Reviewerly/exHarmony huggingface dataset:
FRBNet: Feedback refinement boundary network for semantic segmentation in breast ultrasound images

Ultrasound breast tumour segmentation is a key step in computer-aided diagnosis and provides an important basis for clinical diagnosis and analysis. Accurate segmentation of breast tumours from ultrasound images is a challenging task due to the characteristics of black shadows, blurred boundaries and uneven colour intensity variations between classes. Currently, most breast tumour segmentation methods focus on extracting multi-scale information and fusing contextual information while underestimating the importance of feature information that can assist in identifying object boundaries in segmentation tasks. The loss of boundary feature information can easily lead to discontinuity or inaccuracy of the target boundary when the network generates the final prediction map. To address this problem, we propose a new feedback refinement boundary network (FRBNet) for accurate segmentation of breast tumour regions in ultrasound images, which mainly consists of a channel calibration module (CCM), boundary detection (BD) module, and feedback refinement module (FRM). Specifically, before fusing low-level feature maps with high-level feature maps, CCM first adopts the method of redistributing feature channel responses to enhance the channels carrying key target information and suppress the noisy channels in low-level feature maps. The BD module then improves the quality of the boundaries in the segmentation results by additionally learning the boundaries of breast tumours to provide accurate boundary feature information for subsequent prediction. The FRM employs a feedback mechanism that complementarily fuses the coarse prediction map and the feature map containing the target boundary feature information, thus achieving the best prediction results before generating the final prediction map. Experimental results on a public ultrasound breast dataset show that our network outperforms other medical image segmentation methods.
Constructions of rotation symmetric Bent functions and Bent idempotent functions

The class of rotation symmetric functions is extremely rich in terms of cryptographical significance.However, few constructions of rotation symmetric bent functions, which can correspond to bent idempotent functions, have been presented in the literature.In this paper, for any even integer n ≥ 4, we first construct an n-variable rotation symmetric bent function by modifying the truth table of Rothaus's bent function on the vector space F n 2 , and then obtain a bent idempotent function by corresponding it to the finite field F 2 n .By generalizing the proposed n-variable Boolean function, we obtain a family of Boolean functions which are rotation symmetric bent on the vector space F n 2 and bent idempotent over the finite field F 2 n .
Learning Agile Flight Maneuvers: Deep SE(3) Motion Planning and Control for Quadrotors

Agile flights of autonomous quadrotors in clut-tered environments require constrained motion planning and control subject to translational and rotational dynamics. Tra-ditional model-based methods typically demand complicated design and heavy computation. In this paper, we develop a novel deep reinforcement learning-based method that tackles the challenging task of flying through a dynamic narrow gate. We design a model predictive controller with its adaptive tracking references parameterized by a deep neural network (DNN). These references include the traversal time and the quadrotor SE(3) traversal pose that encourage the robot to fly through the gate with maximum safety margins from various initial conditions. To cope with the difficulty of training in highly dynamic environments, we develop a reinforce-imitate learning framework to train the DNN efficiently that generalizes well to diverse settings. Furthermore, we propose a binary search algorithm that allows online adaption of the SE(3) references to dynamic gates in real-time. Finally, through extensive high-fidelity simulations, we show that our approach is adaptive to different gate trajectories, velocities, and orientations.
Simplifying Aerial Manipulation Using Intentional Collisions

Aerial manipulation describes a process that includes physical interaction between an unmanned aircraft system (UAS) and its environment. We aim to apply aerial manipulation to sample leaves and small branches from rain forest trees. Current approaches to aerial manipulation involve extended periods of UAS-environment interaction, during which forces and moments can lead to a loss in attitude or position control in underactuated multicopters. By adapting intelligent foot placement strategies found in dynamically stable hopping robots, this work proposes a strategy involving carefully managed intentional collisions between the UAS and its environment. We designed an attitude controller denoted a Velocity Matching controller that aligns a UAS-mounted pogo-stick foot with the center of mass velocity vector during collision approach to maximize UAS ability to recover a hover state after collision. We propose the use of a flight envelope involving altitude and horizontal speed states to assess recoverability prior to initiating each approach to collision. We identify this flight envelope from a simulation study built on a model of flight in Conventional Waypoint Following and Velocity Matching control modes as well as a model of collision response. Experimental flight testing evaluates the simulation-based envelope resulting in an actual envelope that is somewhat smaller but similarly shaped to the envelope identified in simulation.
A tool for the data notarization with the Blockchain to ensure security and privacy

