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Twitter is nowadays a powerful means of political propaganda. Its effectiveness can be easily appreciated in the large amounts of messages exchanged by politicians every day. This wealth of data, together with the interactive nature of the social medium, provides an ideal basis for the analysis of a striking feature of political messages, i.e., their implicitness, often achieved using presuppositions, among other strategies. The present work proposes a comparative analysis of British and Italian politicians' use of Twitter by focusing on implicit communication (notably, presuppositions) and the pragmatic functions of tweets. Based on a sample of about 400 tweets, our analysis shows that some of these functions tend to associate either with presuppositional or non-presuppositional communicative devices. Moreover, a critical methodological discussion is offered in order to address the main challenges of quantitative corpus-based pragmatics.

Agile quadrotor flight in challenging environments has the potential to revolutionize shipping, transportation, and search and rescue applications. Nonlinear model predictive control (NMPC) has recently shown promising results for agile quadrotor control, but relies on highly accurate models for maximum performance. Hence, model uncertainties in the form of unmodeled complex aerodynamic effects, varying payloads and parameter mismatch will degrade overall system performance. In this paper, we propose L1-NMPC, a novel hybrid adaptive NMPC to learn model uncertainties online and immediately compensate for them, drastically improving performance over the non-adaptive baseline with minimal computational overhead. Our proposed architecture generalizes to many different environments from which we evaluate wind, unknown payloads, and highly agile flight conditions. The proposed method demonstrates immense flexibility and robustness, with more than 90% tracking error reduction over non-adaptive NMPC under large unknown disturbances and without any gain tuning. In addition, the same controller with identical gains can accurately fly highly agile racing trajectories exhibiting top speeds of 70 km/h, offering tracking performance improvements of around 50% relative to the non-adaptive NMPC baseline., 8 Pages, 6 figures, Accepted RAL 2021

State-of-the-art face restoration methods employ deep convolutional neural networks (CNNs) to learn a mapping between degraded and sharp facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and only deal with task-specific face restoration (e.g., face super-resolution or deblurring). In this paper, we propose cross-tasks and cross-models plug-and-play 3D facial priors to explicitly embed the network with the sharp facial structures for general face restoration tasks. Our 3D priors are the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes (e.g., identity, facial expression, texture, illumination, and face pose). Furthermore, the priors can easily be incorporated into any network and are very efficient in improving the performance and accelerating the convergence speed. Firstly, a 3D face rendering branch is set up to obtain 3D priors of salient facial structures and identity knowledge. Secondly, for better exploiting this hierarchical information (i.e., intensity similarity, 3D facial structure, and identity content), a spatial attention module is designed for the image restoration problems. Extensive face restoration experiments including face super-resolution and deblurring demonstrate that the proposed 3D priors achieve superior face restoration results over the state-of-the-art algorithms.

The most popular code review tools (e.g., Gerrit and GitHub) present the files to review sorted in alphabetical order. Could this choice or, more generally, the relative position in which a file is presented bias the outcome of code reviews? We investigate this hypothesis by triangulating complementary evidence in a two-step study. First, we observe developers' code review activity. We analyze the review comments pertaining to 219,476 Pull Requests (PRs) from 138 popular Java projects on GitHub. We found files shown earlier in a PR to receive more comments than files shown later, also when controlling for possible confounding factors: e.g., the presence of discussion threads or the lines added in a file. Second, we measure the impact of file position on defect finding in code review. Recruiting 106 participants, we conduct an online controlled experiment in which we measure participants' performance in detecting two unrelated defects seeded into two different files. Participants are assigned to one of two treatments in which the position of the defective files is switched. For one type of defect, participants are not affected by its file's position; for the other, they have 64% lower odds to identify it when its file is last as opposed to first. Overall, our findings provide evidence that the relative position in which files are presented has an impact on code reviews' outcome; we discuss these results and implications for tool design and code review. Data and materials: https://doi.org/10.5281/zenodo.6901285, Comment: This paper has been accepted at ESEC/FSE '22 (30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering)

Real-time analysis and classification of bio-signals measured using wearable devices is computationally costly and requires dedicated low-power hardware. One promising approach is to use spiking neural networks implemented using in-memory computing architectures and neuromorphic electronic circuits. However, as these circuits process data in streaming mode without the possibility of storing it in external buffers, a major challenge lies in the processing of spatio-temporal signals that last longer than the time constants present in the network synapses and neurons. Here we propose to extend the memory capacity of a spiking neural network by using parallel delay chains. We show that it is possible to map temporal signals of multiple seconds into spiking activity distributed across multiple neurons which have time constants of few milliseconds. We validate this approach on an ECG anomaly detection task and present experimental results that demonstrate how temporal information is properly preserved in the network activity.

