OpenRaiser/Intern-Atlas huggingface dataset:
SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images
Venue: arXiv cs.CV 2022
Year: 2022

The existing deep learning fusion methods mainly concentrate on the convolutional neural networks, and few attempts are made with transformer. Meanwhile, the convolutional operation is a content-independent interaction between the image and convolution kernel, which may lose some important contexts and further limit fusion performance. Towards this end, we present a simple and strong fusion baseline for infrared and visible images, namely\textit{ Residual Swin Transformer Fusion Network}, termed as SwinFuse. Our SwinFuse includes three parts: the global feature extraction, fusion layer and feature reconstruction. In particular, we build a fully attentional feature encoding backbone to model the long-range dependency, which is a pure transformer network and has a stronger representation ability compared with the convolutional neural networks. Moreover, we design a novel feature fusion strategy based on $L_{1}$-norm for sequence matrices, and measure the corresponding activity levels from row and column vector dimensions, which can well retain competitive infrared brightness and distinct visible details. Finally, we testify our SwinFuse with nine state-of-the-art traditional and deep learning methods on three different datasets through subjective observations and objective comparisons, and the experimental results manifest that the proposed SwinFuse obtains surprising fusion performance with strong generalization ability and competitive computational efficiency. The code will be available at https://github.com/Zhishe-Wang/SwinFuse.
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer
Venue: arXiv cs.LG 2022
Year: 2022

The unmanned aerial vehicle (UAV) plays an vital role in various applications such as delivery, military mission, disaster rescue, communication, etc., due to its flexibility and versatility. This paper proposes a deep reinforcement learning method to solve the UAV online routing problem with wireless power transfer, which can charge the UAV remotely without wires, thus extending the capability of the battery-limited UAV. Our study considers the power consumption of the UAV and the wireless charging process. Unlike the previous works, we solve the problem by a designed deep neural network. The model is trained using a deep reinforcement learning method offline, and is used to optimize the UAV routing problem online. On small and large scale instances, the proposed model runs from four times to 500 times faster than Google OR-tools, the state-of-the-art combinatorial optimization solver, with identical solution quality. It also outperforms different types of heuristic and local search methods in terms of both run-time and optimality. In addition, once the model is trained, it can scale to new generated problem instances with arbitrary topology that are not seen during training. The proposed method is practically applicable when the problem scale is large and the response time is crucial.
Mining and searching association relation of scientific papers based on deep learning
Venue: arXiv cs.DL 2022
Year: 2022

There is a complex correlation among the data of scientific papers. The phenomenon reveals the data characteristics, laws, and correlations contained in the data of scientific and technological papers in specific fields, which can realize the analysis of scientific and technological big data and help to design applications to serve scientific researchers. Therefore, the research on mining and searching the association relationship of scientific papers based on deep learning has far-reaching practical significance.
Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing
Venue: arXiv cs.LG 2022
Year: 2022

We consider the problem of reconstructing rank-one matrices from random linear measurements, a task that appears in a variety of problems in signal processing, statistics, and machine learning. In this paper, we focus on the Alternating Least Squares (ALS) method. While this algorithm has been studied in a number of previous works, most of them only show convergence from an initialization close to the true solution and thus require a carefully designed initialization scheme. However, random initialization has often been preferred by practitioners as it is model-agnostic. In this paper, we show that ALS with random initialization converges to the true solution with $\varepsilon$-accuracy in $O(\log n + \log (1/\varepsilon)) $ iterations using only a near-optimal amount of samples, where we assume the measurement matrices to be i.i.d. Gaussian and where by $n$ we denote the ambient dimension. Key to our proof is the observation that the trajectory of the ALS iterates only depends very mildly on certain entries of the random measurement matrices. Numerical experiments corroborate our theoretical predictions.
PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation
Venue: arXiv cs.CV 2022
Year: 2022

We present a multitask network that supports various deep neural network based pedestrian detection functions. Besides 2D and 3D human pose, it also supports body and head orientation estimation based on full body bounding box input. This eliminates the need for explicit face recognition. We show that the performance of 3D human pose estimation and orientation estimation is comparable to the state-of-the-art. Since very few data sets exist for 3D human pose and in particular body and head orientation estimation based on full body data, we further show the benefit of particular simulation data to train the network. The network architecture is relatively simple, yet powerful, and easily adaptable for further research and applications.
Integrating Prior Knowledge in Post-hoc Explanations
Venue: arXiv cs.AI 2022
Year: 2022

