Negative / Null Result ReportOpen accessComputer Science
D. Hüber, J. L. Friar · 1998 · arXiv
The nucleon-deuteron analyzing power $A_y$ in elastic nucleon-deuteron scattering poses a longstanding puzzle. At energies $E_{lab}$ below approximately 30 MeV $A_y$ cannot be described by any realistic NN force. The inclusion of existing three-nucleon forces does not improve the situation. Because of recent questions about the $^3P_J$ NN phases, we examine whether reasonable changes in the NN force can resolve the puzzle. In order to do this we investigate the effect on the $^3P_J$ waves produced by changes in different parts of the potential (viz., the central force, tensor force, etc.), as
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Rujie Zhu, Xiaodong Xu, Stanisław Radziszowski · 2015 · arXiv
Let $Δ_s=R(K_3,K_s)-R(K_3,K_{s-1})$, where $R(G,H)$ is the Ramsey number of graphs $G$ and $H$ defined as the smallest $n$ such that any edge coloring of $K_n$ with two colors contains $G$ in the first color or $H$ in the second color. In 1980, Erdős and Sós posed some questions about the growth of $Δ_s$. The best known concrete bounds on $Δ_s$ are $3 \le Δ_s \le s$, and they have not improved since the stating of the problem. In this paper we present some constructions, which imply in particular that $R(K_3,K_s) \ge R(K_3,K_{s-1}-e) + 4$. This does not improve the lower bound of 3 on $Δ_s$, b
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Dawei Ding, Yihui Quek, Peter W. Shor et al. · 2019 · arXiv
We prove that the classical capacity of an arbitrary quantum channel assisted by a free classical feedback channel is bounded from above by the maximum average output entropy of the quantum channel. As a consequence of this bound, we conclude that a classical feedback channel does not improve the classical capacity of a quantum erasure channel, and by taking into account energy constraints, we conclude the same for a pure-loss bosonic channel. The method for establishing the aforementioned entropy bound involves identifying an information measure having two key properties: 1) it does not incre
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Jingshu Li, Yitian Yang, Renwen Zhang et al. · 2024 · arXiv
Providing well-calibrated AI confidence can help promote users' appropriate trust in and reliance on AI, which are essential for AI-assisted decision-making. However, calibrating AI confidence -- providing confidence score that accurately reflects the true likelihood of AI being correct -- is known to be challenging. To understand the effects of AI confidence miscalibration, we conducted our first experiment. The results indicate that miscalibrated AI confidence impairs users' appropriate reliance and reduces AI-assisted decision-making efficacy, and AI miscalibration is difficult for users to
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Yeskendir Koishekenov · 2023 · arXiv
Graph Neural Networks (GNNs) have achieved a lot of success with graph-structured data. However, it is observed that the performance of GNNs does not improve (or even worsen) as the number of layers increases. This effect has known as over-smoothing, which means that the representations of the graph nodes of different classes would become indistinguishable when stacking multiple layers. In this work, we propose a new simple, and efficient method to alleviate the effect of the over-smoothing problem in GNNs by explicitly using relations between node embeddings. Experiments on real-world dataset
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Maziar Peihani · 2025 · Alberta Law Review
This article discusses lost premium provisions, often referred to as Con Ed provisions. The article examines the main variants of these provisions and considers how they may conflict with established doctrines in contract and corporate…
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Konstantinos Voudouris, Andrew Barron, Marta Halina et al. · 2025 · arXiv
Transitional accounts of evolution emphasise a few changes that shape what is evolvable, with dramatic consequences for derived lineages. More recently it has been proposed that cognition might also have evolved via a series of major transitions that manipulate the structure of biological neural networks, fundamentally changing the flow of information. We used idealised models of information flow, artificial neural networks (ANNs), to evaluate whether changes in information flow in a network can yield a transitional change in cognitive performance. We compared networks with feed-forward, recur
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Haoze Wu · 2017 · arXiv
In this project, we aimed to improve the runtime of Minisat, a Conflict-Driven Clause Learning (CDCL) solver that solves the Propositional Boolean Satisfiability (SAT) problem. We first used a logistic regression model to predict the satisfiability of propositional boolean formulae after fixing the values of a certain fraction of the variables in each formula. We then applied the logistic model and added a preprocessing period to Minisat to determine the preferable initial value (either true or false) of each boolean variable using a Monte-Carlo approach. Concretely, for each Monte-Carlo trial
