latex_text string | figure_path string |
|---|---|
\begin{figure}
\centering
% Figure 5
\includegraphics[width=\textwidth]{figure_5.pdf}
\caption{The HOSIDF-based validation of the designed control; (a) Open loop, (b) Sensitivity function, (c) Complementary sensitivity function.}
\label{figure_5}
\end{figure}
\begin{figure}
\centering
% Figure 6
\includegraphics[width... | 2510.09445v1_5.png |
level performance measure or Level of Service (LoS). The LoS aggregates individual assets’ conditions into a single representative indicator; for instance, a weighted sum of pavement quality indices for different roads. Maximizing the long-term LoS thus becomes the principal objective, although other objectives—such as... | 2507.18732v1_6.png |
\section{References}
@article{Bai_2023,
author = {Bai, S. and Chen, K. and Liu, X. and Wang, J. and Ge, W. and Song, S. and Dang, K. and Wang, Y. and Wang, S. and Tang, J. and Zhong, H. and Zhu, Y. and Yang, M. and Li, Z. and Wan, J. and Wang, P. and Ding, W. and Fu, Z. and Xu, Y. and Ye, J. and Zhang, X. and Xie, T... | 2509.18711v1_7.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_1.pdf}
\caption{Example of how redescriptions can be expressed in the framework of Problem~\ref{prob:exact}. \textbf{Top:} A pair of queries, forming a redescription. \textbf{Bottom left and middle:} Original data matrices $\mD_L$ and $\mD_R$ for re... | 2507.08745v1_4.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_8.pdf}
\caption{Energy consumption (in Joules) for the hardware-constrained baselines of MUSIC (in black), ESPRIT (in red), and Root-MUSIC (in gray), and the adaptive MUSIC case (in blue), per power mode across the vehicle's trajectory.}
\label{figu... | 2507.20399v1_18.png |
at the active site. This balance is essential for industrial enzymes operating under thermal stress, as excessive rigidity can impair catalytic efficiency.
While these results collectively highlight segment transformer’s ability to guide thermostability-related enzyme engineering, experimental data from a single enzym... | 2507.19755v1_25.png |
\title{Two-Stage TSO-DSO Services Provision Framework for Electric Vehicle Coordination}
\author{Yi Wang, Member, IEEE, Dawei Qiu, Member, IEEE, Fei Teng, Senior Member, IEEE, and Goran Strbac, Member, IEEE}
\begin{abstract}
High renewable penetration has been witnessed in power systems, resulting in reduced system i... | 2507.18110v1_0.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_1.pdf}
\caption{Energy per mole for the topochemical reactions between stoichiometric LiBC and metal salts XM (X = I, Br, Cl, or NO$_3$) resulting in the formation of MBC (M = Cu or Ag). Negative energies indicate exothermic reactions.}
\label{figur... | 2507.14281v2_2.png |
where $b(s)$ is a baseline (often a state-value function) to reduce variance in the gradient estimate. Algorithms like REINFORCE or actor-critic variants (e.g., A2C, PPO) use Monte Carlo or bootstrapping techniques to estimate $G_t$ and update $\boldsymbol{\theta}$ accordingly. In contrast to value-based methods that d... | 2507.19458v1_16.png |
\title{Queue up for Takeoff: A Transferable Deep Learning Framework for Flight Delay Prediction}
\author{Nnamdi Daniel Aghanya, Ta Duong Vu\\
Cranfield University, Cranfield\\
\texttt{\{nnamdi.aghanya, duong.vu\}@cranfield.ac.uk}
\and
Amaëlle Diop, Charlotte Deville, Nour Imane Kerroumi\\
Cranfield University, Cranfie... | 2507.09084v1_0.png |
types tend to specialize in particular biological domains, with limited generalizability beyond their source modality. Among them, literature-derived gene embeddings and language models (LMs) showed relatively strong and consistent performance across a broad range of benchmarks, highlighting their potential as versatil... | 2507.07367v1_1.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_1.pdf}
\caption{(a) The difference between attribute-driven multi-interest analysis and LLM-driven multi-interest analysis. (b) LLM-driven multi-interest analysis across varying levels of granularity. (c) The sparsity of the user's behaviors compare... | 2507.10917v2_1.png |
% Acknowledgements section
\section*{Acknowledgements}
