Negative / Null Result ReportOpen accessEconomics, Econometrics and Finance
Valentina Macchiati, Piero Mazzarisi, Diego Garlaschelli · 2024 · arXiv
Networks of financial exposures are the key propagators of risk and distress among banks, but their empirical structure is not publicly available because of confidentiality. This limitation has triggered the development of methods of network reconstruction from partial, aggregate information. Unfortunately, even the best methods available fail in replicating the number of directed cycles, which on the other hand play a crucial role in determining graph spectra and hence the degree of network stability and systemic risk. Here we address this challenge by exploiting the hypothesis that the stati
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Mert Albaba, Sammy Christen, Thomas Langarek et al. · 2024 · arXiv
Acquiring complex behaviors is essential for artificially intelligent agents, yet learning these behaviors in high-dimensional settings poses a significant challenge due to the vast search space. Traditional reinforcement learning (RL) requires extensive manual effort for reward function engineering. Inverse reinforcement learning (IRL) uncovers reward functions from expert demonstrations but relies on an iterative process that is often computationally expensive. Imitation learning (IL) provides a more efficient alternative by directly comparing an agent's actions to expert demonstrations; how
View details →Negative / Null Result ReportOpen accessPhysics
Naiara Patiño, Nuria Calvet, Gladis Magris et al. · 2026 · arXiv
Magnetospheric accretion is the paradigm for accretion in Classical T-Tauri Stars (CTTS). However, the standard, one-flow magnetospheric accretion model fails to replicate important characteristics such as the observed Balmer decrements. We address this limitation by adopting a model with two axisymmetric magnetospheric accretion flows of different accretion rates and geometries. We calculate the fluxes of the hydrogen $H_α$, $H_β$, and $H_γ$ lines of each flow with the magnetospheric accretion model and use Bayesian statistics to fit the Balmer line fluxes of 139 CTTS in the Orion OB1b subass
View details →Negative / Null Result ReportOpen accessEconomics, Econometrics and Finance
Damien Ackerer, Natasa Tagasovska, Thibault Vatter · 2019 · arXiv
We present a neural network (NN) approach to fit and predict implied volatility surfaces (IVSs). Atypically to standard NN applications, financial industry practitioners use such models equally to replicate market prices and to value other financial instruments. In other words, low training losses are as important as generalization capabilities. Importantly, IVS models need to generate realistic arbitrage-free option prices, meaning that no portfolio can lead to risk-free profits. We propose an approach guaranteeing the absence of arbitrage opportunities by penalizing the loss using soft const
View details →Negative / Null Result ReportOpen accessMathematics
Fintan Costello, Paul Watts · 2022 · arXiv
There is a well-known problem in Null Hypothesis Significance Testing: many statistically significant results fail to replicate in subsequent experiments. We show that this problem arises because standard `point-form null' significance tests consider only within-experiment but ignore between-experiment variation, and so systematically underestimate the degree of random variation in results. We give an extension to standard significance testing that addresses this problem by analysing both within- and between-experiment variation. This `distributional null' approach does not underestimate exper
View details →Negative / Null Result ReportOpen accessComputer Science
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
View details →Negative / Null Result ReportOpen accessComputer Science
Rohan Jha, Reno Kriz, Benjamin Van Durme · 2026 · arXiv
The XTR (conteXtual Token Retrieval) algorithm is a modification to ColBERT retrieval that avoids the costly step of fully gathering and reranking the candidates' embeddings by imputing their missing similarity scores from the initial token retrieval step. The original work proposes a modified training objective as necessary for effective XTR retrieval, arguing that standard ColBERT token scoring is unsuitable for imputation. In this paper, we replicate both the XTR retrieval algorithm and its modified training objective, and extend the evaluation to knowledge-distillation (KD) training and ef
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Patrick Murphy, Misha Perepelitsa, Ilya Timofeyev et al. · 2023 · arXiv
Studies in the collective motility of organisms use a range of analytical approaches to formulate continuous kinetic models of collective dynamics from rules or equations describing agent interactions. However, the derivation of these kinetic models often relies on Boltzmann's hypothesis of "molecular chaos", that correlations between individuals are short-lived. While this assumption is often the simplest way to derive tractable models, it is often not valid in practice due to the high levels of cooperation and self-organization present in biological systems. In this work, we illustrated this
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Rachel Conlon, Margaret Kuzemchak, Lan Yu et al. · 2025 · SLEEP
Abstract Introduction Multidimensional sleep health patterns relate to general health and functioning. We developed the Ru-SATED 4.0 Multidimensional Sleep Health (MDSH) Scale, a self-reported measure of individuals’ Regularity,…