In the field of eXplainable Artificial Intelligence (XAI), post-hoc interpretability methods aim at explaining to a user the predictions of a trained decision model. Integrating prior knowledge into such interpretability methods aims at improving the explanation understandability and allowing for personalised explanations adapted to each user. In this paper, we propose to define a cost function that explicitly integrates prior knowledge into the interpretability objectives: we present a general framework for the optimization problem of post-hoc interpretability methods, and show that user knowledge can thus be integrated to any method by adding a compatibility term in the cost function. We instantiate the proposed formalization in the case of counterfactual explanations and propose a new interpretability method called Knowledge Integration in Counterfactual Explanation (KICE) to optimize it. The paper performs an experimental study on several benchmark data sets to characterize the counterfactual instances generated by KICE, as compared to reference methods.
Efficient Recognition of Subgraphs of Planar Cubic Bridgeless Graphs
Venue: arXiv cs.DS 2022
Year: 2022

It follows from the work of Tait and the Four-Color-Theorem that a planar cubic graph is 3-edge-colorable if and only if it contains no bridge. We consider the question of which planar graphs are subgraphs of planar cubic bridgeless graphs, and hence 3-edge-colorable. We provide an efficient recognition algorithm that given an $n$-vertex planar graph, augments this graph in $O(n^2)$ steps to a planar cubic bridgeless supergraph, or decides that no such augmentation is possible. The main tools involve the Generalized Antifactor-problem for the fixed embedding case, and SPQR-trees for the variable embedding case.
General Interpolation and Strong Amalgamation for Contiguous Arrays
Venue: arXiv cs.LO 2022
Year: 2022

Interpolation is an essential tool in software verification, where first-order theories are used to constrain datatypes manipulated by programs. In this paper, we introduce the datatype theory of contiguous arrays with maxdiff, where arrays are completely defined in their allocation memory and for which maxdiff returns the max index where they differ. This theory is strictly more expressive than the array theories previously studied. By showing via an algebraic analysis that its models strongly amalgamate, we prove that this theory admits quantifier-free interpolants and, notably, that interpolation transfers to theory combinations. Finally, we provide an algorithm that significantly improves the ones for related array theories: it relies on a polysize reduction to general interpolation in linear arithmetics, thus avoiding impractical full terms instantiations and unbounded loops.
Machine learning identification of organic compounds using visible light
Venue: arXiv cs.LG 2022
Year: 2022

Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols or applications.
A Closer Look at Personalization in Federated Image Classification
Venue: arXiv cs.LG 2022
Year: 2022

Federated Learning (FL) is developed to learn a single global model across the decentralized data, while is susceptible when realizing client-specific personalization in the presence of statistical heterogeneity. However, studies focus on learning a robust global model or personalized classifiers, which yield divergence due to inconsistent objectives. This paper shows that it is possible to achieve flexible personalization after the convergence of the global model by introducing representation learning. In this paper, we first analyze and determine that non-IID data harms representation learning of the global model. Existing FL methods adhere to the scheme of jointly learning representations and classifiers, where the global model is an average of classification-based local models that are consistently subject to heterogeneity from non-IID data. As a solution, we separate representation learning from classification learning in FL and propose RepPer, an independent two-stage personalized FL framework.We first learn the client-side feature representation models that are robust to non-IID data and aggregate them into a global common representation model. After that, we achieve personalization by learning a classifier head for each client, based on the common representation obtained at the former stage. Notably, the proposed two-stage learning scheme of RepPer can be potentially used for lightweight edge computing that involves devices with constrained computation power.Experiments on various datasets (CIFAR-10/100, CINIC-10) and heterogeneous data setup show that RepPer outperforms alternatives in flexibility and personalization on non-IID data.
Logistic-ELM: A Novel Fault Diagnosis Method for Rolling Bearings
Venue: arXiv cs.LG 2022
Year: 2022