This work is funded by the European Union Horizon 2020 research and innovation programme under the Marie Sk\l{}odowska--Curie grant agreement No 860801 and the FWF Austrian Science Fund [10.55776/COE12]. Sabrina Kirrane is also funded by the FWF Austrian Science F... | 2510.04652v1_19.png |
\title{Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning*}
\author{Zheng Zhang\\Amazon Web Service\\zha@amazon.com, zzhang@gmail.com}
\begin{abstract}
Large Language Models (LLMs) display striking surface fluency yet systematically fail at tasks requiring symbolic re... | 2507.10624v1_0.png |
\begin{table*}[t]
\centering
\scalebox{0.8}{
\begin{tabular}{l|l|cccccc|ccc|c}
\toprule
Model & Year & BLEU-1 & BLEU-2 & BLEU-3 & BLEU-4 & METEOR & ROUGE & Precision & Recall & F1 & Avg \\
\midrule
R2Gen & ACL 2020 & 0.289 & 0.155 & 0.087 & 0.052 & 0.128 & 0.243 & 0.151 & 0.145 & 0.145 & 0.155 \\
M2... | 2507.07568v1_6.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_4.pdf}
\caption{P95 latency comparison across traffic patterns for KIScaler, GPU baseline, and CPU baseline.}
\label{figure_4}
\end{figure}
\begin{table}[htbp]
\centering
\caption{P95 Latency Comparison: KIScaler vs. Baselines}
\label{table_4}
\res... | 2507.07932v1_6.png |
One of the most important randomized tools for computational linear algebra is matrix sketching, along with its underlying theoretical framework of subspace embeddings \cite{53}. In the context of solving linear systems, sketching-based techniques have focused primarily on the highly over-determined regression setting ... | 2507.11724v1_5.png |
\begin{table}[h!]
\centering
\caption{Cell capacity measurements (in amperhour) on three modules (M1, M2, M3). Data organised by cell position inside each module (H1--6, L1--6)}
\begin{tabular}{c|ccc||c|ccc}
& M1 & M2 & M3 & & M1 & M2 & M3 \\
\hline
H1 & 93.8 & 91.6 & 91.0 & L1 & 93.4 & 88.4 & 89.5 \\
H2 & 92.6 & 88.... | 2507.14020v1_2.png |
Note that $\widetilde{U}(\boldsymbol{\beta}, \boldsymbol{\gamma}, \xi) = \widetilde{D}(\boldsymbol{\beta}, \boldsymbol{\gamma}, \xi)\widetilde{V}^{-1}\widetilde{S}(\boldsymbol{\beta}, \boldsymbol{\gamma}, \xi)$, where
\begin{align*}
\widetilde{D}(\boldsymbol{\beta}, \boldsymbol{\gamma}, \xi) &= \int_{\nu-(\gamma_{0}+\b... | 2507.09468v1_22.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_5.pdf}
\caption{Overview of LGSP-Prompt framework. Our approach leverages both local and global spatial information through three key components: (1) Local Spatial Prompting with dynamic prompt selection for fine-grained feature extraction, (2) Glob... | 2507.09183v2_4.png |
\begin{table}[t]
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{lccccc}
\toprule
\multirow{2}{*}{\textbf{Model}} & \textbf{HQA} & \textbf{2Wiki} & \textbf{MATH} & \textbf{GSM8K} & \textbf{IFEval} \\
& \textbf{EM} & \textbf{EM} & \textbf{Acc} & \textbf{Acc} & \textbf{SAcc} \\
\midrule
\textbf{AutoTIR} & 43.15 & ... | 2507.21836v1_5.png |
differentiable.
\section{Problem Statement}
Optimal transport involves finding the most efficient way to transform one probability distribution into another. The distributions can be thought of as descriptions of how mass (representing probability) is spread over a space. Efficiency here is measured by a predefined co... | 2507.13191v2_1.png |
\begin{table}[t]
\centering
\caption{Comparison of different methods for cancer lesion segmentation with Weighted Averages. Bold values indicate the highest score, and underlined values indicate the second highest. All comparison results yielded $p<0.05$. ("Single" denotes single-task training, "+" indicates nnU-Net wi... | 2507.07126v1_6.png |
\section{References}
@article{1,
author = {Rosi{\'e} et al., V.},
title = {Perception and social evaluation of cloned and recorded voices: Effects of familiarity and self-relevance},
journal = {Computers in Human Behavior: Artificial Humans},
year = {2025}
}
@misc{2,
author = {Peek, S.},
title = {How digi... | 2510.23096v1_4.png |
overlooked, affecting comparison outcomes. Additionally, on average, the hybrid method takes 90 seconds to process a single report pair running on an NVIDIA GeForce RTX 2080 Ti, limiting its scalability for large-scale datasets.