Brain-inspired event-based neuromorphic processing systems have emerged as a promising technology in particular for bio-medical circuits and systems. However, both neuromorphic and biological implementations of neural networks have critical energy and memory constraints. To minimize the use of memory resources in multi-core neuromorphic processors, we propose a network design approach inspired by biological neural networks. We use this approach to design a new routing scheme optimized for small-world networks and, at the same time, to present a hardware-aware placement algorithm that optimizes the allocation of resources for small-world network models. We validate the algorithm with a canonical small-world network and present preliminary results for other networks derived from it, arXiv admin note: substantial text overlap with arXiv:2203.00655

Predictability is a prerequisite for effective human control of artificial intelligence (AI). For example, the inability to predict the malfunctioning of AI impedes timely human intervention. In this paper, we employ a computerized navigation task, a lunar lander game, to investigate empirically how AI's predictability compares to humans' predictability. To this end, we ask participants to guess whether the landings of a spaceship in this game performed by AI and humans will succeed. We show that humans are worse at predicting AI performance than at predicting human performance in this environment. Significantly, participants underestimate the differences in the relative predictability of AI and, at times, overestimate their prediction skills. We link the difference in predictability to differences in the approaches, i.e. different landing patterns, employed by AI and humans to succeed in the task. These results highlight important differences in perception of AI and human with implications for human-computer interaction.

In a busy city street, a pedestrian surrounded by distractions can pick out a single sign if it is relevant to their route. Artificial agents in outdoor Vision-and-Language Navigation (VLN) are also confronted with detecting supervisory signal on environment features and location in inputs. To boost the prominence of relevant features in transformer-based architectures without costly preprocessing and pretraining, we take inspiration from priority maps - a mechanism described in neuropsychological studies. We implement a novel priority map module and pretrain on auxiliary tasks using low-sample datasets with high-level representations of routes and environment-related references to urban features. A hierarchical process of trajectory planning - with subsequent parameterised visual boost filtering on visual inputs and prediction of corresponding textual spans - addresses the core challenges of cross-modal alignment and feature-level localisation. The priority map module is integrated into a feature-location framework that doubles the task completion rates of standalone transformers and attains state-of-the-art performance on the Touchdown benchmark for VLN. Code and data are referenced in Appendix C., Accepted to WACV 2023

We push the boundaries of electronic structure-based ab-initio molecular dynamics (AIMD) beyond 100 million atoms. This scale is otherwise barely reachable with classical force-field methods or novel neural network and machine learning potentials. We achieve this breakthrough by combining innovations in linear-scaling AIMD, efficient and approximate sparse linear algebra, low and mixed-precision floating-point computation on GPUs, and a compensation scheme for the errors introduced by numerical approximations. The core of our work is the non-orthogonalized local submatrix method (NOLSM), which scales very favorably to massively parallel computing systems and translates large sparse matrix operations into highly parallel, dense matrix operations that are ideally suited to hardware accelerators. We demonstrate that the NOLSM method, which is at the center point of each AIMD step, is able to achieve a sustained performance of 324 PFLOP/s in mixed FP16/FP32 precision corresponding to an efficiency of 67.7% when running on 1536 NVIDIA A100 GPUs., Parallel Computing, 111, ISSN:0167-8191, ISSN:1872-7336

