An iterative method is derived for image reconstruction. Among other attributes, this method allows constraints unrelated to the radiation measurements to be incorporated into the reconstructed image. A comparison is made with the widely used Maximum-Likelihood Expectation-Maximization (MLEM) algori
We construct an example of proof within the main formal system from arXiv:1010.4760v3, which is intended to capture the bisimulation equivalence for non-deterministic first-order grammars, and show that its conclusion is semantically false. We then locate and analyze the flawed argument in the sound
We employ the face recognition technology developed in house at face.com to a well accepted benchmark and show that without any tuning we are able to considerably surpass state of the art results. Much of the improvement is concentrated in the high-valued performance point of zero false positive mat
We investigate how LLMs encode sociodemographic attributes of human conversational partners inferred from indirect cues such as names and occupations. We show that LLMs develop linear representations of user demographics within activation space, wherein stereotypically associated attributes are enco
We present an algorithm for generating Poisson-disc patterns taking O(N) time to generate $N$ points. The method is based on a grid of regions which can contain no more than one point in the final pattern, and uses an explicit model of point arrival times under a uniform Poisson process.
The rapid proliferation of Large Language Models (LLMs) and diverse specialized benchmarks necessitates a shift from fragmented, task-specific metrics to a holistic, competitive ranking system that effectively aggregates performance across multiple ability dimensions. Primarily using static scoring,
Robots often fail at everyday tasks because instructions skip commonsense details like hidden preconditions and small subgoals. Traditional symbolic planners need these details to be written explicitly, which is time consuming and often incomplete. In this project we combine a Large Language Model w
A simple example shows that losing all money is compatible with a very high Sharpe ratio (as computed after losing all money). However, the only way that the Sharpe ratio can be high while losing money is that there is a period in which all or almost all money is lost. This note explores the best ac
The material in this note is now superseded by arXiv:1108.5288v4. Bulatov et al. [1] defined the operation of (efficient) pps_omega-definability in order to study the computational complexity of certain approximate counting problems. They asked whether all log-supermodular functions can be defined
'Marching of freely falling plates' is a fluid dynamics video for the Gallery of Fluid Motion submitted to APS-DFD 2011 at Baltimore Maryland. The problem of a freely falling plate is of interest in both fluid mechanics and nonlinear dynamics. The trajectory of the plate can be regular (Willmarth et
In this work, we introduce a new and simple transformation from time series to complex networks based on markov-binary visibility graph(MBVG). Due to the simple structure of this transformation in comparison with other transformations be obtained more precise results. Moreover, several topological a
Non-electromagnetic emissions from high energy particles in extreme environments has been studied in the literature by using several variations of the semi-classical formalism. The detailed mechanisms behind such emissions are of great astrophysical interest since they can alter appreciably the asso
Understanding affective polarization in online discourse is crucial for evaluating the societal impact of social media interactions. This study presents a novel framework that leverages large language models (LLMs) and domain-informed heuristics to systematically analyze and quantify affective polar
Accurate and interpretable image-based diagnosis remains a fundamental challenge in medical AI, particularly under domain shifts and rare-class conditions. Deep learning models often struggle with real-world distribution changes, exhibit bias against infrequent pathologies, and lack the transparency
Agentic memory is emerging as a key enabler for large language models (LLM) to maintain continuity, personalization, and long-term context in extended user interactions, critical capabilities for deploying LLMs as truly interactive and adaptive agents. Agentic memory refers to the memory that provid
The Quantum Approximate Optimization Algorithm (QAOA) is a leading approach for solving combinatorial optimization problems on near-term quantum processors. However, finding good variational parameters remains a significant challenge due to the non-convex energy landscape, often resulting in slow co
This paper studies the training-testing discrepancy (a.k.a. exposure bias) problem for improving the diffusion models. During training, the input of a prediction network at one training timestep is the corresponding ground-truth noisy data that is an interpolation of the noise and the data, and duri
