Negative / Null Result ReportOpen accessComputer Science
D. Hüber, J. L. Friar · 1998 · arXiv
The nucleon-deuteron analyzing power $A_y$ in elastic nucleon-deuteron scattering poses a longstanding puzzle. At energies $E_{lab}$ below approximately 30 MeV $A_y$ cannot be described by any realistic NN force. The inclusion of existing three-nucleon forces does not improve the situation. Because of recent questions about the $^3P_J$ NN phases, we examine whether reasonable changes in the NN force can resolve the puzzle. In order to do this we investigate the effect on the $^3P_J$ waves produced by changes in different parts of the potential (viz., the central force, tensor force, etc.), as
Negative / Null Result ReportOpen accessPhysics
I. Pallecchi, L. Pellegrino, N. Banerjee et al. · 2010 · arXiv
We probe spin transport in Cu_{2}O by measuring spin valve effect in La_{0.7}Sr_{0.3}MnO_{3}/Cu_{2}O/Co and La_{0.7}Sr_{0.3}MnO_{3}/Cu_{2}O/La_{0.7}Sr_{0.3}MnO_{3} epitaxial heterostructures. In La_{0.7}Sr_{0.3}MnO_{3}/Cu_{2}O/Co systems we find that a fraction of out-of-equilibrium spin polarized carrier actually travel across the Cu_{2}O layer up to distances of almost 100 nm at low temperature. The corresponding spin diffusion length dspin is estimated around 40 nm. Furthermore, we find that the insertion of a SrTiO_{3} tunneling barrier does not improve spin injection, likely due to the ma
Failed Experiment ReportOpen accessMathematics
Anh-Huy Phan, Petr Tichavský, Andrzej Cichocki · 2017 · arXiv
In CANDECOMP/PARAFAC tensor decomposition, degeneracy often occurs in some difficult scenarios, e.g., when the rank exceeds the tensor dimension, or when the loading components are highly collinear in several or all modes, or when CPD does not have an optimal solution. In such the cases, norms of some rank-1 terms become significantly large and cancel each other. This makes algorithms getting stuck in local minima while running a huge number of iterations does not improve the decomposition. In this paper, we propose an error preservation correction method to deal with such problem. Our aim is
Negative / Null Result ReportOpen accessComputer Science
Greta Laage, Emma Frejinger, Andrea Lodi et al. · 2021 · arXiv
Airlines and other industries have been making use of sophisticated Revenue Management Systems to maximize revenue for decades. While improving the different components of these systems has been the focus of numerous studies, estimating the impact of such improvements on the revenue has been overlooked in the literature despite its practical importance. Indeed, quantifying the benefit of a change in a system serves as support for investment decisions. This is a challenging problem as it corresponds to the difference between the generated value and the value that would have been generated keepi
Negative / Null Result ReportOpen accessComputer Science
Wataru Masaka, Mitsuki Sakamoto, Kenshi Abe et al. · 2025 · arXiv
We investigate how perturbation does and does not improve the Follow-the-Regularized-Leader (FTRL) algorithm in solving imperfect-information extensive-form games under sampling, where payoffs are estimated from sampled trajectories. While optimistic algorithms are effective under full feedback, they often become unstable in the presence of sampling noise. Payoff perturbation offers a promising alternative for stabilizing learning and achieving \textit{last-iterate convergence}. We present a unified framework for \textit{Perturbed FTRL} algorithms and study two variants: PFTRL-KL (standard KL
Negative / Null Result ReportOpen accessComputer Science
Arwa Arif · 2025 · arXiv
Backtranslation BT is widely used in low resource machine translation MT to generate additional synthetic training data using monolingual corpora. While this approach has shown strong improvements for many language pairs, its effectiveness in high quality, low resource settings remains unclear. In this work, we explore the effectiveness of backtranslation for English Gujarati translation using the multilingual pretrained MBART50 model. Our baseline system, trained on a high quality parallel corpus of approximately 50,000 sentence pairs, achieves a BLEU score of 43.8 on a validation set. We aug
Negative / Null Result ReportOpen accessComputer Science
Lawrence Clegg, John Cartlidge · 2025 · arXiv
Intransitive player dominance, where player A beats B, B beats C, but C beats A, is common in competitive tennis. Yet, there are few known attempts to incorporate it within forecasting methods. We address this problem with a graph neural network approach that explicitly models these intransitive relationships through temporal directed graphs, with players as nodes and their historical match outcomes as directed edges. Our model (65.7% accuracy, 0.214 Brier score) forecasts competitively with established rating systems such as Weighted Elo. Although it does not improve on the baseline in uncond
Negative / Null Result Report
Luh Gd Rahayu Budiarta, I Putu Indra Kusuma · 2024 · Jurnal Pendidikan Bahasa Inggris undiksha
