Negative / Null Result ReportOpen accessPhysics
Md Monirojjaman Monshi, Sadegh Mehdi Aghaei, Irene Calizo · 2017 · arXiv
First-principles calculations based on density functional theory (DFT) have been employed to investigate the structural, electronic, and gas-sensing properties of pure, defected, and doped germanene nanosheets. Our calculations have revealed that while a pristine germanene nanosheet adsorbs CO2 weakly, H2S moderately, and SO2 strongly, the introduction of vacancy defects increases the sensitivity significantly which is promising for future gas-sensing applications.Mulliken population analysis imparts that an appreciable amount of charge transfer occurs between gas molecules and a germanene nan
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P. Sirabella, A. Giuliani, A. Colosimo · 1998 · arXiv
This paper reports about an approach to the classification of proteins' primary structures taking advantage of the Self Organizing Maps algorithm and of a numerical coding of the aminoacids based upon their physico-chemical properties. Hydrophobicity, volume, surface area, hydrophilicity, bulkiness, refractivity and polarity were subjected to a Principal Component Analysis and the first two principal components, explaining 84.8 % of the total observed variability, were used to cluster the aminoacids into 4 or 5 classes through a k-means algorithm. This leads to an economical representation of
View details →Negative / Null Result ReportOpen accessPhysics
C. Alard · 2016 · arXiv
The cosmic horseshoe gravitational lens is analyzed using the perturbative approach. The two first order perturbative fields are expanded in Fourier series. The source is reconstructed using a fine adaptive grid. The expansion of the fields at order 2 produces a higher value of the chi-square. Expanding at order 3 provides a very significant improvement, while order 4 does not bring a significant improvement over order 3. The presence of the order 3 terms is not a consequence of limiting the perturbative expansion to the first order. The amplitude and signs of the third order terms are recover
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
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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
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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
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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
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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
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
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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.
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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
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
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Michael A. Cole, Aristeidis Lamprianidis, Ilya V. Shadrivov et al. · 2018 · arXiv
Metasurfaces are an enabling technology for complex wave manipulation functions, including in the terahertz frequency range, where they are expected to advance security, imaging, sensing, and communications technology. For operation in transmission, Huygens' metasurfaces are commonly used, since their good impedance match to the surrounding media minimizes reflections and maximizes transmission. Recent theoretical work has shown that Huygens' metasurfaces are non-optimal, particularly for large angles of refraction, and that to eliminate reflections and spurious diffracted beams it is necessar
View details →Negative / Null Result ReportOpen accessMathematics
Utkarsh, Chris Elrod, Yingbo Ma et al. · 2022 · arXiv
Numerically solving ordinary differential equations (ODEs) is a naturally serial process and as a result the vast majority of ODE solver software are serial. In this manuscript we developed a set of parallelized ODE solvers using extrapolation methods which exploit "parallelism within the method" so that arbitrary user ODEs can be parallelized. We describe the specific choices made in the implementation of the explicit and implicit extrapolation methods which allow for generating low overhead static schedules to then exploit with optimized multi-threaded implementations. We demonstrate that wh
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Andrew C. Collazzi, Bradley E. Schaefer · 2008 · arXiv
Firmani et al. proposed a new Gamma Ray Burst (GRB) luminosity relation that showed a significant improvement over the L_iso-E_peak relation. The new proposed relation simply modifies the E_peak value by multiplying it by a power of T_0.45, where T_0.45 is a particular measure of the GRB duration. We begin by reproducing the results of Firmani for his 19 bursts. We then test the Firmani relation for the same 19 bursts except that we use independently measured values for L_iso, T_0.45, and E_peak, and we find that the relation deteriorates substantially. We further test the relation by using 60
View details →Failed Experiment ReportOpen accessComputer Science
Sándor Kisfaludi-Bak, Jesper Nederlof, Erik Jan van Leeuwen · 2018 · arXiv
The Planar Steiner Tree problem is one of the most fundamental NP-complete problems as it models many network design problems. Recall that an instance of this problem consists of a graph with edge weights, and a subset of vertices (often called terminals); the goal is to find a subtree of the graph of minimum total weight that connects all terminals. A seminal paper by Erickson et al. [Math. Oper. Res., 1987] considers instances where the underlying graph is planar and all terminals can be covered by the boundary of $k$ faces. Erickson et al. show that the problem can be solved by an algorithm
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Alexander Contreras-Payares, Pablo G. Lustemberg, M. Verónica Ganduglia-Pirovano · 2025 · arXiv
The vibrational frequency of carbon monoxide (CO) adsorbed on ceria-based catalysts serves as a sensitive probe for identifying exposed surface facets, provided that experimental reference data on well-defined single-crystal surfaces and reliable theoretical assignments are available. Previous studies have shown that the hybrid DFT approach using the HSE06 functional yields good agreement with experimental observations, whereas the generalized gradient approximation (GGA) with PBE+U does not. In this work, we assess the performance of different exchange-correlation functionals by comparing the
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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
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Alberto Nardin, Eddy Ardonne, Leonardo Mazza · 2022 · arXiv
We prove a generic spin-statistics relation for the fractional quasiparticles that appear in abelian quantum Hall states on the disk. The proof is based on an efficient way for computing the Berry phase acquired by a generic quasiparticle translated in the plane along a circular path, and on the crucial fact that once the gauge-invariant generator of rotations is projected onto a Landau level, it fractionalizes among the quasiparticles and the edge. Using these results we define a measurable quasiparticle fractional spin that satisfies the spin-statistics relation. As an application, we predic
