OpenRaiser/Intern-Atlas huggingface dataset:
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits
Venue: arXiv cs.LG 2023
Year: 2023

In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent multi-armed bandits. For regret minimization in multi-armed bandits, we present the first set of tradeoffs between the number of rounds of communication among the agents and the regret of the collaborative learning process.
3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
Venue: arXiv cs.CV 2023
Year: 2023

We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models. Our shape representation can encode 3D shapes given as surface models or point clouds, and represents them as neural fields. The concept of neural fields has previously been combined with a global latent vector, a regular grid of latent vectors, or an irregular grid of latent vectors. Our new representation encodes neural fields on top of a set of vectors. We draw from multiple concepts, such as the radial basis function representation and the cross attention and self-attention function, to design a learnable representation that is especially suitable for processing with transformers. Our results show improved performance in 3D shape encoding and 3D shape generative modeling tasks. We demonstrate a wide variety of generative applications: unconditioned generation, category-conditioned generation, text-conditioned generation, point-cloud completion, and image-conditioned generation.
Multi-channel Medium Access Control Protocols for Wireless Networks within Computing Packages
Venue: arXiv cs.ET 2023
Year: 2023

Wireless communications at the chip scale emerge as a interesting complement to traditional wire-based approaches thanks to their low latency, inherent broadcast nature, and capacity to bypass pin constraints. However, as current trends push towards massive and bandwidth-hungry processor architectures, there is a need for wireless chip-scale networks that exploit and share as many channels as possible. In this context, this work addresses the issue of channel sharing by exploring the design space of multi-channel Medium Access Control (MAC) protocols for chip-scale networks. Distinct channel assignment strategies for both random access and token passing are presented and evaluated under realistic traffic patterns. It is shown that, even with the improvements enabled by the multiple channels, both protocols maintain their intrinsic advantages and disadvantages.
Exploring Deep Reinforcement Learning for Holistic Smart Building Control
Venue: arXiv cs.LG 2023
Year: 2023

In this paper, we take a holistic approach to deal with the tradeoffs between energy use and comfort in commercial buildings. We developed a system called OCTOPUS, which employs a novel deep reinforcement learning (DRL) framework that uses a data-driven approach to find the optimal control sequences of all building's subsystems, including HVAC, lighting, blind and window systems. The DRL architecture includes a novel reward function that allows the framework to explore the tradeoffs between energy use and users' comfort, while at the same time enabling the solution of the high-dimensional control problem due to the interactions of four different building subsystems. In order to cope with OCTOPUS's data training requirements, we argue that calibrated simulations that match the target building operational points are the vehicle to generate enough data to be able to train our DRL framework to find the control solution for the target building. In our work, we trained OCTOPUS with 10-year weather data and a building model that is implemented in the EnergyPlus building simulator, which was calibrated using data from a real production building. Through extensive simulations, we demonstrate that OCTOPUS can achieve 14.26% and 8.1% energy savings compared with the state-of-the-art rule-based method in a LEED Gold Certified building and the latest DRL-based method available in the literature respectively, while maintaining human comfort within a desired range.
Deep Industrial Image Anomaly Detection: A Survey
Venue: arXiv cs.CV 2023
Year: 2023

The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.
Safe Posterior Sampling for Constrained MDPs with Bounded Constraint Violation
Venue: arXiv cs.LG 2023
Year: 2023

Constrained Markov decision processes (CMDPs) model scenarios of sequential decision making with multiple objectives that are increasingly important in many applications. However, the model is often unknown and must be learned online while still ensuring the constraint is met, or at least the violation is bounded with time. Some recent papers have made progress on this very challenging problem but either need unsatisfactory assumptions such as knowledge of a safe policy, or have high cumulative regret. We propose the Safe PSRL (posterior sampling-based RL) algorithm that does not need such assumptions and yet performs very well, both in terms of theoretical regret bounds as well as empirically. The algorithm achieves an efficient tradeoff between exploration and exploitation by use of the posterior sampling principle, and provably suffers only bounded constraint violation by leveraging the idea of pessimism. Our approach is based on a primal-dual approach. We establish a sub-linear $\tilde{\mathcal{ O}}\left(H^{2.5} \sqrt{|\mathcal{S}|^2 |\mathcal{A}| K} \right)$ upper bound on the Bayesian reward objective regret along with a bounded, i.e., $\tilde{\mathcal{O}}\left(1\right)$ constraint violation regret over $K$ episodes for an $|\mathcal{S}|$-state, $|\mathcal{A}|$-action and horizon $H$ CMDP.
Repairing DNN Architecture: Are We There Yet?
Venue: arXiv cs.SE 2023
Year: 2023

