Tootfinder

DeepPolar : Breaking the BER-BLER Trade-off with Self-Attention and SMART (SNR-MAtched Redundancy Technique) decoding
Shubham Srivastava, Adrish Banerjee
https://arxiv.org/abs/2506.10166

DeepPolar+: Breaking the BER-BLER Trade-off with Self-Attention and SMART (SNR-MAtched Redundancy Technique) decoding
DeepPolar codes have recently emerged as a promising approach for channel coding, demonstrating superior bit error rate (BER) performance compared to conventional polar codes. Despite their excellent BER characteristics, these codes exhibit suboptimal block error rate (BLER) performance, creating a fundamental BER-BLER trade-off that severely limits their practical deployment in communication systems. This paper introduces DeepPolar+, an enhanced neural polar coding framework that systematicall…

ASTAGEN: Empirical Evaluation of Automated SATD Taxonomy Generation with LLMs
Sota Nakashima, Yuta Ishimoto, Masanari Kondo, Tao Xiao, Yasutaka Kamei
https://arxiv.org/abs/2506.09601

ASTAGEN: Empirical Evaluation of Automated SATD Taxonomy Generation with LLMs
Technical debt refers to suboptimal code that degrades software quality. When developers intentionally introduce such debt, it is called self-admitted technical debt (SATD). Since SATD hinders maintenance, identifying its categories is key to uncovering quality issues. Traditionally, constructing such taxonomies requires manually inspecting SATD comments and surrounding code, which is time-consuming, labor-intensive, and often inconsistent due to annotator subjectivity. This study presents ASTA…

Conclusive Local State Marking: More Nonlocality With No Entanglement
Samrat Sen
https://arxiv.org/abs/2506.09415 https://arxiv.org/p…

Conclusive Local State Marking: More Nonlocality With No Entanglement
Nonlocality exhibited by ensembles of composite quantum states, wherein local operations and classical communication (LOCC) yield suboptimal discrimination probabilities compared to global strategies, is one of the striking nonclassical features of quantum theory. A variant of this phenomenon arises in conclusive local state discrimination, where the correct state must be identified with zero error, albeit allowing for inconclusive outcomes. More recently, the notion of local state marking has …

Beyond Personalization: Federated Recommendation with Calibration via Low-rank Decomposition
Jundong Chen, Honglei Zhang, Haoxuan Li, Chunxu Zhang, Zhiwei Li, Yidong Li
https://arxiv.org/abs/2506.09525

Beyond Personalization: Federated Recommendation with Calibration via Low-rank Decomposition
Federated recommendation (FR) is a promising paradigm to protect user privacy in recommender systems. Distinct from general federated scenarios, FR inherently needs to preserve client-specific parameters, i.e., user embeddings, for privacy and personalization. However, we empirically find that globally aggregated item embeddings can induce skew in user embeddings, resulting in suboptimal performance. To this end, we theoretically analyze the user embedding skew issue and propose Personalized Fe…

This https://arxiv.org/abs/2505.16422 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Unlocking Smarter Device Control: Foresighted Planning with a World Model-Driven Code Execution Approach
The automatic control of mobile devices is essential for efficiently performing complex tasks that involve multiple sequential steps. However, these tasks pose significant challenges due to the limited environmental information available at each step, primarily through visual observations. As a result, current approaches, which typically rely on reactive policies, focus solely on immediate observations and often lead to suboptimal decision-making. To address this problem, we propose \textbf{For…

Rapid cardiac activation prediction for cardiac resynchronization therapy planning using geometric deep learning
Ehsan Naghavi, Haifeng Wang, Vahid Ziaei Rad, Julius Guccione, Ghassan Kassab, Vishnu Boddeti, Seungik Baek, Lik-Chuan Lee
https://arxiv.org/abs/2506.08987

