OpenRaiser/Intern-Atlas huggingface dataset:
Theoretical Analyses of Evolutionary Algorithms on Time-Linkage OneMax with General Weights
Venue: arXiv cs.NE 2023
Year: 2023

Evolutionary computation has shown its superiority in dynamic optimization, but for the (dynamic) time-linkage problems, some theoretical studies have revealed the possible weakness of evolutionary computation. Since the theoretically analyzed time-linkage problem only considers the influence of an extremely strong negative time-linkage effect, it remains unclear whether the weakness also appears in problems with more general time-linkage effects. Besides, understanding in depth the relationship between time-linkage effect and algorithmic features is important to build up our knowledge of what algorithmic features are good at what kinds of problems. In this paper, we analyze the general time-linkage effect and consider the time-linkage OneMax with general weights whose absolute values reflect the strength and whose sign reflects the positive or negative influence. We prove that except for some small and positive time-linkage effects (that is, for weights $0$ and $1$), randomized local search (RLS) and (1+1)EA cannot converge to the global optimum with a positive probability. More precisely, for the negative time-linkage effect (for negative weights), both algorithms cannot efficiently reach the global optimum and the probability of failing to converge to the global optimum is at least $1-o(1)$. For the not so small positive time-linkage effect (positive weights greater than $1$), such a probability is at most $c+o(1)$ where $c$ is a constant strictly less than $1$.
Energy cost and machine learning accuracy impact of k-anonymisation and synthetic data techniques
Venue: arXiv cs.LG 2023
Year: 2023

To address increasing societal concerns regarding privacy and climate, the EU adopted the General Data Protection Regulation (GDPR) and committed to the Green Deal. Considerable research studied the energy efficiency of software and the accuracy of machine learning models trained on anonymised data sets. Recent work began exploring the impact of privacy-enhancing techniques (PET) on both the energy consumption and accuracy of the machine learning models, focusing on k-anonymity. As synthetic data is becoming an increasingly popular PET, this paper analyses the energy consumption and accuracy of two phases: a) applying privacy-enhancing techniques to the concerned data set, b) training the models on the concerned privacy-enhanced data set. We use two privacy-enhancing techniques: k-anonymisation (using generalisation and suppression) and synthetic data, and three machine-learning models. Each model is trained on each privacy-enhanced data set. Our results show that models trained on k-anonymised data consume less energy than models trained on the original data, with a similar performance regarding accuracy. Models trained on synthetic data have a similar energy consumption and a similar to lower accuracy compared to models trained on the original data.
Making Differential Privacy Work for Census Data Users
Venue: arXiv cs.CY 2023
Year: 2023

The U.S. Census Bureau collects and publishes detailed demographic data about Americans which are heavily used by researchers and policymakers. The Bureau has recently adopted the framework of differential privacy in an effort to improve confidentiality of individual census responses. A key output of this privacy protection system is the Noisy Measurement File (NMF), which is produced by adding random noise to tabulated statistics. The NMF is critical to understanding any errors introduced in the data, and performing valid statistical inference on published census data. Unfortunately, the current release format of the NMF is difficult to access and work with. We describe the process we use to transform the NMF into a usable format, and provide recommendations to the Bureau for how to release future versions of the NMF. These changes are essential for ensuring transparency of privacy measures and reproducibility of scientific research built on census data.
Asymmetric feature interaction for interpreting model predictions
Venue: arXiv cs.CL 2023
Year: 2023

In natural language processing (NLP), deep neural networks (DNNs) could model complex interactions between context and have achieved impressive results on a range of NLP tasks. Prior works on feature interaction attribution mainly focus on studying symmetric interaction that only explains the additional influence of a set of words in combination, which fails to capture asymmetric influence that contributes to model prediction. In this work, we propose an asymmetric feature interaction attribution explanation model that aims to explore asymmetric higher-order feature interactions in the inference of deep neural NLP models. By representing our explanation with an directed interaction graph, we experimentally demonstrate interpretability of the graph to discover asymmetric feature interactions. Experimental results on two sentiment classification datasets show the superiority of our model against the state-of-the-art feature interaction attribution methods in identifying influential features for model predictions. Our code is available at https://github.com/StillLu/ASIV.
A Simulator for Fully-Actuated UAVs
Venue: arXiv cs.RO 2023
Year: 2023

