744 real negative results, null findings, and replication failures in Computer Science. Search the index →
WASTE indexes published research — it does not host or republish full papers. Each entry is a metadata record compiled from open scholarly databases; the abstract is shown in full only where the paper is openly licensed, otherwise a short excerpt under fair use. Classifications are automated and approximate.
Fabrice Djatsa · 2019 · Journal of Information Security
As the economy increases its dependence on the internet to increase efficiency and productivity in all aspects of society, close attention has been directed to solve the challenges related to internet security. Despite the large amount of…
View details →Conor Hanrahan · 2019 · ARROW@Dublin Institute of Technology (Dublin Institute of Technology)
The presence of noise in electroencephalography (EEG) signals can significantly reduce the accuracy of the analysis of the signal. This study assesses to what extent stacked autoencoders designed using one-dimensional convolutional neural…
View details →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 →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 →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 →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 →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 →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 →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 →Carmine Abate, Arthur Azevedo de Amorim, Roberto Blanco et al. · 2018 · arXiv
We propose a new formal criterion for evaluating secure compilation schemes for unsafe languages, expressing end-to-end security guarantees for software components that may become compromised after encountering undefined behavior---for example, by accessing an array out of bounds. Our criterion is the first to model dynamic compromise in a system of mutually distrustful components with clearly specified privileges. It articulates how each component should be protected from all the others---in particular, from components that have encountered undefined behavior and become compromised. Each comp
View details →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 →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 →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 →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 →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 →Xinzhe Chen, Jianjiang Li · 2023 · arXiv
The escalating surge in data generation presents formidable challenges to information technology, necessitating advancements in storage, retrieval, and utilization. With the proliferation of artificial intelligence and big data, the "Data Age 2025" report forecasts an exponential increase in global data production. The escalating data volumes raise concerns about efficient data processing. The paper addresses the predicament of achieving a lower compression ratio while maintaining or surpassing the compression performance of state-of-the-art techniques. This paper introduces a lossy compressio
View details →Liyun Zhang, Jiayi Guo · 2026 · arXiv
We document an empirical phenomenon in chain-of-thought and ReAct agents driven by ten large language models from seven architecture families: meaning-bearing perturbations (e.g., paraphrase, synonym) alter final answers more often than presentation perturbations (e.g., formatting, reordering) of comparable severity. Across 68 cells spanning GSM8K, MATH, and HotpotQA (1,530 originals and $\sim$11,150 variants), the inconsistency gap averages +19.69 pp after severity matching (paired $t=9.58$, $p<0.0001$), with 64/68 cells positive. The gap survives four severity-proxy audits and remains signif
View details →Rohit Agarwala, Kalyan Dey · 2025 · arXiv
We employ PYTHIA8 simulations to study forward-backward (FB) correlations in pp collisions at LHC energies, probing non-perturbative QCD dynamics via color reconnection (CR) and QCD radiation (ISR/FSR). Using \texttt{PYTHIA8} (v8.311) under ALICE/ATLAS kinematics, we analyze: extensive: FB multiplicity ($b_{\rm corr}^{\rm mult}$) and summed $p_{\rm T}$ ($b_{\rm corr}^{\sum p_{\rm T}}$) correlations; intensive: FB mean $p_{\rm T}$ ($b_{\rm corr}^{\overline p_{\rm T}}$) correlations; and \textit{strongly intensive:} $Σ_{\rm N_F N_B}$ quantity in symmetric pseudorapidity intervals, validated agai
View details →Jayoo Hwang, Xiaowen Zhang, Vedant Padwal · 2026 · arXiv
Autonomous web navigation remains challenging for LLM agents, and the strongest generalist systems rely on proprietary reasoning models whose inference cost is prohibitive for the repetitive tasks where such agents would be most useful. We argue this gap stems not from insufficient model capability but from agent architectures that fail to replicate three human cognitive advantages: selective attention to relevant page regions, persistent memory of website structure, and procedural fluency with common interaction patterns. We introduce WebChallenger, a web agent framework that addresses each g
View details →Shaoxuan Zhou, Yafei Sun, Jing Zhang et al. · 2026 · arXiv
Short-video platforms like Douyin and Kwai have become central to adolescent digital life, but they also risk exposing teens to algorithmically amplified harmful content. Despite its societal importance, the scale, mechanisms, and real-world impact of this exposure remain poorly understood. Measuring it is challenging: recommendation feeds are personalized black boxes, harmful content employs sophisticated evasion tactics, and naive crawlers fail to replicate authentic teen behavior. To bridge this gap, we propose PHTV-Scout, the first large-scale, behaviorally grounded measurement framework f