The advent of ChatGPT has surprised many English educators, as theoretically, it has many potentials to support English language teaching. However, the empirical results of how ChatGPT influence English as a foreign language (henceforth,…
View details →DOI: 10.23887/jpbi.v12i3.85769 Negative / Null Result ReportMedicine
Shen, Dai, Hu et al. · 2026 · Quantitative imaging in medicine and surgery
Inguinal lymph node (ILN) metastasis significantly affects prognosis and treatment strategies in patients with gynecological malignancies. Conventional ultrasound (US) provides morphological assessment but has limited sensitivity for…
View details →DOI: 10.21037/qims-2025-1314 Negative / Null Result Report
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…
View details →DOI: 10.1200/op.2024.20.10_suppl.327 Negative / Null Result Report
· 2020 · Human Resource Management International Digest
Purpose The author was motivated to focus on the palm oil sector because it is essential to the Indonesian economy. He wanted to discover how to improve performance Design/methodology/approach The author focused on employees of class…
View details →DOI: 10.1108/hrmid-03-2020-0057 Negative / Null Result Report
Zygy Roe-Zurz, Liliana Madrigal, Rahul Sharma et al. · 2025 · Journal of the Endocrine Society
Abstract Disclosure: Z. Roe-Zurz: None. L. Madrigal: None. Graves’ ophthalmopathy (GO) is a serious complication of autoimmune hyperthyroidism, affecting approximately 30% of patients. It is characterized by immune-mediated inflammation of…
View details →DOI: 10.1210/jendso/bvaf149.2207 Negative / Null Result ReportOpen accessComputer Science
Osvaldo Simeone, Haim H. Permuter · 2011 · arXiv
For memoryless sources, delayed side information at the decoder does not improve the rate-distortion function. However, this is not the case for more general sources with memory, as demonstrated by a number of works focusing on the special case of (delayed) feedforward. In this paper, a setting is studied in which the encoder is potentially uncertain about the delay with which measurements of the side information are acquired at the decoder. Assuming a hidden Markov model for the sources, at first, a single-letter characterization is given for the set-up where the side information delay is arb
Negative / Null Result ReportOpen accessComputer Science
Tovly Deutsch, Masoud Jasbi, Stuart Shieber · 2020 · arXiv
Readability assessment aims to automatically classify text by the level appropriate for learning readers. Traditional approaches to this task utilize a variety of linguistically motivated features paired with simple machine learning models. More recent methods have improved performance by discarding these features and utilizing deep learning models. However, it is unknown whether augmenting deep learning models with linguistically motivated features would improve performance further. This paper combines these two approaches with the goal of improving overall model performance and addressing th
Negative / Null Result ReportOpen accessComputer Science
Mitchell A. Gordon, Kevin Duh, Nicholas Andrews · 2020 · arXiv
Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models without requiring more labeled data. While effective, feature extractors like BERT may be prohibitively large for some deployment scenarios. We explore weight pruning for BERT and ask: how does compression during pre-training affect transfer learning? We find that pruning affects transfer learning in three broad regimes. Low levels of pruning (30-40%) do not affect pre-training loss or transfer to downstream tasks a
Negative / Null Result ReportOpen accessComputer Science
Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li · 2025 · arXiv
Multi-Agent Debate~(MAD) has emerged as a promising paradigm for improving the performance of large language models through collaborative reasoning. Despite recent advances, the key factors driving MAD's effectiveness remain unclear. In this work, we disentangle MAD into two key components--Majority Voting and inter-agent Debate--and assess their respective contributions. Through extensive experiments across seven NLP benchmarks, we find that Majority Voting alone accounts for most of the performance gains typically attributed to MAD. To explain this, we propose a theoretical framework that mo
Negative / Null Result ReportOpen accessComputer Science
Yulin Chen, He He, Chen Zhao · 2026 · arXiv
Reinforcement Learning with Verifiable Reward (RLVR) has proven effective in improving Large Language Model's (LLM) reasoning ability. However, the learning dynamics of RLVR remain underexplored. In this paper, we reveal a counterintuitive phenomenon: among hard examples that the model initially struggles with, a substantial subset remains unlearnable even when correct rollouts are present. To understand the phenomenon, we first demonstrate that existing optimization and sampling techniques fail to resolve unlearnability. With cross-example gradient analysis, we show that unlearnable examples