This workshop paper presents the challenges we encountered when simulating fully-actuated Unmanned Aerial Vehicles (UAVs) for our research and the solutions we developed to overcome the challenges. We describe the ARCAD simulator that has helped us rapidly implement and test different controllers ranging from Hybrid Force-Position Controllers to advanced Model Predictive Path Integrals and has allowed us to analyze the design and behavior of different fully-actuated UAVs. We used the simulator to enable real-world deployments of our fully-actuated UAV fleet for different applications. The simulator is further extended to support the physical interaction of UAVs with their environment and allow more UAV designs, such as hybrid VTOLs. The code for the simulator can be accessed from https://github.com/keipour/aircraft-simulator-matlab.
A Wait-free Queue with Polylogarithmic Step Complexity
Venue: arXiv cs.DC 2023
Year: 2023

We present a novel linearizable wait-free queue implementation using single-word CAS instructions. Previous lock-free queue implementations from CAS all have amortized step complexity of $Ω(p)$ per operation in worst-case executions, where $p$ is the number of processes that access the queue. Our new wait-free queue takes $O(\log p)$ steps per enqueue and $O(\log^2 p +\log q)$ steps per dequeue, where $q$ is the size of the queue. A bounded-space version of the implementation has $O(\log p \log(p+q))$ amortized step complexity per operation.
Progressive Material Caching
Venue: arXiv cs.GR 2023
Year: 2023

The evaluation of material networks is a relatively resource-intensive process in the rendering pipeline. Modern production scenes can contain hundreds or thousands of complex materials with massive networks, so there is a great demand for an efficient way of handling material networks. In this paper, we introduce an efficient method for progressively caching the material nodes without an overhead on the rendering performance. We evaluate the material networks as usual in the rendering process. Then, the output value of part of the network is stored in a cache and can be used in the evaluation of the next materials. Using our method, we can render the scene with performance equal to or better than that of the method without caching, with a slight difference in the images rendered with caching and without it.
Better speech synthesis through scaling
Venue: arXiv cs.SD 2023
Year: 2023

In recent years, the field of image generation has been revolutionized by the application of autoregressive transformers and DDPMs. These approaches model the process of image generation as a step-wise probabilistic processes and leverage large amounts of compute and data to learn the image distribution. This methodology of improving performance need not be confined to images. This paper describes a way to apply advances in the image generative domain to speech synthesis. The result is TorToise -- an expressive, multi-voice text-to-speech system. All model code and trained weights have been open-sourced at https://github.com/neonbjb/tortoise-tts.
Digital Twin as a Service (DTaaS): A Platform for Digital Twin Developers and Users
Venue: arXiv cs.SE 2023
Year: 2023

Establishing digital twins is a non-trivial endeavour especially when users face significant challenges in creating them from scratch. Ready availability of reusable models, data and tool assets, can help with creation and use of digital twins. A number of digital twin frameworks exist to facilitate creation and use of digital twins. In this paper we propose a digital twin framework to author digital twin assets, create digital twins from reusable assets and make the digital twins available as a service to other users. The proposed framework automates the management of reusable assets, storage, provision of compute infrastructure, communication and monitoring tasks. The users operate at the level of digital twins and delegate rest of the work to the digital twin as a service framework.
Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient Algorithms
Venue: arXiv cs.LG 2023
Year: 2023

