Negative / Null Result ReportOpen accessPhysics
Laura Stricker, Benjamin Dollet, David Fernandez Rivas et al. · 2013 · arXiv
Acoustically driven air pockets trapped in artificial crevices on a sur- face can emit bubbles which organize in (interacting) bubble clusters. With increasing driving power Fernandez Rivas et al. [Angew. Chem. Int. Ed., 2010] observed three different behaviors: clusters close to the very pits out of which they had been created, clusters pointing toward each other, and merging clusters. The latter behavior is highly undesired for technological purposes as it is associated with a reduction of the radical production and an enhancement of the erosion of the reactor walls. The dependence on the co
Negative / Null Result ReportOpen accessEngineering
Deepak Singla, Soham Chatterjee, Lavanya Ramapantulu et al. · 2020 · arXiv
Applications in the Internet of Video Things (IoVT) domain have very tight constraints with respect to power and area. While neuromorphic vision sensors (NVS) may offer advantages over traditional imagers in this domain, the existing NVS systems either do not meet the power constraints or have not demonstrated end-to-end system performance. To address this, we improve on a recently proposed hybrid event-frame approach by using morphological image processing algorithms for region proposal and address the low-power requirement for object detection and classification by exploring various convolut
Negative / Null Result ReportOpen accessMathematics
Nicolas Banholzer, Thomas Mellan, H Juliette T Unwin et al. · 2023 · arXiv
Short-term forecasts of infectious disease spread are a critical component in risk evaluation and public health decision making. While different models for short-term forecasting have been developed, open questions about their relative performance remain. Here, we compare short-term probabilistic forecasts of popular mechanistic models based on the renewal equation with forecasts of statistical time series models. Our empirical comparison is based on data of the daily incidence of COVID-19 across six large US states over the first pandemic year. We find that, on average, probabilistic forecast
Negative / Null Result ReportOpen accessComputer Science
Evgeniia Razumovskaia, Ivan Vulić, Anna Korhonen · 2024 · arXiv
Supervised fine-tuning (SFT), supervised instruction tuning (SIT) and in-context learning (ICL) are three alternative, de facto standard approaches to few-shot learning. ICL has gained popularity recently with the advent of LLMs due to its simplicity and sample efficiency. Prior research has conducted only limited investigation into how these approaches work for multilingual few-shot learning, and the focus so far has been mostly on their performance. In this work, we present an extensive and systematic comparison of the three approaches, testing them on 6 high- and low-resource languages, thr
Negative / Null Result ReportOpen accessComputer Science
Zijie Zhao, Roy E. Welsch · 2026 · arXiv
Financial retrieval-augmented generation (RAG) systems typically rank evidence by textual relevance, but in financial markets evidence utility depends on event type, forecast horizon, and market context. We study news-triggered event-impact prediction as a point-in-time financial RAG problem. For each company-news anchor, the system retrieves financial news and SEC filing passages, appends a pre-decision market-context card, and predicts multi-horizon residual-return signals. Our method keeps the LLM frozen and adapts retrieval through an external Bayesian source memory updated from matured re
Negative / Null Result ReportOpen accessEngineering
Luca Didaci, Sara Maria Pani, Claudio Frongia et al. · 2023 · arXiv
EEG-based biometric represents a relatively recent research field that aims to recognize individuals based on their recorded brain activity by means of electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, we investigate how the performance of an EEG biometric system varies with respect to different time windows to understand if it is possible to define the optimal duration of EEG signal that can be used to extract those distinctive features. Overall, the results
Negative / Null Result ReportOpen accessComputer Science
Baha Eddine Youcef Belmekki, Abdelkrim Hamza, Benoît Escrig · 2018 · arXiv
Vehicular safety communications (VSCs) are known to provide relevant contributions to avoid congestions and prevent road accidents, and more particularly at road intersections since these areas are more prone to accidents. In this context, one of the main impairments that affect the performance of VSCs are interference. In this paper, we develop a tractable framework to model cooperative transmissions in presence of interference for VSCs at intersections. We use tools from stochastic geometry, and model interferer vehicles locations as a Poisson point process. First, we calculate the outage pr
Negative / Null Result ReportOpen accessComputer Science
Jennifer Haase, Jana Gonnermann-Müller, See Heng Yim et al. · 2026 · arXiv
Large language model (LLM)-based simulations of clinical patients are increasingly used for research and training, yet their validity requires persona stability: coherent maintenance of an assigned psychological profile across and within conversations. We evaluate this prerequisite using eating disorder personas grounded in five published case vignettes, a dual-assessment framework (self-report + independent observer ratings), and validated psychometric instruments (EDE-Q) with known ground-truth scores. Across six LLMs and two experiments (between-conversation stability (Exp. I) and within-co