Rapid cardiac activation prediction for cardiac resynchronization therapy planning using geometric deep learning
Cardiac resynchronization therapy (CRT) is a common intervention for patients with dyssynchronous heart failure, yet approximately one-third of recipients fail to respond due to suboptimal lead placement. Identifying optimal pacing sites remains challenging, largely due to patient-specific anatomical variability and the limitations of current individualized planning strategies. In a step towards constructing an in-silico approach to help address this issue, we develop two geometric deep learnin…

Set-valued regression and cautious suboptimization: From noisy data to optimality
Jaap Eising, Jorge Cortes
https://arxiv.org/abs/2506.07622 https://

Set-valued regression and cautious suboptimization: From noisy data to optimality
This paper deals with the problem of finding suboptimal values of an unknown function on the basis of measured data corrupted by bounded noise. As a prior, we assume that the unknown function is parameterized in terms of a number of basis functions. Inspired by the informativity approach, we view the problem as the suboptimization of the worst-case estimate of the function. The paper provides closed form solutions and convexity results for this function, which enables us to solve the problem. A…

Tram Positioning with Map-Enabled GNSS Data Reconciliation
Jakub Ka\v{s}par, V\'it Fanta, Vladim\'ir Havlena
https://arxiv.org/abs/2506.08032 https…

Tram Positioning with Map-Enabled GNSS Data Reconciliation
This paper presents an approach to tackle the problem of tram localization through utilizing a custom processing of Global Navigation Satellite System (GNSS) observables and the track map. The method is motivated by suboptimal performance in dense urban environments where the direct line of sight to GNSS satellites is often obscured which leads to multipath propagation of GNSS signals. The presented concept is based upon the iterated extended Kalman filter (IEKF) and has linear complexity (with…

Fine-Grained Motion Compression and Selective Temporal Fusion for Neural B-Frame Video Coding
Xihua Sheng, Peilin Chen, Meng Wang, Li Zhang, Shiqi Wang, Dapeng Oliver Wu
https://arxiv.org/abs/2506.07709

Fine-Grained Motion Compression and Selective Temporal Fusion for Neural B-Frame Video Coding
With the remarkable progress in neural P-frame video coding, neural B-frame coding has recently emerged as a critical research direction. However, most existing neural B-frame codecs directly adopt P-frame coding tools without adequately addressing the unique challenges of B-frame compression, leading to suboptimal performance. To bridge this gap, we propose novel enhancements for motion compression and temporal fusion for neural B-frame coding. First, we design a fine-grained motion compressio…

Correcting for Position Bias in Learning to Rank: A Control Function Approach
Md Aminul Islam, Kathryn Vasilaky, Elena Zheleva
https://arxiv.org/abs/2506.06989

Correcting for Position Bias in Learning to Rank: A Control Function Approach
Implicit feedback data, such as user clicks, is commonly used in learning-to-rank (LTR) systems because it is easy to collect and it often reflects user preferences. However, this data is prone to various biases, and training an LTR system directly on biased data can result in suboptimal ranking performance. One of the most prominent and well-studied biases in implicit feedback data is position bias, which occurs because users are more likely to interact with higher-ranked documents regardless …

Let this be a lesson: my point about editing above goes deep. It's not just about writing words and editing words.
It's about decisions of any kind.
While deliberative paralysis is a thing, when you make "do" easier than "deliberate" (and increasingly tip the balance toward "do"), what happens is what's easy, not what's good. It is an accelerator for all kinds of things, a great many of them bad, and even more suboptimal.

