OpenRaiser/Intern-Atlas huggingface dataset:
Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization
Venue: arXiv cs.LG 2023
Year: 2023

Bayesian optimization (BO) is a promising approach for hyperparameter optimization of deep neural networks (DNNs), where each model training can take minutes to hours. In BO, a computationally cheap surrogate model is employed to learn the relationship between parameter configurations and their performance such as accuracy. Parallel BO methods often adopt single manager/multiple workers strategies to evaluate multiple hyperparameter configurations simultaneously. Despite significant hyperparameter evaluation time, the overhead in such centralized schemes prevents these methods to scale on a large number of workers. We present an asynchronous-decentralized BO, wherein each worker runs a sequential BO and asynchronously communicates its results through shared storage. We scale our method without loss of computational efficiency with above 95% of worker's utilization to 1,920 parallel workers (full production queue of the Polaris supercomputer) and demonstrate improvement in model accuracy as well as faster convergence on the CANDLE benchmark from the Exascale computing project.
The Power-Set Construction for Tree Algebras
Venue: arXiv cs.FL 2023
Year: 2023

We study power-set operations on classes of trees and tree algebras. Our main result consists of a distributive law between the tree monad and the upwards-closed power-set monad, in the case where all trees are assumed to be linear. For non-linear ones, we prove that such a distributive law does not exist.
Unified Object Detector for Different Modalities based on Vision Transformers
Venue: arXiv cs.CV 2023
Year: 2023

Traditional systems typically require different models for processing different modalities, such as one model for RGB images and another for depth images. Recent research has demonstrated that a single model for one modality can be adapted for another using cross-modality transfer learning. In this paper, we extend this approach by combining cross/inter-modality transfer learning with a vision transformer to develop a unified detector that achieves superior performance across diverse modalities. Our research envisions an application scenario for robotics, where the unified system seamlessly switches between RGB cameras and depth sensors in varying lighting conditions. Importantly, the system requires no model architecture or weight updates to enable this smooth transition. Specifically, the system uses the depth sensor during low-lighting conditions (night time) and both the RGB camera and depth sensor or RGB caemra only in well-lit environments. We evaluate our unified model on the SUN RGB-D dataset, and demonstrate that it achieves similar or better performance in terms of mAP50 compared to state-of-the-art methods in the SUNRGBD16 category, and comparable performance in point cloud only mode. We also introduce a novel inter-modality mixing method that enables our model to achieve significantly better results than previous methods. We provide our code, including training/inference logs and model checkpoints, to facilitate reproducibility and further research. \url{https://github.com/liketheflower/UODDM}
Scalable Polar Code Construction for Successive Cancellation List Decoding: A Graph Neural Network-Based Approach
Venue: arXiv cs.IT 2023
Year: 2023

While constructing polar codes for successive-cancellation decoding can be implemented efficiently by sorting the bit-channels, finding optimal polar codes for cyclic-redundancy-check-aided successive-cancellation list (CA-SCL) decoding in an efficient and scalable manner still awaits investigation. This paper first maps a polar code to a unique heterogeneous graph called the polar-code-construction message-passing (PCCMP) graph. Next, a heterogeneous graph-neural-network-based iterative message-passing (IMP) algorithm is proposed which aims to find a PCCMP graph that corresponds to the polar code with minimum frame error rate under CA-SCL decoding. This new IMP algorithm's major advantage lies in its scalability power. That is, the model complexity is independent of the blocklength and code rate, and a trained IMP model over a short polar code can be readily applied to a long polar code's construction. Numerical experiments show that IMP-based polar-code constructions outperform classical constructions under CA-SCL decoding. In addition, when an IMP model trained on a length-128 polar code directly applies to the construction of polar codes with different code rates and blocklengths, simulations show that these polar code constructions deliver comparable performance to the 5G polar codes.
Model-Free 3D Shape Control of Deformable Objects Using Novel Features Based on Modal Analysis
Venue: arXiv cs.RO 2023
Year: 2023