View details →Negative / Null Result ReportOpen accessPhysics
Li-Xin Li, Jeremiah P. Ostriker · 2000 · arXiv
We use the semi-analytical approach to analyze gravitational lensing of quasars by dark halos in various cold dark matter (CDM) cosmologies, in order to determine the sensitivity of the prediction probabilities of images separations to the input assumptions regarding halos and cosmologies. The mass function of dark halos is assumed to be given by the Press-Schechter function. The mass density profile of dark halos is alternatively taken to be the singular isothermal sphere (SIS), the Navarro-Frenk-White (NFW) profile, or the generalized NFW profile. The cosmologies include: the Einstein-de Sit
View details →Negative / Null Result ReportMedicine
Peng, Nik Othman, Zakaria et al. · 2026 · European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
While the application of tuning fork Weber (TFW) test in adult is well-documented, its accuracy in the paediatric population remains uncertain although conductive hearing loss is frequently identified in children. Similarly, the…
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Sayan Dutta, Maria C. Lesniewski, Muhammad Nawaz Qaisrani et al. · 2025 · arXiv
Conjugated organic molecules play a central role in a wide range of optoelectronic devices, including organic light-emitting diodes, organic field-effect transistors, and organic solar cells. A major bottleneck in the computational design of these materials is the discrepancy between simulation and experimental time and length scales. Coarse-graining (CG) offers a promising solution to bridge this gap by reducing redundant degrees of freedom and smoothing the potential energy landscape, thereby significantly accelerating molecular dynamics simulations. However, standard CG models are typically
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Gülise Gökce · 2025 · Brazilian Journal of Business
Following the September 11, 2001 attacks, the concept of "failed states" quickly gained prominence in international security literature, particularly under the influence of the United States. The 2002 U.S. National Security Strategy…
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Angelo Di Porzio, Marco Coraggio · 2025 · arXiv
The deployment of autonomous virtual avatars (in extended reality) and robots in human group activities -- such as rehabilitation therapy, sports, and manufacturing -- is expected to increase as these technologies become more pervasive. Designing cognitive architectures and control strategies to drive these agents requires realistic models of human motion. Furthermore, recent research has shown that each person exhibits a unique velocity signature, highlighting how individual motor behaviors are both rich in variability and internally consistent. However, existing models only provide simplifie
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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…
View details →Negative / Null Result ReportOpen accessComputer Science
Tianjiao Cao, Jiahao Lyu, Weichao Zeng et al. · 2025 · arXiv
Scene text detection has seen the emergence of high-performing methods that excel on academic benchmarks. However, these detectors often fail to replicate such success in real-world scenarios. We uncover two key factors contributing to this discrepancy through extensive experiments. First, a \textit{Fine-tuning Gap}, where models leverage \textit{Dataset-Specific Optimization} (DSO) paradigm for one domain at the cost of reduced effectiveness in others, leads to inflated performances on academic benchmarks. Second, the suboptimal performance in practical settings is primarily attributed to the
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Christopher Brady, Xu Wu · 2025 · arXiv
The Organization for Economic Cooperation and Development (OECD) Working Party on Nuclear Criticality Safety (WPNCS) proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian Inverse Uncertainty Quantification (IUQ) as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of Generalized Linear Least Squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed
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Yu-Han Huang · 2024 · Roadsides
This essay scrutinizes the soil-cement brick (SCB), a half-earthen, half-concrete building material, and its use in U.S.-aided housing projects in Cold War-era Taiwan. Made of cement and natural earth with manually operated ‘brickmaker’…
View details →Negative / Null Result ReportOpen accessComputer Science
Meimingwei Li, Yuanhao Ding, Esteban Garces Arias et al. · 2026 · arXiv
Recent work has identified a counterintuitive phenomenon termed "Hyperfitting", where fine-tuning Large Language Models (LLMs) to near-zero training loss on small datasets surprisingly enhances open-ended generation quality and mitigates repetition in greedy decoding. While effective, the underlying mechanism remains poorly understood, with the extremely low-entropy output distributions suggesting a potential equivalence to simple temperature scaling. In this work, we demonstrate that this phenomenon is fundamentally distinct from distribution sharpening; entropy-matched control experiments re
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John Nyland, Ryan Krupp · 2022 · Arthroscopy
Abstract Severe anterior shoulder instability with glenoid bone loss can be very difficult to treat. A recent cadaveric, biomechanical, time‐zero study compared the stability of Bankart repair with long head of the biceps brachi transfer…