Fault-tolerant control is crucial for safety-critical systems, such as quadrotors. State-of-art flight controllers can stabilize and control a quadrotor even when subjected to the complete loss of a rotor. However, these methods rely on external sensors, such as GPS or motion capture systems, for state estimation. To the best of our knowledge, this has not yet been achieved with only onboard sensors. In this paper, we propose the first algorithm that combines fault-tolerant control and onboard vision-based state estimation to achieve position control of a quadrotor subjected to complete failure of one rotor. Experimental validations show that our approach is able to accurately control the position of a quadrotor during a motor failure scenario, without the aid of any external sensors. The primary challenge to vision-based state estimation stems from the inevitable high-speed yaw rotation (over 20 rad/s) of the damaged quadrotor, causing motion blur to cameras, which is detrimental to visual inertial odometry (VIO). We compare two types of visual inputs to the vision-based state estimation algorithm: standard frames and events. Experimental results show the advantage of using an event camera especially in low light environments due to its inherent high dynamic range and high temporal resolution. We believe that our approach will render autonomous quadrotors safer in both GPS denied or degraded environments. We release both our controller and VIO algorithm open source., Comment: 8 pages, 10 figures

Exploiting 4D-flow magnetic resonance imaging (MRI) data to quantify hemodynamics requires an adequate spatio-temporal vector field resolution at a low noise level. To address this challenge, we provide a learned solution to super-resolve in vivo 4D-flow MRI data at a post-processing level. We propose a deep convolutional neural network (CNN) that learns the inter-scale relationship of the velocity vector map and leverages an efficient residual learning scheme to make it computationally feasible. A novel, direction-sensitive, and robust loss function is crucial to learning vector-field data. We present a detailed comparative study between the proposed super-resolution and the conventional cubic B-spline based vector-field super-resolution. Our method improves the peak-velocity to noise ratio of the flow field by 10 and 30% for in vivo cardiovascular and cerebrovascular data, respectively, for 4 × super-resolution over the state-of-the-art cubic B-spline. Significantly, our method offers 10x faster inference over the cubic B-spline. The proposed approach for super-resolution of 4D-flow data would potentially improve the subsequent calculation of hemodynamic quantities.

Multirotor aerial robots are becoming widely used for the inspection of powerlines. To enable continuous, robust inspection without human intervention, the robots must be able to perch on the powerlines to recharge their batteries. Highly versatile perching capabilities are necessary to adapt to the variety of configurations and constraints that are present in real powerline systems. This paper presents a novel perching trajectory generation framework that computes perception-aware, collision-free, and dynamically-feasible maneuvers to guide the robot to the desired final state. Trajectory generation is achieved via solving a Nonlinear Programming problem using the Primal-Dual Interior Point method. The problem considers the full dynamic model of the robot down to its single rotor thrusts and minimizes the final pose and velocity errors while avoiding collisions and maximizing the visibility of the powerline during the maneuver. The generated maneuvers consider both the perching and the posterior recovery trajectories. The framework adopts costs and constraints defined by efficient mathematical representations of powerlines, enabling online onboard execution in resource-constrained hardware. The method is validated on-board an agile quadrotor conducting powerline inspection and various perching maneuvers with final pitch values of up to 180 degrees. The developed code is available online at: https://github.com/grvcPerception/pa_powerline_perching, IEEE Robotics and Automation Letters (2022)

Neural networks predictions are unreliable when the input sample is out of the training distribution or corrupted by noise. Being able to detect such failures automatically is fundamental to integrate deep learning algorithms into robotics. Current approaches for uncertainty estimation of neural networks require changes to the network and optimization process, typically ignore prior knowledge about the data, and tend to make over-simplifying assumptions which underestimate uncertainty. To address these limitations, we propose a novel framework for uncertainty estimation. Based on Bayesian belief networks and Monte-Carlo sampling, our framework not only fully models the different sources of prediction uncertainty, but also incorporates prior data information, e.g. sensor noise. We show theoretically that this gives us the ability to capture uncertainty better than existing methods. In addition, our framework has several desirable properties: (i) it is agnostic to the network architecture and task; (ii) it does not require changes in the optimization process; (iii) it can be applied to already trained architectures. We thoroughly validate the proposed framework through extensive experiments on both computer vision and control tasks, where we outperform previous methods by up to 23% in accuracy., Accepted for publication in the Robotics and Automation Letters 2020, and for presentation at the International Conference on Robotics and Automation (ICRA) 2020

Previous research has hypothesized that human sequential processing may be dependent upon hearing experience (the “auditory scaffolding hypothesis”), predicting that sequential rule learning abilities should be hindered by congenital deafness. To test this hypothesis, we compared deaf signer and hearing individuals’ ability to acquire rules of different computational complexity in a visual artificial grammar learning task using sequential stimuli. As a group, deaf participants succeeded at all levels of the task; Bayesian analysis indicates that they successfully acquired each of several target grammars at ascending levels of the formal language hierarchy. Overall, these results do not support the auditory scaffolding hypothesis. However, age- and education-matched hearing participants did outperform deaf participants in two out of three tested grammars. We suggest that this difference may be related to verbal recoding strategies in the two groups. Any verbal recoding strategies used by the deaf signers would be less effective because they would have to use the same visual channel required for the experimental task.