Classical reinforcement learning (RL) aims to optimize the expected cumulative reward. In this work, we consider the RL setting where the goal is to optimize the quantile of the cumulative reward. We parameterize the policy controlling actions by neural networks, and propose a novel policy gradient algorithm called Quantile-Based Policy Optimization (QPO) and its variant Quantile-Based Proximal Policy Optimization (QPPO) for solving deep RL problems with quantile objectives. QPO uses two coupled iterations running at different timescales for simultaneously updating quantiles and policy parameters, whereas QPPO is an off-policy version of QPO that allows multiple updates of parameters during one simulation episode, leading to improved algorithm efficiency. Our numerical results indicate that the proposed algorithms outperform the existing baseline algorithms under the quantile criterion.
Performance Analysis of RIS-Aided NOMA Networks in $α-μ$ & $κ-μ$ Generalized Fading Channel
Venue: arXiv cs.IT 2023
Year: 2023

For forthcoming 5G networks, Non-Orthogonal Multiple Access (NOMA) is a very promising techniques. and in today's world, Line of Sight communication is becoming increasingly harder to achieve. Hence, technologies like Reconfigurable Intelligent Surfaces (RIS) emerge. RIS-aided NOMA networks is a widely researched implementation of RIS. The environment where these networks are employed are non-homogeneous & non-linear in nature. The effectiveness of these systems must thus be evaluated using generalized fading channels. In this paper, the performance of a RIS-aided NOMA is compared with conventional NOMA in alpha-mu and kappa-mu channels. This paper also shows that the well-known fading distribution are special cases of these generalized fading channels, both analytically and through simulation.
The 3rd Anti-UAV Workshop & Challenge: Methods and Results
Venue: arXiv cs.CV 2023
Year: 2023

The 3rd Anti-UAV Workshop & Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UAV Challenge has been publicly released. There are two main differences between this year's competition and the previous two. First, we have expanded the existing dataset, and for the first time, released a training set so that participants can focus on improving their models. Second, we set up two tracks for the first time, i.e., Anti-UAV Tracking and Anti-UAV Detection & Tracking. Around 76 participating teams from the globe competed in the 3rd Anti-UAV Challenge. In this paper, we provide a brief summary of the 3rd Anti-UAV Workshop & Challenge including brief introductions to the top three methods in each track. The submission leaderboard will be reopened for researchers that are interested in the Anti-UAV challenge. The benchmark dataset and other information can be found at: https://anti-uav.github.io/.
MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation with Bird's Eye View based Appearance and Motion Features
Venue: arXiv cs.CV 2023
Year: 2023

Identifying moving objects is an essential capability for autonomous systems, as it provides critical information for pose estimation, navigation, collision avoidance, and static map construction. In this paper, we present MotionBEV, a fast and accurate framework for LiDAR moving object segmentation, which segments moving objects with appearance and motion features in the bird's eye view (BEV) domain. Our approach converts 3D LiDAR scans into a 2D polar BEV representation to improve computational efficiency. Specifically, we learn appearance features with a simplified PointNet and compute motion features through the height differences of consecutive frames of point clouds projected onto vertical columns in the polar BEV coordinate system. We employ a dual-branch network bridged by the Appearance-Motion Co-attention Module (AMCM) to adaptively fuse the spatio-temporal information from appearance and motion features. Our approach achieves state-of-the-art performance on the SemanticKITTI-MOS benchmark. Furthermore, to demonstrate the practical effectiveness of our method, we provide a LiDAR-MOS dataset recorded by a solid-state LiDAR, which features non-repetitive scanning patterns and a small field of view.
Detecting Coordinated Inauthentic Behavior in Likes on Social Media: Proof of Concept
Venue: arXiv cs.CY 2023
Year: 2023