This paper will describe a trustworthy blockchain-based framework to ensure secure, immutable and pseudo anonymized data collection at field level. This paper will describe the context in which the framework is being conceived and developed, starting from the analysis of the state of the art of DLT (Distributed Ledger Technology) and Blockchain, to highlight the potential uses cases in terms of data management, in particular. A specific focus will be given to the FIWARE framework, since the final objective is the creation of a tool for data gathering, as a FIWARE context broker extension. The core of the tool will be the two smart contracts implementing the PoE (Proof of Existence) and the RT-MDN (Reat Time-Monitoring Data Notarization). The smart contracts will be used for the certification of single documents and bulk of monitoring data, respectively. The second case is characterized by a periodicity of production and represent the most innovative part of the work. A first PoC (Proof of Concept) will be implemented, and first early results will be presented as well. The work is being developed in the framework of the EU co-funded “DigiBUILD” project.
Search Algorithms for Multi-Agent Teamwise Cooperative Path Finding

Multi-Agent Path Finding (MA-PF) computes a set of collision-free paths for multiple agents from their respective starting locations to destinations. This paper considers a generalization of MA-PF called Multi-Agent Teamwise Cooperative Path Finding (MA-TC-PF), where agents are grouped as multiple teams and each team has its own objective to be minimized. For example, an objective can be the sum or max of individual arrival times of the agents. In general, there is more than one team, and MA-TC-PF is thus a multi-objective planning problem with the goal of finding the entire Pareto-optimal front that represents all possible trade-offs among the objectives of the teams. To solve MA-TC-PF, we propose two algorithms TC-CBS and TC-M*, which leverage the existing CBS and M* for conventional MA-PF. We discuss the conditions under which the proposed algorithms are complete and are guaranteed to find the Pareto-optimal front. We present numerical results for several types of MA-TC-PF problems.
Testing Rare Downstream Safety Violations via Upstream Adaptive Sampling of Perception Error Models

Testing black-box perceptual-control systems in simulation faces two difficulties. Firstly, perceptual inputs in simulation lack the fidelity of real-world sensor inputs. Secondly, for a reasonably accurate perception system, encountering a rare failure trajectory may require running infeasibly many simulations. This paper combines perception error models-surrogates for a sensor-based detection system-with state-dependent adaptive importance sampling. This allows us to efficiently assess the rare failure probabilities for real-world perceptual control systems within simulation. Our experiments with an autonomous braking system equipped with an RGB obstacle-detector show that our method can calculate accurate failure probabilities with an inexpensive number of simulations. Further, we show how choice of safety metric can influence the process of learning proposal distributions capable of reliably sampling high-probability failures.
Collision-free Coverage Path Planning for the Variable-speed Curvature-constrained Robot

Dubins coverage has been extensively researched to address the coverage path planning (CPP) problem of a known environment for the curvature-constrained robot. However, its fixed-speed assumption prevents the robot from accelerating to reduce the time and limits its flexibility to avoid obstacles. Therefore, this paper presents a collision-free CPP approach (CFC) for the obstacle-constrained environment, which enhances time efficiency by constructing the variable-speed Dubins paths and ensures robot safety by building a risk potential surface for representing the possibility of collision. Furthermore, CFC models the CPP problem as an asymmetric traveling salesman problem (ATSP) and utilizes a graph pruning strategy to reduce the computational cost. Comparison tests with other Dubins coverage methods demonstrate that CFC provides shorter coverage times and better runtimes than the other Dubins coverage methods while preventing collision risk between the robot and obstacles. Physical experiments in a laboratory setting demonstrate the applicability of CFC to the physical robot.
Source-free Unsupervised Domain Adaptation for 3D Object Detection in Adverse Weather

A domain shift exists between the distributions of large scale, outdoor lidar datasets due to being captured using different types of lidar sensors, in different locations, and under varying weather conditions. Inclement weather in particular affects the quality of lidar data, adding artifacts such as scattered and missed points, leading to a drop in performance of 3D object detection networks trained on standard lidar datasets. Domain adaptation methods seek to adapt source-trained neural networks to a target domain. Pseudo-label based self training approaches are popular methods for source-free unsupervised domain adaptation. However, their efficacy depends on the quality of the labels generated by the source trained model. These labels may be incorrect with high confidence, rendering thresholding methods ineffective. In order to avoid reinforcing errors caused by label noise, we propose an uncertainty-aware mean teacher framework which implicitly filters incorrect pseudo-labels during training. Leveraging model uncertainty allows the mean teacher network to perform implicit filtering by down-weighing losses corresponding to uncertain pseudo-labels. Effectively, we perform automatic soft-sampling of pseudo-labeled data while aligning predictions from the student and teacher networks. We demonstrate our domain adaptation method on an adverse weather dataset created by augmenting lidar scenes from KITTI with rain, snow, and fog and show that it out-performs current domain adaptation frameworks. We make our code publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/deeptibhegde/UncertaintyAwareMeanTeacher.
Optimal Allocation of Many Robot Guards for Sweep-Line Coverage

