OpenRaiser/Intern-Atlas huggingface dataset:
FCB-SwinV2 Transformer for Polyp Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Polyp segmentation within colonoscopy video frames using deep learning models has the potential to automate the workflow of clinicians. This could help improve the early detection rate and characterization of polyps which could progress to colorectal cancer. Recent state-of-the-art deep learning polyp segmentation models have combined the outputs of Fully Convolutional Network architectures and Transformer Network architectures which work in parallel. In this paper we propose modifications to the current state-of-the-art polyp segmentation model FCBFormer. The transformer architecture of the FCBFormer is replaced with a SwinV2 Transformer-UNET and minor changes to the Fully Convolutional Network architecture are made to create the FCB-SwinV2 Transformer. The performance of the FCB-SwinV2 Transformer is evaluated on the popular colonoscopy segmentation bench-marking datasets Kvasir-SEG and CVC-ClinicDB. Generalizability tests are also conducted. The FCB-SwinV2 Transformer is able to consistently achieve higher mDice scores across all tests conducted and therefore represents new state-of-the-art performance. Issues found with how colonoscopy segmentation model performance is evaluated within literature are also re-ported and discussed. One of the most important issues identified is that when evaluating performance on the CVC-ClinicDB dataset it would be preferable to ensure no data leakage from video sequences occurs during the training/validation/test data partition.
Modeling opinion polarization on social media: application to Covid-19 vaccination hesitancy in Italy
Venue: arXiv cs.SI 2023
Year: 2023

The SARS-CoV-2 pandemic reminded us how vaccination can be a divisive topic on which the public conversation is permeated by misleading claims, and thoughts tend to polarize, especially on online social networks. In this work, motivated by recent natural language processing techniques to systematically extract and quantify opinions from text messages, we present a differential framework for bivariate opinion formation dynamics that is coupled with a compartmental model for fake news dissemination. Thanks to a mean-field analysis we demonstrate that the resulting Fokker-Planck system permits to reproduce bimodal distributions of opinions as observed in polarization dynamics. The model is then applied to sentiment analysis data from social media platforms in Italy, in order to analyze the evolution of opinions about Covid-19 vaccination. We show through numerical simulations that the model is capable to describe correctly the formation of the bimodal opinion structure observed in the vaccine-hesitant dataset, which is witness of the known polarization effects that happen within closed online communities.
Deep Learning Based Predictive Beamforming Design
Venue: arXiv cs.IT 2023
Year: 2023

This paper investigates deep learning techniques to predict transmit beamforming based on only historical channel data without current channel information in the multiuser multiple-input-single-output downlink. This will significantly reduce the channel estimation overhead and improve the spectrum efficiency especially in high-mobility vehicular communications. Specifically, we propose a joint learning framework that incorporates channel prediction and power optimization, and produces prediction for transmit beamforming directly. In addition, we propose to use the attention mechanism in the Long Short-Term Memory Recurrent Neural Networks to improve the accuracy of channel prediction. Simulation results using both a simple autoregressive process model and the more realistic 3GPP spatial channel model verify that our proposed predictive beamforming scheme can significantly improve the effective spectrum efficiency compared to traditional channel estimation and the method that separately predicts channel and then optimizes beamforming.
Approximate Reversible Circuits for NISQ-Era Quantum Computers
Venue: arXiv cs.ET 2023
Year: 2023

The synthesis approaches for quantum circuits typically aim at minimizing the number of lines or gates. Given the tight restrictions on those logical resources in physical implementations, we propose to view the problem fundamentally different: Given noisy gates and a fixed number of lines, how can we use them to perform a computation as precisely as possible? In this paper we show approximate circuits can be deployed for computations with limited resources. Performing experiments on a QC simulator, we show that under the influence of noise, approximate circuits can have lower error-rates than exact circuits.
Metaverse: Requirements, Architecture, Standards, Status, Challenges, and Perspectives
Venue: arXiv cs.AI 2023
Year: 2023

The Metaverse is driving the next wave of innovation for new opportunities by replacing the digital world (Internet) with the virtual world through a single, shared, immersive, persistent 3D virtual space. In this paper, we present requirements, architecture, standards, challenges, and solutions for Metaverse. Specifically, we provide Metaverse architecture and requirements, and different standards for Metaverse which serve as the basis for the development and deployment. Moreover, we present recent status, challenges such as integration of AI and Metaverse, security and privacy in Metaverse, etc., and perspectives and solutions.
UW-CVGAN: UnderWater Image Enhancement with Capsules Vectors Quantization
Venue: arXiv cs.CV 2023
Year: 2023

