Negative / Null Result ReportOpen accessComputer Science
Eisha Hassan, Fazila‐Tun‐Nesa Malik, Qazi Waqas Khan et al. · 2025 · IEEE Access
Document Clustering has attracted the interest of many researchers who have created several solutions to this problem by combining different techniques, models, and algorithms. While famous for its simplicity, the most commonly used algorithm, K-Means, suffers from issues such as finding the optimal value for k and random initialization of the centroids. In this paper, we propose a hybrid methodology combining K-Means++ with the metaheuristic algorithm PSO to overcome the challenges of both these algorithms. K-Means++ is a smart initialization technique that selects clusters based on probabili
View details →Negative / Null Result ReportOpen accessComputer Science
Sterling Williams-Ceci, Maurice Jakesch, Advait Bhat et al. · 2026 · Science Advances
Artificial intelligence (AI) writing assistants powered by large language models (LLMs) are increasingly used to make autocomplete suggestions to people as they write text. Can these AI writing assistants affect people’s attitudes in this…
View details →Negative / Null Result ReportOpen accessComputer Science
Muhammad Dawood, Kim Branson, Sabine Tejpar et al. · 2026 · Nature Biomedical Engineering
Deep learning models that infer clinically relevant biomarker status from tissue images are being explored as rapid and low-cost alternatives to molecular testing. Here we show, through statistical analysis across multiple cancer types, datasets and modelling approaches, that the datasets used to train these models contain strong dependencies between biomarkers and clinicopathological features, which prevent models from isolating the effect of a single biomarker and lead them to learn confounded signals. Consequently, their prediction accuracy varies substantially with the status of codependen
View details →Negative / Null Result ReportOpen accessComputer Science
Andrea Espis, Chiara Marzi, Stefano Diciotti · 2025 · Scientific Reports
Self-supervised learning (SSL) in computer vision has shown its potential to reduce reliance on labeled data. However, most studies focused on balanced, large, broad-domain datasets like ImageNet, whereas, in real-world medical applications, dataset size is typically limited. This study compares the performance of SSL versus supervised learning (SL) on small, imbalanced medical imaging datasets. We experimented with four binary classification tasks: age prediction and diagnosis of Alzheimer's disease from brain magnetic resonance imaging scans, pneumonia from chest radiograms, and retinal dise
View details →Negative / Null Result ReportOpen accessComputer Science
M. Said Doğru, Emily Faulconer · 2025 · Research in Science Education
Abstract The emergence of ChatGPT brings an opportunity to lighten the workload of in-person teaching assistance in science laboratory courses. We investigated the use of the language model developed by OpenAI as a virtual teaching assistant for an introductory undergraduate biology laboratory course. Student-generated questions related to a laboratory exercise on enzyme activity were separately provided to ChatGPT and a cohort of graduate teaching assistants (TAs). Responses were evaluated for content accuracy and teaching effectiveness. Results revealed that human TAs were more accurate in t
View details →Negative / Null Result ReportComputer Science
Simon Holmbacka, Robert Müller · 2017
Current benchmark suites for evaluating energy efficiency of modern computer systems fail to replicate real-world streaming applications accurately enough because of indeterministic load pattern which depends heavily on the input data of…
View details →Negative / Null Result ReportOpen accessComputer Science
Michael Vaccaro, Mikayla Friday, Arash E. Zaghi · 2025 · Applied Sciences
Multi-agent large language models promise flexible, modular architectures for delivering personalized educational content. Drawing on a pilot randomized controlled trial with middle school students (n = 23), we introduce a two-agent GPT-4 framework in which a Profiler agent infers learner-specific preferences and a Rewrite agent dynamically adapts science passages via an explicit message-passing protocol. We implement structured system and user prompts as inter-agent communication schemas to enable real-time content adaptation. The results of an ordinal logistic regression analysis hinted that
View details →Negative / Null Result ReportOpen accessComputer Science
Tristan Piater, Niklas Penzel, Gideon Stein et al. · 2024 · arXiv
A substantial body of research has focused on developing systems that assist medical professionals during labor-intensive early screening processes, many based on convolutional deep-learning architectures. Recently, multiple studies explored the application of so-called self-attention mechanisms in the vision domain. These studies often report empirical improvements over fully convolutional approaches on various datasets and tasks. To evaluate this trend for medical imaging, we extend two widely adopted convolutional architectures with different self-attention variants on two different medical