Humans race drones faster than algorithms, despite being limited to a fixed camera angle, body rate control, and response latencies in the order of hundreds of milliseconds. A better understanding of the ability of human pilots of selecting appropriate motor commands from highly dynamic visual information may provide key insights for solving current challenges in vision-based autonomous navigation. This paper investigates the relationship between human eye movements, control behavior, and flight performance in a drone racing task. We collected a multimodal dataset from 21 experienced drone pilots using a highly realistic drone racing simulator, also used to recruit professional pilots. Our results show task-specific improvements in drone racing performance over time. In particular, we found that eye gaze tracks future waypoints (i.e., gates), with first fixations occurring on average 1.5 seconds and 16 meters before reaching the gate. Moreover, human pilots consistently looked at the inside of the future flight path for lateral (i.e., left and right turns) and vertical maneuvers (i.e., ascending and descending). Finally, we found a strong correlation between pilots eye movements and the commanded direction of quadrotor flight, with an average visual-motor response latency of 220 ms. These results highlight the importance of coordinated eye movements in human-piloted drone racing. We make our dataset publicly available., Comment: 8 pages, 6 figures

Artificial vision systems of autonomous agents face very difficult challenges, as their vision sensors are required to transmit vast amounts of information to the processing stages, and to process it in real-time. One first approach to reduce data transmission is to use event-based vision sensors, whose pixels produce events only when there are changes in the input. However, even for event-based vision, transmission and processing of visual data can be quite onerous. Currently, these challenges are solved by using high-speed communication links and powerful machine vision processing hardware. But if resources are limited, instead of processing all the sensory information in parallel, an effective strategy is to divide the visual field into several small sub-regions, choose the region of highest saliency, process it, and shift serially the focus of attention to regions of decreasing saliency. This strategy, commonly used also by the visual system of many animals, is typically referred to as ``selective attention''. Here we present a digital architecture implementing a saliency-based selective visual attention model for processing asynchronous event-based sensory information received from a DVS. For ease of prototyping, we use a standard digital design flow and map the architecture on an FPGA. We describe the architecture block diagram highlighting the efficient use of the available hardware resources demonstrated through experimental results exploiting a hardware setup where the FPGA interfaced with the DVS camera., Comment: 5 pages, 5 figures

Autonomous car racing is a major challenge in robotics. It raises fundamental problems for classical approaches such as planning minimum-time trajectories under uncertain dynamics and controlling the car at the limits of its handling. Besides, the requirement of minimizing the lap time, which is a sparse objective, and the difficulty of collecting training data from human experts have also hindered researchers from directly applying learning-based approaches to solve the problem. In the present work, we propose a learning-based system for autonomous car racing by leveraging a high-fidelity physical car simulation, a course-progress proxy reward, and deep reinforcement learning. We deploy our system in Gran Turismo Sport, a world-leading car simulator known for its realistic physics simulation of different race cars and tracks, which is even used to recruit human race car drivers. Our trained policy achieves autonomous racing performance that goes beyond what had been achieved so far by the built-in AI, and, at the same time, outperforms the fastest driver in a dataset of over 50,000 human players., Comment: Accepted for Publication at the IEEE Robotics and Automation Letters (RA-L) 2021, and International Conference on Robots and Automation (ICRA) 2021

To avoid software vulnerabilities, organizations are shifting security to earlier stages of the software development, such as at code review time. In this paper, we aim to understand the developers' perspective on assessing software security during code review, the challenges they encounter, and the support that companies and projects provide. To this end, we conduct a two-step investigation: we interview 10 professional developers and survey 182 practitioners about software security assessment during code review. The outcome is an overview of how developers perceive software security during code review and a set of identified challenges. Our study revealed that most developers do not immediately report to focus on security issues during code review. Only after being asked about software security, developers state to always consider it during review and acknowledge its importance. Most companies do not provide security training, yet expect developers to still ensure security during reviews. Accordingly, developers report the lack of training and security knowledge as the main challenges they face when checking for security issues. In addition, they have challenges with third-party libraries and to identify interactions between parts of code that could have security implications. Moreover, security may be disregarded during reviews due to developers' assumptions about the security dynamic of the application they develop. Data and materials: https://doi.org/10.5281/zenodo.6875435, Comment: This paper has been accepted at ESEC/FSE '22 (30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering)