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Ni Yang, Rui He, Philipp Homan et al. · 2026 · arXiv
Large language models (LLMs) reliably predict neural activity during language comprehension and transformer depth has been interpreted as mirroring hierarchical cortical organization. However, it remains unclear whether such alignment extends to subcortical regions, overlaps spatially across languages, and what the computational roots of such alignment are. Here, we used a multilingual, whole-brain encoding framework to examine brain-LLM alignment across three typologically distinct languages: Mandarin, English, and French during naturalistic story listening. Our results show that across langu
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Kaito Hosono, Shunsuke Ono, Takamichi Miyata · 2020 · arXiv
Low-rank tensor recovery has attracted much attention among various tensor recovery approaches. A tensor rank has several definitions, unlike the matrix rank--e.g. the CP rank and the Tucker rank. Many low-rank tensor recovery methods are focused on the Tucker rank. Since the Tucker rank is nonconvex and discontinuous, many relaxations of the Tucker rank have been proposed, e.g., the tensor nuclear norm, weighted tensor nuclear norm, and weighted tensor Schatten-$p$ norm. In particular, the weighted tensor Schatten-p norm has two parameters, the weight and $p$, and the tensor nuclear norm and
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Chiman Salavati, Shannon Song, Willmar Sosa Diaz et al. · 2024 · arXiv
Biased information (recently termed bisinformation) continues to be taught in medical curricula, often long after having been debunked. In this paper, we introduce BRICC, a firstin-class initiative that seeks to mitigate medical bisinformation using machine learning to systematically identify and flag text with potential biases, for subsequent review in an expert-in-the-loop fashion, thus greatly accelerating an otherwise labor-intensive process. A gold-standard BRICC dataset was developed throughout several years, and contains over 12K pages of instructional materials. Medical experts meticul
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Mirac Suzgun, Nathan Scales, Nathanael Schärli et al. · 2022 · arXiv
BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models. Language models have already made good progress on this benchmark, with the best model in the BIG-Bench paper outperforming average reported human-rater results on 65% of the BIG-Bench tasks via few-shot prompting. But on what tasks do language models fall short of average human-rater performance, and are those tasks actually unsolvable by current language models? In this work, we focus on a suite of 23 challenging BIG-Bench tasks which we c
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Zhen Xuen Brandon Low, Rory Zhang, Hang Min et al. · 2025 · arXiv
Enlarged perivascular spaces (PVS) are increasingly recognized as biomarkers of cerebral small vessel disease, Alzheimer's disease, stroke, and aging-related neurodegeneration. However, manual segmentation of PVS is time-consuming and subject to moderate inter-rater reliability, while existing automated deep learning models have moderate performance and typically fail to generalize across diverse clinical and research MRI datasets. We adapted MedNeXt-L-k5, a Transformer-inspired 3D encoder-decoder convolutional network, for automated PVS segmentation. Two models were trained: one using a homog
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Zhiqiang Shen, Zechun Liu, Jie Qin et al. · 2021 · arXiv
Previous studies dominantly target at self-supervised learning on real-valued networks and have achieved many promising results. However, on the more challenging binary neural networks (BNNs), this task has not yet been fully explored in the community. In this paper, we focus on this more difficult scenario: learning networks where both weights and activations are binary, meanwhile, without any human annotated labels. We observe that the commonly used contrastive objective is not satisfying on BNNs for competitive accuracy, since the backbone network contains relatively limited capacity and re
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Mehdi S. M. Sajjadi, Morteza Alamgir, Ulrike von Luxburg · 2015 · arXiv
Peer grading is the process of students reviewing each others' work, such as homework submissions, and has lately become a popular mechanism used in massive open online courses (MOOCs). Intrigued by this idea, we used it in a course on algorithms and data structures at the University of Hamburg. Throughout the whole semester, students repeatedly handed in submissions to exercises, which were then evaluated both by teaching assistants and by a peer grading mechanism, yielding a large dataset of teacher and peer grades. We applied different statistical and machine learning methods to aggregate t
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T. Gent, S. Huber, K. Mimasu et al. · 2025 · arXiv