View details →Negative / Null Result ReportOpen accessComputer Science
J. X. Lu, Shibaji Roy, Zhao-Long Wang et al. · 2007 · arXiv
Following \cite{Bai:2006vv} we here consider the supergravity solutions representing the charged non-supersymmetric $p$-brane (for $1\leq p \leq 6$) intersecting with chargeless non-supersymmetric 1-brane and 0-brane of type II string theories. We show how these solutions nicely interpolate between black $p$-branes and the Kaluza-Klein "bubble of nothing" (BON) by continuously varying some parameters characterizing the solutions from one set of values to another. By performing a time symmetric general bubble initial data analysis, we show that the interpolation implies a possible transition fr
View details →Negative / Null Result ReportOpen accessComputer Science
Joao V. C. Evangelista, Georges Kaddoum, Zeeshan Sattar · 2021 · arXiv
5G cellular networks are designed to support a new range of applications not supported by previous standards. Among these, ultra-reliable low-latency communication (URLLC) applications are arguably the most challenging. URLLC service requires the user equipment (UE) to be able to transmit its data under strict latency constraints with high reliability. To address these requirements, new technologies, such as mini-slots, semi-persistent scheduling and grant-free access were introduced in 5G standards. In this work, we formulate a spatiotemporal mathematical model to evaluate the user-plane late
View details →Negative / Null Result ReportOpen accessComputer Science
Thorsten Brants · 1995 · arXiv
A technique for reducing a tagset used for n-gram part-of-speech disambiguation is introduced and evaluated in an experiment. The technique ensures that all information that is provided by the original tagset can be restored from the reduced one. This is crucial, since we are interested in the linguistically motivated tags for part-of-speech disambiguation. The reduced tagset needs fewer parameters for its statistical model and allows more accurate parameter estimation. Additionally, there is a slight but not significant improvement of tagging accuracy.
View details →Negative / Null Result ReportOpen accessComputer Science
Shamima Mithun, Leila Kosseim · 2017 · arXiv
The work presented in this paper attempts to evaluate and quantify the use of discourse relations in the context of blog summarization and compare their use to more traditional and factual texts. Specifically, we measured the usefulness of 6 discourse relations - namely comparison, contingency, illustration, attribution, topic-opinion, and attributive for the task of text summarization from blogs. We have evaluated the effect of each relation using the TAC 2008 opinion summarization dataset and compared them with the results with the DUC 2007 dataset. The results show that in both textual genr
View details →Negative / Null Result ReportOpen accessComputer Science
Zhifei Dou, Shabnam Hassani, Ou Wei · 2026 · arXiv
Flowcharts are widely used in industrial requirements, but usually remain embedded as static images. Vision Language Models (VLMs) show promise in the conversion of these flowcharts into machine-readable models for RE activities, yet, when directly applied to flowchart conversion, they often fail on topology-critical visual details. To address this, we propose EdgeFlow that augments a VLM's original input with a deterministically extracted Canny edge map-acting as a structural prior-to improve flowchart-to-Mermaid conversion, without requiring annotated training data or domain-specific model f
View details →Negative / Null Result ReportOpen accessComputer Science
Yibo Wang, Yuanyuan Mao, Lik-Hang Lee et al. · 2024 · arXiv
The AR 3D book has shown significant potential in enhancing students' learning outcomes. However, the creation process of 3D books requires a significant investment of time, effort, and specialized skills. Thus, in this paper, we first conduct a three-day workshop investigating how AI can support the automated creation of 3D books. Informed by the design insights derived from the workshop, we developed Metabook, a system that enables even novice users to create 3D books from text automatically. To our knowledge, Metabook is the first system to offer end-to-end 3D book generation. A follow-up s
View details →Negative / Null Result ReportOpen accessComputer Science
Mandar Patil, Pankaj S. Joshi, Masashi Kimura et al. · 2011 · arXiv
We explore the Reissner-Nordström naked singularities with a charge $Q$ larger than its mass $M$ from the perspective of the particle acceleration. We first consider a collision between two test particles following the radial geodesics in the Reissner-Nordström naked singular geometry. An initially radially ingoing particle turns back due to the repulsive effect of gravity in the vicinity of naked singularity. Such a particle then collides with an another radially ingoing particle. We show that the center of mass energy of collision taking place at $r \approx M$ is unbound, in the limit where