Process Models (PM) are visual documentations of the business processes within or across enterprises. Activities (tasks) are arranged together into a model (i.e., similar to flowcharts). This study aimed at understanding the underlying structure of PM comprehension. Though standards for describing PM have been defined, the cognitive work load they evoke, their structure, and the efficacy of information transmission are only partially understood. Two studies were conducted to better differentiate the concept of visual literacy (VL) and logical reasoning in interpreting PM. Study I: A total of 1047 students from 52 school classes were assessed. Three different process models of increasing complexity were presented on tablets. Additionally, written labels of the models’ elements were randomly allocated to scholars in a 3-group between-subjects design. Comprehension of process models was assessed by a series of 3 × 4 (=12) dichotomous test items. Latent Class Analysis of solved items revealed 6 qualitatively differing solution patterns, suggesting that a single test score is insufficient to reflect participants’ performance. Study II: Overall, 21 experts and 15 novices with respect to visual literacy were presented the same set of PMs as in Study I, while wearing eye tracking glasses. The fixation duration on relevant parts of the PM and on questions were recorded, as well as the total time needed to solve all 12 test items. The number of gaze transitions between process model and comprehension questions was measured as well. Being an expert in visual literacy did not alter the capability of correctly understanding graphical logical PMs. Presenting PMs that are labelled by single letters had a significant influence on reducing the time spent on irrelevant model parts but did not affect the fixation duration on relevant areas of interest. Both samples’ participants required longer response times with increasing model complexity. The number of toggles (i.e., gaze transitions between model and statement area of interest) was predictive for membership in one of the latent classes. Contrary to expectations, denoting the PM events and decisions not with real-world descriptions, but with single letters, led to lower cognitive workload in responding to comprehension questions and to better results. Visual Literacy experts could neither outperform novices nor high-school students in comprehending PM.

Children’s spontaneous speech may not reflect true productivity with grammar rules. We investigated Turkish-learning children’s rule-based understanding of causative morphology by combining experimental and corpus work. We asked (1) when the generalization of causative morphology emerges and (2) what role input plays in this development. To answer the first question, Study 1 experimentally tested 106 children aged 2;6–6;1 on a language judgment task using pseudo-verbs. Children preferred the causative marker -DIr over an incongruent suffix for causativized events earliest at age 4;10. Further, Study 2 tested 38 children aged 3;6–4;6 and revealed no preference of -DIr over no suffix. Study 3 examined a corpus for child-directed input and showed that the variation of verb stems to which -DIr was attached remained lower than variation of verbs eligible to take -DIr, up to age 3. The findings suggest that rule-based understanding of morphological causatives emerges much later than previously proposed productivity at age 2, which might be accounted for by the insufficient variation of morphological causatives in the early input.

Segmentation of anatomical structures is a fundamental image analysis task for many applications in the medical field. Deep learning methods have been shown to perform well, but for this purpose large numbers of manual annotations are needed in the first place, which necessitate prohibitive levels of resources that are often unavailable. In an active learning framework of selecting informed samples for manual labeling, expert clinician time for manual annotation can be optimally utilized, enabling the establishment of large labeled datasets for machine learning. In this paper, we propose a novel method that combines representativeness with uncertainty in order to estimate ideal samples to be annotated, iteratively from a given dataset. Our novel representativeness metric is based on Bayesian sampling, by using information-maximizing autoencoders. We conduct experiments on a shoulder magnetic resonance imaging (MRI) dataset for the segmentation of four musculoskeletal tissue classes. Quantitative results show that the annotation of representative samples selected by our proposed querying method yields an improved segmentation performance at each active learning iteration, compared to a baseline method that also employs uncertainty and representativeness metrics. For instance, with only 10% of the dataset annotated, our method reaches within 5% of Dice score expected from the upper bound scenario of all the dataset given as annotated (an impractical scenario due to resource constraints), and this gap drops down to a mere 2% when less than a fifth of the dataset samples are annotated. Such active learning approach to selecting samples to annotate enables an optimal use of the expert clinician time, being often the bottleneck in realizing machine learning solutions in medicine., Comment: 10 pages, 7 figures

