Negative / Null Result ReportOpen accessPhysics
Chris Pankow, Laura Sampson, Leah Perri et al. · 2016 · arXiv
The detection of electromagnetic counterparts to gravitational waves has great promise for the investigation of many scientific questions. It has long been hoped that in addition to providing extra, non-gravitational information about the sources of these signals, the detection of an electromagnetic signal in conjunction with a gravitational wave could aid in the analysis of the gravitational signal itself. That is, knowledge of the sky location, inclination, and redshift of a binary could break degeneracies between these extrinsic, coordinate-dependent parameters and the physical parameters,
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Sergei Manzhos, Pavlo Golub · 2020 · arXiv
We study the dependence of kinetic energy densities (KED) on density-dependent variables that have been suggested in previous works on kinetic energy functionals (KEF) for orbital-free DFT (OF-DFT). We focus on the role of data distribution and on data and regressor selection. We compare unweighted and weighted linear and Gaussian process regressions of KED for light metals and a semiconductor. We find that good quality linear regression resulting in good energy-volume dependence is possible over density-dependent variables suggested in previous literature. This is achieved with weighted fitti
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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
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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
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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
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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
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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
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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 →Negative / Null Result ReportOpen accessMathematics
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
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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
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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
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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
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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
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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
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Tom Kouwenhoven, Max Peeperkorn, Bram van Dijk et al. · 2024 · arXiv
Natural language has the universal properties of being compositional and grounded in reality. The emergence of linguistic properties is often investigated through simulations of emergent communication in referential games. However, these experiments have yielded mixed results compared to similar experiments addressing linguistic properties of human language. Here we address representational alignment as a potential contributing factor to these results. Specifically, we assess the representational alignment between agent image representations and between agent representations and input images.
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Luigi Giannelli, Ralf Betzholz, Laura Kreiner et al. · 2016 · arXiv
We theoretically analyse the cooling dynamics of a high-Q mode of a mechanical resonator, when the structure is also an optical cavity and is coupled with a NV center. The NV center is driven by a laser and interacts with the cavity photon field and with the strain field of the mechanical oscillator, while radiation pressure couples mechanical resonator and cavity field. Starting from the full master equation we derive the rate equation for the mechanical resonator's motion, whose coefficients depend on the system parameters and on the noise sources. We then determine the cooling regime, the c
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Elias Hossain, Md Mehedi Hasan Nipu, Maleeha Sheikh et al. · 2025 · arXiv
Clinical language models often assign high confidence to incorrect predictions, particularly in high-severity and out-of-distribution cases. We present MedBayes-Lite, a retraining-free uncertainty governance layer for transformer-based clinical predictors. It combines Monte Carlo dropout, predictive calibration, and confidence-guided abstention to defer low-confidence predictions for human review, adding no trainable parameters. Evaluated on MedMCQA and MedQA-USMLE, MedBayes-Lite reduces expected calibration error by 0.23 to 0.33 and drives harmful overconfident errors (confident, incorrect, h
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Lei Wang · 2026 · arXiv
The endpoint region $ζ\to1$ of the NLO forward jet vertex has not been systematically separated from BFKL energy-scale terms in Mueller-Navelet phenomenology. Starting from the small-cone NLO vertex, we isolate the quark and gluon plus distributions and construct a BFKL-aware threshold matching scheme that preserves exact NLO accuracy. The conservative Scheme-II exponent resums only the ordinary endpoint logarithms and leaves the $χ(n,γ)\ln\bar N$ term in the fixed-order coefficient, avoiding an uncontrolled tower of mixed endpoint-BFKL logarithms. In fixed-baseline CMS tests, this matched ver
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Jaroslaw Zdebik · 2008 · arXiv
The work was carried out in the framework of the KLOE collaboration studying the decays of the phi meson produced in the DAFNE accelerator in the collisions of electron and positron. The main aim of this thesis was investigation of the influence of the merging and splitting of clusters in decays with the high multiplicity of gamma quanta, which are at most biased by these effects. For this aim we implemented the full geometry and realistic material composition of the barrel electromagnetic calorimeter in FLUKA package. The prepared Monte Carlo based simulation program permits to achieve a fast
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Silas L. Fong, Vincent Y. F. Tan · 2017 · arXiv
This paper investigates the asymptotic expansion for the maximum rate of fixed-length codes over a parallel Gaussian channel with feedback under the following setting: A peak power constraint is imposed on every transmitted codeword, and the average error probability of decoding the transmitted message is non-vanishing as the blocklength increases. It is well known that the presence of feedback does not increase the first-order asymptotics of the channel, i.e., capacity, in the asymptotic expansion, and the closed-form expression of the capacity can be obtained by the well-known water-filling
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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
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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
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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
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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
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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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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
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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
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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
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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
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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
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