The fault diagnosis of rolling bearings is a critical technique to realize predictive maintenance for mechanical condition monitoring. In real industrial systems, the main challenges for the fault diagnosis of rolling bearings pertain to the accuracy and real-time requirements. Most existing methods focus on ensuring the accuracy, and the real-time requirement is often neglected. In this paper, considering both requirements, we propose a novel fast fault diagnosis method for rolling bearings, based on extreme learning machine (ELM) and logistic mapping, named logistic-ELM. First, we identify 14 kinds of time-domain features from the original vibration signals according to mechanical vibration principles and adopt the sequential forward selection (SFS) strategy to select optimal features from them to ensure the basic predictive accuracy and efficiency. Next, we propose the logistic-ELM for fast fault classification, where the biases in ELM are omitted and the random input weights are replaced by the chaotic logistic mapping sequence which involves a higher uncorrelation to obtain more accurate results with fewer hidden neurons. We conduct extensive experiments on the rolling bearing vibration signal dataset of the Case Western Reserve University (CWRU) Bearing Data Centre. The experimental results show that the proposed approach outperforms existing SOTA comparison methods in terms of the predictive accuracy, and the highest accuracy is 100% in seven separate sub data environments. The relevant code is publicly available at https://github.com/TAN-OpenLab/logistic-ELM.
Multi-objective Pointer Network for Combinatorial Optimization
Venue: arXiv cs.LG 2022
Year: 2022

Multi-objective combinatorial optimization problems (MOCOPs), one type of complex optimization problems, widely exist in various real applications. Although meta-heuristics have been successfully applied to address MOCOPs, the calculation time is often much longer. Recently, a number of deep reinforcement learning (DRL) methods have been proposed to generate approximate optimal solutions to the combinatorial optimization problems. However, the existing studies on DRL have seldom focused on MOCOPs. This study proposes a single-model deep reinforcement learning framework, called multi-objective Pointer Network (MOPN), where the input structure of PN is effectively improved so that the single PN is capable of solving MOCOPs. In addition, two training strategies, based on representative model and transfer learning, respectively, are proposed to further enhance the performance of MOPN in different application scenarios. Moreover, compared to classical meta-heuristics, MOPN only consumes much less time on forward propagation to obtain the Pareto front. Meanwhile, MOPN is insensitive to problem scale, meaning that a trained MOPN is able to address MOCOPs with different scales. To verify the performance of MOPN, extensive experiments are conducted on three multi-objective traveling salesman problems, in comparison with one state-of-the-art model DRL-MOA and three classical multi-objective meta-heuristics. Experimental results demonstrate that the proposed model outperforms all the comparative methods with only 20\% to 40\% training time of DRL-MOA.
Quo Vadis, Optical Network Architecture? Towards an Optical-processing-enabled Paradigm
Venue: arXiv cs.NI 2022
Year: 2022

Among various aspects in optical network architectures, handling transit traffic at intermediate nodes represents a defining characteristic for classification. In this context, the transition from the first generation of optical-electrical-optical (O-E-O) mode to the second generation of optical-bypass marked a paradigm shift in redesigning optical transport networks towards greater network efficiency. Optical-bypass operation has then become the \textit{de facto} approach adopted by the majority of carriers in both metro and backbone networks in the last two decades and has remained basically unchanged. However, in optical-bypass network, the fact that in-transit lightpaths crossing a common intermediate node must be separated in either time, frequency or spatial domain to avoid adversarial interference appears to be a critical shortcoming as the interaction of such lightpaths in optical domain may result in efficient computing and/or signal processing operations for saving spectral resources. Inspired by the accelerated progresses in optical signal processing technologies and the integration of computing and communications, we introduce in this paper a new architectural paradigm for future optical networks and highlight how this new architecture has the potential to shatter the \textit{status quo}. Indeed, our proposal is centered on exploiting the superposition of in-transit lightpaths at intermediate nodes to generate more spectrally efficient lightpaths and how to harness this opportunity from network design perspectives. We present two case studies featuring optical aggregation and optical XOR encoding to demonstrate the merit of optical-processing-enabled operation compared to its counterpart, optical-bypass. Numerical results on realistic network typologies are provided, revealing that a spectral saving up to $30\%$ could be achieved thanks to adopting optical-processing network.
Temporal Relevance Analysis for Video Action Models
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature. We first propose a new approach to quantify the temporal relationships between frames captured by CNN-based action models based on layer-wise relevance propagation. We then conduct comprehensive experiments and in-depth analysis to provide a better understanding of how temporal modeling is affected by various factors such as dataset, network architecture, and input frames. With this, we further study some important questions for action recognition that lead to interesting findings. Our analysis shows that there is no strong correlation between temporal relevance and model performance; and action models tend to capture local temporal information, but less long-range dependencies. Our codes and models will be publicly available.
The equivalence of GRS codes and EGRS codes
Venue: arXiv cs.IT 2022
Year: 2022