The ability to store information in verbal working memory (WM) closely interacts with our linguistic knowledge. For instance, we can hold semantically related words (e.g., “cat, dog, bird”) better in our WM than unrelated ones (e.g., “desk, pillow, mouse”). This study investigates boundary conditions of the beneficial effect of semantic relatedness of words on immediate memory for lists. Independently varying the encoding and recall order for lists of related words, we unraveled several mechanistic explanations of the semantic relatedness effect. We first tested a semantic cueing mechanism, according to which the recall of an item facilitates the recall of other semantically related items. We next disentangled an interactive activation versus feature overlap account of semantic relatedness. Whereas the former predicts that the semantic relatedness effect emerges from the temporal co-activation of related items, the latter predicts that it emerges from the superposition of semantic features bound to similar contexts. Our results demonstrate that semantic relatedness affects WM performance at the encoding stage of WM processing, which rules out semantic cueing as a plausible mechanism. Further, the temporal order in which words are presented was the most important determinant of the semantic relatedness effect, in agreement with the interactive activation account. This study supports a model in which semantic relatedness supports WM through interactive activation occurring in semantic long-term memory.

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibitively expensive, we introduce a self-supervised detection and segmentation approach that can work with single images captured by a potentially moving camera. At the heart of our approach lies the observation that object segmentation and background reconstruction are linked tasks, and that, for structured scenes, background regions can be re-synthesized from their surroundings, whereas regions depicting the moving object cannot. We encode this intuition into a self-supervised loss function that we exploit to train a proposal-based segmentation network. To account for the discrete nature of the proposals, we developed a Monte Carlo-based training strategy that allows the algorithm to explore the large space of object proposals. We apply our method to human detection and segmentation in images that visually depart from those of standard benchmarks and outperform existing self-supervised methods.

A circadian rhythm is a roughly 24-h cycle in biological processes and physiological phenomena such as sleep, feeding, and photosynthesis for many organisms on Earth. The circadian patterns are coordinated by rhythmical gene expression of clock genes. Time-course transcriptomic analyses involving statistical methods have shown coordination of periodic gene expression in many organisms. Here we applied the cosine fitting method COSOPT to identify novel oscillating genes in microarray data for the chordate Ciona intestinalis. This organism showed rhythmic oxygen consumption in our previous study, but there were few homologous clock genes showing rhythmic mRNA expression. To understand circadian behavior at the transcriptomic level, we analyzed the 817 of 21,938 probes showing a 23- to 25-h period by means of COSOPT. Coupling the analysis of period detection with functional annotations indicated that previously unknown rhythmic mRNA expression might exist in C. intestinalis. In addition, we are releasing our implementation of COSOPT by means of R and C. All source code and supplementary information are available from https://github.com/mhiromi/cosopt .

The potential capacity for robots to deceive has received considerable attention recently. Many papers explore the technical possibility for a robot to engage in deception for beneficial purposes (e.g., in education or health). In this short experimental paper, I focus on a more paradigmatic case: robot lying (lying being the textbook example of deception) for nonbeneficial purposes as judged from the human point of view. More precisely, I present an empirical experiment that investigates the following three questions: (a) Are ordinary people willing to ascribe deceptive intentions to artificial agents? (b) Are they as willing to judge a robot lie as a lie as they would be when human agents engage in verbal deception? (c) Do people blame a lying artificial agent to the same extent as a lying human agent? The response to all three questions is a resounding yes. This, I argue, implies that robot deception and its normative consequences deserve considerably more attention than they presently receive.

During gait neurorehabilitation, many factors influence the quality of gait patterns, particularly the chosen body-weight support (BWS) device. Consequently, robotic BWS devices play a key role in gait rehabilitation of people with neurological disorders. The device transparency, support force vector direction, and attachment to the harness vary widely across existing robotic BWS devices, but the influence of these factors on the production of gait remains unknown. Because this information is key to designing an optimal BWS, we systematically studied these determinants in this work. We report that with a highly transparent device and a conventional harness, healthy participants select a small backward force when asked for optimal BWS conditions. This unexpected finding challenges the view that during human-robot interactions, humans predominantly optimize energy efficiency. Instead, they might seek to increase their feeling of stability and safety. We also demonstrate that the location of the attachment points on the harness strongly affects gait patterns, yet harness attachment is hardly reported in literature. Our results establish principles for the design of BWS devices and personalization of BWS settings for gait neurorehabilitation.