Coordinated inauthentic behavior is used as a tool on social media to shape public opinion by elevating or suppressing topics using systematic engagements -- e.g. through *likes* or similar reactions. In an honest world, reactions may be informative to users when selecting on what to spend their attention: through the wisdom of crowds, summed reactions may help identifying relevant and high-quality content. This is nullified by coordinated inauthentic liking. To restore wisdom-of-crowds effects, it is therefore desirable to separate the inauthentic agents from the wise crowd, and use only the latter as a voting *jury* on the relevance of a post. To this end, we design two *jury selection procedures* (JSPs) that discard agents classified as inauthentic. Using machine learning techniques, both cluster on binary vote data -- one using a Gaussian Mixture Model (GMM JSP), one the k-means algorithm (KM JSP) -- and label agents by logistic regression. We evaluate the jury selection procedures with an agent-based model, and show that the GMM JSP detects more inauthentic agents, but both JSPs select juries with vastly increased correctness of vote by majority. This proof of concept provides an argument for the release of reactions data from social media platforms through a direct use-case in the fight against online misinformation.
Improved Lower Bounds for Monotone q-Multilinear Boolean Circuits
Venue: arXiv cs.CC 2023
Year: 2023

A monotone Boolean circuit is composed of OR gates, AND gates and input gates corresponding to the input variables and the Boolean constants. It is $q$-multilinear if for each its output gate $o$ and for each prime implicant $s$ of the function computed at $o$, the arithmetic version of the circuit resulting from the replacement of OR and AND gates by addition and multiplication gates, respectively, computes a polynomial at $o$ which contains a monomial including the same variables as $s$ and each of the variables in $s$ has degree at most $q$ in the monomial. First, we study the complexity of computing semi-disjoint bilinear Boolean forms in terms of the size of monotone $q$-multilinear Boolean circuits. In particular, we show that any monotone $1$-multilinear Boolean circuit computing a semi-disjoint Boolean form with $p$ prime implicants includes at least $p$ AND gates. We also show that any monotone $q$-multilinear Boolean circuit computing a semi-disjoint Boolean form with $p$ prime implicants has $Ω(\frac p {q^4})$ size. Next, we study the complexity of the monotone Boolean function $Isol_{k,n}$ that verifies if a $k$-dimensional Boolean matrix has at least one $1$ in each line (e.g., each row and column when $k=2$), in terms of monotone $q$-multilinear Boolean circuits. We show that that any $Σ_3$ monotone Boolean circuit for $Isol_{k,n}$ has an exponential in $n$ size or it is not $(k-1)$-multilinear.
S-REINFORCE: A Neuro-Symbolic Policy Gradient Approach for Interpretable Reinforcement Learning
Venue: arXiv cs.LG 2023
Year: 2023

This paper presents a novel RL algorithm, S-REINFORCE, which is designed to generate interpretable policies for dynamic decision-making tasks. The proposed algorithm leverages two types of function approximators, namely Neural Network (NN) and Symbolic Regressor (SR), to produce numerical and symbolic policies, respectively. The NN component learns to generate a numerical probability distribution over the possible actions using a policy gradient, while the SR component captures the functional form that relates the associated states with the action probabilities. The SR-generated policy expressions are then utilized through importance sampling to improve the rewards received during the learning process. We have tested the proposed S-REINFORCE algorithm on various dynamic decision-making problems with low and high dimensional action spaces, and the results demonstrate its effectiveness and impact in achieving interpretable solutions. By leveraging the strengths of both NN and SR, S-REINFORCE produces policies that are not only well-performing but also easy to interpret, making it an ideal choice for real-world applications where transparency and causality are crucial.
One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering
Venue: arXiv cs.LG 2023
Year: 2023

The bipartite graph structure has shown its promising ability in facilitating the subspace clustering and spectral clustering algorithms for large-scale datasets. To avoid the post-processing via k-means during the bipartite graph partitioning, the constrained Laplacian rank (CLR) is often utilized for constraining the number of connected components (i.e., clusters) in the bipartite graph, which, however, neglects the distribution (or normalization) of these connected components and may lead to imbalanced or even ill clusters. Despite the significant success of normalized cut (Ncut) in general graphs, it remains surprisingly an open problem how to enforce a one-step normalized cut for bipartite graphs, especially with linear-time complexity. In this paper, we first characterize a novel one-step bipartite graph cut (OBCut) criterion with normalized constraints, and theoretically prove its equivalence to a trace maximization problem. Then we extend this cut criterion to a scalable subspace clustering approach, where adaptive anchor learning, bipartite graph learning, and one-step normalized bipartite graph partitioning are simultaneously modeled in a unified objective function, and an alternating optimization algorithm is further designed to solve it in linear time. Experiments on a variety of general and large-scale datasets demonstrate the effectiveness and scalability of our approach.
Deep Deterministic Policy Gradient for End-to-End Communication Systems without Prior Channel Knowledge
Venue: arXiv cs.NI 2023
Year: 2023