As Deep Neural Networks (DNNs) are rapidly being adopted within large software systems, software developers are increasingly required to design, train, and deploy such models into the systems they develop. Consequently, testing and improving the robustness of these models have received a lot of attention lately. However, relatively little effort has been made to address the difficulties developers experience when designing and training such models: if the evaluation of a model shows poor performance after the initial training, what should the developer change? We survey and evaluate existing state-of-the-art techniques that can be used to repair model performance, using a benchmark of both real-world mistakes developers made while designing DNN models and artificial faulty models generated by mutating the model code. The empirical evaluation shows that random baseline is comparable with or sometimes outperforms existing state-of-the-art techniques. However, for larger and more complicated models, all repair techniques fail to find fixes. Our findings call for further research to develop more sophisticated techniques for Deep Learning repair.
Chirp-based Hierarchical Beam Training for Extremely Large-Scale Massive MIMO
Venue: arXiv cs.IT 2023
Year: 2023

XL-MIMO promises to provide ultrahigh data rates in Terahertz (THz) spectrum. However, the spherical-wavefront wireless transmission caused by large aperture array presents huge challenges for channel state information (CSI) acquisition. Two independent parameters (physical angles and transmission distance) should be simultaneously considered in XL-MIMO beamforming, which brings severe overhead consumption and beamforming degradation. To address this problem, we exploit the near-field channel characteristic and propose one low-overhead hierarchical beam training scheme for near-field XL-MIMO system. Firstly, we project near-field channel into spatial-angular domain and slope-intercept domain to capture detailed representations. Secondly, a novel spatial-chirp beam-aided codebook and corresponding hierarchical update policy are proposed. Theoretical analyses and numerical simulations are also displayed to verify the superior performances on beamforming and training overhead.
Khaos: The Impact of Inter-procedural Code Obfuscation on Binary Diffing Techniques
Venue: arXiv cs.CR 2023
Year: 2023

Software obfuscation techniques can prevent binary diffing techniques from locating vulnerable code by obfuscating the third-party code, to achieve the purpose of protecting embedded device software. With the rapid development of binary diffing techniques, they can achieve more and more accurate function matching and identification by extracting the features within the function. This makes existing software obfuscation techniques, which mainly focus on the intra-procedural code obfuscation, no longer effective. In this paper, we propose a new inter-procedural code obfuscation mechanism Khaos, which moves the code across functions to obfuscate the function by using compilation optimizations. Two obfuscation primitives are proposed to separate and aggregate the function, which are called fission and fusion respectively. A prototype of Khaos is implemented based on the LLVM compiler and evaluated on a large number of real-world programs including SPEC CPU 2006 & 2017, CoreUtils, JavaScript engines, etc. Experimental results show that Khaos outperforms existing code obfuscations and can significantly reduce the accuracy rates of five state-of-the-art binary diffing techniques (less than 19%) with lower runtime overhead (less than 7%).
Information Entropy-based Camera Path Estimation for In-Situ Visualization
Venue: arXiv cs.GR 2023
Year: 2023

In-situ processing has widely been recognized as an effective approach for the visualization and analysis of large-scale simulation outputs from modern HPC systems. One of the most common approaches for batch-based in-situ visualization is the image- or video-based approach. In this kind of approach, a large number of rendered images are generated from different viewpoints at each time step and has proven useful for detailed analysis of the main simulation results. However, during test runs and model calibration runs before the main simulation run, a quick overview might be sufficient and useful. In this work, we focused on selecting the viewpoints which provide as much information as possible by using information entropy to maximize the subsequent visual analysis task. However, by simply following the selected viewpoints at each of the visualization time steps will probably lead to a rapidly changing video, which can impact the understanding. Therefore, we have also worked on an efficient camera path estimation approach for connecting selected viewpoints, at regular intervals, to generate a smooth video. This resulting video is expected to assist in rapid understanding of the underlying simulation phenomena and can be helpful to narrow down the temporal region of interest to minimize the turnaround time during detailed visual exploration via image- or video-based visual analysis of the main simulation run. We implemented and evaluated the proposed approach using the OpenFOAM CFD application, on an x86-based Server and an ARM A64FX-based supercomputer (Fugaku), and we obtained positive evaluations from domain scientists.
Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties
Venue: arXiv cs.LG 2023
Year: 2023

Constrained Reinforcement Learning has been employed to enforce safety constraints on policy through the use of expected cost constraints. The key challenge is in handling expected cost accumulated using the policy and not just in a single step. Existing methods have developed innovative ways of converting this cost constraint over entire policy to constraints over local decisions (at each time step). While such approaches have provided good solutions with regards to objective, they can either be overly aggressive or conservative with respect to costs. This is owing to use of estimates for "future" or "backward" costs in local cost constraints. To that end, we provide an equivalent unconstrained formulation to constrained RL that has an augmented state space and reward penalties. This intuitive formulation is general and has interesting theoretical properties. More importantly, this provides a new paradigm for solving constrained RL problems effectively. As we show in our experimental results, we are able to outperform leading approaches on multiple benchmark problems from literature.
Semi-Supervised Machine Learning: a Homological Approach
Venue: arXiv cs.LG 2023
Year: 2023

