694 real negative results, null findings, and replication failures in Computer Science · Negative / Null Result Report. 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.
B. Selivany, A. Yousif · 2020 · The Journal Of Duhok University
Aim : The study aimed to compare Shaping ability of WaveOne Gold, Reciproc Blue and 2Shape NiTi systems having different design and metallurgic properties. Method : Forty five extracted human single root with 10 mm root length were…
View details →Rongxia Zhuang, Haiguang Fang, Yan Zhang et al. · 2017 · Smart Learning Environments
In the perspective of lifelong learning, lifewide learning and learning society, learning environments have expanded from schools to a more broader space, and even to the whole city. School, family, community, workplace, and museum can be…
View details →A. Ali, Khaled Mohammad Abu Sheirah · 2021 · International Journal of Education and Information Technologies
The study aims to investigate the perceptions of faculty members in the preparatory year at the University of Ha’il concerning the use of the Blackboard learning management system, and to identify the impact of the study variables (gender,…
View details →Shruti Hegde, Mabon Ninan, Jonathan R. Dillman et al. · 2025 · arXiv.org
General-purpose clinical natural language processing (NLP) tools are increasingly used for the automatic labeling of clinical reports. However, independent evaluations for specific tasks, such as pediatric chest radiograph (CXR) report…
View details →K. Krapinec, V. Jumić, Matija Balekić et al. · 2022 · Symmetry
Three FA indices showed correlations with age and magnitude of traits, but not in general. Significant correlations between magnitude of traits and their FA were more pronounced in AFA (12 traits) than in RFA (10 traits) in all age classes…
View details →Yang Liu, Yixing Luo, Xiaofeng Li et al. · 2026 · arXiv
Time series anomaly detection (TSAD) is essential for ensuring the safety and reliability of aerospace software systems. Although large language models (LLMs) provide a promising training-free alternative to unsupervised approaches, their effectiveness in aerospace settings remains under-examined because of complex telemetry, misaligned evaluation metrics, and the absence of domain knowledge. To address this gap, we introduce ATSADBench, the first benchmark for aerospace TSAD. ATSADBench comprises nine tasks that combine three pattern-wise anomaly types, univariate and multivariate signals, an
View details →Mandhapati P. Raju, Siddhartha Khaitan · 2009 · arXiv
The study deals with the parallelization of finite element based Navier-Stokes codes using domain decomposition and state-ofart sparse direct solvers. There has been significant improvement in the performance of sparse direct solvers. Parallel sparse direct solvers are not found to exhibit good scalability. Hence, the parallelization of sparse direct solvers is done using domain decomposition techniques. A highly efficient sparse direct solver PARDISO is used in this study. The scalability of both Newton and modified Newton algorithms are tested.
View details →Gi-Chol Cho, Kaoru Hagiwara · 1999 · arXiv
We study constraints on the supersymmetric standard model from the updated electroweak precision measurements --- the Z-pole experiments and the $W$-boson mass measurements. The supersymmetric-particle contributions to the universal gauge-boson-propagator corrections are parametrized by the three oblique parameters Sz, Tz and mw. The oblique corrections, the Zqq and Zll vertex corrections, and the vertex and box corrections to the μ-decay width are separately studied in detail. We first study individual contribution from the four sectors of the model, the squarks, the sleptons, the supersymmet
View details →Hao-jie Xu · 2023 · arXiv
In these proceedings, we present the three proposed observables to probe the neutron skin and symmetry energy with relativistic isobar collisions, namely, the isobar ratios of the produced hadron multiplicities ($N_{\rm ch}$), the mean transverse momenta ($\langle p_{\perp} \rangle$), and the net charge multiplicities ($ΔQ$). Our findings suggest potentially significant improvement to neutron skin and symmetry energy determination over traditional low energy methods.