Negative / Null Result ReportOpen accessComputer Science
Vitaly Kurin, Maximilian Igl, Tim Rocktäschel et al. · 2020 · arXiv
Multitask Reinforcement Learning is a promising way to obtain models with better performance, generalisation, data efficiency, and robustness. Most existing work is limited to compatible settings, where the state and action space dimensions are the same across tasks. Graph Neural Networks (GNN) are one way to address incompatible environments, because they can process graphs of arbitrary size. They also allow practitioners to inject biases encoded in the structure of the input graph. Existing work in graph-based continuous control uses the physical morphology of the agent to construct the inpu
Negative / Null Result ReportOpen accessPhysics
Atsushi Taruya, Takahiro Nishimichi, Donghui Jeong · 2021 · arXiv
Perturbation theory (PT) has been used to interpret the observed nonlinear large-scale structure statistics at the quasi-linear regime. To facilitate the PT-based analysis, we have presented the GridSPT algorithm, a grid-based method to compute the nonlinear density and velocity fields in standard perturbation theory (SPT) from a given linear power spectrum. Here, we further put forward the approach by taking the redshift-space distortions into account. With the new implementation, we have, for the first time, generated the redshift-space density field to the fifth order and computed the next-
Negative / Null Result ReportOpen accessComputer Science
Jin Zeng, Yang Liu, Gene Cheung et al. · 2022 · arXiv
A graph convolutional network (GCN) employs a graph filtering kernel tailored for data with irregular structures. However, simply stacking more GCN layers does not improve performance; instead, the output converges to an uninformative low-dimensional subspace, where the convergence rate is characterized by the graph spectrum -- this is the known over-smoothing problem in GCN. In this paper, we propose a sparse graph learning algorithm incorporating a new spectrum prior to compute a graph topology that circumvents over-smoothing while preserving pairwise correlations inherent in data. Specifica
Negative / Null Result ReportOpen accessComputer Science
Wentao Zhao, Dalin Zhou, Xinguo Qiu et al. · 2020 · arXiv
Graph neural networks (GNNs) have been investigated for potential applicability in multiple fields that employ graph data. However, there are no standard training settings to ensure fair comparisons among new methods, including different model architectures and data augmentation techniques. We introduce a standard, reproducible benchmark to which the same training settings can be applied for node classification. For this benchmark, we constructed 9 datasets, including both small- and medium-scale datasets from different fields, and 7 different models. We design a k-fold model assessment strate
Negative / Null Result ReportOpen accessComputer Science
Charles Jin, Melinda Sun, Martin Rinard · 2021 · arXiv
We propose a novel clustering mechanism based on an incompatibility property between subsets of data that emerges during model training. This mechanism partitions the dataset into subsets that generalize only to themselves, i.e., training on one subset does not improve performance on the other subsets. Leveraging the interaction between the dataset and the training process, our clustering mechanism partitions datasets into clusters that are defined by--and therefore meaningful to--the objective of the training process. We apply our clustering mechanism to defend against data poisoning attacks,
Negative / Null Result ReportOpen accessPhysics
Fabien Robineau, Frédéric Boy, Jean-Pierre Orliaguet et al. · 2006 · arXiv
Performing minimal-invasive surgical punctures require guiding a needle toward an intracorporeal clinically-defined target. As this technique does not involve cutting the body open, a visualization system is employed to provide the surgeon with indirect visual spatial information about the intracorporeal positions of the tool. One may consider that such systems reduce the ergonomics of the situation as they generate a decorrelation between the actual movement of the needle and the displayed information about this movement. The present study aims at assessing the guidance of an intracorporeal p
Negative / Null Result ReportOpen accessComputer Science
Alexia Jolicoeur-Martineau · 2019 · arXiv
This paper provides a more rigorous look at Relativistic Generative Adversarial Networks (RGANs). We prove that the objective function of the discriminator is a statistical divergence for any concave function $f$ with minimal properties ($f(0)=0$, $f'(0) \neq 0$, $\sup_x f(x)>0$). We also devise a few variants of relativistic $f$-divergences. Wasserstein GAN was originally justified by the idea that the Wasserstein distance (WD) is most sensible because it is weak (i.e., it induces a weak topology). We show that the WD is weaker than $f$-divergences which are weaker than relativistic $f$-diver
Negative / Null Result ReportOpen accessEngineering
Neil Irwin Bernardo, Jingge Zhu, Jamie Evans · 2021 · arXiv