In this paper we describe the mathematical foundations of a new approach to semi-supervised Machine Learning. Using techniques of Symbolic Computation and Computer Algebra, we apply the concept of persistent homology to obtain a new semi-supervised learning method.
Codes for Correcting Asymmetric Adjacent Transpositions and Deletions
Venue: arXiv cs.IT 2023
Year: 2023

Codes in the Damerau--Levenshtein metric have been extensively studied recently owing to their applications in DNA-based data storage. In particular, Gabrys, Yaakobi, and Milenkovic (2017) designed a length-$n$ code correcting a single deletion and $s$ adjacent transpositions with at most $(1+2s)\log n$ bits of redundancy. In this work, we consider a new setting where both asymmetric adjacent transpositions (also known as right-shifts or left-shifts) and deletions may occur. We present several constructions of the codes correcting these errors in various cases. In particular, we design a code correcting a single deletion, $s^+$ right-shift, and $s^-$ left-shift errors with at most $(1+s)\log (n+s+1)+1$ bits of redundancy where $s=s^{+}+s^{-}$. In addition, we investigate codes correcting $t$ $0$-deletions, $s^+$ right-shift, and $s^-$ left-shift errors with both uniquely-decoding and list-decoding algorithms. Our main contribution here is the construction of a list-decodable code with list size $O(n^{\min\{s+1,t\}})$ and with at most $(\max \{t,s+1\}) \log n+O(1)$ bits of redundancy, where $s=s^{+}+s^{-}$. Finally, we construct both non-systematic and systematic codes for correcting blocks of $0$-deletions with $\ell$-limited-magnitude and $s$ adjacent transpositions.
Semantic Network Model for Sign Language Comprehension
Venue: arXiv cs.CL 2023
Year: 2023

In this study, the authors propose a computational cognitive model for sign language (SL) perception and comprehension with detailed algorithmic descriptions based on cognitive functionalities in human language processing. The semantic network model (SNM) that represents semantic relations between concepts, it is used as a form of knowledge representation. The proposed model is applied in the comprehension of sign language for classifier predicates. The spreading activation search method is initiated by labeling a set of source nodes (e.g. concepts in the semantic network) with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes. The results demonstrate that the proposed search method improves the performance of sign language comprehension in the SNM.
The Exploration of Knowledge-Preserving Prompts for Document Summarisation
Venue: arXiv cs.CL 2023
Year: 2023

Despite the great development of document summarisation techniques nowadays, factual inconsistencies between the generated summaries and the original texts still occur from time to time. This study explores the possibility of adopting prompts to incorporate factual knowledge into generated summaries. We specifically study prefix-tuning that uses a set of trainable continuous prefix prompts together with discrete natural language prompts to aid summary generation. Experimental results demonstrate that the trainable prefixes can help the summarisation model extract information from discrete prompts precisely, thus generating knowledge-preserving summaries that are factually consistent with the discrete prompts. The ROUGE improvements of the generated summaries indicate that explicitly adding factual knowledge into the summarisation process could boost the overall performance, showing great potential for applying it to other natural language processing tasks.
Program Dependence Net and On-demand Slicing for Property Verification of Concurrent System and Software
Venue: arXiv cs.SE 2023
Year: 2023

When checking concurrent software using a finite-state model, we face a formidable state explosion problem. One solution to this problem is dependence-based program slicing, whose use can effectively reduce verification time. It is orthogonal to other model-checking reduction techniques. However, when slicing concurrent programs for model checking, there are conversions between multiple irreplaceable models, and dependencies need to be found for variables irrelevant to the verified property, which results in redundant computation. To resolve this issue, we propose a Program Dependence Net (PDNet) based on Petri net theory. It is a unified model that combines a control-flow structure with dependencies to avoid conversions. For reduction, we present a PDNet slicing method to capture the relevant variables' dependencies when needed. PDNet in verifying linear temporal logic and its on-demand slicing can be used to significantly reduce computation cost. We implement a model-checking tool based on PDNet and its on-demand slicing, and validate the advantages of our proposed methods.
Two-Server Private Information Retrieval with Optimized Download Rate and Result Verification
Venue: arXiv cs.IT 2023
Year: 2023