This https://arxiv.org/abs/2502.15786 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-Text Decoding
Decoding functional magnetic resonance imaging (fMRI) signals into text has been a key challenge in the neuroscience community, with the potential to advance brain-computer interfaces and uncover deeper insights into brain mechanisms. However, existing approaches often struggle with suboptimal predictive performance, limited task variety, and poor generalization across subjects. In response to this, we propose MindLLM, a model designed for subject-agnostic and versatile fMRI-to-text decoding. M…

Fun fact: #Windows 95 (and in some cases the later versions) had a very suboptimal implementation of idle state — they would run a busy loop, heating the CPU unnecessarily. Of course, you could work around this by installing a third-party program that would detect this state and use a more optimal HLT instruction instead.
So you could say that the purpose of such a program was to pretend that it's doing something, so that the system wouldn't fall into its own idle loop.
http://www.benchtest.com/cooler.html

AdaDec: Uncertainty-Guided Adaptive Decoding for LLM-based Code Generation
Kaifeng He, Mingwei Liu, Chong Wang, Zike Li, Yanlin Wang, Xin Peng, Zibin Zheng
https://arxiv.org/abs/2506.08980

AdaDec: Uncertainty-Guided Adaptive Decoding for LLM-based Code Generation
Code generation with large language models (LLMs) is highly sensitive to token selection during decoding, particularly at uncertain decision points that influence program logic. While standard strategies like greedy and beam search treat all tokens uniformly, they overlook code-specific uncertainty patterns, leading to suboptimal performance. This paper presents an empirical study revealing that many generation errors stem from ranking mistakes at high-uncertainty steps, where the correct token…

This https://arxiv.org/abs/2409.10289 has been replaced.
link: https://scholar.google.com/scholar?q=a

ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework
Empathetic response generation necessitates the integration of emotional and intentional dynamics to foster meaningful interactions. Existing research either neglects the intricate interplay between emotion and intent, leading to suboptimal controllability of empathy, or resorts to large language models (LLMs), which incur significant computational overhead. In this paper, we introduce ReflectDiffu, a lightweight and comprehensive framework for empathetic response generation. This framework inc…

SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model
Zhao Yang, Jiwei Zhu, Bing Su
https://arxiv.org/abs/2506.01833 https://

SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model
Inspired by the success of unsupervised pre-training paradigms, researchers have applied these approaches to DNA pre-training. However, we argue that these approaches alone yield suboptimal results because pure DNA sequences lack sufficient information, since their functions are regulated by genomic profiles like chromatin accessibility. Here, we demonstrate that supervised training for genomic profile prediction serves as a more effective alternative to pure sequence pre-training. Furthermore,…

Knowledge-Guided Attention-Inspired Learning for Task Offloading in Vehicle Edge Computing
Ke Ma, Junfei Xie
https://arxiv.org/abs/2506.04456 https://

Knowledge-Guided Attention-Inspired Learning for Task Offloading in Vehicle Edge Computing
Vehicle edge computing (VEC) brings abundant computing resources close to vehicles by deploying them at roadside units (RSUs) or base stations, thereby enabling diverse computation-intensive and delay sensitive applications. Existing task offloading strategies are often computationally expensive to execute or generate suboptimal solutions. In this paper, we propose a novel learning-based approach, Knowledge-guided Attention-inspired Task Offloading (KATO), designed to efficiently offload tasks …

CoMemo: LVLMs Need Image Context with Image Memory
Shi Liu, Weijie Su, Xizhou Zhu, Wenhai Wang, Jifeng Dai
https://arxiv.org/abs/2506.06279 https://…

CoMemo: LVLMs Need Image Context with Image Memory
Recent advancements in Large Vision-Language Models built upon Large Language Models have established aligning visual features with LLM representations as the dominant paradigm. However, inherited LLM architectural designs introduce suboptimal characteristics for multimodal processing. First, LVLMs exhibit a bimodal distribution in attention allocation, leading to the progressive neglect of middle visual content as context expands. Second, conventional positional encoding schemes fail to preser…

CiteEval: Principle-Driven Citation Evaluation for Source Attribution
Yumo Xu, Peng Qi, Jifan Chen, Kunlun Liu, Rujun Han, Lan Liu, Bonan Min, Vittorio Castelli, Arshit Gupta, Zhiguo Wang
https://arxiv.org/abs/2506.01829