Memory-augmented spiking neural networks (SNNs) promise energy-efficient neuromorphic computing, yet their generalization across sensory modalities remains unexplored. We present the first comprehensive cross-modal ablation study of memory mechanisms in SNNs, evaluating Hopfield networks, Hierarchic
Most available tools propose simulation frameworks to study models of biological systems, but simulation only explores a few of the most probable behaviours of the system. On the contrary, techniques such as model checking, coming from IT-systems analysis, explore all the possible behaviours of the
Many early-type stars are in binary systems. A number of them shows radio emissivity with periodic variability. This variability is associated with non-thermal synchrotron radiation emitted by relativistic electrons. The strong shocks necessary to accelerate the electrons up to high energies are pro
Trajectory prediction is crucial for the reliability and safety of autonomous driving systems, yet it remains a challenging task in complex interactive scenarios due to noisy trajectory observations and intricate agent interactions. Existing methods often struggle to filter redundant scene data for
Moral actions are judged not only by their outcomes but by the context in which they occur. We present COMETH (Contextual Organization of Moral Evaluation from Textual Human inputs), a framework that integrates a probabilistic context learner with LLM-based semantic abstraction and human moral evalu
The primary value of AI agents in software development lies in their ability to extend the developer's capacity for reasoning and action, not to supplant human involvement. To showcase how to use agents working in tandem with developers, we designed a novel approach for carrying out coordinated rena
Manual software beta testing is costly and time-consuming, while single-agent large language model (LLM) approaches suffer from hallucinations and inconsistent behavior. We propose a multi-agent committee framework in which diverse vision-enabled LLMs collaborate through a three-round voting protoco
A multicanonical update relation for calculation of the microcanonical entropy $S_{micro}(E)$ by means of the estimates of the inverse statistical temperature $beta_S$, is proposed. This inverse temperature is obtained from the recently proposed statistical temperature weighted histogram analysis me
Theoretical models suggest that the first stars in the universe could have been very massive, with typical masses $gtrsim$ 100 Msun. Many of them might have died as energetic thermonuclear explosions known as pair-instability supernovae (PSNe). We present multidimensional numerical simulations of PS
Event-based Cameras (EBCs) are widely utilized in surveillance and autonomous driving applications due to their high speed and low power consumption. Corners are essential low-level features in event-driven computer vision, and novel algorithms utilizing event-based representations, such as Threshol
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, temporal reasoning, particularly under complex temporal constraints, remains a major challenge. To this end, existing approaches have explored symbolic methods, wh
Accurate yet interpretable image-based diagnosis remains a central challenge in medical AI, particularly in settings characterized by limited data, subtle visual cues, and high-stakes clinical decision-making. Most existing vision models rely on purely data-driven learning and produce black-box pred
We study neutrino spin oscillations in black hole backgrounds. In the case of a charged black hole, the maximum frequency of oscillations is a monotonically increasing function of the charge. For a rotating black hole, the maximum frequency decreases with increasing the angular momentum. In both cas
We have recently proposed that Force-Free Electrodynamics (FFE) does not apply to pulsars -- pulsars should be described by the high-conductivity limit of Strong-Field Electrodynamics (SFE), which predicts an order-unity damping of the Poynting flux, while FFE postulates zero damping. The strong dam
Over the past 3 years, Auckland University of Technology has established the first radio astronomical observatory in New Zealand, which, because of its remote geographic location, has quickly become a member of a number of international VLBI networks, in particular the IVS and the LBA. Not only has
We present the next-to-next-to-leading order post-Newtonian (PN) spin(1)-spin(2) Hamiltonian for two self-gravitating spinning compact objects. If both objects are rapidly rotating, then the corresponding interaction is comparable in strength to a 4PN effect. The Hamiltonian is checked via the globa
Large Language Models (LLMs) inherit societal biases from their training data, potentially leading to harmful or unfair outputs. While various techniques aim to mitigate these biases, their effects are often evaluated only along the dimension of the bias being targeted. This work investigates the cr
A new model for the stock market price analysis is proposed. It is suggested to look at price as an everywhere discontinuous function of time of bounded variation.