View details →S. Schneider, J. Haidenbauer, C. Hanhart et al. · 2002 · arXiv
We study pion absorption on 3He employing trinucleon wave functions calculated from modern realistic NN interactions (Paris, CD Bonn). Even though the use of the new wave functions leads to a significant improvement over older calculations with regard to both cross section and polarization data, there are hints that polarization data with quasifree kinematics cannot be described by just two-nucleon absorption mechanisms.
View details →O. Behrend, R. Harris, P. Warren · 1993 · arXiv
We present a numerical analysis of the validity of classical and generalized hydrodynamics for Lattice Boltzmann Equation (LBE) and Lattice BGK methods in two and three dimensions, as a function of the collision parameters of these models. Our analysis is based on the wave-number dependence of the evolution operator. Good ranges of validity are found for BGK models as long as the relaxation time is chosen smaller than or equal to unity. The additional freedom in the choice of collision parameters for LBE models does not seem to give significant improvement.
View details →V. S. Raghu Parupudi · 2025 · arXiv
Vector comparison in high dimensions is a fundamental task in NLP, yet it is dominated by two baselines: the raw dot product, which is unbounded and sensitive to vector norms, and the cosine similarity, which discards magnitude information entirely. This paper challenges both standards by proposing and rigorously evaluating a new class of parameter-free, magnitude-aware similarity metrics. I introduce two such functions, Overlap Similarity (OS) and Hyperbolic Tangent Similarity (HTS), designed to integrate vector magnitude and alignment in a more principled manner. To ensure that my findings a
View details →Sungjin Park, Seungwoo Ryu, Edward Choi · 2022 · arXiv
Recent success of pre-trained language models (PLMs) has stimulated interest in their ability to understand and work with numbers. Yet, the numerical reasoning over measurements has not been formally studied despite their importance. In this study, we show that PLMs lack the capability required for reasoning over measurements. Furthermore, we find that a language model trained on a measurement-rich corpus shows better performance on understanding measurements. We propose a simple embedding strategy to better distinguish between numbers and units, which leads to a significant improvement in the
View details →Ramnath Kumar, Tristan Deleu, Yoshua Bengio · 2022 · arXiv
Recent studies show that task distribution plays a vital role in the meta-learner's performance. Conventional wisdom is that task diversity should improve the performance of meta-learning. In this work, we find evidence to the contrary; (i) our experiments draw into question the efficacy of our learned models: similar manifolds can be learned with a subset of the data (lower task diversity). This finding questions the advantage of providing more data to the model, and (ii) adding diversity to the task distribution (higher task diversity) sometimes hinders the model and does not lead to a signi
View details →Andreas Hocker · 2001 · arXiv
The recent precise measurement of the muon magnetic anomaly (g-2)_{mu} at BNL opens a window into possible new physics, provided the contribution from hadronic vacuum polarization is well understood. This talk summarizes the development in the evaluation of the leading order hadronic contributions. Significant improvement has been achieved in a series of analyses which is presented historically in three steps: (1), use of tau spectral functions in addition to e+e- cross sections, (2), extended use of perturbative QCD and (3), application of QCD sum rule techniques. The uncertainties, in partic
View details →Wojciech Zaremba, Evgenia Nitishinskaya, Boaz Barak et al. · 2025 · arXiv
We conduct experiments on the impact of increasing inference-time compute in reasoning models (specifically OpenAI o1-preview and o1-mini) on their robustness to adversarial attacks. We find that across a variety of attacks, increased inference-time compute leads to improved robustness. In many cases (with important exceptions), the fraction of model samples where the attack succeeds tends to zero as the amount of test-time compute grows. We perform no adversarial training for the tasks we study, and we increase inference-time compute by simply allowing the models to spend more compute on reas
View details →Hongxu Zhou · 2026 · arXiv