CiteEval: Principle-Driven Citation Evaluation for Source Attribution
Citation quality is crucial in information-seeking systems, directly influencing trust and the effectiveness of information access. Current evaluation frameworks, both human and automatic, mainly rely on Natural Language Inference (NLI) to assess binary or ternary supportiveness from cited sources, which we argue is a suboptimal proxy for citation evaluation. In this work we introduce CiteEval, a citation evaluation framework driven by principles focusing on fine-grained citation assessment wit…

Solving Nash Equilibria in Nonlinear Differential Games for Common-Pool Resources
Yongyang Cai, Anastasios Xepapadeas, Aart de Zeeuw
https://arxiv.org/abs/2506.06646

Solving Nash Equilibria in Nonlinear Differential Games for Common-Pool Resources
Many resources are provided by an ecological system that is vulnerable to tipping when exceeding a certain level of pollution, with a sudden big loss of ecosystem services. An ecological system is usually also a common-pool resource and therefore vulnerable to suboptimal use resulting from non-cooperative behavior. An analysis requires methods to derive cooperative and non-cooperative solutions for managing a dynamical system with tipping points. Such a game is a differential game which has two…

This https://arxiv.org/abs/2503.02769 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

InSerter: Speech Instruction Following with Unsupervised Interleaved Pre-training
Recent advancements in speech large language models (SpeechLLMs) have attracted considerable attention. Nonetheless, current methods exhibit suboptimal performance in adhering to speech instructions. Notably, the intelligence of models significantly diminishes when processing speech-form input as compared to direct text-form input. Prior work has attempted to mitigate this semantic inconsistency between speech and text representations through techniques such as representation and behavior align…

This https://arxiv.org/abs/2304.14990 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Robust Stackelberg Equilibria
This paper provides a systematic study of the robust Stackelberg equilibrium (RSE), which naturally generalizes the widely adopted solution concept of the strong Stackelberg equilibrium (SSE). The RSE accounts for any possible up-to-$δ$ suboptimal follower responses in Stackelberg games and is adopted to improve the robustness of the leader's strategy. While a few variants of robust Stackelberg equilibrium have been considered in previous literature, the RSE solution concept we consider is imp…

This https://arxiv.org/abs/2412.00418 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Mixture of Experts for Node Classification
Nodes in the real-world graphs exhibit diverse patterns in numerous aspects, such as degree and homophily. However, most existent node predictors fail to capture a wide range of node patterns or to make predictions based on distinct node patterns, resulting in unsatisfactory classification performance. In this paper, we reveal that different node predictors are good at handling nodes with specific patterns and only apply one node predictor uniformly could lead to suboptimal result. To mitigate …

CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients
Mengda Ji, Genjiu Xu, Liying Wang
https://arxiv.org/abs/2506.04265 http…

CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients
This work focuses on the credit assignment problem in cooperative multi-agent reinforcement learning (MARL). Sharing the global advantage among agents often leads to suboptimal policy updates as it fails to account for the distinct contributions of agents. Although numerous methods consider global or individual contributions for credit assignment, a detailed analysis at the coalition level remains lacking in many approaches. This work analyzes the over-updating problem during multi-agent policy…

SSCard: Substring Cardinality Estimation using Suffix Tree-Guided Learned FM-Index
Yirui Zhan, Wen Nie, Jun Gao
https://arxiv.org/abs/2505.24312 https://…

SSCard: Substring Cardinality Estimation using Suffix Tree-Guided Learned FM-Index
Accurate cardinality estimation of substring queries, which are commonly expressed using the SQL LIKE predicate, is crucial for query optimization in database systems. While both rule-based methods and machine learning-based methods have been developed to optimize various aspects of cardinality estimation, their absence of error bounds may result in substantial estimation errors, leading to suboptimal execution plans. In this paper, we propose SSCard, a novel SubString Cardinality estimator tha…