Neuromorphic chips that use spikes to encode information could provide fast and energy-efficient computing for ubiquitous embedded systems. A bio-plausible spike-timing solution for training spiking neural networks that makes the most of sparsity is implemented on the BrainScaleS-2 hardware platform.

Methods for processing point cloud information have seen a great success in collider physics applications. One recent breakthrough in machine learning is the usage of Transformer networks to learn semantic relationships between sequences in language processing. In this work, we apply a modified Transformer network called Point Cloud Transformer as a method to incorporate the advantages of the Transformer architecture to an unordered set of particles resulting from collision events. To compare the performance with other strategies, we study jet-tagging applications for highly-boosted particles., Comment: 12 pages, 3 figures

Describing our visual experiences improves their retention in visual working memory, yielding a labeling benefit. Labels vary, however, in categorical distinctiveness: they can be applied broadly or narrowly to categorize stimuli. Does categorical distinctiveness constrain the labeling benefit? Here, we varied the number of terms used to label continuously varying colors (Experiment 1) and shapes (Experiment 2). Participants memorized four items, and later recalled them using a continuous color or shape wheel. During study, participants articulated “bababa” or labeled the items with two, four, or their preferred term. Recall error decreased with increases in the number of labels. Mixture modeling showed that labeling increased the probability of recall. Memory precision, however, varied with categorical distinctiveness: broad labels reduced precision, whereas categorically distinct labels increased precision compared to no-labels. In sum, in-the-moment labeling activates categorical knowledge that facilitates the storage of visual details. Data and analysis scripts are available at: https://osf.io/mqg4k/

Data analysis in high-dimensional spaces aims at obtaining a synthetic description of a data set, revealing its main structure and its salient features. We here introduce an approach providing this description in the form of a topography of the data, namely a human-readable chart of the probability density from which the data are harvested. The approach is based on an unsupervised extension of Density Peak clustering and a non-parametric density estimator that measures the probability density in the manifold containing the data. This allows finding automatically the number and the height of the peaks of the probability density, and the depth of the "valleys" separating them. Importantly, the density estimator provides a measure of the error, which allows distinguishing genuine density peaks from density fluctuations due to finite sampling. The approach thus provides robust and visual information about the density peaks' height, their statistical reliability, and their hierarchical organization, offering a conceptually powerful extension of the standard clustering partitions. We show that this framework is particularly useful in the analysis of complex data sets., There is a Supplementary Information document in the ancillary files folder

While the field of algorithmic fairness has brought forth many ways to measure and improve the fairness of machine learning models, these findings are still not widely used in practice. We suspect that one reason for this is that the field of algorithmic fairness came up with a lot of definitions of fairness, which are difficult to navigate. The goal of this paper is to provide data scientists with an accessible introduction to group fairness metrics and to give some insight into the philosophical reasoning for caring about these metrics. We will do this by considering in which sense socio-demographic groups are compared for making a statement on fairness., Comment: Accepted full paper at SDS2021, the 8th Swiss Conference on Data Science

Chronic diseases can greatly benefit from bioelectronic medicine approaches. Neuromorphic electronic circuits present ideal characteristics for the development of brain-inspired low-power implantable processing systems that can be interfaced with biological systems. These circuits, therefore, represent a promising additional tool in the tool-set of bioelectronic medicine. In this paper, we describe the main features of neuromorphic circuits that are ideally suited for continuously monitoring the physiological parameters of the body and interact with them in real-time. We propose examples of computational primitives that can be used for real-time pattern generation and present a neuromorphic implementation of neural oscillators for the generation of sequence activation patterns. We demonstrate the features of such systems with an implementation of a three-phase network that models the dynamics of the respiratory Central Pattern Generator (CPG) and the heart chambers rhythm, and that could be used to build an adaptive pacemaker., Comment: 4 pages; 7 figures; to be published in 10th International IEEE EMBS Conference On Neural Engineering (NER'21), Special Session on Bioelectronics medicine