View details →Negative / Null Result ReportOpen accessComputer Science
Keith Harrigian, Tina Tang, Anthony Gonzales et al. · 2023 · arXiv
Diabetic eye disease is a major cause of blindness worldwide. The ability to monitor relevant clinical trajectories and detect lapses in care is critical to managing the disease and preventing blindness. Alas, much of the information necessary to support these goals is found only in the free text of the electronic medical record. To fill this information gap, we introduce a system for extracting evidence from clinical text of 19 clinical concepts related to diabetic eye disease and inferring relevant attributes for each. In developing this ophthalmology phenotyping system, we are also afforded
View details →Negative / Null Result ReportOpen accessComputer Science
Harsh Deshpande, Kushal Chawla, Sangwoo Cho et al. · 2026 · arXiv
Production agentic systems routinely face evolving constraints and must comply from the very next interaction. Scenarios like a tool-call notification changing a compliance threshold or a policy update adding disclosure requirements fit this criteria, having close to no room for errors in production. This proactive adaptation setting is common in deployment, but absent from current benchmarks, which assume either static constraint sets or reactive protocols with evaluation feedback. We introduce RECAP, a benchmark that measures continual-learning phenomena (forgetting, regression, forward tran
View details →Negative / Null Result ReportOpen accessComputer Science
Jingjing Huo, Christian Herold, Yingbo Gao et al. · 2020 · arXiv
Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e.g., document-level translation, or having meta-information. Although there exist various architectures and analyses, the effectiveness of different context-aware NMT models is not well explored yet. This paper analyzes the performance of document-level NMT models on four diverse domains with a varied amount of parallel document-level bilingual data. We conduct a comprehensive set of experiments to investigate the impact of document-level NMT. We
View details →Negative / Null Result ReportOpen accessComputer Science
Muhammad Asif Ayub, Khubaib Ahmad, Kashif Ahmad et al. · 2021 · arXiv
This paper presents our contributions to the MediaEval 2021 task namely "WaterMM: Water Quality in Social Multimedia". The task aims at analyzing social media posts relevant to water quality with particular focus on the aspects like watercolor, smell, taste, and related illnesses. To this aim, a multimodal dataset containing both textual and visual information along with meta-data is provided. Considering the quality and quantity of available content, we mainly focus on textual information by employing three different models individually and jointly in a late-fusion manner. These models includ
View details →Negative / Null Result ReportOpen accessComputer Science
Tao Han, Benjamin Nachman, Xing Wang · 2018 · arXiv
It is extremely challenging to probe the charm-quark Yukawa coupling at hadron colliders primarily due to the large Standard Model (SM) background (including $h\to b\bar b$) and the lack of an effective trigger for the signal $h\to c\bar c$. We examine the feasibility of probing this coupling at the LHC via a Higgs radiative decay $h\rightarrow c\bar{c}γ$. The existence of an additional photon in the final state may help for the signal identification and background suppression. Adopting a refined triggering strategy and utilizing basic machine learning, we find that a coupling limit of about 8
View details →Negative / Null Result ReportOpen accessComputer Science
K. A. Assamagan, N. Gollub · 2004 · arXiv
The feasibility of detecting a heavy charged Higgs boson, m(H^{+-})>m(t)+m(b), decaying in the H^{+-}->tb channel is studied with the fast simulation of the ATLAS detector. We study the gg->H^{+-}tb production process at the LHC which together with the aforementioned decay channel leads to four b-quarks in the final state. The whole production and decay chain reads gg->H^{+-}tb->t\bar{t}b\bar{b}->b\bar{b}b\bar{b}lν\bar{q}q'. Combinatorial background is a major difficulty in this multi-jet environment but can be overcome by employing multivariate techniques in the event reconstruction. Requirin
View details →Negative / Null Result ReportOpen accessComputer Science
Changhun Oh, Youngrong Lim, Bill Fefferman et al. · 2021 · arXiv
Sampling from probability distributions of quantum circuits is a fundamentally and practically important task which can be used to demonstrate quantum supremacy using noisy intermediate-scale quantum devices. In the present work, we examine classical simulability of sampling from the output photon-number distribution of linear-optical circuits composed of random beam splitters with equally distributed squeezed vacuum states and single-photon states input. We provide efficient classical algorithms to simulate linear-optical random circuits and show that the algorithms' error is exponentially sm
View details →Negative / Null Result ReportOpen accessComputer Science
Minghao Wu, Alham Fikri Aji · 2023 · arXiv