Foundation segmentation models (such as SAM and SAM-2) perform well on natural images but struggle with brain MRIs where structures like the caudate and thalamus lack sharp boundaries and have poor contrast. Rather than fine-tune these models (e.g., MedSAM), we propose a compositional alternative wh
LLMs have shown early promise in generating RTL code, yet evaluating their capabilities in realistic setups remains a challenge. So far, RTL benchmarks have been limited in scale, skewed toward trivial designs, offering minimal verification rigor, and remaining vulnerable to data contamination. To o
We revisit the classical problem of nucleated polymerisation and derive a range of exact results describing polymerisation in systems intermediate between the well-known limiting cases of a reaction starting from purely soluble material and for a reaction where no new growth nuclei are formed.
We introduce the Nemotron 3 family of models - Nano, Super, and Ultra. These models deliver strong agentic, reasoning, and conversational capabilities. The Nemotron 3 family uses a Mixture-of-Experts hybrid Mamba-Transformer architecture to provide best-in-class throughput and context lengths of up
We investigate the possibility of obscuring supersoft X-ray sources in the winds of companion stars. We derive limits on the amount of circumstellar material needed to fully obscure a 'canonical' supersoft X-ray source in the Large Magellanic Cloud, as observed with the Chandra X-ray Observatory.
Ideal axisymmetric pulsar magnetosphere is calculated from the standard stationary force-free equation but with a new boundary condition at the equator. The new solution predicts Ohmic heating. About 50% of the Poynting power is dissipated in the equatorial current layer outside the light cylinder,
Okun's law for the biggest developed countries is re-estimated using the most recent data on real GDP per capita and the rate of unemployment. Our results show that the change in unemployment rate can be predicted with a high accuracy. The link needs the introduction of a structural break which migh
We show that in any subset of the vertices of $n$-dimensional cube that contains at least $2^{n-1}+1$ vertices ($ngeq 4$), there are four vertices that induce a claw, or there are eight vertices that induce the cycle of length eight.
We give improved lower bounds on the minimum number of $k$-holes (empty convex $k$-gons) in a set of $n$ points in general position in the plane, for $k=5,6$.
Some inconsistencies in the passive pharmacokinetics has been observed many years ago. It is possible these inconsistencies result from the lack of proper evolution equations. In this communication, this possibility is illustrated on the example of an evolution equation for an one compartment model.
Recently, Choi emph{et al}. proposed an assumption on Mayers-Lo-Chau (MLC) no-go theorem that the state of the entire quantum system is invariable to both participants before the unveiling phase. This means that the theorem is only applicable to static quantum bit commitment (QBC). This paper find t
A set S is independent in a graph G if no two vertices from S are adjacent. The independence number alpha(G) is the cardinality of a maximum independent set, while mu(G) is the size of a maximum matching in G. If alpha(G)+mu(G)=|V|, then G=(V,E) is called a Konig-Egervary graph. The number d_{c}(G)=
A planar straight-line graph which causes the non-termination Ruppert's algorithm for a minimum angle threshold larger than about 29.5 degrees is given. The minimum input angle of this example is about 74.5 degrees meaning that failure is not due to small input angles. Additionally, a similar non-ac
The intermediate-duration gamma-ray bursts (GRBs) identified in the data of the RHESSI satellite are investigated with respect to their spectral lags, peak count rates, redshifts, supernova observations, and star formation rates of their host galaxies. Standard statistical tests like Kolmogorov-Smir
Humor is a salient testbed for human-like creative thinking in large language models (LLMs). We study humor using the Japanese creative response game Oogiri, in which participants produce witty responses to a given prompt, and ask the following research question: What makes such responses funny to h
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