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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
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Yining Lu, Dixuan Wang, Tianjian Li et al. · 2024 · arXiv
As LLMs become increasingly prevalent, it is interesting to consider how ``creative'' these models can be. From cognitive science, creativity consists of at least two key characteristics: \emph{convergent} thinking (purposefulness to achieve a given goal) and \emph{divergent} thinking (adaptability to explore new environments or constraints) \citep{runco2003critical}. In this work, we introduce a framework for quantifying LLM creativity that incorporates the two design ingredients: (1) We introduce DENIAL PROMPTING which pushes LLMs to develop more creative solutions to a given problem by incr
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Jason Dury · 2026 · arXiv
Dense retrieval systems rank passages by embedding similarity to a query, but multi-hop questions require passages that are associatively related through shared reasoning chains. We introduce Association-Augmented Retrieval (AAR), a lightweight transductive reranking method that trains a small MLP (4.2M parameters) to learn associative relationships between passages in embedding space using contrastive learning on co-occurrence annotations. At inference time, AAR reranks an initial dense retrieval candidate set using bi-directional association scoring. On HotpotQA, AAR improves passage Recall@
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Xiaolong Jin, Xuandong Zhao, Wenbo Guo et al. · 2026 · arXiv
Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in large language model reasoning, but relies on ground-truth supervision that is costly or infeasible, especially in coding tasks. Recent work addresses this by deriving rewards from a model's own signals, such as majority voting or confidence-based scores, achieving notable success on mathematical reasoning benchmarks. However, code generation poses distinct challenges: programs are structurally complex, semantically equivalent solutions may differ syntactically, and verification typically requires executio
View details →Negative / Null Result ReportOpen accessMathematics
Nguyen Ngoc Hung · 2015 · arXiv
A classical theorem of John Thompson on character degrees asserts that if the degree of every ordinary irreducible character of a finite group $G$ is 1 or divisible by a prime $p$, then $G$ has a normal $p$-complement. We obtain a significant improvement of this result by considering the average of $p'$-degrees of irreducible characters. We also consider fields of character values and prove several improvements of earlier related results.
View details →Negative / Null Result ReportOpen accessEngineering
Max Langtry, Vijja Wichitwechkarn, Rebecca Ward et al. · 2024 · arXiv
Data is required to develop forecasting models for use in Model Predictive Control (MPC) schemes in building energy systems. However, data is costly to both collect and exploit. Determining cost optimal data usage strategies requires understanding of the forecast accuracy and resulting MPC operational performance it enables. This study investigates the performance of both simple and state-of-the-art machine learning prediction models for MPC in multi-building energy systems using a simulated case study with historic building energy data. The impact on forecast accuracy of measures to improve m
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Jimmy Lin, Daniel Campos, Nick Craswell et al. · 2021 · arXiv
Leaderboards are a ubiquitous part of modern research in applied machine learning. By design, they sort entries into some linear order, where the top-scoring entry is recognized as the "state of the art" (SOTA). Due to the rapid progress being made in information retrieval today, particularly with neural models, the top entry in a leaderboard is replaced with some regularity. These are touted as improvements in the state of the art. Such pronouncements, however, are almost never qualified with significance testing. In the context of the MS MARCO document ranking leaderboard, we pose a specific
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Jordan M. Slagowski, Erik Pearson, Rajit Tummala et al. · 2025 · arXiv
Techniques for preclinical intensity modulated radiation therapy are being developed to improve translation by replicating the clinical paradigm. This study presents the first treatment planning comparison between small animal IMRT (SA-IMRT) and three-dimensional conformal radiotherapy (CRT) in a model application, oxygen-guided dose painting of tumor hypoxia, using actual mouse data. A novel compensator-based platform was employed to generate SA-IMRT and CRT plans with 2-15 beam angles for seventeen mice with fibrosarcoma tumors. The whole tumor received a dose of 22.5 Gy, with a simultaneous
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Timothy Garn, Philip Best · 2010 · arXiv
We investigate how the typical dust extinction of H-alpha luminosity from a star-forming galaxy depends upon star formation rate (SFR), metallicity and stellar mass independently, using a sample of ~90,000 galaxies from Data Release 7 of the Sloan Digital Sky Survey (SDSS). We measure extinctions directly from the Balmer decrement of each source, and while higher values of extinction are associated with an increase in any of the three parameters, we demonstrate that the fundamental property that governs extinction is stellar mass. After this mass-dependent relationship is removed, there is ver
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Jason Z Wang · 2026 · arXiv
We introduce MIRROR, a benchmark comprising eight experiments across four metacognitive levels that evaluates whether large language models can use self-knowledge to make better decisions. We evaluate 16 models from 8 labs across approximately 250,000 evaluation instances using five independent behavioral measurement channels. Core experiments are run across the full model roster; experiments with specialized infrastructure requirements report explicitly marked model subsets. We find two phenomena with direct implications for agentic deployment: (1) compositional self-prediction fails universa
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Mohd Zaki, Jayadeva, Mausam et al. · 2023 · arXiv
Information extraction and textual comprehension from materials literature are vital for developing an exhaustive knowledge base that enables accelerated materials discovery. Language models have demonstrated their capability to answer domain-specific questions and retrieve information from knowledge bases. However, there are no benchmark datasets in the materials domain that can evaluate the understanding of the key concepts by these language models. In this work, we curate a dataset of 650 challenging questions from the materials domain that require the knowledge and skills of a materials st
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