This https://arxiv.org/abs/2502.09493 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Quantitative estimates for high-contrast random media
This paper studies quantitative homogenization of elliptic equations with random, uniformly elliptic coefficients that vanish in a union of random holes. Assuming an upper bound on the size of the holes and a separation condition between them, we derive optimal bounds for the regularity radius $r_*$ and suboptimal growth estimates for the corrector. These results are key ingredients for error analysis in stochastic homogenization and serve as crucial input for recent developments in the double-…

Re-experiment Smart: a Novel Method to Enhance Data-driven Prediction of Mechanical Properties of Epoxy Polymers
Wanshan Cui, Yejin Jeong, Inwook Song, Gyuri Kim, Minsang Kwon, Donghun Lee
https://arxiv.org/abs/2506.01994

Re-experiment Smart: a Novel Method to Enhance Data-driven Prediction of Mechanical Properties of Epoxy Polymers
Accurate prediction of polymer material properties through data-driven approaches greatly accelerates novel material development by reducing redundant experiments and trial-and-error processes. However, inevitable outliers in empirical measurements can severely skew machine learning results, leading to erroneous prediction models and suboptimal material designs. To address this limitation, we propose a novel approach to enhance dataset quality efficiently by integrating multi-algorithm outlier …

This https://arxiv.org/abs/2505.21810 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

SHARAD Illuminates Deeper Martian Subsurface Structures with a Boost from Very Large Rolls of the MRO Spacecraft
Throughout its mission, the Mars Reconnaissance Orbiter (MRO) has often rolled about its along-track axis by up to 28° to partially compensate for the suboptimal location of the Shallow Radar (SHARAD) antenna along an edge of the spacecraft that is opposite the imaging payload deck, thereby enhancing the signal-to-noise ratio (S/N) of echoes returned from the surface. After recent modeling work predicted that a much larger roll would improve the S/N by ~10 dB relative to nadir-pointed observin…

Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning
Esra Adiyeke, Tianqi Liu, Venkata Sai Dheeraj Naganaboina, Han Li, Tyler J. Loftus, Yuanfang Ren, Benjamin Shickel, Matthew M. Ruppert, Karandeep Singh, Ruogu Fang, Parisa Rashidi, Azra Bihorac, Tezcan Ozrazgat-Baslanti
https://

Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning
Traditional methods of surgical decision making heavily rely on human experience and prompt actions, which are variable. A data-driven system generating treatment recommendations based on patient states can be a substantial asset in perioperative decision-making, as in cases of intraoperative hypotension, for which suboptimal management is associated with acute kidney injury (AKI), a common and morbid postoperative complication. We developed a Reinforcement Learning (RL) model to recommend opti…

This https://arxiv.org/abs/2505.16865 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

LARES: Latent Reasoning for Sequential Recommendation
Sequential recommender systems have become increasingly important in real-world applications that model user behavior sequences to predict their preferences. However, existing sequential recommendation methods predominantly rely on non-reasoning paradigms, which may limit the model's computational capacity and result in suboptimal recommendation performance. To address these limitations, we present LARES, a novel and scalable LAtent REasoning framework for Sequential recommendation that enhance…

A High-Performance Multilevel Framework for Quantum Layout Synthesis
Shuohao Ping, Naren Sathishkumar, Wan-Hsuan Lin, Hanyu Wang, Jason Cong
https://arxiv.org/abs/2505.24169

A High-Performance Multilevel Framework for Quantum Layout Synthesis
Quantum Layout Synthesis (QLS) is a critical compilation stage that adapts quantum circuits to hardware constraints with an objective of minimizing the SWAP overhead. While heuristic tools demonstrate good efficiency, they often produce suboptimal solutions, and exact methods suffer from limited scalability. In this work, we propose ML-SABRE, a high-performance multilevel framework for QLS that improves both solution quality and compilation time through a hierarchical optimization approach. We …

IKMo: Image-Keyframed Motion Generation with Trajectory-Pose Conditioned Motion Diffusion Model
Yang Zhao, Yan Zhang, Xubo Yang
https://arxiv.org/abs/2505.21146