Future work will focus on several directions: (1) Exploring how the choice of NER model af... | 2510.03102v2_11.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_3.pdf}
\caption{Illustration of cluster Prototype-Guided cross-modal Alignment (PGA): (a) Instance-based image-text contrastive learning aims to draw positive pairs together while separating negative pairs. (b \& c) Cluster Prototype-Guided Cross-mo... | 2507.09256v1_7.png |
\title{AU-LLM: Micro-Expression Action Unit Detection via Enhanced LLM-Based Feature Fusion}
\author{Zhishu Liu$^{1\ast}$, Kaishen Yuan$^{1\ast}$, Bo Zhao$^{1}$, Yong Xu$^{2}$, and Zitong Yu$^{1(\boxtimes)}$\\
$^{1}$Great Bay University\\
$^{2}$Harbin Institute of Technology, Shenzhen}
\begin{abstract}
The detection ... | 2507.21778v1_0.png |
\begin{table*}[tbp]
\centering
\caption{Image-text retrieval performance comparison on Flickr30K and MSCOCO. $^\star$ and $^\dagger$ denote visual semantic description generated by Florence-2-large-ft-0.77B and MiniCPMV2.6-8B respectively, while * indicates ensemble model results.}
\label{tab:table_1}
\begin{tabular}{l... | 2507.08590v1_3.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_1.pdf}
\caption{Design of PoliAnalyzer. Solid arrows represent data flow within PoliAnalyzer; dashed arrows represent main user flows.}
\label{figure_1}
\end{figure}
We use each \emph{line} of the privacy policy as a segment, as a balance between a... | 2507.14214v1_2.png |
\begin{table}[htbp]
\centering
\resizebox{\columnwidth}{!}{
\begin{tabular}{|l|r|r|r|r|l|}
\hline
\emph{dastgāh}/\emph{āvāz} & \emph{gūsheh} Count & Number of Notes & Total Duration (unit) & MIDI Performance Duration (second) & Pitch Range \\
\hline
\emph{Shur} & 29 & 4830 & 9839 & 1966 ... | 2507.10456v3_4.png |
\begin{table*}[htp]
\centering
\setlength{\tabcolsep}{3pt}
\begin{tabular}{c|cc|cc|cc|cc}
\toprule
\multirow{2}{*}{Approach} & \multicolumn{2}{c|}{Facescrub/ResNet} & \multicolumn{2}{c|}{SVHN/VGG} & \multicolumn{2}{c|}{CIFAR-10/ViT} & \multicolumn{2}{c}{CIFAR-100/ResNet}\\
& $Acc_{\mathcal{D}_{r... | 2507.21738v1_4.png |
\title{Revealing the Hidden Temporal Structure of HubertSoft Embeddings based on the Russian Phonetic Corpus}
\author{Anastasia Ananeva$^{1}$, Anton Tomilov$^{2}$, and Marina Volkova$^{2}$\\
$^{1}$ ITMO University, Saint Petersburg, Russia\\
$^{2}$ STC-innovations Ltd., Russia\\
{\tt ananeva@itmo.ru}\\
{\tt tomilov, v... | 2507.06794v1_0.png |
\begin{table}[t]
\centering
\caption{Top-1 Accuracy Comparison of Token Compression Methods on AutoFormer-S in Off-the-Shelf and Retrained Settings (Acc1(ots) vs. Acc1(re-train)), with Inference Efficiency Measured by GFLOPs and Throughput (img/s).}
\label{table_2}
\begin{tabular}{lcccc}
\hline
Method & Acc1(ots)$\upar... | 2507.09702v1_3.png |
relation-aware attention mechanism \cite{28}, HRAN aggregates features along relational paths \cite{21}, and KGT5 and HittER leverage Transformers for scalable and context-aware representation learning \cite{27,4}. Despite their success, embedding-based models still struggle with distinguishing semantically similar but... | 2507.20643v2_4.png |
(BTA) matrix. While $\Q_c$ stores information on the conditional dependencies, its inverse $\boldsymbol{\Sigma}_c = \Q_c^{-1}$ contains information on the linear dependence between two variables in the off-diagonal entries, while its diagonal entries store the variances of the individual variables which are of interest... | 2507.06938v2_3.png |
Starobinsky gravity, both neutron stars~\cite{37} and black holes~\cite{42} can support massive scalar hair, with their exterior spacetime exhibiting nonzero curvature, while the Schwarzschild black hole remains a special case with vanishing exterior curvature. (Similar types of hairy solutions also arise in general qu... | 2507.18916v1_2.png |
\begin{figure}[t]
\centering
\begin{subfigure}{0.45\linewidth}
\centering
\includegraphics[width=\linewidth]{figure_4_1.pdf}
\caption{Valencian Region}
\end{subfigure}
\hfill
\begin{subfigure}{0.45\linewidth}
\centering
\includegraphics[width=\linewidth]{figure_4_2.pdf}
\caption{Castile and Leon}
\end{subfigure}
\vspa... | 2507.08376v1_13.png |
the concept of grain size, which defines the level at which engagement is conceptualized and measured. Grain size ranges from macro-level (e.g., group engagement) to micro-level (e.g., momentary individual engagement in a specific task). Micro-level engagement can be assessed using physiological signals such as blink r... | 2507.17959v1_1.png |
