While much research has shown the presence of AI's 'under-the-hood' biases (e.g., algorithmic, training data, etc.), what about 'over-the-hood' inclusivity biases: barriers in user-facing AI products that disproportionately exclude users with certain problem-solving approaches? Recent research has b
Despite the importance of Type Ia supernovae as standard candles for cosmology and to the chemical evolution of the Universe, we still have no consistent picture of the nature of these events. Much progress has been made in the hydrodynamical explosion modelling of supernovae Ia in the last few year
This paper presents 3Description, an experimental human-AI collaborative approach for intuitive 3D modeling. 3Description aims to address accessibility and usability challenges in traditional 3D modeling by enabling non-professional individuals to co-create 3D models using verbal and gesture descrip
Deep learning has had a great impact on various fields of computer science by enabling data-driven representation learning in a decade. Because science and technology policy decisions for a nation can be made on the impact of each technology, quantifying research impact is an important task. The num
📝 Original Info Title: A Comprehensive Quantification of Inconsistencies in Memory Dumps ArXiv ID: 2503.15065 Date: 2025-03-19 Authors: Andrea Oliveri, Davide Balzarotti 📝 Abstract None 💡 Deep Analysis 📄 Full Content 📄 Read Full PDF on ArXiv …
Discrete exterior calculus offers a coordinate-free discretization of exterior calculus especially suited for computations on curved spaces. In this work, we present a wedge product on 2-dimensional pseudomanifolds, whose faces are any polygons. We prove that this polygonal wedge product is compatib
In this work, we investigate the use of Large Language Models (LLMs) within a graph-based Retrieval Augmented Generation (RAG) architecture for Energy Efficiency (EE) Question Answering. First, the system automatically extracts a Knowledge Graph (KG) from guidance and regulatory documents in the ene
How can short-term energy consumption be accurately forecasted when sensor data is noisy, incomplete, and lacks contextual richness? This question guided our participation in the textit{2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics}, which chall
The problem of the existence of an equi-partition of a curve in $R^n$ has recently been raised in the context of computational geometry. The problem is to show that for a (continuous) curve $Gamma : [0,1] to R^n$ and for any positive integer N, there exist points $t_0=0<t_1<...<t_{N-1}<1=t_N$, such
Motivated by the large-scale nature of modern aerospace engineering simulations, this paper presents a detailed description of distributed Operator Inference (dOpInf), a recently developed parallel algorithm designed to efficiently construct physics-based reduced-order models (ROMs) for problems wit
Question answering (QA) plays a central role in financial education, yet existing large language model (LLM) approaches often fail to capture the nuanced and specialized reasoning required for financial problem-solving. The financial domain demands multistep quantitative reasoning, familiarity with
📝 Original Info Title: Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim ArXiv ID: 2501.07701 Date: 2025-01-13 Authors: Anas Abdelrehim, Dhairya Gandhi, Sharan Yalburgi, Ashutosh Bharambe, Ranjan Anantharaman, Chris Rackauckas 📝 …
Hierarchical data pervades diverse machine learning applications, including natural language processing, computer vision, and social network analysis. Hyperbolic space, characterized by its negative curvature, has demonstrated strong potential in such tasks due to its capacity to embed hierarchical
Let $A$ be an expanding $2 times 2$ matrix with rational entries and $mathbb{Z}^2[A]$ be the smallest $A$-invariant $mathbb{Z}$-module containing $mathbb{Z}^2$. Let $mathcal{D}$ be a finite subset of $mathbb{Z}^2[A]$ which is a complete residue system of $mathbb{Z}^2[A]/Amathbb{Z}^2[A]$. The pair $(
Accurate and reliable motion forecasting is essential for the safe deployment of autonomous vehicles (AVs), particularly in rare but safety-critical scenarios known as corner cases. Existing models often underperform in these situations due to an over-representation of common scenes in training data
The design of complex machines stands as both a marker of human intelligence and a foundation of engineering practice. Given recent advances in large language models (LLMs), we ask whether they, too, can learn to create. We approach this question through the lens of compositional machine design: a t
Modern manufacturing systems must meet hard delivery deadlines while coping with stochastic task durations caused by process noise, equipment variability, and human intervention. Traditional deterministic schedules break down when reality deviates from nominal plans, triggering costly last-minute re