Negative / Null Result ReportOpen accessAgricultural and Biological Sciences
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
Negative / Null Result ReportOpen accessComputer Science
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
Negative / Null Result ReportOpen accessComputer Science
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
Negative / Null Result ReportOpen accessComputer Science
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
Negative / Null Result ReportOpen accessComputer Science
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
Negative / Null Result ReportOpen accessComputer Science
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
Negative / Null Result ReportOpen accessComputer Science
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
Negative / Null Result ReportOpen accessPhysics
Ilias Magoulas, Karthik Gururangan, Piotr Piecuch et al. · 2021 · arXiv
The short answer to the question in the title is 'no'. We identify classes of truncated configuration interaction (CI) wave functions for which the externally corrected coupled-cluster (ec-CC) approach using the three-body ($T_{3}$) and four-body ($T_{4}$) components of the cluster operator extracted from CI does not improve the results of the underlying CI calculations. Implications of our analysis, illustrated by numerical examples, for the ec-CC computations using truncated and selected CI methods are discussed. We also introduce a novel ec-CC approach using the $T_{3}$ and $T_{4}$ amplitud
Negative / Null Result ReportOpen accessPhysics
Susanne Still, David A. Sivak, Anthony J. Bell et al. · 2012 · arXiv
A system responding to a stochastic driving signal can be interpreted as computing, by means of its dynamics, an implicit model of the environmental variables. The system's state retains information about past environmental fluctuations, and a fraction of this information is predictive of future ones. The remaining nonpredictive information reflects model complexity that does not improve predictive power, and thus represents the ineffectiveness of the model. We expose the fundamental equivalence between this model inefficiency and thermodynamic inefficiency, measured by dissipation. Our result
Negative / Null Result ReportOpen accessAgricultural and Biological Sciences
Roy Henha Eyono, Daniel Levenstein, Arna Ghosh et al. · 2026 · arXiv
Normalization is a critical operation in neural circuits. In the brain, there is evidence that normalization is implemented via inhibitory interneurons and allows neural populations to adjust to changes in the distribution of their inputs. In artificial neural networks (ANNs), normalization is used to improve learning in tasks that involve complex input distributions. However, it is unclear whether inhibition-mediated normalization in biological neural circuits also improves learning. Here, we explore this possibility using ANNs with separate excitatory and inhibitory populations trained on an
Negative / Null Result ReportOpen accessEngineering
Siqi Ye, Yong Long, Il Yong Chun · 2020 · arXiv
This paper applies the recent fast iterative neural network framework, Momentum-Net, using appropriate models to low-dose X-ray computed tomography (LDCT) image reconstruction. At each layer of the proposed Momentum-Net, the model-based image reconstruction module solves the majorized penalized weighted least-square problem, and the image refining module uses a four-layer convolutional neural network (CNN). Experimental results with the NIH AAPM-Mayo Clinic Low Dose CT Grand Challenge dataset show that the proposed Momentum-Net architecture significantly improves image reconstruction accuracy,
Negative / Null Result ReportOpen accessComputer Science
Guanzi Chen, Jiying Zhang, Xi Xiao et al. · 2022 · arXiv
In recent years, hypergraph learning has attracted great attention due to its capacity in representing complex and high-order relationships. However, current neural network approaches designed for hypergraphs are mostly shallow, thus limiting their ability to extract information from high-order neighbors. In this paper, we show both theoretically and empirically, that the performance of hypergraph neural networks does not improve as the number of layers increases, which is known as the over-smoothing problem. To avoid this issue, we develop a new deep hypergraph convolutional network called De
Negative / Null Result ReportOpen accessComputer Science
Chenming Tang, Xiuyu Wu, Yunfang Wu · 2023 · arXiv
Model ensemble has been in widespread use for Grammatical Error Correction (GEC), boosting model performance. We hypothesize that model ensemble based on the perplexity (PPL) computed by pre-trained language models (PLMs) should benefit the GEC system. To this end, we explore several ensemble strategies based on strong PLMs with four sophisticated single models. However, the performance does not improve but even gets worse after the PLM-based ensemble. This surprising result sets us doing a detailed analysis on the data and coming up with some insights on GEC. The human references of correct s