The degradation in the underwater images is due to wavelength-dependent light attenuation, scattering, and to the diversity of the water types in which they are captured. Deep neural networks take a step in this field, providing autonomous models able to achieve the enhancement of underwater images. We introduce Underwater Capsules Vectors GAN UWCVGAN based on the discrete features quantization paradigm from VQGAN for this task. The proposed UWCVGAN combines an encoding network, which compresses the image into its latent representation, with a decoding network, able to reconstruct the enhancement of the image from the only latent representation. In contrast with VQGAN, UWCVGAN achieves feature quantization by exploiting the clusterization ability of capsule layer, making the model completely trainable and easier to manage. The model obtains enhanced underwater images with high quality and fine details. Moreover, the trained encoder is independent of the decoder giving the possibility to be embedded onto the collector as compressing algorithm to reduce the memory space required for the images, of factor $3\times$. \myUWCVGAN{ }is validated with quantitative and qualitative analysis on benchmark datasets, and we present metrics results compared with the state of the art.
A comparative study of statistical and machine learning models on near-real-time daily emissions prediction
Venue: arXiv cs.AI 2023
Year: 2023

The rapid ascent in carbon dioxide emissions is a major cause of global warming and climate change, which pose a huge threat to human survival and impose far-reaching influence on the global ecosystem. Therefore, it is very necessary to effectively control carbon dioxide emissions by accurately predicting and analyzing the change trend timely, so as to provide a reference for carbon dioxide emissions mitigation measures. This paper is aiming to select a suitable model to predict the near-real-time daily emissions based on univariate daily time-series data from January 1st, 2020 to September 30st, 2022 of all sectors (Power, Industry, Ground Transport, Residential, Domestic Aviation, International Aviation) in China. We proposed six prediction models, which including three statistical models: Grey prediction (GM(1,1)), autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average with exogenous factors (SARIMAX); three machine learning models: artificial neural network (ANN), random forest (RF) and long short term memory (LSTM). To evaluate the performance of these models, five criteria: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination () are imported and discussed in detail. In the results, three machine learning models perform better than that three statistical models, in which LSTM model performs the best on five criteria values for daily emissions prediction with the 3.5179e-04 MSE value, 0.0187 RMSE value, 0.0140 MAE value, 14.8291% MAPE value and 0.9844 value.
Deep COVID-19 Forecasting for Multiple States with Data Augmentation
Venue: arXiv cs.LG 2023
Year: 2023

In this work, we propose a deep learning approach to forecasting state-level COVID-19 trends of weekly cumulative death in the United States (US) and incident cases in Germany. This approach includes a transformer model, an ensemble method, and a data augmentation technique for time series. We arrange the inputs of the transformer in such a way that predictions for different states can attend to the trends of the others. To overcome the issue of scarcity of training data for this COVID-19 pandemic, we have developed a novel data augmentation technique to generate useful data for training. More importantly, the generated data can also be used for model validation. As such, it has a two-fold advantage: 1) more actual observations can be used for training, and 2) the model can be validated on data which has distribution closer to the expected situation. Our model has achieved some of the best state-level results on the COVID-19 Forecast Hub for the US and for Germany.
Vehicle Fault-Tolerant Robust Power Transmission Line Inspection Planning
Venue: arXiv cs.RO 2023
Year: 2023

This paper concerns fault-tolerant power transmission line inspection planning as a generalization of the multiple traveling salesmen problem. The addressed inspection planning problem is formulated as a single-depot multiple-vehicle scenario, where the inspection vehicles are constrained by the battery budget limiting their inspection time. The inspection vehicle is assumed to be an autonomous multi-copter with a wide range of possible flight speeds influencing battery consumption. The inspection plan is represented by multiple routes for vehicles providing full coverage over inspection target power lines. On an inspection vehicle mission interruption, which might happen at any time during the execution of the inspection plan, the inspection is re-planned using the remaining vehicles and their remaining battery budgets. Robustness is introduced by choosing a suitable cost function for the initial plan that maximizes the time window for successful re-planning. It enables the remaining vehicles to successfully finish all the inspection targets using their respective remaining battery budgets. A combinatorial metaheuristic algorithm with various cost functions is used for planning and fast re-planning during the inspection.
Multi-Tour Set Traveling Salesman Problem in Planning Power Transmission Line Inspection
Venue: arXiv cs.RO 2023
Year: 2023