In recent years, several hardware-based countermeasures proposed to mitigate Spectre attacks have been shown to be insecure. To enable the development of effective secure speculation countermeasures, we need easy-to-use tools that can automatically test their security guarantees early-on in the desi
Capacity, Robustness, & Perceptual quality of watermark data are very important issues to be considered. A lot of research is going on to increase these parameters for watermarking of the digital images, as there is always a tradeoff among them. . In this paper an efficient watermarking algorithm to
Analyzing high-dimensional data presents challenges due to the 'curse of dimensionality'', making computations intensive. Dimension reduction techniques, categorized as linear or non-linear, simplify such data. Non-linear methods are particularly essential for efficiently visualizing and processing
📝 Original Info Title: Appeal prediction for AI up-scaled Images ArXiv ID: 2502.14013 Date: 2025-02-19 Authors: Steve Göring, Rasmus Merten, Alexander Raake 📝 Abstract None 💡 Deep Analysis 📄 Full Content 📄 Read Full PDF on ArXiv Reference This …
Low-dose PET imaging is crucial for reducing patient radiation exposure but faces challenges like noise interference, reduced contrast, and difficulty in preserving physiological details. Existing methods often neglect both projection-domain physics knowledge and patient-specific meta-information, w
Neural Radiance Fields (NeRF) offer significant promise for generating photorealistic images and videos. However, existing mainstream neural rendering models often fall short in meeting the demands for immediacy and power efficiency in practical applications. Specifically, these models frequently ex
Medical order extraction is essential for structuring actionable clinical information, supporting decision-making, and enabling downstream applications such as documentation and workflow automation. Orders may be embedded in diverse sources, including electronic health records, discharge summaries,
Large-scale enterprise systems utilizing Protocol Buffers (protobuf) present significant challenges for performance testing, particularly when targeting intermediate business interfaces with complex nested data structures. Traditional test data generation approaches are inadequate for handling the i
This paper deals with discrete-time Markov control processes on a general state space. A long-run risk-sensitive average cost criterion is used as a performance measure. The one-step cost function is nonnegative and possibly unbounded. Using the vanishing discount factor approach, the optimality ine
A recent advancement in Multimodal Large Language Models (MLLMs) research is the emergence of 'reasoning MLLMs' that offer explicit control over their internal thinking processes (normally referred as the 'thinking mode') alongside the standard 'non-thinking mode'. This capability allows these model
Road surface is the sole contact medium for wheels or robot feet. Reconstructing road surface is crucial for unmanned vehicles and mobile robots. Recent studies on Neural Radiance Fields (NeRF) and Gaussian Splatting (GS) have achieved remarkable results in scene reconstruction. However, they typica
Despite their successes in vision and language, foundation models have stumbled in pathology, revealing low accuracy, instability, and heavy computational demands. These shortcomings stem not from tuning problems but from deeper conceptual mismatches: dense embeddings cannot represent the combinator
We show that a weighted homogeneous complex surface singularity is metrically conical (i.e., bi-Lipschitz equivalent to a metric cone) only if its two lowest weights are equal. We also give an example of a pair of weighted homogeneous complex surface singularities that are topologically equivalent b
📝 Original Info Title: Blending 3D Geometry and Machine Learning for Multi-View Stereopsis ArXiv ID: 2505.03470 Date: 2025-05-06 Authors: Vibhas Vats, Md. Alimoor Reza, David Crandall, Soon-heung Jung 📝 Abstract None 💡 Deep Analysis 📄 Full Content 📄 …
In this paper, we tackle the copy-paste image-to-image composition problem with a focus on object placement learning. Prior methods have leveraged generative models to reduce the reliance for dense supervision. However, this often limits their capacity to model complex data distributions. Alternativ
We prove a nearly optimal bound on the number of stable homotopy types occurring in a k-parameter semi-algebraic family of sets in $R^ell$, each defined in terms of m quadratic inequalities. Our bound is exponential in k and m, but polynomial in $ell$. More precisely, we prove the following. Let $R$