IKMo: Image-Keyframed Motion Generation with Trajectory-Pose Conditioned Motion Diffusion Model
Existing human motion generation methods with trajectory and pose inputs operate global processing on both modalities, leading to suboptimal outputs. In this paper, we propose IKMo, an image-keyframed motion generation method based on the diffusion model with trajectory and pose being decoupled. The trajectory and pose inputs go through a two-stage conditioning framework. In the first stage, the dedicated optimization module is applied to refine inputs. In the second stage, trajectory and pose …

This https://arxiv.org/abs/2505.19577 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

MFA-KWS: Effective Keyword Spotting with Multi-head Frame-asynchronous Decoding
Keyword spotting (KWS) is essential for voice-driven applications, demanding both accuracy and efficiency. Traditional ASR-based KWS methods, such as greedy and beam search, explore the entire search space without explicitly prioritizing keyword detection, often leading to suboptimal performance. In this paper, we propose an effective keyword-specific KWS framework by introducing a streaming-oriented CTC-Transducer-combined frame-asynchronous system with multi-head frame-asynchronous decoding (…

This https://arxiv.org/abs/2505.01136 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Descriptor: C++ Self-Admitted Technical Debt Dataset (CppSATD)
In software development, technical debt (TD) refers to suboptimal implementation choices made by the developers to meet urgent deadlines and limited resources, posing challenges for future maintenance. Self-Admitted Technical Debt (SATD) is a sub-type of TD, representing specific TD instances ``openly admitted'' by the developers and often expressed through source code comments. Previous research on SATD has focused predominantly on the Java programming language, revealing a significant gap in …

This https://arxiv.org/abs/2503.02077 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality
Designing effective reward functions in multi-agent reinforcement learning (MARL) is a significant challenge, often leading to suboptimal or misaligned behaviors in complex, coordinated environments. We introduce Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality ($\text{M}^3\text{HF}$), a novel framework that integrates multi-phase human feedback of mixed quality into the MARL training process. By involving humans with diverse expertise levels to provide iterat…

This https://arxiv.org/abs/2502.13128 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation
Text-to-song generation, the task of creating vocals and accompaniment from textual inputs, poses significant challenges due to domain complexity and data scarcity. Existing approaches often employ multi-stage generation procedures, leading to cumbersome training and inference pipelines, as well as suboptimal overall generation quality due to error accumulation across stages. In this paper, we propose SongGen, a fully open-source, single-stage auto-regressive transformer designed for controllab…

Model-Free Deep Hedging with Transaction Costs and Light Data Requirements
Pierre Brugi\`ere, Gabriel Turinici
https://arxiv.org/abs/2505.22836 https://

Model-Free Deep Hedging with Transaction Costs and Light Data Requirements
Option pricing theory, such as the Black and Scholes (1973) model, provides an explicit solution to construct a strategy that perfectly hedges an option in a continuous-time setting. In practice, however, trading occurs in discrete time and often involves transaction costs, making the direct application of continuous-time solutions potentially suboptimal. Previous studies, such as those by Buehler et al. (2018), Buehler et al. (2019) and Cao et al. (2019), have shown that deep learning or reinf…

This https://arxiv.org/abs/2505.08944 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Toward Cost-Efficient Serving of Mixture-of-Experts with Asynchrony
Mixture-of-Experts (MoE) architectures offer the promise of larger model capacity without the prohibitive costs of fully dense designs. However, in real-world inference serving, load skew across experts often leads to suboptimal device utilization and excessive synchronization overheads. This paper introduces Asynchronous Expert Parallelism (AEP), a new paradigm that decouples layer execution from barrier-style synchronization. By dynamically queuing tokens at each layer (referred to as $μ$-qu…

This https://arxiv.org/abs/2505.22598 has been replaced.
initial toot: https://mastoxiv.page/@arX…