Generalized Reed-Solomon and extended generalized Reed-Solomon (abbreviation to GRS and EGRS) codes are the most well-known family of MDS codes with wide applications in coding theory and practice. Let $\mathbb{F}_q$ be the $q$ elements finite field, where $q$ is the power of a prime. For a linear code $\mathcal{C}$ over $\mathbb{F}_q$ with length $2\le n\le q$, we prove that $\mathcal{C}$ is a GRS code if and only if $\mathcal{C}$ is a EGRS code.
Bug Characteristics in Quantum Software Ecosystem
Venue: arXiv cs.SE 2022
Year: 2022

With the advance in quantum computing in recent years, quantum software becomes vital for exploring the full potential of quantum computing systems. Quantum programming is different from classical programming, for example, the state of a quantum program is probabilistic in nature, and a quantum computer is error-prone due to the instability of quantum mechanisms. Therefore, the characteristics of bugs in quantum software projects may be very different from that of classical software projects. This work aims to understand the characteristics of bugs in quantum software projects, in order to provide insights to help devise effective testing and debugging mechanisms. To achieve this goal, we conduct an empirical study on the bug reports of 125 quantum software projects. We observe that quantum software projects are more buggy than classical software projects and that quantum project bugs are more costly to fix than classical project bugs. We also identify the types of the bugs and the quantum programming components where they occurred. Our study shows that the bugs are spread across different components, but quantum-specific bugs particularly appear in the compiler, gate operation, and state preparation components. The three most occurring types of bugs are Program anomaly bugs, Configuration bugs, and Data type and structure bugs. Our study highlights some particularly challenging areas in quantum software development, such as the lack of scientific quantum computation libraries that implement comprehensive mathematical functions for quantum computing. Quantum developers also seek specialized data manipulation libraries for quantum software engineering like Numpy for quantum computing. Our findings also provide insights for future work to advance the quantum program development, testing, and debugging of quantum software, such as providing tooling support for debugging low-level circuits.
An Efficient Dynamic Sampling Policy For Monte Carlo Tree Search
Venue: arXiv cs.AI 2022
Year: 2022

We consider the popular tree-based search strategy within the framework of reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of finite-horizon Markov decision process. We propose a dynamic sampling tree policy that efficiently allocates limited computational budget to maximize the probability of correct selection of the best action at the root node of the tree. Experimental results on Tic-Tac-Toe and Gomoku show that the proposed tree policy is more efficient than other competing methods.
ISTRBoost: Importance Sampling Transfer Regression using Boosting
Venue: arXiv cs.LG 2022
Year: 2022

Current Instance Transfer Learning (ITL) methodologies use domain adaptation and sub-space transformation to achieve successful transfer learning. However, these methodologies, in their processes, sometimes overfit on the target dataset or suffer from negative transfer if the test dataset has a high variance. Boosting methodologies have been shown to reduce the risk of overfitting by iteratively re-weighing instances with high-residual. However, this balance is usually achieved with parameter optimization, as well as reducing the skewness in weights produced due to the size of the source dataset. While the former can be achieved, the latter is more challenging and can lead to negative transfer. We introduce a simpler and more robust fix to this problem by building upon the popular boosting ITL regression methodology, two-stage TrAdaBoost.R2. Our methodology,~\us{}, is a boosting and random-forest based ensemble methodology that utilizes importance sampling to reduce the skewness due to the source dataset. We show that~\us{}~performs better than competitive transfer learning methodologies $63\%$ of the time. It also displays consistency in its performance over diverse datasets with varying complexities, as opposed to the sporadic results observed for other transfer learning methodologies.