To solve the parameter sensitive issue of the traditional RED (random early detection) algorithm, an adaptive buffer management algorithm called PAFD (packet adaptive fair dropping) is proposed. This algorithm supports DiffServ (differentiated services) model of QoS (quality of service). In this alg
Understanding the flow of information across today's fragmented digital media landscape requires scalable, cross-platform infrastructure. In this paper, we present the Canadian Media Ecosystem Observatory, a national-scale infrastructure designed to monitor political and media discourse across platf
Computer-aided design (CAD) is the digital construction of 2D and 3D objects, and is central to a wide range of engineering and manufacturing applications like automobile and aviation. Despite its importance, CAD modeling remains largely a time-intensive, manual task. Recent works have attempted to
Capturing and rendering three-dimensional (3D) objects in real time remain a significant challenge, yet hold substantial potential for applications in augmented reality, digital twin systems, remote collaboration and prototyping. We present an end-to-end pipeline that leverages 3D Gaussian Splatting
The recognition of Activities of Daily Living (ADLs) from event-triggered ambient sensors is an essential task in Ambient Assisted Living, yet existing methods remain constrained by representation-level limitations. Sequence-based approaches preserve temporal order of sensor activations but are sens
Critical appraisal of scientific literature is an essential skill in the biomedical field. While large language models (LLMs) can offer promising support in this task, their reliability remains limited, particularly for critical reasoning in specialized domains. We introduce CareMedEval, an original
This paper introduces a novel stability measure for edit distances between merge trees of piecewise linear scalar fields. We apply the new measure to various metrics introduced recently in the field of scalar field comparison in scientific visualization. While previous stability measures are unable
Disentangling complex causal relationships is important for accurate detection of anomalies. In multivariate time series analysis, dynamic interactions among data variables over time complicate the interpretation of causal relationships. Traditional approaches assume statistical independence between
Language models are traditionally designed around causal masking. In domains with spatial or relational structure, causal masking is often viewed as inappropriate, and sequential linearizations are instead used. Yet the question of whether it is viable to accept the information loss introduced by ca
📝 Original Info Title: CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classification ArXiv ID: 2510.17584 Date: 2025-10-20 Authors: Ludi Li, Junbin Mao, Hanhe Lin, Xu Tian, Fang-Xiang Wu, Jin Liu 📝 Abstract None …
This paper considers a millimeter-wave wideband point-to-point MIMO system with fully digital transceivers at the base station and the user equipment (UE), focusing on mobile UE scenarios. A main challenge when building a digital UE combining is the large volume of baseband samples to handle. To mit
Windblown sand creates multiscale bedforms on Earth, Mars, and other planetary bodies. According to conventional wisdom, decameter-scale dunes and decimeter-scale ripples emerge via distinct mechanisms on Earth: a hydrodynamic instability related to a phase shift between the turbulent flow and the t
📝 Original Info Title: Cognitive Load Traces as Symbolic and Visual Accounts of Deep Model Cognition ArXiv ID: 2510.15980 Date: 2025-10-13 Authors: Dong Liu, Yanxuan Yu 📝 Abstract None 💡 Deep Analysis 📄 Full Content 📄 Read Full PDF on ArXiv Reference …
Recently, scientists from The Craig J. Venter Institute reported construction of very long DNA molecules using a variety of experimental procedures adopting a number of working hypotheses. Finding a mathematical rule for generation of such a long sequence would revolutionize our thinking on various
📝 Original Info Title: Componentwise Automata Learning for System Integration (Extended Version) ArXiv ID: 2508.04458 Date: 2025-08-06 Authors: Hiroya Fujinami, Masaki Waga, Jie An, Kohei Suenaga, Nayuta Yanagisawa, Hiroki Iseri, Ichiro Hasuo 📝 …
Tractography is a unique method for mapping white matter connections in the brain, but tractography algorithms suffer from an inherent trade-off between sensitivity and specificity that limits accuracy. Incorporating prior knowledge of white matter anatomy is an effective strategy for improving accu
Continual fine-tuning of Large Language Models (LLMs) is hampered by the trade-off between efficiency and expressiveness. Low-Rank Adaptation (LoRA) offers efficiency but constrains the model's ability to learn new tasks and transfer knowledge due to its low-rank nature and reliance on explicit para
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