Intrinsic self-correction in Large Language Models (LLMs) frequently fails in open-ended reasoning tasks due to ``hallucination snowballing,'' a phenomenon in which models recursively justify early errors during free-text reflection. While structured feedback can mitigate this issue, existing approaches often rely on externally trained critics or symbolic tools, reducing agent autonomy. This study investigates whether enforcing structured reflection purely through Outlines-based constrained decoding can disrupt error propagation without additional training. Evaluating an 8-billion-parameter mo
View details →Linas Vepstas · 2013 · arXiv
This report describes the suicidality prediction models created under the DARPA DCAPS program in association with the Durkheim Project [http://durkheimproject.org/]. The models were built primarily from unstructured text (free-format clinician notes) for several hundred patient records obtained from the Veterans Health Administration (VHA). The models were constructed using a genetic programming algorithm applied to bag-of-words and bag-of-phrases datasets. The influence of additional structured data was explored but was found to be minor. Given the small dataset size, classification between c
View details →Shreya Chandrasekhar, Chieh-Yang Huang, Ting-Hao 'Kenneth' Huang · 2023 · arXiv
The rapid growth of scientific publications, particularly during the COVID-19 pandemic, emphasizes the need for tools to help researchers efficiently comprehend the latest advancements. One essential part of understanding scientific literature is research aspect classification, which categorizes sentences in abstracts to Background, Purpose, Method, and Finding. In this study, we investigate the impact of different datasets on model performance for the crowd-annotated CODA-19 research aspect classification task. Specifically, we explore the potential benefits of using the large, automatically
View details →Egor Shevchenko, Elena Bruches · 2026 · arXiv
Data quality is a critical factor in the effectiveness of machine learning models. Label errors, present even in widely used benchmarks, introduce noise into training data and reduce model generalization. In this work, we conduct a comparative analysis of two automatic label error detection methods - Confident Learning and Dataset Cartography - on three Russian text classification corpora of varying size, number of classes, and domain: ru_emotion_e-culture (49,123 examples, emotion classification), RuCoLA (8,524 examples, linguistic acceptability), and TERRa (2,337 examples, textual entailment
View details →Mario Sanz-Guerrero, Katharina von der Wense · 2025 · arXiv
In-context learning (ICL) has transformed the use of large language models (LLMs) for NLP tasks, enabling few-shot learning by conditioning on labeled examples without finetuning. Despite its effectiveness, ICL is prone to errors, especially for challenging examples. With the goal of improving the performance of ICL, we propose corrective in-context learning (CICL), an approach that incorporates a model's incorrect predictions alongside ground truth corrections into the prompt, aiming to enhance classification accuracy through self-correction. However, contrary to our hypothesis, extensive exp
View details →Hui Zhang, Kiduk Yang, Elin Jacob · 2015 · arXiv
Despite limited success, information retrieval (IR) systems today are not intelligent or reliable. IR systems return poor search results when users formulate their information needs into incomplete or ambiguous queries (i.e., weak queries). Therefore, one of the main challenges in modern IR research is to provide consistent results across all queries by improving the performance on weak queries. However, existing IR approaches such as query expansion are not overly effective because they make little effort to analyze and exploit the meanings of the queries. Furthermore, word sense disambiguati
View details →Philippe Laban, Tobias Schnabel, Jennifer Neville · 2026 · arXiv
Large Language Models (LLMs) are poised to disrupt knowledge work, with the emergence of delegated work as a new interaction paradigm (e.g., vibe coding). Delegation requires trust - the expectation that the LLM will faithfully execute the task without introducing errors into documents. We introduce DELEGATE-52 to study the readiness of AI systems in delegated workflows. DELEGATE-52 simulates long delegated workflows that require in-depth document editing across 52 professional domains, such as coding, crystallography, and music notation. Our large-scale experiment with 19 LLMs reveals that cu
View details →Prabhav Jain, Nikolai Miklin, Mariami Gachechiladze · 2026 · arXiv
