19,859 real negative results, null findings, and replication failures · 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.
Kagami M, Kataoka Y, Hirao Y et al. · 2026 · IEEE transactions on visualization and computer graphics
Augmented Reality (AR) can enhance accessibility by anchoring virtual windows to the user's body. Among common approaches, head-following windows help maintain floating virtual windows within the user's field of view. Previous studies have…
View details →Myllylä IL, Wiklund M, Haakana V et al. · 2026 · Clinical linguistics & phonetics
Speech of persons on the autism spectrum is often described as prosodically distinctive, yet the perceptual basis and acoustic correlates of these judgements remain undefined. We investigated how prosodic typicality is perceived in…
View details →Filtness AJ, Miller KA, Maynard S et al. · 2026 · Scientific reports
Driver sleepiness contributes to a substantial proportion of road crashes. Drivers experiencing sleepiness are advised to take a break and have a caffeinated drink followed by a short nap (caffeine nap). However, previous research…
View details →Widłak P, Malara M, Kuk A et al. · 2026 · Preprint
Abstract Movement quality and postural control are crucial for performance and injury prevention in team-sport athletes. Although FMS and YBT are commonly used, their ability to predict injury risk is limited when used alone. Therefore,…
View details →Etoundi JC, Juhel A · 2026 · Preprint
Social media use has been identified as a significant risk factor for the development and maintenance of eating disorders, particularly among young adults. Despite the growing urgency of this public health issue, effective prevention…
View details →Chowdhury A, Dasgupta D · 2026 · Anthropologischer Anzeiger; Bericht uber die biologisch-anthropologische Literatur
Objective : Present study delineates to explore the relation of actual body weight and body self-image perception among tribal adolescent girls and boys. Materials and methods : This cross sectional study is conducted at Purulia district…
View details →Zanco B, Mirth CK, Sgrò CM et al. · 2026 · npj aging
Dietary restriction may extend lifespan by improving late-life gut health. Because micronutrients mediate the effects of macronutrient ratios on longevity, we examined how cholesterol limitation affects gut health in female Drosophila…
View details →Yoav Simhon, Alex Segal, Ofer Amrani et al. · 2025 · arXiv
Scintillator-SiPM Particle Detectors (SSPDs) are compact, low-power devices with applications including particle physics, underground tomography, cosmic-ray studies, and space instrumentation. They are based on a prism-shaped scintillator with corner-mounted SiPMs. Previous work has demonstrated that analytic algorithms based on a physical model of light propagation can reconstruct particle impinging positions and tracks and estimate deposited energy and Linear Energy Transfer (LET) with moderate accuracy. In this study, we enhance this approach by applying machine learning (ML) methods, speci
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 →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 →Guido De Philippis, Michael Goldman, Berardo Ruffini · 2023 · arXiv
We consider a branched transport type problem which describes the magnetic flux through type-I superconductors in a regime of very weak applied fields. At the boundary of the sample, deviation of the magnetization from being uniform is penalized through a negative Sobolev norm. It was conjectured by S. Conti, F. Otto and S. Serfaty that as a result, the trace of the magnetization on the boundary should be a measure of Hausdorff dimension $8/5$. We prove that this conjecture is equivalent to the proof of local energy bounds with an optimal exponent. We then obtain local bounds which are however
View details →Saahil Ognawala, Justin Bayer · 2014 · arXiv
Advancements in parallel processing have lead to a surge in multilayer perceptrons' (MLP) applications and deep learning in the past decades. Recurrent Neural Networks (RNNs) give additional representational power to feedforward MLPs by providing a way to treat sequential data. However, RNNs are hard to train using conventional error backpropagation methods because of the difficulty in relating inputs over many time-steps. Regularization approaches from MLP sphere, like dropout and noisy weight training, have been insufficiently applied and tested on simple RNNs. Moreover, solutions have been
View details →Yuda Bi, Vince D Calhoun · 2026 · arXiv
We address the attribution problem for apparent slow collective dynamics: is the observed persistence intrinsic, or inherited from a persistent driver? For the leading eigenvalue fraction $ψ_1=λ_{\max}/N$ of S\&P 500 60-day rolling correlation matrices ($237$ stocks, 2004--2023), a VIX-coupled Ornstein--Uhlenbeck model reduces the effective relaxation time from $298$ to $61$ trading days and improves the fit over bare mean reversion by $Δ$BIC$=109$. On the decomposition sample, an informational residual of $\log(\mathrm{VIX})$ alone retains most of that gain ($Δ$BIC$=78.6$), whereas a mechanic
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 →Md Mamun Al-Rashid, Jayasimha Atulasimha, Supriyo Bandyopadhyay · 2014 · arXiv
Strain-clocked dipole-coupled nanomagnetic logic is an energy-efficient Boolean logic paradigm whose progress has been stymied by its propensity for high error rates. In an effort to mitigate this problem, we have studied the effect of nanomagnet geometry on error rates, focusing on elliptical and cylindrical geometries. We had previously reported that the out-of-plane excursion of the magnetization vector during switching creates a precessional torque that is responsible for high switching error probability in elliptical nanomagnet geometries. The absence of this torque in cylindrical magnets
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 →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 →Giulio Cantone, Venera Tomaselli · 2022 · arXiv
Snowball sampling is the common name for sampling designs on human populations where respondents are requested to share the questionnaire among their social ties. With some exceptions, estimates from snowball samplings are considered biased. However, the magnitude of the bias is influenced by a combination of elements of the sampling design and features of the target population. Hybrid Probabilistic-Snowball Sampling Designs (HPSSD) aims to reduce the main source of bias in the snowball sample through randomly oversampling the first stage 0 of the snowball. To check the behaviour of HPSSD for
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 →Florian Sammüller, Sophie Hermann, Matthias Schmidt · 2022 · arXiv
We reexamine results obtained with the recently proposed density functional theory framework based on forces (force-DFT) [Tschopp et al., Phys. Rev. E 106, 014115 (2022)]. We compare inhomogeneous density profiles for hard sphere fluids to results from both standard density functional theory and from computer simulations. Test situations include the equilibrium hard sphere fluid adsorbed against a planar hard wall and the dynamical relaxation of hard spheres in a switched harmonic potential. The comparison to grand canonical Monte Carlo simulation profiles shows that equilibrium force-DFT alon
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 →Ryosuke Kohita, Seiichiro Yoshioka · 2026 · arXiv
Memes are a popular element of modern web communication, used not only as static artifacts but also as interactive replies within conversations. While computational research has focused on analyzing the intrinsic properties of memes, the dynamic and contextual use of memes to create humor remains an understudied area of web science. To address this gap, we introduce the Meme Reply Selection task and present MaMe-Re (Manga Meme Reply Benchmark), a benchmark of 100,000 human-annotated pairs (500,000 total annotations from 2,325 unique annotators) consisting of openly licensed Japanese manga pane
View details →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
View details →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
View details →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
View details →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
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