\title{HH-Codec: High Compression High-fidelity Discrete Neural Codec for Spoken Language Modeling}
\author{Rongkun Xue$^{1}$ \and Yazhe Niu$^{2,3}$ \and Shuai Hu$^{4}$ \and Zixin Yin$^{4}$ \and Yongqiang Yao$^{5}$ \and Jing Yang$^{1}$}
\begin{abstract}
Discrete speech tokenization is a fundamental component in speec... | 2507.18897v1_0.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_3.pdf}
\caption{The diurnal variation of AfriTEC model with F10.7 and Dst index value comparision for model evaluation with GNSS data in MAL2 station in October 12--20, 2016.}
\label{figure_3}
\end{figure}
\begin{figure}[t]
\centering
\includegraph... | 2507.10275v1_9.png |
The main sources of theoretical uncertainties in cross section calculations are known, and some of them have been outlined in ref.~\cite{7}. The state of the art in nuclear reaction theory is well described in the classic book of Satchler~\cite{8}, and on this basis, a hierarchy of theoretical treatments can be establi... | 2507.11591v1_1.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_2.pdf}
\caption{Directed graph of conjugated capabilities according to Table~\ref{tab:table_4}, adjusted according to the correlation of Figure~\ref{fig:figure_1}.}
\label{fig:figure_2}
\end{figure}
On the set of paths with minimal length, we solve... | 2507.07560v1_5.png |
\section{Agent}
Our agent development consists of two main steps. First, \emph{behavior cloning} on curated expert replays establishes an initial policy. This policy is then refined via \emph{self-play fine-tuning} with reinforcement learning, where the agent iteratively improves by competing against a dynamic pool of ... | 2507.06825v2_4.png |
\begin{table}[t]
\caption{The average scores of Diffuser, Decision Diffuser, Diffusion-QL, Consistency-AC, Consistency Planning and our method on D4RL locomotion tasks are shown. The results of previous work are quoted from \cite{Ding_Jin_2024}, \cite{Ajay_2023} and \cite{Wang_2024}.}
\label{table_1}
\centering
\begin{... | 2507.09534v1_8.png |
\begin{table}
\centering
\caption{Different DLT Structures for CBDC. DLTs used in CBDC, adopted from \cite{32,57}.}
\label{tab:table_2}
\end{table}
and cryptocurrencies into two types: ABC (Account Blockchain) and TBC (Trading Blockchain). As their names imply, ABC stores and transmits account information, while TBC m... | 2507.08880v1_15.png |
\title{Infinite Video Understanding}
\author{Dell Zhang \\
Institute of Artificial Intelligence (TeleAI), China Telecom, China \\ \and
Xiangyu Chen \\
Institute of Artificial Intelligence (TeleAI), China Telecom, China \\ \and
Jixiang Luo \\
Institute of Artificial Intelligence (TeleAI), China Telecom, China \\ \and
M... | 2507.09068v2_0.png |
\title{SELF-CONSISTENCY IN VISION-LANGUAGE MODELS FOR PRECISION AGRICULTURE: MULTI-RESPONSE CONSENSUS FOR CROP DISEASE MANAGEMENT}
\author{Mihir Gupta$^{*}$ \and Abhay Mangla$^{\dagger}$ \and Pratik Desai$^{\ddagger}$ \and Ross Greer$^{\S}$\\
$^{*}$The Harker School, USA\\
$^{\dagger}$Dougherty Valley High School, USA... | 2507.08024v1_0.png |
\begin{figure}
\centering
\includegraphics[width=\linewidth]{figure_2.pdf}
\caption{Bubble terminal velocity $v_t^{*}$ as a function of the diameter $D^{*}$ for clean water. Black symbols and line correspond to experimental results of Duineveld~\cite{6} and the numerical predictions of Herrada and Egggers~\cite{32} wit... | 2507.12422v1_4.png |
and may not be fully reliable due to factors such as human error, subjective or ambiguous labelling tasks, or inaccurate automated labelling systems \cite{15}. Incorrectly labelled data—`label noise’—has been shown to negatively impact generalizability and training dynamics \cite{2}, comprising an often overlooked but ... | 2507.17996v1_1.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_2.pdf}
\caption{The overview of our proposed quantity retrieval via description parsing (the blue parts) and weak supervision (the green parts).}
\label{figure_2}
\end{figure}
model finds the most similar description in the corpus and returns its q... | 2507.08322v2_2.png |
mation systems, database systems, etc. To identify the research fields of articles, we utilize a zero-shot classification technique to automatically categorize research articles based on their titles and abstracts into predefined research fields. The list of research fields was sourced from the ORKG, to ensure that our... | 2507.13143v1_8.png |