Information Causality was introduced as a physical principle for constraining the set of nonlocal correlations. In recent work, we proposed an extension of Information Causality that allows correlations among Alice's inputs. This extended principle yields tighter constraints than the original formulation and recovers part of the quantum boundary in certain Bell scenarios. In this work, we further investigate the implications of extended Information Causality and apply it to scenarios beyond binary inputs and outputs. We derive a family of quantum Bell inequalities that strengthen previously kn
View details →Jirui Qi, Raquel Fernández, Arianna Bisazza · 2023 · arXiv
Multilingual large-scale Pretrained Language Models (PLMs) have been shown to store considerable amounts of factual knowledge, but large variations are observed across languages. With the ultimate goal of ensuring that users with different language backgrounds obtain consistent feedback from the same model, we study the cross-lingual consistency (CLC) of factual knowledge in various multilingual PLMs. To this end, we propose a Ranking-based Consistency (RankC) metric to evaluate knowledge consistency across languages independently from accuracy. Using this metric, we conduct an in-depth analys
View details →Gil Katz, Pablo Piantanida, Merouane Debbah · 2016 · arXiv
A collaborative distributed binary decision problem is considered. Two statisticians are required to declare the correct probability measure of two jointly distributed memoryless process, denoted by $X^n=(X_1,\dots,X_n)$ and $Y^n=(Y_1,\dots,Y_n)$, out of two possible probability measures on finite alphabets, namely $P_{XY}$ and $P_{\bar{X}\bar{Y}}$. The marginal samples given by $X^n$ and $Y^n$ are assumed to be available at different locations. The statisticians are allowed to exchange limited amount of data over multiple rounds of interactions, which differs from previous work that deals mai
View details →Rumman Ahmed Prodhan, Sumya Akter, Tanmoy Sarkar Pias et al. · 2023 · arXiv
The human brain is a complex organ, still completely undiscovered, that controls almost all the parts of the body. Apart from survival, the human brain stimulates emotions. Recent research indicates that brain signals can be very effective for emotion recognition. However, which parts of the brain exhibit most of the emotions is still under-explored. In this study, we empirically analyze the contribution of each part of the brain in exhibiting emotions. We use the DEAP dataset to find the most optimal electrode set which eventually leads to the effective brain part associated with emotions. We
View details →Roberto Casadio, Fabio Finelli, Mattia Luzzi et al. · 2004 · arXiv
Improved Wentzel-Kramers-Brillouin (WKB)-type approximations are presented in order to study cosmological perturbations beyond the lowest order. Our methods are based on functions which approximate the true perturbation modes over the complete range of the independent (Langer) variable, from sub-horizon to super-horizon scales, and include the region near the turning point. We employ both a perturbative Green's function technique and an adiabatic (or ``semiclassical'') expansion (for a linear turning point) in order to compute higher order corrections. Improved general expressions for the WKB
View details →Abdul Waheed, Zhen Wu, Dareen Alharthi et al. · 2025 · arXiv
Precisely evaluating video understanding models remains challenging: commonly used metrics such as BLEU, ROUGE, and BERTScore fail to capture the fineness of human judgment, while obtaining such judgments through manual evaluation is costly. Recent work has explored using large language models (LLMs) or multimodal LLMs (MLLMs) as evaluators, but their extension to video understanding remains relatively unexplored. In this work, we introduce VideoJudge, a 3B and 7B-sized MLLM judge specialized to evaluate outputs from video understanding models (\textit{i.e.}, text responses conditioned on vide
View details →Gokhan Alkac, Luca Basanisi, Ercan Kilicarslan et al. · 2017 · arXiv
We revisit the problem of the bulk-boundary unitarity clash in 2 + 1 dimensional gravity theories, which has been an obstacle in providing a viable dual two-dimensional conformal field theory for bulk gravity in anti-de Sitter (AdS) spacetime. Chiral gravity, which is a particular limit of cosmological topologically massive gravity (TMG), suffers from pertur- bative log-modes with negative energies inducing a non-unitary logarithmic boundary field theory. We show here that any f(R) extension of TMG does not improve the situation. We also study the perturbative modes in the metric formulation o
View details →