We study the problem of allocating many mobile robots for the execution of a pre-defined sweep schedule in a known two-dimensional environment, with applications toward search and rescue, coverage, surveillance, monitoring, pursuit-evasion, and so on. The mobile robots (or agents) are assumed to have one-dimensional sensing capability with probabilistic guarantees that deteriorate as the sensing distance increases. In solving such tasks, a time-parameterized distribution of robots along the sweep frontier must be computed, to minimize the number of robots used to achieve some desired coverage quality guarantee or to maximize the probabilistic guarantee for a given the number of robots. We propose a max-flow-based algorithm for solving the allocation task, which builds on a decomposition technique of the workspace as a generalization of the well-known boustrophedon decomposition. Our proposed algorithm has a very low polynomial running time and completes in under two seconds for polygonal environments with over 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> vertices. Simulation experiments are carried out on three realistic use cases with randomly generated obstacles of varying shapes, sizes, and spatial distributions, demonstrating our proposed method's applicability and scalability. Introduction video: https://youtu.be/8taX92rzC5k.
Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain Adaptation

Autonomous Underwater Vehicles (AUVs) conduct regular visual surveys of marine environments to characterise and monitor the composition and diversity of the benthos. The use of machine learning classifiers for this task is limited by the low numbers of annotations available and the many fine-grained classes involved. In addition to these challenges, there are domain shifts between image sets acquired during different AUV surveys due to changes in camera systems, imaging altitude, illumination and water column properties leading to a drop in classification performance for images from a different survey where some or all these elements may have changed. This paper proposes a framework to improve the performance of a benthic morphospecies classifier when used to classify images from a different survey compared to the training data. We adapt the SymmNet state-of-the-art Unsupervised Domain Adaptation method with an efficient bilinear pooling layer and image scaling to normalise spatial resolution, and show improved classification accuracy. We test our approach on two datasets with images from AUV surveys with different imaging payloads and locations. The results show that generic domain adaptation can be enhanced to produce a significant increase in accuracy for images from an AUV survey that differs from the training images.
Efficient Bimanual Handover and Rearrangement via Symmetry-Aware Actor-Critic Learning

Bimanual manipulation is important for building intelligent robots that unlock richer skills than single arms. We consider a multi-object bimanual rearrangement task, where a reinforcement learning (RL) agent aims to jointly control two arms to rearrange these objects as fast as possible. Solving this task efficiently is challenging for an RL agent due to the requirement of discovering precise intra-arm coordination in an exponentially large control space. We develop a symmetry-aware actor-critic framework that leverages the interchangeable roles of the two manipulators in the bimanual control setting to reduce the policy search space. To handle the compositionality over multiple objects, we augment training data with an object-centric relabeling technique. The overall approach produces an RL policy that can rearrange up to 8 objects with a success rate of over 70% in simulation. We deploy the policy to two Franka Panda arms and further show a successful demo on human-robot collaboration. Videos can be found at https://sites.google.com/view/bimanual.
Early detection of colorectal cancer by leveraging Dutch primary care consultation notes with free text embeddings

We aimed to assess the added predictive performance that free-text Dutch consultation notes provide in detecting colorectal cancer in primary care, in comparison to currently used models. We developed, evaluated and compared three prediction models for colorectal cancer (CRC) in a large primary care database with 60,641 patients. The prediction model with both known predictive features and free-text data (with TabTxt AUROC: 0.823) performs statistically significantly better (p < 0.05) than the other two models with only tabular (as used nowadays) and text data, respectively (AUROC Tab: 0.767; Txt: 0.797). The specificity of the two models that use demographics and known CRC features (with specificity Tab: 0.321; TabTxt: 0.335) are higher than that of the model with only free-text (specificity Txt: 0.234). The Txt and, to a lesser degree, TabTxt model are well calibrated, while the Tab model shows slight underprediction at both tails. As expected with an outcome prevalence below 0.01, all models show much uncalibrated predictions in the extreme upper tail (top 1%). Free-text consultation notes show promising results to improve the predictive performance over established prediction models that only use structured features. Clinical future implications for our CRC use case include that such improvement may help lowering the number of referrals for suspected CRC to medical specialists.
Assessing the impact of network factors and Twitter data on Ethereum's popularity