Private Information Retrieval (PIR) schemes allow a client to retrieve any file of interest, while hiding the file identity from the database servers. In contrast to most existing PIR schemes that assume honest-but-curious servers, we study the case of dishonest servers. The latter provide incorrect answers and try to persuade the client to output the wrong result. We introduce several PIR schemes with information-theoretic privacy and result verification for the case of two servers. Security guarantees can be information-theoretical or computational, and the verification keys can be public or private. In this work, our main performance metric is the download rate.
Information-Theoretic Privacy-Preserving Schemes Based On Perfect Privacy
Venue: arXiv cs.IT 2023
Year: 2023

Consider a pair of random variables $(X,Y)$ distributed according to a given joint distribution $p_{XY}$. A curator wishes to maximally disclose information about $Y$, while limiting the information leakage incurred on $X$. Adopting mutual information to measure both utility and privacy of this information disclosure, the problem is to maximize $I(Y;U)$, subject to $I(X;U)\leqε$, where $U$ denotes the released random variable and $ε$ is a given privacy threshold. Two settings are considered, where in the first one, the curator has access to $(X,Y)$, and hence, the optimization is over $p_{U|XY}$, while in the second one, the curator can only observe $Y$ and the optimization is over $p_{U|Y}$. In both settings, the utility-privacy trade-off is investigated from theoretical and practical perspective. More specifically, several privacy-preserving schemes are proposed in these settings based on generalizing the notion of statistical independence. Moreover, closed-form solutions are provided in certain scenarios. Finally, convexity arguments are provided for the utility-privacy trade-off as functionals of the joint distribution $p_{XY}$.
Interpreting learning in biological neural networks as zero-order optimization method
Venue: arXiv cs.LG 2023
Year: 2023

Recently, significant progress has been made regarding the statistical understanding of artificial neural networks (ANNs). ANNs are motivated by the functioning of the brain, but differ in several crucial aspects. In particular, the locality in the updating rule of the connection parameters in biological neural networks (BNNs) makes it biologically implausible that the learning of the brain is based on gradient descent. In this work, we look at the brain as a statistical method for supervised learning. The main contribution is to relate the local updating rule of the connection parameters in BNNs to a zero-order optimization method. It is shown that the expected values of the iterates implement a modification of gradient descent.
Bi-AM-RRT*: A Fast and Efficient Sampling-Based Motion Planning Algorithm in Dynamic Environments
Venue: arXiv cs.RO 2023
Year: 2023

The efficiency of sampling-based motion planning brings wide application in autonomous mobile robots. The conventional rapidly exploring random tree (RRT) algorithm and its variants have gained significant successes, but there are still challenges for the optimal motion planning of mobile robots in dynamic environments. In this paper, based on Bidirectional RRT and the use of an assisting metric (AM), we propose a novel motion planning algorithm, namely Bi-AM-RRT*. Different from the existing RRT-based methods, the AM is introduced in this paper to optimize the performance of robot motion planning in dynamic environments with obstacles. On this basis, the bidirectional search sampling strategy is employed to reduce the search time. Further, we present a new rewiring method to shorten path lengths. The effectiveness and efficiency of the proposed Bi-AM-RRT* are proved through comparative experiments in different environments. Experimental results show that the Bi-AM-RRT* algorithm can achieve better performance in terms of path length and search time, and always finds near-optimal paths with the shortest search time when the diffusion metric is used as the AM.
HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera
Venue: arXiv cs.RO 2023
Year: 2023

This paper proposes a novel visual simultaneous localization and mapping (SLAM) system called Hybrid Depth-augmented Panoramic Visual SLAM (HDPV-SLAM), that employs a panoramic camera and a tilted multi-beam LiDAR scanner to generate accurate and metrically-scaled trajectories. RGB-D SLAM was the design basis for HDPV-SLAM, which added depth information to visual features. It aims to solve the two major issues hindering the performance of similar SLAM systems. The first obstacle is the sparseness of LiDAR depth, which makes it difficult to correlate it with the extracted visual features of the RGB image. A deep learning-based depth estimation module for iteratively densifying sparse LiDAR depth was suggested to address this issue. The second issue pertains to the difficulties in depth association caused by a lack of horizontal overlap between the panoramic camera and the tilted LiDAR sensor. To surmount this difficulty, we present a hybrid depth association module that optimally combines depth information estimated by two independent procedures, feature-based triangulation and depth estimation. During a phase of feature tracking, this hybrid depth association module aims to maximize the use of more accurate depth information between the triangulated depth with visual features tracked and the deep learning-based corrected depth. We evaluated the efficacy of HDPV-SLAM using the 18.95 km-long York University and Teledyne Optech (YUTO) MMS dataset. The experimental results demonstrate that the two proposed modules contribute substantially to the performance of HDPV-SLAM, which surpasses that of the state-of-the-art (SOTA) SLAM systems.