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Found 41 papers, 15 papers with code
CarbonClipper: Optimal Algorithms for Carbon-Aware Spatiotemporal Workload Management
We formalize this as an online problem called spatiotemporal online allocation with deadline constraints ($\mathsf{SOAD}$), in which an online player completes a workload (e. g., a batch compute job) by moving and scheduling the workload across a network subject to a deadline $T$.
Competitive Algorithms for Online Knapsack with Succinct Predictions
In the online knapsack problem, the goal is to pack items arriving online with different values and weights into a capacity-limited knapsack to maximize the total value of the accepted items.
TutteNet: Injective 3D Deformations by Composition of 2D Mesh Deformations
This work proposes a novel representation of injective deformations of 3D space, which overcomes existing limitations of injective methods: inaccuracy, lack of robustness, and incompatibility with general learning and optimization frameworks.
Chance-Constrained Economic Dispatch with Flexible Loads and RES
In this paper, we propose an enhanced day-ahead energy market, where the MDFs of aggregate loads are traded to minimize the generation cost and mitigate the volatility of locational marginal prices (LMPs) in the transmission network.
Chasing Convex Functions with Long-term Constraints
We introduce and study a family of online metric problems with long-term constraints.
360ORB-SLAM: A Visual SLAM System for Panoramic Images with Depth Completion Network
To enhance the performance and effect of AR/VR applications and visual assistance and inspection systems, visual simultaneous localization and mapping (vSLAM) is a fundamental task in computer vision and robotics.
Instance-aware 3D Semantic Segmentation powered by Shape Generators and Classifiers
In the experiments, our method significantly outperform existing approaches in 3D semantic segmentation on several public benchmarks, such as Waymo Open Dataset, SemanticKITTI and ScanNetV2.
Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression
Evaluation results show our method improves visual quality by 14%, prompt alignment by 16. 2% and scene diversity by 15. 3%, compared to prompt engineering the base Emu model for stickers generation.
Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms
We introduce competitive (robust) threshold-based algorithms for both the minimization and maximization variants of this problem, and show they are optimal among deterministic online algorithms.
Online Algorithms with Uncertainty-Quantified Predictions
The burgeoning field of algorithms with predictions studies the problem of using possibly imperfect machine learning predictions to improve online algorithm performance.
Weakly Supervised Point Clouds Transformer for 3D Object Detection
The teacher network supervises the classification and regression of the student network using the pre-trained model on ImageNet.
Time Fairness in Online Knapsack Problems
We propose a parameterized deterministic algorithm where the parameter precisely captures the Pareto-optimal trade-off between fairness (static pricing) and competitiveness (dynamic pricing).
CSED: A Chinese Semantic Error Diagnosis Corpus
Our experiments show that powerful pre-trained models perform poorly on this corpus.
GenCorres: Consistent Shape Matching via Coupled Implicit-Explicit Shape Generative Models
GenCorres addresses this issue by learning an implicit generator from the input shapes, which provides intermediate shapes between two arbitrary shapes.
LiDAR-Based 3D Object Detection via Hybrid 2D Semantic Scene Generation
Bird's-Eye View (BEV) features are popular intermediate scene representations shared by the 3D backbone and the detector head in LiDAR-based object detectors.
The Projection-Enhancement Network (PEN)
Contemporary approaches to instance segmentation in cell science use 2D or 3D convolutional networks depending on the experiment and data structures.
Online Search with Predictions: Pareto-optimal Algorithm and its Applications in Energy Markets
This paper develops learning-augmented algorithms for energy trading in volatile electricity markets.
IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning
In this paper, we investigate the joint offloading, communication and computation resource allocation for IRS-assisted NOMA MEC system.
PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation
Our key insight is to copy and deform patches from the partial input to complete missing regions.
Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition
These errors have been studied extensively and are relatively simple for humans.
Emotion Recognition using Machine Learning and ECG signals
Various emotions can produce variations in electrocardiograph (ECG) signals, distinct emotions can be distinguished by different changes in ECG signals.