where $\epsilon_t \in \mathbb{R}^n$ is a stochastic noise vector with zero mean. In such settings, the optimizer must filter out the noise to ensure a stable descent. LyAm mitigates these challenges via two key mechanisms: adaptive learning rate scaling and bias-corrected moment estimation. This adaptive scaling has tw... | 2507.11262v1_6.png |
While the color weights considered in this work still ignore correlations between different coordinates, this class of models provides a very flexible parametrization for the nuclear color structure that can be tested against experimental data. With the precise data expected to be collected at the upcoming Electron--Io... | 2507.18711v1_22.png |
\begin{table}
\centering
\caption{Mask construction rules for the five tasks.}
\label{table_vii}
\begin{tabular}{ll}
\hline
Task $t$ & Rule $\mathbf{M}=\mathcal{F}_t(\mathbf{m})$ \\
\hline
DeNovo & $M_i=1$ (all atoms) \\
LINKER & Mask $\leq 2$ atoms on the shortest path between anchors \\
FRAGMENT & Mask exit atom + 2-... | 2507.07201v1_4.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_2.pdf}
\caption{The overall architecture of \texttt{RAGSum}.}
\label{figure_2}
\end{figure}
twice to produce two representation vectors, $q_i$ and $q_i^+$. The model is trained to minimize the distance between these two representations while maximi... | 2507.12558v2_2.png |
putational efficiency, we introduce a gating SSM mechanism that selectively integrates long-range dependencies, ensuring the model captures the most relevant temporal information. Furthermore, we design innovative scanning strategies at both the BEV and instance query levels, which incorporate multi-directional and spa... | 2507.20224v1_1.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_2_1.pdf}
\\
\includegraphics[width=\linewidth]{figure_2_2.pdf}
\caption{(a) Overall architecture of our model. Our model takes the current input and $N$ history inputs as input, where the history inputs are fed into a history compressor before being... | 2507.21369v1_2.png |
a multiply-with-carry generator taken from \cite{48}. For a more detailed discussion see the Appendix A of Ref. \cite{8}.
In preliminary runs with about $10^6$ update cycles at our preliminary estimate of $K_{1,c}$ for $K_3 = 0.0415$ we determined the integrated autocorrelation times of the energy density and the magn... | 2507.19265v1_17.png |
\title{Autonomy for Older Adult-Agent Interaction}
\author{Jiaxin An \\
The University of Texas at Austin, USA}
\begin{abstract}
As the global population ages, artificial intelligence (AI)-powered agents have emerged as potential tools to support older adults’ caregiving. Prior research has explored agent autonomy by... | 2507.12767v1_0.png |
i.e., $D_{\mathtt{init}}$ and $D_{\mathtt{goal}}$, as valuable guidance for generating task-aware contact maps.
Task-aware contact maps integrate environmental context (e.g., multiple objects) and task-relevant details (e.g., relocating one object onto another) to facilitate human grasp synthesis. The representation c... | 2507.11287v1_4.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_4.pdf}
\caption{Spectral sequence of SN~2020aeuh at late phases displaying our 7 spectra between 81 and 181 days past first detection, when the SN is clearly dominated by CSM interaction. The smoothed spectra (using a Savitzky--Golay filter) are sho... | 2507.08532v1_5.png |
\begin{figure}[t]
\centering
\begin{subfigure}{0.45\linewidth}
\centering
\includegraphics[width=\linewidth]{figure_1_1.pdf}
\caption{ICAR Variances, Valencian Region}
\end{subfigure}
\hfill
\begin{subfigure}{0.45\linewidth}
\centering
\includegraphics[width=\linewidth]{figure_1_2.pdf}
\caption{ICAR Variances, Castile ... | 2507.08376v1_4.png |
\paragraph{}
appear well-suited for leveraging heterogeneous observations, enabling the model to generalize coupling patterns beyond the regions with dense coverage. This highlights a central strength of GraphDOP: the flexibility to incorporate new or unconventional observations without the need for predefined observat... | 2510.20416v1_14.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_1.pdf}
\caption{(a) An illustration of a recurrent neural network (RNN) in the rolled version on the left-hand side and the unrolled version on the right-hand side. Each RNN cell (in green) receives a one-hot encoding $\bm{\sigma}_{n-1}$ of the spin... | 2507.18700v1_2.png |
\begin{align}
s_{24} &= (q_2 + q_4)^2\,, & s_{35} &= (q_3 + q_5)^2\,,
\end{align}
and massive and massless external momenta,
\begin{align}
q_1^2 &= q_2^2 = m_1^2\,, & q_3^2 &= q_5^2 = m_2^2\,, & q_4^2 &= 0\,.