This letter concerns optimal power transmission line inspection formulated as a proposed generalization of the traveling salesman problem for a multi-route one-depot scenario. The problem is formulated for an inspection vehicle with a limited travel budget. Therefore, the solution can be composed of multiple runs to provide full coverage of the given power lines. Besides, the solution indicates how many vehicles can perform the inspection in a single run. The optimal solution of the problem is solved by the proposed Integer Linear Programming (ILP) formulation, which is, however, very computationally demanding. Therefore, the computational requirements are addressed by the combinatorial metaheuristic. The employed greedy randomized adaptive search procedure is significantly less demanding while providing competitive solutions and scales better with the problem size than the ILP-based approach. The proposed formulation and algorithms are demonstrated in a real-world scenario to inspect power line segments at the electrical substation.
Imitating careful experts to avoid catastrophic events
Venue: arXiv cs.LG 2023
Year: 2023

RL is increasingly being used to control robotic systems that interact closely with humans. This interaction raises the problem of safe RL: how to ensure that a RL-controlled robotic system never, for instance, injures a human. This problem is especially challenging in rich, realistic settings where it is not even possible to clearly write down a reward function which incorporates these outcomes. In these circumstances, perhaps the only viable approach is based on IRL, which infers rewards from human demonstrations. However, IRL is massively underdetermined as many different rewards can lead to the same optimal policies; we show that this makes it difficult to distinguish catastrophic outcomes (such as injuring a human) from merely undesirable outcomes. Our key insight is that humans do display different behaviour when catastrophic outcomes are possible: they become much more careful. We incorporate carefulness signals into IRL, and find that they do indeed allow IRL to disambiguate undesirable from catastrophic outcomes, which is critical to ensuring safety in future real-world human-robot interactions.
Causal Lifting and Link Prediction
Venue: arXiv cs.LG 2023
Year: 2023

Existing causal models for link prediction assume an underlying set of inherent node factors -- an innate characteristic defined at the node's birth -- that governs the causal evolution of links in the graph. In some causal tasks, however, link formation is path-dependent: The outcome of link interventions depends on existing links. Unfortunately, these existing causal methods are not designed for path-dependent link formation, as the cascading functional dependencies between links (arising from path dependence) are either unidentifiable or require an impractical number of control variables. To overcome this, we develop the first causal model capable of dealing with path dependencies in link prediction. In this work we introduce the concept of causal lifting, an invariance in causal models of independent interest that, on graphs, allows the identification of causal link prediction queries using limited interventional data. Further, we show how structural pairwise embeddings exhibit lower bias and correctly represent the task's causal structure, as opposed to existing node embeddings, e.g., graph neural network node embeddings and matrix factorization. Finally, we validate our theoretical findings on three scenarios for causal link prediction tasks: knowledge base completion, covariance matrix estimation and consumer-product recommendations.
Towards Evology: a Market Ecology Agent-Based Model of US Equity Mutual Funds II
Venue: arXiv cs.MA 2023
Year: 2023

Agent-based models (ABMs) are fit to model heterogeneous, interacting systems like financial markets. We present the latest advances in Evology: a heterogeneous, empirically calibrated market ecology agent-based model of the US stock market. Prices emerge endogenously from the interactions of market participants with diverse investment behaviours and their reactions to fundamentals. This approach allows testing trading strategies while accounting for the interactions of this strategy with other market participants and conditions. Those early results encourage a closer association between ABMs and ML algorithms for testing and optimising investment strategies using machine learning algorithms.
Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric
Venue: arXiv cs.LG 2023
Year: 2023

Heterogeneous graphs offer powerful data representations for traffic, given their ability to model the complex interaction effects among a varying number of traffic participants and the underlying road infrastructure. With the recent advent of graph neural networks (GNNs) as the accompanying deep learning framework, the graph structure can be efficiently leveraged for various machine learning applications such as trajectory prediction. As a first of its kind, our proposed Python framework offers an easy-to-use and fully customizable data processing pipeline to extract standardized graph datasets from traffic scenarios. Providing a platform for GNN-based autonomous driving research, it improves comparability between approaches and allows researchers to focus on model implementation instead of dataset curation.
A Framework for the Automated Verification of Algebraic Effects and Handlers (extended version)
Venue: arXiv cs.LO 2023
Year: 2023

Algebraic effects and handlers are a powerful abstraction to build non-local control-flow mechanisms such as resumable exceptions, lightweight threads, co-routines, generators, and asynchronous I/O. All of such features have very evolved semantics, hence they pose very interesting challenges to deductive verification techniques. In fact, there are very few proposed techniques to deductively verify programs featuring these constructs, even fewer when it comes to automated proofs. In this paper, we present an extension to Cameleer, a deductive verification tool for OCaml code, that allows one to reason about algebraic effects and handlers. Our proposal embeds the behavior of effects and handlers using exceptions and employs defunctionalization to deal with continuations exposed by effect handlers.
Large language models predict human sensory judgments across six modalities
Venue: arXiv cs.CL 2023
Year: 2023