As large language models (LLMs) continue to advance, accurately and comprehensively evaluating their performance becomes increasingly challenging. Ranking the relative performance of LLMs based on Elo ratings, according to human judgment, is gaining more popularity. However, the extent to which humans and LLMs are capable evaluators remains uncertain. This study investigates the behavior of crowd-sourced and expert annotators, as well as LLMs, when comparing outputs from different models. To achieve this, we curate a dataset of intentionally flawed machine-generated answers. Our findings revea
View details →Negative / Null Result ReportOpen accessComputer Science
Conrad Borchers, Dalia Sara Gala, Benjamin Gilburt et al. · 2022 · arXiv
The growing capability and availability of generative language models has enabled a wide range of new downstream tasks. Academic research has identified, quantified and mitigated biases present in language models but is rarely tailored to downstream tasks where wider impact on individuals and society can be felt. In this work, we leverage one popular generative language model, GPT-3, with the goal of writing unbiased and realistic job advertisements. We first assess the bias and realism of zero-shot generated advertisements and compare them to real-world advertisements. We then evaluate prompt
View details →Negative / Null Result ReportOpen accessComputer Science
Daniel Rajchwald, Natasha Markuzon · 2016 · arXiv
The importance of peer influence on consumer actions plays a vital role in marketing efforts. However, peer influence effects are often confounded with latent homophily, which are unobserved commonalities that drive friendship. Understanding causality has become one of the pressing issues of current research. We present an approach to explicitly account for various causal influences. We implement a simulation framework to show the effectiveness of two latent homophily proxies, latent coordinates and community membership, in improving peer influence effect estimates on game downloads in a Japan
View details →Negative / Null Result ReportOpen accessComputer Science
V. Bertin, E. Nezri, J. Orloff · 2002 · arXiv
We study potential signals of neutralino dark matter indirect detection by neutrino telescopes in a wide range of CMSSM parameters. We also compare with direct detection potential signals taking into account in both cases present and future experiment sensitivities. Only models with neutralino annihilation into gauge bosons can satisfy cosmological constraints and current neutrino indirect detection sensitivities. For both direct and indirect detection, only next generation experiments will be able to really test this kind of models.
View details →Negative / Null Result ReportOpen accessComputer Science
Maxim Krasnov, Ufuk Aydemir, Maxim Khlopov · 2025 · arXiv
We investigate the particle production by the Nambu-Goldstone boson in the spontaneous baryogenesis scenario for large misalignment angles. Studying numerically the case of an arbitrary initial phase, we reproduce the cubic dependence on the initial value of the phase of baryon asymmetry in the case of small oscillations and present our results for an initial phase close to π. Our calculations indicate that there is a saturation in particle production as the initial phase approaches π in Minkowski spacetime. Furthermore, we present numerical solutions in the Friedmann-Lemaître-Robertson-Walker
View details →Negative / Null Result ReportOpen accessComputer Science
Seyed Amir Kasaei, Arash Marioriyad, Mahbod Khaleti et al. · 2026 · arXiv
Large Vision-Language Models (LVLMs) have achieved remarkable proficiency in explicit visual recognition, effectively describing what is directly visible in an image. However, a critical cognitive gap emerges when the visual input serves only as a clue rather than the answer. We identify that current models struggle with the complex, multi-step reasoning required to solve problems where information is not explicitly depicted. Successfully solving a rebus puzzle requires a distinct cognitive workflow: the model must extract visual and textual attributes, retrieve linguistic prior knowledge (suc
View details →Negative / Null Result ReportOpen accessComputer Science
S. Haouat, K. Nouicer · 2013 · arXiv
In this paper we have studied the problem of scalar particles pair creation by an electric field in the presence of a minimal length. Two sets of exact solutions for the Klein Gordon equation are given in momentum space. Then the canonical method based on Bogoliubov transformation connecting the "in" with the "out" states is applied to calculate the probability to create a pair of particles and the mean number of created particles. The number of created particles per unit of time per unit of length, which is related directly to the experimental measurements, is calculated. It is shown that, wi
View details →Negative / Null Result ReportOpen accessComputer Science
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal et al. · 2024 · arXiv
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and
View details →