\end{align}
As a first example, we implement a one-loop five-point topology with 7, 8 and 9 complex kinemat... | 2507.12548v1_13.png |
\section*{Acknowledgments}
We thank the referee for some very insightful comments that have significantly improved the manuscript. EPK and DLC acknowledge financial support from the STFC/UKRI grant ST/Y001834/1. EPK is supported by the Leverhulme Trust (Research Fellowship RF-2025-357). AGE was supported by NASA’s Hel... | 2509.17861v1_8.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_6.pdf}
\caption{Zoomed view of the near-optimal region comparing HDRL, DQL, and Hybrid LP-GA in cost-condition space (10 sewersheds). Marginal KDEs along the axes highlight the density of solutions for each method.}
\label{figure_6}
\end{figure}
Fo... | 2507.19458v1_31.png |
% Figure 4
\begin{figure}
\centering
% Subfigures omitted for brevity in layout
\includegraphics[width=\linewidth]{figure_4.pdf}
\caption{Metrics per BEMA power--EMA power bins in OthelloGPT length generalization.}
\label{figure_4}
\end{figure}
% Figure 5
\begin{figure}
\centering
\includegraphics[width=0.7\linewidth]... | 2510.08341v1_7.png |
explored in the literature. A common strategy in unsupervised domain adaptation (DA) is to calculate target-specific statistics to normalize or align the raw target data \cite{42,48,49,50}. More advanced methods \cite{35,51} further adapt the model’s batch normalization layers to standardize the data between layers. \c... | 2507.06779v1_6.png |
% Figures
\begin{figure}
\centering
\includegraphics{figure_5.pdf}
\caption{}
\label{figure_5}
\end{figure}
\begin{figure}
\centering
\includegraphics{figure_6.pdf}
\caption{}
\label{figure_6}
\end{figure}
% Main text
all three observations, produced with the \textit{PCUBE} algorithm from \textit{ixpeobssim} \cite{Ba... | 2507.07232v1_5.png |
\begin{equation}
\frac{\partial \rho \mathbf{u}}{\partial t} + \nabla \cdot \left( \rho \mathbf{u} \otimes \mathbf{u} + \left( p + \frac{1}{2} \alpha(\rho') - \alpha(\rho) \|\mathbf{j}\|^2 \right) I \right) + \alpha(\rho) \mathbf{j} \otimes \mathbf{j} = 0,
\tag{2.1b}
\end{equation}
\begin{equation}
\frac{\partial \mat... | 2507.21351v2_2.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_2.pdf}
\caption{Overview of the intent-verification pipeline: The system extracts structured goals and constraints from user input using a foundation model. Confidence estimation is performed to identify uncertain outputs. When uncertainty is high, ... | 2507.11352v1_3.png |
\section{References}
@article{Makdissi_Yazbeck_2014,
author = {Makdissi, Paul and Yazbeck, Myra},
title = {Measuring socioeconomic health inequalities in presence of multiple categorical information},
journal = {Journal of Health Economics},
volume = {34},
pages = {84--95},
year = {2014}
}
@article{Meissn... | 2510.09590v2_50.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_1.pdf}
\caption{Illustration of three different visual analytics paradigms.}
\label{fig:figure_1}
\end{figure}
\title{ProactiveVA: Proactive Visual Analytics with LLM-Based UI Agent}
\author{Yuheng Zhao, Xueli Shu, Liwen Fan, Lin Gao, Yu Zhang, Si... | 2507.18165v1_0.png |
The computation of the convolution over the residual map:
\[
\mathrm{FLOPs}_{Res} = \alpha \beta Conv_{ops}
\]
As a result, the overall computation of our framework is:
\[
\mathrm{FLOPs}_{MEVC} = \mathrm{FLOPs}_{ME} + \mathrm{FLOPs}_{Unmatched} + \mathrm{FLOPs}_{Res}
\]
The acceleration ratio is:
\[
Acceleration =... | 2501.15119v2_4.png |
In our implementation, we first solve the equilibrium equations~(\ref{zeroorder})--(\ref{zeroordervac}) independently to determine appropriate initial values for $\lambda_0$ and $\phi_0$, as well as to obtain physical quantities such as the mass $M$, radius $R$, and scalar hair $\phi_c$. Once the background solution is... | 2507.18916v1_14.png |
\begin{verbatim}