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M. Telwak, P. Voglewede, M. B. Silver-Thorn · 2014 · Journal of Medical Devices
Recent advances in lower limb prostheses have involved the design of active, powered prosthetic knee and ankle-foot components capable of generating knee and ankle torques similar to that of normal gait. The associated componentry results…
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Sabatini, Molinero-Mourelle, Limones et al. · 2026 · The Journal of prosthetic dentistry
Whether the manufacturing protocol (build orientation and sintering schedule) of additively manufactured (AM) zirconia impacts the bond strength compared with subtractively manufactured (SM) zirconia remains unclear. The purpose of this in…
View details →Negative / Null Result ReportOpen accessComputer Science
Ping Chen, Zezhou Chen, Xingpeng Zhang et al. · 2026 · arXiv
Current 2D-to-3D conversion methods achieve geometric accuracy but are artistically deficient, failing to replicate the immersive and emotionally resonant experience of professional 3D cinema. This is because geometric reconstruction paradigms mistake deliberate artistic intent, such as strategic zero-plane shifts for pop-out effects and local depth sculpting, for data noise or ambiguity. This paper argues for a new paradigm: Artistic Disparity Synthesis, shifting the goal from physically accurate disparity estimation to artistically coherent disparity synthesis. We propose Art3D, a preliminar
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Maria Zenkova · 2025 · ISTORIYA
The author examines the practice of veneration of St. Clement of Rome within the Western and Eastern Christian churches, as well as in Iceland, where his cult was less widespread. Based on the image of St. Clement in Old Norse literature,…
View details →Negative / Null Result ReportOpen accessAgricultural and Biological Sciences
Takuto Yamamoto, Hirosato Akahoshi, Shigeru Kitazawa · 2024 · arXiv
Many models of visual attention have been proposed so far. Traditional bottom-up models, like saliency models, fail to replicate human gaze patterns, and deep gaze prediction models lack biological plausibility due to their reliance on supervised learning. Vision Transformers (ViTs), with their self-attention mechanisms, offer a new approach but often produce dispersed attention patterns if trained with supervised learning. This study explores whether self-supervised DINO (self-DIstillation with NO labels) training enables ViTs to develop attention mechanisms resembling human visual attention.
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Joseph Cullen, Gregory Holloway · 2025 · Glottodidactica
This article presents an evaluation of the LSP-TEOC.Pro project. It sets out the evaluation methodology applied, how it was implemented and the key evaluation findings. Given the exploratory nature of the project, the range and complexity…
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Chenglong Ma, Ziqi Xu, Yongli Ren et al. · 2025 · arXiv
Traditional offline evaluation methods for recommender systems struggle to capture the complexity of modern platforms due to sparse behavioural signals, noisy data, and limited modelling of user personality traits. While simulation frameworks can generate synthetic data to address these gaps, existing methods fail to replicate behavioural diversity, limiting their effectiveness. To overcome these challenges, we propose the Personality-driven User Behaviour Simulator (PUB), an LLM-based simulation framework that integrates the Big Five personality traits to model personalised user behaviour. PU
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Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh et al. · 2022 · arXiv
Robust imitation learning using disturbance injections overcomes issues of limited variation in demonstrations. However, these methods assume demonstrations are optimal, and that policy stabilization can be learned via simple augmentations. In real-world scenarios, demonstrations are often of diverse-quality, and disturbance injection instead learns sub-optimal policies that fail to replicate desired behavior. To address this issue, this paper proposes a novel imitation learning framework that combines both policy robustification and optimal demonstration learning. Specifically, this combinato
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Carly Cunningham, Stuart Cantlay, Joseph Horzempa · 2019 · Proceedings of the West Virginia Academy of Science
CARLY CUNNINGHAM, STUART CANTLAY, AND JOSEPH HORZEMPA, Department of Natural Sciences and Mathematics, West Liberty University, West Liberty, WV USA. The ability of VBNC F. tularensis to replicate within THP-1 cells. Francisella…
View details →Negative / Null Result ReportOpen accessPhysics
María Pereda · 2024 · arXiv
The problem of free-riding arises when individuals benefit from a shared resource, service, or public good without contributing proportionately to its provision. This conduct often leads to a collective action problem, as individuals pursue personal gains while relying on the contributions of others. In this study, we present a Bayesian inference model to elucidate the behaviour of participants in a Public Goods Game, a conceptual framework that captures the essence of the free-riding problem. Here, individuals possess information on the distribution of group donations to the public good. Our
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