We perform a detailed investigation of the viable baryogenesis parameter space of a non-minimal Higgs sector consisting of two Higgs doublets and a singlet pseudoscalar (2HDM$+a$). In such a model, an early Universe period of transient CP violation may occur, driven by a nonvanishing vacuum expectation value of the CP-odd scalar $a$. This naturally avoids the stringent electric dipole moment experimental constraints on beyond-the-Standard-Model sources of CP violation. We provide a state-of-art computation of the baryon asymmetry, providing several important improvements over existing baryogen
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Youlong Wu, Michèle Wigger · 2014 · arXiv
We propose two coding schemes for the two-receiver discrete memoryless broadcast channel (BC) with rate-limited feedback from one or both receivers. They improve over the nofeedback capacity region for a large class of channels, including the class of \emph{strictly essentially less-noisy BCs} that we introduce in this article. Examples of strictly essentially less-noisy BCs are the binary symmetric BC (BSBC) or the binary erasure BC (BEBC) with unequal cross-over or erasure probabilities at the two receivers. When the feedback rates are sufficiently large, our schemes recover all previously k
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T. M. Salman, N. Lugaz, C. J. Farrugia et al. · 2018 · arXiv
Long periods of strong southward magnetic fields are known to be the primary cause of intense geomagnetic storms. The majority of such events are caused by the passage over Earth of a magnetic ejecta. Irrespective of the interplanetary cause, fast-forward shocks often precede such strong southward B$_{z}$ periods. Here, we first look at all long periods of strong southward magnetic fields as well as fast-forward shocks measured by the \textit{Wind} spacecraft in a 22.4-year span. We find that 76{\%} of strong southward B$_{z}$ periods are preceded within 48 hours by at least a fast-forward sho
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Nandakishore Santhi, Alexander Vardy · 2006 · arXiv
We consider the problem of estimating the probability of error in multi-hypothesis testing when MAP criterion is used. This probability, which is also known as the Bayes risk is an important measure in many communication and information theory problems. In general, the exact Bayes risk can be difficult to obtain. Many upper and lower bounds are known in literature. One such upper bound is the equivocation bound due to Rényi which is of great philosophical interest because it connects the Bayes risk to conditional entropy. Here we give a simple derivation for an improved equivocation bound. We
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Wenbin Wu · 2026 · arXiv
Reinforcement Learning from Human Feedback (RLHF) assumes annotator preferences reflect stable internal states. We challenge this through three experiments spanning the preference pipeline. In a human choice blindness study, 91% of surreptitiously swapped preferences go undetected, extending choice blindness to third-person evaluative comparison of unfamiliar text. Testing fifteen LLM judges as potential replacements, we find detection relies on shallow text matching rather than genuine self-monitoring: removing prior reasoning from context causes blindness to surge from near-zero to over 50%,
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Md Nahid Haque Shazon, Piyush Kumar, Luqiao Liu et al. · 2025 · arXiv
This paper presents physical modeling and benchmarking for two-terminal spin-orbit torque magnetic random-access memory (2T-SOT-MRAM). The results indicate that the common SOT materials that provide only in-plane torque can provide little to no improvement over spin-transfer-torque (STT) MRAM in terms of write energy. However, emerging SOT materials that provide out-of-plane torques with efficiencies as small as 0.1 can result in significant improvements in the write energy for such 2-terminal devices, especially when the magnet lateral dimensions are scaled down to 30 or 20 nm. Additionally,
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Gabriel Grand, Valerio Pepe, Jacob Andreas et al. · 2024 · arXiv
Questions combine our mastery of language with our remarkable facility for reasoning about uncertainty. How do people navigate vast hypothesis spaces to pose informative questions given limited cognitive resources? We study these tradeoffs in a classic grounded question-asking task based on the board game Battleship. Our language-informed program sampling (LIPS) model uses large language models (LLMs) to generate natural language questions, translate them into symbolic programs, and evaluate their expected information gain. We find that with a surprisingly modest resource budget, this simple M
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Shibdas Roy, Ian R. Petersen, Elanor H. Huntington · 2015 · arXiv
We study a coherent-classical estimation scheme for a class of linear quantum systems, where the estimator is a mixed quantum-classical system that may or may not involve coherent feedback. We show that when the quantum plant or the quantum part of the estimator (coherent controller) is an annihilation operator only system, coherent-classical estimation without coherent feedback can provide no improvement over purely-classical estimation. Otherwise, coherent-classical estimation without feedback can be better than classical-only estimation for certain homodyne detector angles, although the for