Shape control of deformable objects is a challenging and important robotic problem. This paper proposes a model-free controller using novel 3D global deformation features based on modal analysis. Unlike most existing controllers using geometric features, our controller employs a physically-based deformation feature by decoupling 3D global deformation into low-frequency mode shapes. Although modal analysis is widely adopted in computer vision and simulation, it has not been used in robotic deformation control. We develop a new model-free framework for modal-based deformation control under robot manipulation. Physical interpretation of mode shapes enables us to formulate an analytical deformation Jacobian matrix mapping the robot manipulation onto changes of the modal features. In the Jacobian matrix, unknown geometry and physical properties of the object are treated as low-dimensional modal parameters which can be used to linearly parameterize the closed-loop system. Thus, an adaptive controller with proven stability can be designed to deform the object while online estimating the modal parameters. Simulations and experiments are conducted using linear, planar, and solid objects under different settings. The results not only confirm the superior performance of our controller but also demonstrate its advantages over the baseline method.
Deep Contrastive One-Class Time Series Anomaly Detection
Venue: arXiv cs.LG 2023
Year: 2023

The accumulation of time-series data and the absence of labels make time-series Anomaly Detection (AD) a self-supervised deep learning task. Single-normality-assumption-based methods, which reveal only a certain aspect of the whole normality, are incapable of tasks involved with a large number of anomalies. Specifically, Contrastive Learning (CL) methods distance negative pairs, many of which consist of both normal samples, thus reducing the AD performance. Existing multi-normality-assumption-based methods are usually two-staged, firstly pre-training through certain tasks whose target may differ from AD, limiting their performance. To overcome the shortcomings, a deep Contrastive One-Class Anomaly detection method of time series (COCA) is proposed by authors, following the normality assumptions of CL and one-class classification. It treats the original and reconstructed representations as the positive pair of negative-sample-free CL, namely "sequence contrast". Next, invariance terms and variance terms compose a contrastive one-class loss function in which the loss of the assumptions is optimized by invariance terms simultaneously and the "hypersphere collapse" is prevented by variance terms. In addition, extensive experiments on two real-world time-series datasets show the superior performance of the proposed method achieves state-of-the-art.
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
Venue: arXiv cs.LG 2023
Year: 2023

In this paper, we study differentially private empirical risk minimization (DP-ERM). It has been shown that the worst-case utility of DP-ERM reduces polynomially as the dimension increases. This is a major obstacle to privately learning large machine learning models. In high dimension, it is common for some model's parameters to carry more information than others. To exploit this, we propose a differentially private greedy coordinate descent (DP-GCD) algorithm. At each iteration, DP-GCD privately performs a coordinate-wise gradient step along the gradients' (approximately) greatest entry. We show theoretically that DP-GCD can achieve a logarithmic dependence on the dimension for a wide range of problems by naturally exploiting their structural properties (such as quasi-sparse solutions). We illustrate this behavior numerically, both on synthetic and real datasets.
Selectively increasing the diversity of GAN-generated samples
Venue: arXiv cs.CV 2023
Year: 2023

Generative Adversarial Networks (GANs) are powerful models able to synthesize data samples closely resembling the distribution of real data, yet the diversity of those generated samples is limited due to the so-called mode collapse phenomenon observed in GANs. Especially prone to mode collapse are conditional GANs, which tend to ignore the input noise vector and focus on the conditional information. Recent methods proposed to mitigate this limitation increase the diversity of generated samples, yet they reduce the performance of the models when similarity of samples is required. To address this shortcoming, we propose a novel method to selectively increase the diversity of GAN-generated samples. By adding a simple, yet effective regularization to the training loss function we encourage the generator to discover new data modes for inputs related to diverse outputs while generating consistent samples for the remaining ones. More precisely, we maximise the ratio of distances between generated images and input latent vectors scaling the effect according to the diversity of samples for a given conditional input. We show the superiority of our method in a synthetic benchmark as well as a real-life scenario of simulating data from the Zero Degree Calorimeter of ALICE experiment in LHC, CERN.
First Price Auction is $1-1/e^2$ Efficient
Venue: arXiv cs.GT 2023
Year: 2023

We prove that the {\sf PoA} of {\sf First Price Auctions} is $1 - 1/e^2 \approx 0.8647$, closing the gap between the best known bounds $[0.7430,\, 0.8689]$.
Manipulator Differential Kinematics: Part 1: Kinematics, Velocity, and Applications
Venue: arXiv cs.RO 2023
Year: 2023

