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Found 28 papers, 13 papers with code
Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data
Our data is a significant contribution to the rare selection of available multimodal datasets.
Federated Temporal Graph Clustering
Temporal graph clustering is a complex task that involves discovering meaningful structures in dynamic graphs where relationships and entities change over time.
Can LLMs Understand Time Series Anomalies?
Inspired by conjectures about LLMs' behavior from time series forecasting research, we formulate key hypotheses about LLMs' capabilities in time series anomaly detection.
Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework
Existing methods for anomaly detection often fall short due to their inability to handle the complexity, heterogeneity, and high dimensionality inherent in real-world mobility data.
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5, 101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions.
NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks
Collecting real-world mobility data is challenging.
Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models
We demonstrate that an attacker can embed a backdoor in LLMs, which, when activated by a specific trigger in the input, manipulates the model's uncertainty without affecting the final output.
Learning operando impedance function for battery health with aging-aware equivalent circuit model
The wide usage of Lithium-ion batteries (LIBs) requires a deep understanding about battery health.
Non-destructive Degradation Pattern Decoupling for Ultra-early Battery Prototype Verification Using Physics-informed Machine Learning
Manufacturing complexities and uncertainties have impeded the transition from material prototypes to commercial batteries, making prototype verification critical to quality assessment.
Data-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Process
Mechanical Circulatory Support (MCS) devices, implemented as a probabilistic deep sequence model.
Towards Real-World HDR Video Reconstruction: A Large-Scale Benchmark Dataset and A Two-Stage Alignment Network
In this work, to facilitate the development of real-world HDR video reconstruction, we present Real-HDRV, a large-scale real-world benchmark dataset for HDR video reconstruction, featuring various scenes, diverse motion patterns, and high-quality labels.
AttackEval: How to Evaluate the Effectiveness of Jailbreak Attacking on Large Language Models
To deal with such risks, we introduce an innovative framework that can help evaluate the effectiveness of jailbreak attacks on LLMs.
Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents
By incorporating the guidance from the teacher agent, the student agent can distill the prior knowledge of the LLM into its own model.
Automatic Integration for Spatiotemporal Neural Point Processes
In this paper, we introduce a novel paradigm: AutoSTPP (Automatic Integration for Spatiotemporal Neural Point Processes) that extends the dual network approach to 3D STPP.
MathAttack: Attacking Large Language Models Towards Math Solving Ability
Instead of attacking prompts in the use of LLMs, we propose a MathAttack model to attack MWP samples which are closer to the essence of security in solving math problems.
Solving Math Word Problem with Problem Type Classification
Firstly, We propose a problem type classifier that combines the strengths of the tree-based solver and the LLM solver.
Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data
Using a newly generated dataset from 225 nickel-manganese-cobalt/graphite Li-ion cells aged under a wide range of conditions, we demonstrate a lifetime prediction of in-distribution cells with 15. 1% mean absolute percentage error using no more than the first 15% of data, for most cells.
Learning by Analogy: Diverse Questions Generation in Math Word Problem
We then feed them to a question generator together with the scenario to obtain the corresponding diverse questions, forming a new MWP with a variety of questions and equations.
Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach
We find that this problem can be naturally formulated by a Markov decision process (MDP), and propose When2Ask, a reinforcement learning based approach that learns when it is necessary to query LLMs for high-level instructions to accomplish a target task.
On Context Distribution Shift in Task Representation Learning for Offline Meta RL
Offline Meta Reinforcement Learning (OMRL) aims to learn transferable knowledge from offline datasets to enhance the learning process for new target tasks.
Bayesian hierarchical modelling for battery lifetime early prediction
Here, a hierarchical Bayesian linear model is proposed for battery life prediction, combining both individual cell features (reflecting manufacturing variability) with population-wide features (reflecting the impact of cycling conditions on the population average).
Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data
Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data.
Efficient demodulation scheme for multilevel modulation based optical camera communication
We proposed and experimentally demonstrated a new hybrid code structure based on the overlapping of two light sources to produce the effect of multi-voltage amplitudes.
Neural Point Process for Learning Spatiotemporal Event Dynamics
The key construction of our approach is the nonparametric space-time intensity function, governed by a latent process.
Multi-Frequency Wireless Channel Measurements and Characteristics Analysis in Indoor Corridor Scenarios
In this paper, we conduct wireless channel measurements in indoor corridor scenarios at 2. 4, 5 and 6 GHz bands with bandwidth of 320 MHz.
Neural Point Process for Forecasting Spatiotemporal Events
To the best of our knowledge, this is the first neural point process model that can jointly predict both the space and time of events.
Recognition and evaluation of constellation diagram using deep learning based on underwater wireless optical communication
More specifically, an constellation diagram analyzer for UWOC system based on convolutional neural network (CNN) is designed for modulation format recognition (MFR), optical signal noise ratio (OSNR) and phase error estimation.