Determining the extent to which the perceptual world can be recovered from language is a longstanding problem in philosophy and cognitive science. We show that state-of-the-art large language models can unlock new insights into this problem by providing a lower bound on the amount of perceptual information that can be extracted from language. Specifically, we elicit pairwise similarity judgments from GPT models across six psychophysical datasets. We show that the judgments are significantly correlated with human data across all domains, recovering well-known representations like the color wheel and pitch spiral. Surprisingly, we find that a model (GPT-4) co-trained on vision and language does not necessarily lead to improvements specific to the visual modality. To study the influence of specific languages on perception, we also apply the models to a multilingual color-naming task. We find that GPT-4 replicates cross-linguistic variation in English and Russian illuminating the interaction of language and perception.
Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search
Venue: arXiv cs.LG 2023
Year: 2023

Serving large-scale machine learning (ML) models efficiently and with low latency has become challenging owing to increasing model size and complexity. Quantizing models can simultaneously reduce memory and compute requirements, facilitating their widespread access. However, for large models not all layers are equally amenable to the same numerical precision and aggressive quantization can lead to unacceptable loss in model accuracy. One approach to prevent this accuracy degradation is mixed-precision quantization, which allows different tensors to be quantized to varying levels of numerical precision, leveraging the capabilities of modern hardware. Such mixed-precision quantiztaion can more effectively allocate numerical precision to different tensors `as needed' to preserve model accuracy while reducing footprint and compute latency. In this paper, we propose a method to efficiently determine quantization configurations of different tensors in ML models using post-training mixed precision quantization. We analyze three sensitivity metrics and evaluate them for guiding configuration search of two algorithms. We evaluate our method for computer vision and natural language processing and demonstrate latency reductions of up to 27.59% and 34.31% compared to the baseline 16-bit floating point model while guaranteeing no more than 1% accuracy degradation.
Understanding and contextualising diffusion models
Venue: arXiv cs.CV 2023
Year: 2023

The latest developments in Artificial Intelligence include diffusion generative models, quite popular tools which can produce original images both unconditionally and, in some cases, conditioned by some inputs provided by the user. Apart from implementation details, which are outside the scope of this work, all of the main models used to generate images are substantially based on a common theory which restores a new image from a completely degraded one. In this work we explain how this is possible by focusing on the mathematical theory behind them, i.e. without analyzing in detail the specific implementations and related methods. The aim of this work is to clarify to the interested reader what all this means mathematically and intuitively.
Complexity of Solo Chess with Unlimited Moves
Venue: arXiv cs.CC 2023
Year: 2023

We analyze Solo Chess puzzles, where the input is an $n \times n$ board containing some standard Chess pieces of the same color, and the goal is to make a sequence of capture moves to reduce down to a single piece. Prior work analyzes this puzzle for a single piece type when each piece is limited to make at most two capture moves (as in the Solo Chess puzzles on chess.com). By contrast, we study when each piece can make an unlimited number of capture moves. We show that any single piece type can be solved in polynomial time in a general model of piece types, while any two standard Chess piece types are NP-complete. We also analyze the restriction (as on chess.com) that one piece type is unique and must be the last surviving piece, showing that in this case some pairs of piece types become tractable while others remain hard.
Monolithic Six-DOF Parallel Positioning System for High-precision and Large-range Applications
Venue: arXiv cs.RO 2023
Year: 2023

A compact large-range six-degrees-of-freedom (six-DOF) parallel positioning system with high resolution, high resonant frequency, and high repeatability was proposed. It mainly consists of three identical kinematic sections. Each kinematic section consists of two identical displacement amplification and guiding mechanisms, which are finally connected to a limb. Each limb was designed with a universal joint at each end and connected to a moving stage. A computational model of the positioner was built in the ANSYS software package, hence, the input stiffness, output compliance, range, and modal analysis of the system were found. Furthermore, a monolithic prototype made of Acrylonitrile Butadiene Styrene (ABS) was successfully manufactured by the 3D-printing process. It was actuated and sensed by piezoelectric actuators (PEAs) and capacitive displacement sensors, respectively. Finally, the performances of this proposed positioner were experimentally investigated. The positioning resolution was achieved as 10.5nm {\times} 10.5nm {\times} 15nm {\times} 1.8μrad {\times} 1.3μrad {\times} 0.5μrad. The experimental results validate the behavior and capabilities of the proposed positioning system, and also verify the nanometer-scale spatial positioning accuracy within the overall stroke range. Practical applications of the proposed system can be expanded to pick-and-place manipulation, vibration-canceling in microsurgery/micro-assembly, and collaborative manipulators systems.