Manipulator kinematics is concerned with the motion of each link within a manipulator without considering mass or force. In this article, which is the first in a two-part tutorial, we provide an introduction to modelling manipulator kinematics using the elementary transform sequence (ETS). Then we formulate the first-order differential kinematics, which leads to the manipulator Jacobian, which is the basis for velocity control and inverse kinematics. We describe essential classical techniques which rely on the manipulator Jacobian before exhibiting some contemporary applications. Part II of this tutorial provides a formulation of second and higher-order differential kinematics, introduces the manipulator Hessian, and illustrates advanced techniques, some of which improve the performance of techniques demonstrated in Part I. We have provided Jupyter Notebooks to accompany each section within this tutorial. The Notebooks are written in Python code and use the Robotics Toolbox for Python, and the Swift Simulator to provide examples and implementations of algorithms. While not absolutely essential, for the most engaging and informative experience, we recommend working through the Jupyter Notebooks while reading this article. The Notebooks and setup instructions can be accessed at https://github.com/jhavl/dkt.
Multimodal Frame-Scoring Transformer for Video Summarization
Venue: arXiv cs.LG 2023
Year: 2023

As the number of video content has mushroomed in recent years, automatic video summarization has come useful when we want to just peek at the content of the video. However, there are two underlying limitations in generic video summarization task. First, most previous approaches read in just visual features as input, leaving other modality features behind. Second, existing datasets for generic video summarization are relatively insufficient to train a caption generator used for extracting text information from a video and to train the multimodal feature extractors. To address these two problems, this paper proposes the Multimodal Frame-Scoring Transformer (MFST), a framework exploiting visual, text, and audio features and scoring a video with respect to frames. Our MFST framework first extracts each modality features (audio-visual-text) using pretrained encoders. Then, MFST trains the multimodal frame-scoring transformer that uses multimodal representation based on extracted features as inputs and predicts frame-level scores. Our extensive experiments with previous models and ablation studies on TVSum and SumMe datasets demonstrate the effectiveness and superiority of our proposed method by a large margin in both F1 score and Rank-based evaluation.
Toward Explainable and Fine-Grained 3D Grounding through Referring Textual Phrases
Venue: arXiv cs.CV 2023
Year: 2023

Recent progress in 3D scene understanding has explored visual grounding (3DVG) to localize a target object through a language description. However, existing methods only consider the dependency between the entire sentence and the target object, ignoring fine-grained relationships between contexts and non-target ones. In this paper, we extend 3DVG to a more fine-grained and interpretable task, called 3D Phrase Aware Grounding (3DPAG). The 3DPAG task aims to localize the target objects in a 3D scene by explicitly identifying all phrase-related objects and then conducting the reasoning according to contextual phrases. To tackle this problem, we manually labeled about 227K phrase-level annotations using a self-developed platform, from 88K sentences of widely used 3DVG datasets, i.e., Nr3D, Sr3D and ScanRefer. By tapping on our datasets, we can extend previous 3DVG methods to the fine-grained phrase-aware scenario. It is achieved through the proposed novel phrase-object alignment optimization and phrase-specific pre-training, boosting conventional 3DVG performance as well. Extensive results confirm significant improvements, i.e., previous state-of-the-art method achieves 3.9%, 3.5% and 4.6% overall accuracy gains on Nr3D, Sr3D and ScanRefer respectively.
Twelve Scientific Challenges for 6G: Rethinking the Foundations of Communications Theory
Venue: arXiv cs.IT 2023
Year: 2023

The research in the sixth generation of communication networks needs to tackle new challenges in order to meet the requirements of emerging applications in terms of high data rate, low latency, high reliability, and massive connectivity. To this end, the entire communication chain needs to be optimized, including the channel and the surrounding environment, as it is no longer sufficient to control the transmitter and/or the receiver only. Investigating large intelligent surfaces, ultra massive multiple-input multiple-output, and smart constructive environments will contribute to this direction. In addition, to allow the exchange of high dimensional sensing data between connected intelligent devices, semantic and goal oriented communications need to be considered for a more efficient and context-aware information encoding. In particular, for multi-agent systems, where agents are collaborating together to achieve a complex task, emergent communications, instead of hard coded communications, can be learned for more efficient task execution and communication resources use. Moreover, new physics phenomenon should be exploited such as the thermodynamics of communication as well as the the interaction between information theory and electromagnetism to better understand the physical limitations of different technologies, e.g, holographic communications. Another new communication paradigm is to consider the end-to-end approach instead of block-by-block optimization, which requires exploiting machine learning theory, non-linear signal processing theory, and non-coherent communications theory. Within this context, we identify twelve scientific challenges for rebuilding the theoretical foundations of communications, and we overview each of the challenges while providing research opportunities and open questions for the research community.
Identification of Threat Regions From a Dynamic Occupancy Grid Map for Situation-Aware Environment Perception
Venue: arXiv cs.RO 2023
Year: 2023