We analyze the symbol error probability (SEP) of $M$-ary pulse amplitude modulation ($M$-PAM) receivers equipped with optimal low-resolution quantizers. We first show that the optimum detector can be reduced to a simple decision rule. Using this simplification, an exact SEP expression for quantized $M$-PAM receivers is obtained when Nakagami-$m$ fading channel is considered. The derived expression enables the optimization of the quantizer and/or constellation under the minimum SEP criterion. Our analysis of optimal quantization for equidistant $M$-PAM receiver reveals the existence of error fl
Negative / Null Result ReportOpen accessComputer Science
Zachary Baker, Yuxiao Li · 2025 · arXiv
Sparse Autoencoders (SAEs) have emerged as a promising approach for interpreting neural network representations by learning sparse, human-interpretable features from dense activations. We investigate whether incorporating variational methods into SAE architectures can improve feature organization and interpretability. We introduce the Variational Sparse Autoencoder (vSAE), which replaces deterministic ReLU gating with stochastic sampling from learned Gaussian posteriors and incorporates KL divergence regularization toward a standard normal prior. Our hypothesis is that this probabilistic sampl
Negative / Null Result ReportOpen accessMathematics
Ben Adcock, Gregor Maier, Rahul Parhi · 2025 · arXiv
We develop a minimax theory for operator learning, where the goal is to estimate an unknown operator between separable Hilbert spaces from finitely many noisy input-output samples. For uniformly bounded Lipschitz operators, we prove information-theoretic lower bounds together with matching or near-matching upper bounds, covering both fixed and random designs under Hilbert-valued Gaussian noise and Gaussian white noise errors. The rates are controlled by the spectrum of the covariance operator of the measure that defines the error metric. Our setup is very general and allows for measures with u
Negative / Null Result ReportOpen accessComputer Science
Ali Subhan, Ashir Raza · 2026 · arXiv
DragDiffusion is a diffusion-based method for interactive point-based image editing that enables users to manipulate images by directly dragging selected points. The method claims that accurate spatial control can be achieved by optimizing a single diffusion latent at an intermediate timestep, together with identity-preserving fine-tuning and spatial regularization. This work presents a reproducibility study of DragDiffusion using the authors' released implementation and the DragBench benchmark. We reproduce the main ablation studies on diffusion timestep selection, LoRA-based fine-tuning, mas
Replication FailureOpen accessComputer Science
Zhichao Fan, Yanhang Li, Zexin Zhuang · 2026 · arXiv
Forced chain-of-thought (CoT) is widely assumed to make vision-language models more reliable on video question answering. We propose a small three-probe evaluation recipe to test that assumption: paired accuracy across direct, CoT, answer-first, and no-video conditions; a counterfactual video-swap diagnostic over the CoT chains; and a four-rung visual-degradation ladder. Each probe is reported under both a strict and a permissive regex scorer, with multiplicity correction over a manuscript-declared primary family. Applied to Qwen2.5-VL on Video-MME subsets, the recipe returns a two-part findin
Replication FailureOpen accessComputer Science
Xiqi Hao, Zengqing Wu, Yu-Xuan Qiu et al. · 2026 · arXiv
Multi-agent debate (MAD) is a promising strategy for improving LLM reasoning, but when agents converge on a shared answer, it is unclear whether that convergence reflects genuine deliberation or social compliance. We show that the conventional answer flip rate conflates three distinct mechanisms: spontaneous instability, stance-induced conformity, and reasoning-induced persuasion. Our three-source decomposition framework isolates each through controlled counterfactual conditions. In the primary MMLU-Pro setting, 37% of agent-question observations change under self-reflection alone, while robus
Replication FailureOpen accessComputer Science
J. Jon Ryu, Pavan Yeddanapudi, Xiangxiang Xu et al. · 2025 · arXiv
The InfoNCE objective, originally introduced for contrastive representation learning, has become a popular choice for mutual information (MI) estimation, despite its indirect connection to MI. In this paper, we demonstrate why InfoNCE should not be regarded as a valid MI estimator, and we introduce a simple modification, which we refer to as InfoNCE-anchor, for accurate MI estimation. Our modification introduces an auxiliary anchor class, enabling consistent density ratio estimation and yielding a plug-in MI estimator with significantly reduced bias. Beyond this, we generalize our framework us
Replication FailureOpen accessMathematics
Natesh Pillai, Aaron Smith · 2026 · arXiv
In Diaconis and Saloff-Coste (1996), the authors introduced the simple ``transvection" walk on $\mathrm{GL}_n(\mathbb F_2)$: at each step, choose two distinct rows and add one to the other. In Ben-Hamou (2025), the author recently proved that this walk has mixing time $O(n^2\log n)$. Inspired by applications in cryptography (see Sotiraki (2016)), Ben-Hamou and Peres (2018) conjectured that the first $k$ columns of this walk mixed in $O(nk \log(n))$ steps. Our main result is a proof of this conjecture uniformly in $n$ and $k.$ Our proof is based on a local-to-global entropy estimate, in the spi