Python Function:
{function}
Input Text:
{text}
AND THIS PARAMETER SPECIFICATION:
{parameters}
PRODUCE THIS OUTPUT:
\end{verbatim}
\subsection{A.3. Prompts Used for Validation}
\subsubsection*{Prompt: Validation Analysis}
You are an expert \textbf{Natural Language Understanding (NLU)} and \textbf{log... | 2510.05156v1_18.png |
budget should be allocated to year~$t$. This fraction is mapped to an admissible annual budget $b_t$ while enforcing constraints on yearly expenditures:
\begin{equation}
\label{eq:annualBudgetMapping}
b_t = \max\Bigl( b_t^{l} + (a_t^{(1)}+1)\,(b_t^{u} - b_t^{l})/2, \; b^{\text{total}} - \sum_{k=1}^{t-1} b_k - \sum_{k=t... | 2507.19458v1_18.png |
% Table 3
\begin{table}
\centering
\caption{Comparison of the properties of transverse oscillations in different works}
\label{table_3}
\begin{tabular}{lllllll}
\hline
Area & Type & Band & Displacement (km) & Period (s) & Velocity amplitude (km s$^{-1}$) & Phase speed (km s$^{-1}$) \\
\hline
QS$^{a}$ & On-disk Type I &... | 2509.18646v1_8.png |
\subsection{Methodological Limitations}
Existing validation protocols predominantly rely on localized comparisons with traditional MCMC results~\cite{129}, and they lack systematic evaluation metrics for high-dimensional parameter spaces. Neural networks may exhibit progressive biases in extreme parameter regions that ... | 2507.11192v3_10.png |
parameter estimation round with a bit error is shown in the following.
\begin{equation}
\begin{aligned}
P_{\mathrm{est,bit}}^{u}=
&\frac{
\sum_{x}\operatorname{tr}\Big[(\ket{\mathrm{est}}_{\mathrm{pe}_u}\bra{\mathrm{est}}_{\mathrm{pe}_u})\big(\mathcal{P}\{(\ket{++}_{A_uB_u}-\ket{--}_{A_uB_u})/\sqrt{2}\}+\mathcal{P}\{(... | 2507.11243v1_4.png |
Since \( \mathbf{H} \) is built from \( \mathbf{w}_i \)'s, it becomes sparser as \( \lambda \) increases and denser as \( \lambda \) decreases.
\subsection{Spectral Clustering on Line Graph}
To detect overlapping communities, we first construct the weighted line graph of a hypergraph \(\Gamma(\mathbf{H}) \in \mathbb{... | 2507.08999v1_2.png |
\begin{figure}
\centering
\includegraphics{figure_5.pdf}
\caption{Beam composition during data taking in December 2024 at 12.5 A GeV/c. The time-of-flight difference relative to fragments with A/Z = 2 is shown on the y-axis, and the charge number Z, measured with a scintillator located just upstream of the NA61 target,... | 2510.17973v1_6.png |
\subsection{Scoring and Metrics}
Since each pass only hides a random subset of positions, we compute all evaluation metrics by aggregating only over those $i$ for which $m_i = 1$. Concretely:
\begin{itemize}
\item \textbf{Per-token log-prob:}
$\ell_i = \log \hat p_{i,x_i}$, summed only over masked positions.
\... | 2507.07586v2_5.png |
\begin{equation}
v = \sqrt{ \frac{r}{\gamma^3} \left( a_{\max} \tanh\left( \frac{GM}{a_{\max} r^2} \right) + a_{\min} \left[ 1 - \tanh\left( \frac{GM}{a_{\min} r^2} \right) \right] \right) }\, \hat{\boldsymbol{r}}.
\tag{51}
\end{equation}
For small speeds $v \ll c$ and in the low acceleration regime, as we are in we... | 2507.11524v1_12.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_1.pdf}
\caption{Mesh configuration: primal Voronoi cells (generators $\mathbf{x}_c$, volumes $|\omega_c|$) and dual Delaunay triangles (circumcenters $\mathbf{x}_p$, volumes $|\omega_p|$). The subcell $\omega_{pc}$ is highlighted in red.}
\label{fig... | 2507.21351v2_3.png |
\section{References}
@inproceedings{1,
author = {Muhammad Umer Anwaar and Egor Labintcev and Martin Klietmann},
title = {Compositional learning of image-text query for image retrieval},
booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
pages = {1139--1148},
year ... | 2510.08003v1_8.png |
The \textsl{spectral moment of order} $r$ is the integer:
\begin{equation}
\label{MOMENT}
\kappa_r(f)=\frac{1}{q^2}\sum_{a\in \mathbb{F}_2^m} \widehat{f}(a)^r.