The advance towards higher levels of automation within the field of automated driving is accompanied by increasing requirements for the operational safety of vehicles. Induced by the limitation of computational resources, trade-offs between the computational complexity of algorithms and their potential to ensure safe operation of automated vehicles are often encountered. Situation-aware environment perception presents one promising example, where computational resources are distributed to regions within the perception area that are relevant for the task of the automated vehicle. While prior map knowledge is often leveraged to identify relevant regions, in this work, we present a lightweight identification of safety-relevant regions that relies solely on online information. We show that our approach enables safe vehicle operation in critical scenarios, while retaining the benefits of non-uniformly distributed resources within the environment perception.
Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications
Venue: arXiv cs.LG 2023
Year: 2023

The use of Deep Reinforcement Learning (DRL) schemes has increased dramatically since their first introduction in 2015. Though uses in many different applications are being found, they still have a problem with the lack of interpretability. This has bread a lack of understanding and trust in the use of DRL solutions from researchers and the general public. To solve this problem, the field of Explainable Artificial Intelligence (XAI) has emerged. This entails a variety of different methods that look to open the DRL black boxes, ranging from the use of interpretable symbolic Decision Trees (DT) to numerical methods like Shapley Values. This review looks at which methods are being used and for which applications. This is done to identify which models are the best suited to each application or if a method is being underutilised.
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
Venue: arXiv cs.MA 2023
Year: 2023

Successful deployment of multi-agent reinforcement learning often requires agents to adapt their behaviour. In this work, we discuss the problem of teamwork adaptation in which a team of agents needs to adapt their policies to solve novel tasks with limited fine-tuning. Motivated by the intuition that agents need to be able to identify and distinguish tasks in order to adapt their behaviour to the current task, we propose to learn multi-agent task embeddings (MATE). These task embeddings are trained using an encoder-decoder architecture optimised for reconstruction of the transition and reward functions which uniquely identify tasks. We show that a team of agents is able to adapt to novel tasks when provided with task embeddings. We propose three MATE training paradigms: independent MATE, centralised MATE, and mixed MATE which vary in the information used for the task encoding. We show that the embeddings learned by MATE identify tasks and provide useful information which agents leverage during adaptation to novel tasks.
Understanding blockchain: definitions, architecture, design, and system comparison
Venue: arXiv cs.NI 2023
Year: 2023

The explosive advent of the blockchain technology has led to hundreds of blockchain systems in the industry, thousands of academic papers published over the last few years, and an even larger number of new initiatives and projects. Despite the emerging consolidation efforts, the area remains highly turbulent without systematization, educational materials, or cross-system comparative analysis. In this paper, we provide a systematic and comprehensive study of four popular yet widely different blockchain systems: Bitcoin, Ethereum, Hyperledger Fabric, and IOTA. The study is presented as a cross-system comparison, which is organized by clearly identified aspects: definitions, roles of the participants, entities, and the characteristics and design of each of the commonly used layers in the cross-system blockchain architecture. Our exploration goes deeper compared to what is currently available in academic surveys and tutorials. For example, we provide the first extensive coverage of the storage layer in Ethereum and the most comprehensive explanation of the consensus protocol in IOTA. The exposition is due to the consolidation of fragmented information gathered from white and yellow papers, academic publications, blogs, developer documentation, communication with the developers, as well as additional analysis gleaned from the source code. We hope that this survey will help the readers gain in-depth understanding of the design principles behind blockchain systems and contribute towards systematization of the area.
A Dataset on Malicious Paper Bidding in Peer Review
Venue: arXiv cs.CR 2023
Year: 2023

In conference peer review, reviewers are often asked to provide "bids" on each submitted paper that express their interest in reviewing that paper. A paper assignment algorithm then uses these bids (along with other data) to compute a high-quality assignment of reviewers to papers. However, this process has been exploited by malicious reviewers who strategically bid in order to unethically manipulate the paper assignment, crucially undermining the peer review process. For example, these reviewers may aim to get assigned to a friend's paper as part of a quid-pro-quo deal. A critical impediment towards creating and evaluating methods to mitigate this issue is the lack of any publicly-available data on malicious paper bidding. In this work, we collect and publicly release a novel dataset to fill this gap, collected from a mock conference activity where participants were instructed to bid either honestly or maliciously. We further provide a descriptive analysis of the bidding behavior, including our categorization of different strategies employed by participants. Finally, we evaluate the ability of each strategy to manipulate the assignment, and also evaluate the performance of some simple algorithms meant to detect malicious bidding. The performance of these detection algorithms can be taken as a baseline for future research on detecting malicious bidding.