Negative / Null Result ReportOpen accessComputer Science
Paul Röttger, Janet B. Pierrehumbert · 2021 · arXiv
Language use differs between domains and even within a domain, language use changes over time. For pre-trained language models like BERT, domain adaptation through continued pre-training has been shown to improve performance on in-domain downstream tasks. In this article, we investigate whether temporal adaptation can bring additional benefits. For this purpose, we introduce a corpus of social media comments sampled over three years. It contains unlabelled data for adaptation and evaluation on an upstream masked language modelling task as well as labelled data for fine-tuning and evaluation on
Negative / Null Result ReportOpen accessComputer Science
Mireille El-Assal, Pierre Tirilly, Ioan Marius Bilasco · 2023 · arXiv
Video analysis is a computer vision task that is useful for many applications like surveillance, human-machine interaction, and autonomous vehicles. Deep Convolutional Neural Networks (CNNs) are currently the state-of-the-art methods for video analysis. However they have high computational costs, and need a large amount of labeled data for training. In this paper, we use Convolutional Spiking Neural Networks (CSNNs) trained with the unsupervised Spike Timing-Dependent Plasticity (STDP) learning rule for action classification. These networks represent the information using asynchronous low-ener
Negative / Null Result ReportOpen accessComputer Science
Óscar García-Sierra, Ana Fernández-Pampillón Cesteros, Miguel Ortega-Martín · 2024 · arXiv
Subword tokenization algorithms used by Large Language Models are significantly more efficient and can independently build the necessary vocabulary of words and subwords without human intervention. However, those subwords do not always align with real morphemes, potentially impacting the models' performance, though it remains uncertain when this might occur. In previous research, we proposed a method to assess the morphological quality of vocabularies, focusing on the overlap between these vocabularies and the morphemes of a given language. Our evaluation method was built on three quality meas
Negative / Null Result ReportOpen accessNutrition. Foods and food supply
Kaijun Wang, Kaijun Wang, Jie Ma et al. · 2022 · Frontiers in Nutrition
Artemisia argyi leaf is a well-known species in traditional Chinese medicine, and its essential oil (AAEO) has been identified to exert various physiological activities. The aim of this study was to investigate the effects of AAEO on lipid metabolism and the potential microbial role in high-fat diet (HFD)-fed mice. A total of 50 male mice were assigned to five groups for feeding with a control diet (Con), a high-fat diet (HFD), and the HFD plus the low (LEO), medium (MEO), and high (HEO) doses of AAEO. The results demonstrated that dietary HFD markedly increased the body weight gain compared w
View details →DOI: 10.3389/fnut.2022.1024722 Negative / Null Result ReportOpen accessMedicine
Julie Labrosse, Marie Osdoit, Anne-Sophie Hamy et al. · 2020 · PLoS ONE
Although identified to be at a higher risk of relapse, no consensus exists on the treatment of breast cancer (BC) patients with no pathological complete response after neoadjuvant chemotherapy (NAC). The benefit of adjuvant chemotherapy (ADJ) in this context has scarcely been studied. We evaluated the benefit of administrating adjuvant chemotherapy in a real life cohort of BC patients with invasive residual disease after NAC. 1199 female BC patients with T1-3NxM0 invasive tumors receiving NAC at Institut Curie from 2002 to 2012 were included in the analysis. 1061 had been treated by NAC only,
View details →DOI: 10.1371/journal.pone.0234173 Negative / Null Result ReportOpen accessOrthopedic surgery
Yakup Ekinci, Mithat Oner, Ibrahim Karaman et al. · 2020 · Acta Orthopaedica et Traumatologica Turcica
Abstract Objective: The aim of this study was to compare simultaneous bilateral total knee arthroplasty (BTKA) and unilateral total knee arthroplasty (UTKA) in terms of morbidity, clinical and radiological findings and quality of life. Methods: The study included 48 simultaneous BTKAs (46 females, 2 males; mean age: 64.00±8.31 years) and 53 UTKAs (46 females, 7 males; mean age: 64.40±7.45 years) performed between November 2007 and June 2012. Groups were compared with respect to comorbidity, complications, blood transfusion, hospital stay, clinical and radiological (American Knee Society Score)
View details →DOI: 10.3944/aott.2014.3226 Negative / Null Result ReportOpen accessArctic medicine. Tropical medicine
Yuelin Xiong, Huatang Luo, Hao Wang et al. · 2026 · PLoS Neglected Tropical Diseases
Background Schistosomiasis, a neglected tropical disease (NTD), remains a public health concern in China. Health education is a fundamental intervention for its control. Even in transmission-interrupted areas like Wuhan, sustained awareness is crucial. However, recent literature on school-based interventions evaluating knowledge, attitudes and practices (KAP) among children in such areas is limited. Objective This study aimed to evaluate and compare the effectiveness of peer-led education versus routine health education in improving schistosomiasis-related KAP among elementary students in an u
View details →DOI: 10.1371/journal.pntd.0013857 