic acid did not reduce the impact of neoadjuvant breast cancer chemotherapy on quality of life or exercise behaviour

Claire M. Douglas, Marnie Newell, Susan Goruk et al. · 2025 · PLOS One

Supplementation of omega-3 (n-3) polyunsaturated fatty acids has been associated with reduced side effects and improved quality of life (QoL) in breast cancer patients receiving chemotherapy. The current study reports secondary outcomes…

View details →DOI: 10.1371/journal.pone.0322178
Negative / Null Result ReportOpen accessComputer Science

Logistic Regression: Tight Bounds for Stochastic and Online Optimization

Elad Hazan, Tomer Koren, Kfir Y. Levy · 2014 · arXiv

The logistic loss function is often advocated in machine learning and statistics as a smooth and strictly convex surrogate for the 0-1 loss. In this paper we investigate the question of whether these smoothness and convexity properties make the logistic loss preferable to other widely considered options such as the hinge loss. We show that in contrast to known asymptotic bounds, as long as the number of prediction/optimization iterations is sub exponential, the logistic loss provides no improvement over a generic non-smooth loss function such as the hinge loss. In particular we show that the c

Abandoned HypothesisOpen accessComputer Science

Adaptive estimation of a time-varying phase with coherent states: smoothing can give an unbounded improvement over filtering

Kiarn T. Laverick, Howard M. Wiseman, Hossien T. Dinani et al. · 2017 · arXiv

The problem of measuring a time-varying phase, even when the statistics of the variation is known, is considerably harder than that of measuring a constant phase. In particular, the usual bounds on accuracy - such as the $1/(4\bar{n})$ standard quantum limit with coherent states - do not apply. Here, restricting to coherent states, we are able to analytically obtain the achievable accuracy - the equivalent of the standard quantum limit - for a wide class of phase variation. In particular, we consider the case where the phase has Gaussian statistics and a power-law spectrum equal to $κ^{p-1}/|ω

Negative / Null Result ReportOpen accessComputer Science

Coherent-Classical Estimation versus Purely-Classical Estimation for Linear Quantum Systems

Shibdas Roy, Ian R. Petersen, Elanor H. Huntington · 2014 · arXiv

We consider a coherent-classical estimation scheme for a class of linear quantum systems. It comprises an estimator that is a mixed quantum-classical system without involving coherent feedback. The estimator yields a classical estimate of a variable for the quantum plant. We demonstrate that for a passive plant that can be characterized by annihilation operators only, such coherent-classical estimation provides no improvement over purely-classical estimation. An example is also given which shows that if the plant is not assumed to be an annihilation operator only quantum system, it is possible

Negative / Null Result ReportOpen accessComputer Science

An Improvement Over Threads Communications on Multi-Core Processors

Reza Fotohi, Mehdi Effatparvar, Fateme Sarkohaki et al. · 2019 · arXiv

Multicore is an integrated circuit chip that uses two or more computational engines (cores) places in a single processor. This new approach is used to split the computational work of a threaded application and spread it over multiple execution cores, so that the computer system can benefits from a better performance and better responsiveness of the system. A thread is a unit of execution inside a process that is created and maintained to execute a set of actions/ instructions. Threads can be implemented differently from an operating system to another, but the operating system is in most cases

Negative / Null Result ReportOpen accessComputer Science

On an Improvement over Rényi's Equivocation Bound

Nandakishore Santhi, Alexander Vardy · 2006 · arXiv

We consider the problem of estimating the probability of error in multi-hypothesis testing when MAP criterion is used. This probability, which is also known as the Bayes risk is an important measure in many communication and information theory problems. In general, the exact Bayes risk can be difficult to obtain. Many upper and lower bounds are known in literature. One such upper bound is the equivocation bound due to Rényi which is of great philosophical interest because it connects the Bayes risk to conditional entropy. Here we give a simple derivation for an improved equivocation bound. We

Negative / Null Result ReportOpen accessComputer Science

Coding Schemes with Rate-Limited Feedback that Improve over the Nofeedback Capacity for a Large Class of Broadcast Channels

Youlong Wu, Michèle Wigger · 2014 · arXiv

We propose two coding schemes for the two-receiver discrete memoryless broadcast channel (BC) with rate-limited feedback from one or both receivers. They improve over the nofeedback capacity region for a large class of channels, including the class of \emph{strictly essentially less-noisy BCs} that we introduce in this article. Examples of strictly essentially less-noisy BCs are the binary symmetric BC (BSBC) or the binary erasure BC (BEBC) with unequal cross-over or erasure probabilities at the two receivers. When the feedback rates are sufficiently large, our schemes recover all previously k

Negative / Null Result ReportOpen accessComputer Science

Towards precise baryogenesis in the 2HDM$+a$

T. Gent, S. Huber, K. Mimasu et al. · 2025 · arXiv

We perform a detailed investigation of the viable baryogenesis parameter space of a non-minimal Higgs sector consisting of two Higgs doublets and a singlet pseudoscalar (2HDM$+a$). In such a model, an early Universe period of transient CP violation may occur, driven by a nonvanishing vacuum expectation value of the CP-odd scalar $a$. This naturally avoids the stringent electric dipole moment experimental constraints on beyond-the-Standard-Model sources of CP violation. We provide a state-of-art computation of the baryon asymmetry, providing several important improvements over existing baryogen