Improved Sensing and Positioning via 5G and mmWave radar for Airport Surveillance
This paper explores an integrated approach for improved sensing and positioning with applications in air traffic management (ATM) and in the Advanced Surface Movement Guidance and Control System (A-SMGCS).
Using Wi-Fi Signal as Sensing Medium: Passive Radar, Channel State Information and Followups
The idea of exploiting the Wi-Fi bursts as the medium for sensing purposes, particularly for the human targets in the indoor environment, was cultivated in both radar and computer science communities and it has became a noticeable research genre with cross-disciplinary impact in security, healthcare, human-machine interaction etc. This article comparatively introduces passive radar based and channel state information (CSI) based approaches.
Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems
This paper leverages machine-learned predictions to design competitive algorithms for online conversion problems with the goal of improving the competitive ratio when predictions are accurate (i. e., consistency), while also guaranteeing a worst-case competitive ratio regardless of the prediction quality (i. e., robustness).
ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators
Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy.
Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction from RGB
To reconstruct spectral signals from multi-channel observations, in particular trichromatic RGBs, has recently emerged as a promising alternative to traditional scanning-based spectral imager.
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected objects.
Ranked #6 on
Cross-Domain Few-Shot Object Detection
on Artaxor
Contrastive Learning
Cross-Domain Few-Shot Object Detection
+4
Online Network Utility Maximization: Algorithm, Competitive Analysis, and Applications
In addition, since worst-case scenarios rarely occur in practice, we devise an adaptive implementation of our algorithm to improve its average-case performance and validate its effectiveness via simulations.
Data Structures and Algorithms
Learning-Based Predictive Control via Real-Time Aggregate Flexibility
To be used effectively, an aggregator must be able to communicate the available flexibility of the loads they control, as known as the aggregate flexibility to a system operator.
Optimization and Control Systems and Control Systems and Control
Learning Order Parameters from Videos of Dynamical Phases for Skyrmions with Neural Networks
The main purposes of this paper are to use neural networks for classifying the dynamical phases of some videos and to demonstrate that neural networks can learn physical concepts from them.
Extracellular Matrix regulates the morphodynamics of 3D migrating cancer cells
As a result, we map cell morphodynamics at mesoscale into the temporal evolution of morphological phenotypes.
H3DNet: 3D Object Detection Using Hybrid Geometric Primitives
We show how to convert the predicted geometric primitives into object proposals by defining a distance function between an object and the geometric primitives.
Ranked #3 on
3D Object Detection
on ARKitScenes
Modeling cell migration regulated by cell-ECM micromechanical coupling
During migration, individual cells can generate active pulling forces via actin filament contraction, which are transmitted to the ECM fibers through focal adhesion complexes, remodel the ECM, and eventually propagate to and can be sensed by other cells in the system.
Oriented Point Sampling for Plane Detection in Unorganized Point Clouds
We named this method Oriented Point Sampling (OPS) to contrast with more conventional techniques that require the sampling of three unoriented points to generate plane hypotheses.
Adversarial Defense by Stratified Convolutional Sparse Coding
We propose an adversarial defense method that achieves state-of-the-art performance among attack-agnostic adversarial defense methods while also maintaining robustness to input resolution, scale of adversarial perturbation, and scale of dataset size.
MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image
In this paper, we address the problem of reconstructing an object's surface from a single image using generative networks.
The morphodynamics of 3D migrating cancer cells
As a result, cell morphodynamics is mapped into temporal evolution of morphological phenotypes.
Stay On-Topic: Generating Context-specific Fake Restaurant Reviews
Automatically generated fake restaurant reviews are a threat to online review systems.
The Stochastic Replica Approach to Machine Learning: Stability and Parameter Optimization
We introduce a statistical physics inspired supervised machine learning algorithm for classification and regression problems.
Inference of hidden structures in complex physical systems by multi-scale clustering
We survey the application of a relatively new branch of statistical physics--"community detection"-- to data mining.
An interacting replica approach applied to the traveling salesman problem
The {\it ensemble} of replicas evolves as to maximize the inter-replica correlation while simultaneously minimize the local intra-replica cost function (e. g., the total path length in the Traveling Salesman Problem within each replica).