View details →Ali Ebrahimpour-Boroojeny · 2025 · arXiv
We propose new methodologies for both unlearning random set of samples and class unlearning and show that they outperform existing methods. The main driver of our unlearning methods is the similarity of predictions to a retrained model on both the forget and remain samples. We introduce Adversarial Machine UNlearning (AMUN), which surpasses prior state-of-the-art methods for image classification based on SOTA MIA scores. AMUN lowers the model's confidence on forget samples by fine-tuning on their corresponding adversarial examples. Through theoretical analysis, we identify factors governing AM
View details →Nilesh Kumar Sahu, Snehil Gupta, Haroon R Lone · 2025 · arXiv
Social Anxiety Disorder (SAD) is a widespread mental health condition, yet its lack of objective markers hinders timely detection and intervention. While previous research has focused on behavioral and non-verbal markers of SAD in structured activities (e.g., speeches or interviews), these settings fail to replicate real-world, unstructured social interactions fully. Identifying non-verbal markers in naturalistic, unstaged environments is essential for developing ubiquitous and non-intrusive monitoring solutions. To address this gap, we present AnxietyFaceTrack, a study leveraging facial video
View details →Syed Kazmi, Berk Gorgulu, Mucahit Cevik et al. · 2023 · arXiv
Wind power forecasting helps with the planning for the power systems by contributing to having a higher level of certainty in decision-making. Due to the randomness inherent to meteorological events (e.g., wind speeds), making highly accurate long-term predictions for wind power can be extremely difficult. One approach to remedy this challenge is to utilize weather information from multiple points across a geographical grid to obtain a holistic view of the wind patterns, along with temporal information from the previous power outputs of the wind farms. Our proposed CNN-RNN architecture combine
View details →Emily L. Aiken, Andre T. Nguyen, Mauricio Santillana · 2019 · arXiv
We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Internet search data. We find that a GRU has lower prediction error than current state-of-the-art methods for data-driven influenza prediction at time horizons of over two weeks. In contrast with other machine learning approaches, the inclusion of real-time Internet search data does not improve GRU predictions.
View details →Sungmin Cha, Kyunghyun Cho · 2024 · arXiv
Continual learning (CL) aims to train a model on a sequence of tasks (i.e., a CL scenario) while balancing the trade-off between plasticity (learning new tasks) and stability (retaining prior knowledge). The dominantly adopted conventional evaluation protocol for CL algorithms selects the best hyperparameters (e.g., learning rate, mini-batch size, regularization strengths, etc.) within a given scenario and then evaluates the algorithms using these hyperparameters in the same scenario. However, this protocol has significant shortcomings: it overestimates the CL capacity of algorithms and relies
View details →Jiarui Xie, Mutahar Safdar, Andrei Mircea et al. · 2024 · arXiv
Machine learning (ML)-based cyber-physical systems (CPSs) have been extensively developed to improve the print quality of additive manufacturing (AM). However, the reproducibility of these systems, as presented in published research, has not been thoroughly investigated due to a lack of formal evaluation methods. Reproducibility, a critical component of trustworthy artificial intelligence, is achieved when an independent team can replicate the findings or artifacts of a study using a different experimental setup and achieve comparable performance. In many publications, critical information nec
View details →Xun Liang, Huayi Lai, Hanyu Wang et al. · 2025 · arXiv
Large language models (LLMs) have gained significant traction in medical decision support systems, particularly in the context of medical question answering and role-playing simulations. A common practice, Prompt-Based Role Playing (PBRP), instructs models to adopt different clinical roles (e.g., medical students, residents, attending physicians) to simulate varied professional behaviors. However, the impact of such role prompts on model reasoning capabilities remains unclear. This study introduces the RP-Neuron-Activated Evaluation Framework(RPNA) to evaluate whether role prompts induce disti
View details →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 →Sarah Ball, Simeon Allmendinger, Frauke Kreuter et al. · 2025 · arXiv
Generative AI (GenAI) is increasingly used in survey contexts to simulate human preferences. While many research endeavors evaluate the quality of synthetic GenAI data by comparing model-generated responses to gold-standard survey results, fundamental questions about the validity and reliability of using LLMs as substitutes for human respondents remain. Our study provides a technical analysis of how demographic attributes and prompt variations influence latent opinion mappings in large language models (LLMs) and evaluates their suitability for survey-based predictions. Using 14 different model
View details →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
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