On the performance of machine-learning assisted Monte Carlo in sampling from simple statistical physics models
Recent years have seen a rise in the application of machine learning techniques to aid the simulation of hard-to-sample systems that cannot be studied using traditional methods. Despite the introduction of many different architectures and procedures, a wide theoretical understanding is still lacking, with the risk of suboptimal implementations. As a first step to address this gap, we provide here a complete analytic study of the widely-used Sequential Tempering procedure applied to a shallow MA…

This https://arxiv.org/abs/2409.05546 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Generative Recommender with End-to-End Learnable Item Tokenization
Generative recommendation systems have gained increasing attention as an innovative approach that directly generates item identifiers for recommendation tasks. Despite their potential, a major challenge is the effective construction of item identifiers that align well with recommender systems. Current approaches often treat item tokenization and generative recommendation training as separate processes, which can lead to suboptimal performance. To overcome this issue, we introduce ETEGRec, a nov…

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback
Rex Chen, Karen Wu, John McCartney, Norman Sadeh, Fei Fang
https://arxiv.org/abs/2505.21349

Out of the Past: An AI-Enabled Pipeline for Traffic Simulation from Noisy, Multimodal Detector Data and Stakeholder Feedback
How can a traffic simulation be designed to faithfully reflect real-world traffic conditions? Past data-driven approaches to traffic simulation in the literature have relied on unrealistic or suboptimal heuristics. They also fail to adequately account for the effects of uncertainty and multimodality in the data on simulation outcomes. In this work, we integrate advances in AI to construct a three-step, end-to-end pipeline for generating a traffic simulation from detector data: computer vision f…

Offline Map Matching Based on Localization Error Distribution Modeling
Ruilin Xu, Yuchen Song, Kaijie Li, Xitong Gao, Kejiang Ye, Fan Zhang, Juanjuan Zhao
https://arxiv.org/abs/2505.23123

Offline Map Matching Based on Localization Error Distribution Modeling
Offline map matching involves aligning historical trajectories of mobile objects, which may have positional errors, with digital maps. This is essential for applications in intelligent transportation systems (ITS), such as route analysis and traffic pattern mining. Existing methods have two main limitations: (i) they assume a uniform Localization Error Distribution (LED) across urban areas, neglecting environmental factors that lead to suboptimal path search ranges, and (ii) they struggle to ef…

SHARAD Illuminates Deeper Martian Subsurface Structures with a Boost from Very Large Rolls of the MRO Spacecraft
Nathaniel E. Putzig, Gareth A. Morgan, Matthew R. Perry, Bruce A. Campbell, Jennifer L. Whitten, Fabrizio Bernardini, Alessandro DiCarlofelice, Pierfrancesco Lombardo
https://arxiv.org/abs/2505.21810

SHARAD Illuminates Deeper Martian Subsurface Structures with a Boost from Very Large Rolls of the MRO Spacecraft
Throughout its mission, the Mars Reconnaissance Orbiter (MRO) has often rolled about its along-track axis by up to 28° to partially compensate for the suboptimal location of the Shallow Radar (SHARAD) antenna along an edge of the spacecraft that is opposite the imaging payload deck, thereby enhancing the signal-to-noise ratio (S/N) of echoes returned from the surface. After recent modeling work predicted that a much larger roll would improve the S/N by ~10 dB relative to nadir-pointed observin…

On the performance of machine-learning assisted Monte Carlo in sampling from simple statistical physics models
Luca Maria Del Bono, Federico Ricci-Tersenghi, Francesco Zamponi
https://arxiv.org/abs/2505.22598

On the performance of machine-learning assisted Monte Carlo in sampling from simple statistical physics models
Recent years have seen a rise in the application of machine learning techniques to aid the simulation of hard-to-sample systems that cannot be studied using traditional methods. Despite the introduction of many different architectures and procedures, a wide theoretical understanding is still lacking, with the risk of suboptimal implementations. As a first step to address this gap, we provide here a complete analytic study of the widely-used Sequential Tempering procedure applied to a shallow MA…