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John T. Holodnak, Allan B. Wollaber · 2021 · arXiv
Multiple techniques for producing calibrated predictive probabilities using deep neural networks in supervised learning settings have emerged that leverage approaches to ensemble diverse solutions discovered during cyclic training or training from multiple random starting points (deep ensembles). However, only a limited amount of work has investigated the utility of exploring the local region around each diverse solution (posterior mode). Using three well-known deep architectures on the CIFAR-10 dataset, we evaluate several simple methods for exploring local regions of the weight space with re
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R. L. Bates, C. Da'Via, S. D'Auria et al. · 1997 · arXiv
Semi-insulating, undoped, Liquid Encapsulated Czochralski (SI-U LEC) GaAs detectors have been irradiated with 1MeV neutrons, 24GeV/c protons, and 300MeV/c pions. The maximum fluences used were 6, 3, and 1.8~10$^{14}$ particles/cm$^{2}$ respectively. For all three types of irradiation the charge collection efficiencies (cce) of the detector are reduced due to the reduction in the electron and hole mean free paths. Pion and proton irradiations produce a greater reduction in cce than neutron irradiation with the pions having the greatest effect. The effect of annealing the detectors at room tempe
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Victor Lagerkvist, Johanna Groven, Leif Eriksson · 2026 · arXiv
The region connection calculus ($RCC$) and Allen's interval algebra ($IA$) are two well-known NP-hard spatial-temporal qualitative reasoning problems. They are solvable in $2^{O(n \log n)}$ time, where $n$ is the number of variables, and $IA$ is additionally known to be solvable in $o(n)^n$ time. However, no improvement over exhaustive search is known for $RCC$, and if they are also solvable in single exponential time $2^{O(n)}$ is unknown. We investigate multiple avenues towards reaching such bounds. First, we show that branching is insufficient since there are too many non-redundant constrai
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Lucas Oliveira Souza, Gabriel de Oliveira Ramos, Celia Ghedini Ralha · 2019 · arXiv
The idea of experience sharing between cooperative agents naturally emerges from our understanding of how humans learn. Our evolution as a species is tightly linked to the ability to exchange learned knowledge with one another. It follows that experience sharing (ES) between autonomous and independent agents could become the key to accelerate learning in cooperative multiagent settings. We investigate if randomly selecting experiences to share can increase the performance of deep reinforcement learning agents, and propose three new methods for selecting experiences to accelerate the learning p
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Andreas Noack, Randolf Rotta · 2008 · arXiv
Modularity is one of the most widely used quality measures for graph clusterings. Maximizing modularity is NP-hard, and the runtime of exact algorithms is prohibitive for large graphs. A simple and effective class of heuristics coarsens the graph by iteratively merging clusters (starting from singletons), and optionally refines the resulting clustering by iteratively moving individual vertices between clusters. Several heuristics of this type have been proposed in the literature, but little is known about their relative performance. This paper experimentally compares existing and new coarsenin
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George Sterpu, Christian Saam, Naomi Harte · 2020 · arXiv
Audio-Visual Speech Recognition (AVSR) seeks to model, and thereby exploit, the dynamic relationship between a human voice and the corresponding mouth movements. A recently proposed multimodal fusion strategy, AV Align, based on state-of-the-art sequence to sequence neural networks, attempts to model this relationship by explicitly aligning the acoustic and visual representations of speech. This study investigates the inner workings of AV Align and visualises the audio-visual alignment patterns. Our experiments are performed on two of the largest publicly available AVSR datasets, TCD-TIMIT and
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Raghav Gupta, Akanksha Jain, Abraham Gonzalez et al. · 2026 · arXiv
Agile hardware design flows are a critically needed force multiplier to meet the exploding demand for compute. Recently, agentic generative AI systems have demonstrated significant advances in algorithm design, improving code efficiency, and enabling discovery across scientific domains. Bridging these worlds, we present ArchAgent, an automated computer architecture discovery system built on AlphaEvolve. We show ArchAgent's ability to automatically design/implement state-of-the-art (SoTA) cache replacement policies (architecting new mechanisms/logic, not only changing parameters), broadly withi
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