\end{equation}
It is an EA-invariant when $r$ is even and we normalize the $4$th-order spectral moment:
\begin{equation}
\label{KAPPA}
\kappa(f)=\frac{1}{q}\kap... | 2507.12853v1_1.png |
\section{Regression-Based Anxiety Estimation}
To extract HRV features from wearable IBI data, we applied a 5-minute sliding window with a 0.25-second step size. Implausible IBI values (below 300 ms or above 2000 ms) were removed to reduce the impact of artefacts. Following this, we computed two standard time-domain HR... | 2507.13795v1_3.png |
chronologically. Focus on detecting and interpreting sequence markers like `then,' `twice,' `again,' and other words indicating repetitions or transitions.
Ensure that your decomposition explicitly outlines:
1. The initial state of the posture or action.
2. Detailed intermediate steps.
3. The final state.
\subsection... | 2501.15058v1_3.png |
\begin{table}
\centering
\caption{Three-class confusion matrix.}
\label{tab:table_1}
\begin{tabular}{c|ccc}
\hline
& \multicolumn{3}{c}{Predicted} \\
\cline{2-4}
Actual & Class 1 & Class 2 & Class 3 \\
\hline
Class 1 & TP$_1$ & FP$_{1,2}$ & FP$_{1,3}$ \\
Class 2 & FP$_{2,1}$ & TP$_2$ & FP$_{2,3}$ \\
Class 3 & FP$_{3,1... | 2507.14072v4_4.png |
evaluation.
Sections \ref{chap:results} shows the empirical results, while Section \ref{sec:discussion} discusses them analyzing their implications. Section \ref{sec:threat} exposes the limitations of this research work and, finally, Section \ref{sec:conclusion} provides our final remarks and comments.
\section{Backgr... | 2507.19156v1_2.png |
\subsection{Inclusion and Exclusion Criteria}
All works not directly related to ASB---including those focused on narrowly scoped or tangential subjects, those not employing an ML-based approach, and those that were either not peer-reviewed or not openly accessible---were excluded from our review. One of the major chal... | 2507.20614v1_8.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_2.pdf}
\caption{Calibration curve illustrating the relationship between confidence and average accuracy. Perfect calibration is shown by the dashed line. \textbf{Prompt4Trust demonstrates better calibration, particularly in the high-confidence regio... | 2507.09279v4_5.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_1.pdf}
\caption{Comparison of models fine-tuned using diverse strategies based on InternVL3-8B.}
\label{figure_1}
\end{figure}
\title{EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO}
\author{Wei Guan$^{1,2}$, Jun L... | 2507.21619v1_0.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_1.pdf}
\caption{The architecture of TinierHAR}
\label{figure_1}
\end{figure}
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_2.pdf}
\caption{Evaluation result of TinierHAR on 14 HAR datasets. Averages across datasets: 2.7x, 4... | 2507.07949v1_2.png |
is typically of the same order \cite{81}. Furthermore, to ensure reliable computation over large programs, the architecture must accommodate both spatial and temporal variations---such as fabrication-induced defects, cosmic ray-induced transient faults, and workload-induced congestion---which further increase the qubit... | 2507.12253v1_24.png |
The concatenation-based approach directly integrates conditioning features with the noisy image through channel concatenation. For example, SR3~\cite{28} concatenates low-resolution images with noisy images to guide the denoising process, producing high-resolution outputs. Similarly, Zero123~\cite{15} employs the refer... | 2507.19201v1_2.png |
Finally, in Section \ref{Sec:Disc} we summarize our findings and provide some recommendations.
\section{Low Information Under Collinearity: Potential Problem and a Possible Solution}\label{Sec:problem}
It is generally considered desirable to jointly estimate the mean and the non-diagonal covariance matrix in Bayesian ... | 2507.17975v1_3.png |
\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figure_2.pdf}
\caption{The plot of the potential which realizes $N_{DW}=1$ with ${\cal N}$ given in (5.3) taking $\Lambda_1 = \Lambda_2$ to make the minimum clearly visible. We can see that the minimum is unique at $(\theta_1, \theta_2) = (0,0)$.}
\label{f... | 2507.07973v1_9.png |
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