End-to-End (E2E) learning-based concept has been recently introduced to jointly optimize both the transmitter and the receiver in wireless communication systems. Unfortunately, this E2E learning architecture requires a prior differentiable channel model to jointly train the deep neural networks (DNNs) at the transceivers, which is hardly obtained in practice. This paper aims to solve this issue by developing a deep deterministic policy gradient (DDPG)-based framework. In particular, the proposed solution uses the loss value of the receiver DNN as the reward to train the transmitter DNN. The simulation results then show that our proposed solution can jointly train the transmitter and the receiver without requiring the prior channel model. In addition, we demonstrate that the proposed DDPG-based solution can achieve better detection performance compared to the state-of-the-art solutions.
Automata with Timers
Venue: arXiv cs.FL 2023
Year: 2023

In this work, we study properties of deterministic finite-state automata with timers, a subclass of timed automata proposed by Vaandrager et al. as a candidate for an efficiently learnable timed model. We first study the complexity of the configuration reachability problem for such automata and establish that it is PSPACE-complete. Then, as simultaneous timeouts (we call these, races) can occur in timed runs of such automata, we study the problem of determining whether it is possible to modify the delays between the actions in a run, in a way to avoid such races. The absence of races is important for modelling purposes and to streamline learning of automata with timers. We provide an effective characterization of when an automaton is race-avoiding and establish that the related decision problem is in 3EXP and PSPACE-hard.
BactInt: A domain driven transfer learning approach and a corpus for extracting inter-bacterial interactions from biomedical text
Venue: arXiv cs.IR 2023
Year: 2023

The community of different types of microbes present in a biological niche plays a very important role in functioning of the system. The crosstalk or interactions among the different microbes contributes to the building blocks of such microbial community structures. Evidence reported in biomedical text serves as a reliable source for predicting such interactions. However, going through the vast and ever-increasing volume of biomedical literature is an intimidating and time consuming process. This necessitates development of automated methods capable of accurately extracting bacterial relations reported in biomedical literature. In this paper, we introduce a method for automated extraction of microbial interactions (specifically between bacteria) from biomedical literature along with ways of using transfer learning to improve its accuracy. We also describe a pipeline using which relations among specific bacteria groups can be mined. Additionally, we introduce the first publicly available dataset which can be used to develop bacterial interaction extraction methods.
A Comprehensive Analysis of Adapter Efficiency
Venue: arXiv cs.CL 2023
Year: 2023

Adapters have been positioned as a parameter-efficient fine-tuning (PEFT) approach, whereby a minimal number of parameters are added to the model and fine-tuned. However, adapters have not been sufficiently analyzed to understand if PEFT translates to benefits in training/deployment efficiency and maintainability/extensibility. Through extensive experiments on many adapters, tasks, and languages in supervised and cross-lingual zero-shot settings, we clearly show that for Natural Language Understanding (NLU) tasks, the parameter efficiency in adapters does not translate to efficiency gains compared to full fine-tuning of models. More precisely, adapters are relatively expensive to train and have slightly higher deployment latency. Furthermore, the maintainability/extensibility benefits of adapters can be achieved with simpler approaches like multi-task training via full fine-tuning, which also provide relatively faster training times. We, therefore, recommend that for moderately sized models for NLU tasks, practitioners should rely on full fine-tuning or multi-task training rather than using adapters. Our code is available at https://github.com/AI4Bharat/adapter-efficiency.