Negative / Null Result ReportOpen accessComputer Science

Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines

Mehdi S. M. Sajjadi, Morteza Alamgir, Ulrike von Luxburg · 2015 · arXiv

Peer grading is the process of students reviewing each others' work, such as homework submissions, and has lately become a popular mechanism used in massive open online courses (MOOCs). Intrigued by this idea, we used it in a course on algorithms and data structures at the University of Hamburg. Throughout the whole semester, students repeatedly handed in submissions to exercises, which were then evaluated both by teaching assistants and by a peer grading mechanism, yielding a large dataset of teacher and peer grades. We applied different statistical and machine learning methods to aggregate t

Negative / Null Result ReportOpen accessComputer Science

Improve SAT-solving with Machine Learning

Haoze Wu · 2017 · arXiv

In this project, we aimed to improve the runtime of Minisat, a Conflict-Driven Clause Learning (CDCL) solver that solves the Propositional Boolean Satisfiability (SAT) problem. We first used a logistic regression model to predict the satisfiability of propositional boolean formulae after fixing the values of a certain fraction of the variables in each formula. We then applied the logistic model and added a preprocessing period to Minisat to determine the preferable initial value (either true or false) of each boolean variable using a Monte-Carlo approach. Concretely, for each Monte-Carlo trial

Negative / Null Result ReportOpen accessComputer Science

When LLMs fall short in Deductive Coding: Model Comparison and Human AI Collaboration Workflow Design

Zijian Li, Luzhen Tang, Mengyu Xia et al. · 2025 · arXiv

With generative artificial intelligence driving the growth of dialogic data in education, automated coding is a promising direction for learning analytics to improve efficiency. This surge highlights the need to understand the nuances of student-AI interactions, especially those rare yet crucial. However, automated coding may struggle to capture these rare codes due to imbalanced data, while human coding remains time-consuming and labour-intensive. The current study examined the potential of large language models (LLMs) to approximate or replace humans in deductive, theory-driven coding, while

Negative / Null Result ReportOpen accessEngineering

3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning

Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg et al. · 2020 · arXiv

This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients with bone metastases from breast cancer. The CT images were refined prior to registration by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of VoxelMorph when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images. In a 4-fold cross-validation scheme

Negative / Null Result ReportOpen accessComputer Science

Exploring Multi-Modality Dynamics: Insights and Challenges in Multimodal Fusion for Biomedical Tasks

Laura Wenderoth · 2024 · arXiv

This paper investigates the MM dynamics approach proposed by Han et al. (2022) for multi-modal fusion in biomedical classification tasks. The MM dynamics algorithm integrates feature-level and modality-level informativeness to dynamically fuse modalities for improved classification performance. However, our analysis reveals several limitations and challenges in replicating and extending the results of MM dynamics. We found that feature informativeness improves performance and explainability, while modality informativeness does not provide significant advantages and can lead to performance degr

Negative / Null Result ReportOpen accessComputer Science

Reducing Over-smoothing in Graph Neural Networks Using Relational Embeddings

Yeskendir Koishekenov · 2023 · arXiv

Graph Neural Networks (GNNs) have achieved a lot of success with graph-structured data. However, it is observed that the performance of GNNs does not improve (or even worsen) as the number of layers increases. This effect has known as over-smoothing, which means that the representations of the graph nodes of different classes would become indistinguishable when stacking multiple layers. In this work, we propose a new simple, and efficient method to alleviate the effect of the over-smoothing problem in GNNs by explicitly using relations between node embeddings. Experiments on real-world dataset

Negative / Null Result ReportOpen accessComputer Science

Entropy Bound for the Classical Capacity of a Quantum Channel Assisted by Classical Feedback

Dawei Ding, Yihui Quek, Peter W. Shor et al. · 2019 · arXiv

We prove that the classical capacity of an arbitrary quantum channel assisted by a free classical feedback channel is bounded from above by the maximum average output entropy of the quantum channel. As a consequence of this bound, we conclude that a classical feedback channel does not improve the classical capacity of a quantum erasure channel, and by taking into account energy constraints, we conclude the same for a pure-loss bosonic channel. The method for establishing the aforementioned entropy bound involves identifying an information measure having two key properties: 1) it does not incre

Negative / Null Result ReportOpen accessMathematics

A step forwards on the Erdős-Sós problem concerning the Ramsey numbers $R(3,k)$

Rujie Zhu, Xiaodong Xu, Stanisław Radziszowski · 2015 · arXiv

Let $Δ_s=R(K_3,K_s)-R(K_3,K_{s-1})$, where $R(G,H)$ is the Ramsey number of graphs $G$ and $H$ defined as the smallest $n$ such that any edge coloring of $K_n$ with two colors contains $G$ in the first color or $H$ in the second color. In 1980, Erdős and Sós posed some questions about the growth of $Δ_s$. The best known concrete bounds on $Δ_s$ are $3 \le Δ_s \le s$, and they have not improved since the stating of the problem. In this paper we present some constructions, which imply in particular that $R(K_3,K_s) \ge R(K_3,K_{s-1}-e) + 4$. This does not improve the lower bound of 3 on $Δ_s$, b