In this letter, we study the effects that perception latency has on the maximum speed a robot can reach to safely navigate through an unknown cluttered environment. We provide a general analysis that can serve as a baseline for future quantitative reasoning for design tradeoffs in autonomous robot navigation. We consider the case where the robot is modeled as a linear second-order system with bounded input and navigates through static obstacles. Also, we focus on a scenario where the robot wants to reach a target destination in as little time as possible, and therefore cannot change its longitudinal velocity to avoid obstacles. We show how the maximum latency that the robot can tolerate to guarantee safety is related to the desired speed, the range of its sensing pipeline, and the actuation limitations of the platform (i.e., the maximum acceleration it can produce). As a particular case study, we compare monocular and stereo frame-based cameras against novel, low-latency sensors, such as event cameras, in the case of quadrotor flight. To validate our analysis, we conduct experiments on a quadrotor platform equipped with an event camera to detect and avoid obstacles thrown towards the robot. To the best of our knowledge, this is the first theoretical work in which perception and actuation limitations are jointly considered to study the performance of a robotic platform in high-speed navigation.

There is a recent surge of interest in maintenance processes in working memory, such as articulatory rehearsal, elaboration, and attentional refreshing. Yet, we know little about the central attentional demand of these processes. It has been assumed that articulatory rehearsal does not require central attention at all (Vergauwe, Camos, & Barrouillet, 2014), being in essence a cost-free strategy. In contrast, elaboration and attentional refreshing are assumed to incur large and continuous costs on central attention. We tested these assumptions in three experiments in which participants were presented with a varying number of words to rehearse. Participants were instructed to rehearse the words aloud, or to elaborate them by creating interactive images. Attentional refreshing was examined in a condition in which words were to be maintained during articulatory suppression. During retention participants carried out a series of choice reaction tasks, which were used to measure central attentional demands of the maintenance strategies. Articulatory rehearsal had costs on processing RTs that lasted for 10 s. Maintenance of words during articulatory suppression did not yield persistent costs on central attention, implying that participants did not continuously refresh the words. Finally, the current results cast doubt on the idea that elaboration requires central attention for an extended period of time. All experimental scripts and data sets reported here are available online ( https://osf.io/69p8j/ ).

Spiking neural networks have shown great promise for the design of low-power sensory-processing and edge-computing hardware platforms. However, implementing on-chip learning algorithms on such architectures is still an open challenge, especially for multi-layer networks that rely on the back-propagation algorithm. In this paper, we present a spike-based learning method that approximates back-propagation using local weight update mechanisms and which is compatible with mixed-signal analog/digital neuromorphic circuits. We introduce a network architecture that enables synaptic weight update mechanisms to back-propagate error signals across layers and present a network that can be trained to distinguish between two spike-based patterns that have identical mean firing rates, but different spike-timings. This work represents a first step towards the design of ultra-low power mixed-signal neuromorphic processing systems with on-chip learning circuits that can be trained to recognize different spatio-temporal patterns of spiking activity (e.g. produced by event-based vision or auditory sensors)., 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

International audience; It is now firmly established that long-term memory knowledge, such as semantic knowledge, supports the temporary maintenance of verbal information in Working Memory (WM). This support from semantic knowledge is well-explained by models assuming that verbal items are directly activated in long-term memory, and that this activation provides the representational basis for WM maintenance. However, the exact mechanisms underlying semantic influence on WM performance remain poorly understood. We manipulated the presence of between-item semantic relatedness in an immediate serial recall task, by mixing triplets composed of semantically related and unrelated items (e.g. leaf – tree – branch – wall – beer – dog; hand – father – truck – cloud – sky – rain). Compared to unrelated items, related items were better recalled, as had been classically observed. Critically, semantic relatedness also impacted WM maintenance in a complex manner, as observed by the presence of proactive benefit effects on subsequent unrelated items, and the absence of retroactive effects. The complexity of these interactions is well-captured by TBRS*-S, a decay-based computational architecture in which the activation occurring in long-term memory is described. The present study suggests that semantic knowledge can be used to free up WM resources that can be reallocated for maintenance purposes, and supports models postulating thatlong-term memory knowledge constrains WM maintenance processes.