In March 2021, we witnessed a surge in Bitcoin price. The cause seemed to be a tweet by Elon Musk. Are other blockchains as sensitive to social media as Bitcoin? And more precisely, could Ethereum's popularity be explained using social media data? This work aims to explore the determinants of Ethereum's popularity. We use both data from Etherscan to retrieve the relevant historic Ethereum factors, and Twitter data. Our sample consists of data ranging from 2015 to 2022. We use Ordinary Least Squares to assess the relationship between these factors (Ethereum characteristics and Twitter data) and Ethereum's popularity. Our findings show that Ethereum's popularity - translated here by the number of daily new addresses - is related to the following elements: the Ether (ETH) price, the transaction fees, and the polarity of tweets related to Ethereum. The results could have multiple practical implications, for both researchers and practitioners. First of all, we believe that it will enable readers to better understand the technology Ethereum and its stake. Secondly, it will help the community to identify pointers for the anticipation or explanation of the popularity of existing or future platforms. And finally, the results could help in understanding the factors facilitating the design of future platforms.
Classification of white blood cells based on modified <scp>U‐Net</scp> and <scp>SVM</scp>

Summary Manual investigation of blood cell count is sometimes erroneous due to interoperability error, fatigue error, requiring expert skill and time consuming too. In particular, investigation of white blood cell (WBC) gains importance in identifying diseases like leukemia, leukopenia, etc. WBC does not possess regular structure because they move throughout the blood stream and hence analyzing WBC and its types for structure and shape is quite challenging. To aid in hematology, this work provides classification of WBC classification based on modified U‐Net and support vector machines (SVM). A modified U‐Net architecture is developed to segment WBC followed by feature extraction and classification by radial basis function‐support vector machine (RBF‐SVM). Experiments indicated that the modified U Net segmentation can detect the WBC nucleus with a dice similarity coefficient of 0.972. The proposed U‐Net‐SVM can recognize WBCs in Raabin‐WBC, LISC, and BCCD datasets with an accuracy of 99.45%, 98.62%, and 98.81%, respectively. Further investigation on leukemia dataset, ALL‐IDB2, revealed an accuracy of 99.42% with 100% sensitivity and specificity. The proposed model can be used to investigate WBCs and hence provide a great support to the hematologists in analyzing the blood smear for various disease identifications.
Adaptive image noise level estimation with Chi-square distribution on the flat patches selected by improved PCANet and ResNet101

Additive Gaussian white noise present in images adversely affects their quality, impeding effective human visualization. Current denoising approaches commonly rely on manual setting of the noise level, leading to insufficient noise removal or excessive smoothing. Traditional noise level estimation methods are not adaptive and their generalization ability is limited. To address this issue, this paper developed an adaptive image noise level estimation with Chi-square distribution on the flat patches selected by improved PCANet and ResNet101, including two key procedures: flat image patches selection and noise level estimation. The former was achieved using the improved PCANet and ResNet101 methods while the latter was completed by applying Chi-square distribution to the selected flat patches. Experimental results demonstrated that the reported method achieved high accuracy and powerful generalization ability, making it more suitable for extensive applications compared to existing methods.
Multiobjective variational quantum optimization for constrained problems: an application to cash handling

Abstract Combinatorial optimization problems are ubiquitous in industry. In addition to finding a solution with minimum cost, problems of high relevance involve a number of constraints that the solution must satisfy. Variational quantum algorithms (VQAs) have emerged as promising candidates for solving these problems in the noisy intermediate-scale quantum stage. However, the constraints are often complex enough to make their efficient mapping to quantum hardware difficult or even infeasible. An alternative standard approach is to transform the optimization problem to include these constraints as penalty terms, but this method involves additional hyperparameters and does not ensure that the constraints are satisfied due to the existence of local minima. In this paper, we introduce a new method for solving combinatorial optimization problems with challenging constraints using VQAs. We propose the multi-objective variational constrained optimizer (MOVCO) to classically update the variational parameters by a multiobjective optimization performed by a genetic algorithm. This optimization allows the algorithm to progressively sample only states within the in-constraints space, while optimizing the energy of these states. We test our proposal on a real-world problem with great relevance in finance: the cash handling problem. We introduce a novel mathematical formulation for this problem, and compare the performance of MOVCO versus a penalty based optimization. Our empirical results show a significant improvement in terms of the cost of the achieved solutions, but especially in the avoidance of local minima that do not satisfy any of the mandatory constraints.