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Increasing anthropomorphic robot behavioral design could affect trust and cooperation positively. However, studies have shown contradicting results and suggest a task-dependent relationship between robots that display emotions and trust. Therefore, this study analyzes the effect of robots that display human-like emotions on trust, cooperation, and participants' emotions. In the between-group study, participants play the coin entrustment game with an emotional and a non-emotional robot. The results show that the robot that displays emotions induces more anxiety than the neutral robot. Accordingly, the participants trust the emotional robot less and are less likely to cooperate. Furthermore, the perceived intelligence of a robot increases trust, while a desire to outcompete the robot can reduce trust and cooperation. Thus, the design of robots expressing emotions should be task dependent to avoid adverse effects that reduce trust and cooperation., Comment: Accepted at 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

The main challenge of quantum computing on its way to scalability is the erroneous behaviour of current devices. Understanding and predicting their impact on computations is essential to counteract these errors with methods such as quantum error mitigation. Thus, it is necessary to construct and evaluate accurate noise models. However, the evaluation of noise models does not yet follow a systematic approach, making it nearly impossible to estimate the accuracy of a model for a given application. Therefore, we developed and present a systematic approach to benchmark noise models for quantum computing applications. It compares the results of hardware experiments to predictions of noise models for a representative set of quantum circuits. We also construct a noise model and optimize its parameters with a series of training circuits. We then perform a volumetric benchmark comparing our model to other models from the literature.

Deep Reinforcement Learning agents often suffer from catastrophic forgetting, forgetting previously found solutions in parts of the input space when training on new data. Replay Memories are a common solution to the problem, decorrelating and shuffling old and new training samples. They naively store state transitions as they come in, without regard for redundancy. We introduce a novel cognitive-inspired replay memory approach based on the Grow-When-Required (GWR) self-organizing network, which resembles a map-based mental model of the world. Our approach organizes stored transitions into a concise environment-model-like network of state-nodes and transition-edges, merging similar samples to reduce the memory size and increase pair-wise distance among samples, which increases the relevancy of each sample. Overall, our paper shows that map-based experience replay allows for significant memory reduction with only small performance decreases.

Saliency prediction refers to the computational task of modeling overt attention. Social cues greatly influence our attention, consequently altering our eye movements and behavior. To emphasize the efficacy of such features, we present a neural model for integrating social cues and weighting their influences. Our model consists of two stages. During the first stage, we detect two social cues by following gaze, estimating gaze direction, and recognizing affect. These features are then transformed into spatiotemporal maps through image processing operations. The transformed representations are propagated to the second stage (GASP) where we explore various techniques of late fusion for integrating social cues and introduce two sub-networks for directing attention to relevant stimuli. Our experiments indicate that fusion approaches achieve better results for static integration methods, whereas non-fusion approaches for which the influence of each modality is unknown, result in better outcomes when coupled with recurrent models for dynamic saliency prediction. We show that gaze direction and affective representations contribute a prediction to ground-truth correspondence improvement of at least 5% compared to dynamic saliency models without social cues. Furthermore, affective representations improve GASP, supporting the necessity of considering affect-biased attention in predicting saliency., Comment: International Joint Conference on Artificial Intelligence (IJCAI-21)

Recent developments in explainable artificial intelligence promise the potential to transform human-robot interaction: Explanations of robot decisions could affect user perceptions, justify their reliability, and increase trust. However, the effects on human perceptions of robots that explain their decisions have not been studied thoroughly. To analyze the effect of explainable robots, we conduct a study in which two simulated robots play a competitive board game. While one robot explains its moves, the other robot only announces them. Providing explanations for its actions was not sufficient to change the perceived competence, intelligence, likeability or safety ratings of the robot. However, the results show that the robot that explains its moves is perceived as more lively and human-like. This study demonstrates the need for and potential of explainable human-robot interaction and the wider assessment of its effects as a novel research direction., Comment: Accepted at 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

Readout errors are among the most dominant errors on current noisy intermediate-scale quantum devices. Recently, an efficient and scaleable method for mitigating such errors has been developed, based on classical bit-flip correction. In this talk, we compare the performance of this method for IBM's and Rigetti's quantum devices, demonstrating how the method improves the noisy measurements of observables obtained on the quantum hardware. Moreover, we examine the variance amplification to the data after applying of our mitigation procedure, which is common to all mitigation strategies. We derive a new expression for the variance of the mitigated Pauli operators in terms of the corrected expectation values and the noisy variances.Our hardware results show good agreement with the theoretical prediction, and we demonstrate that the increase of the variance due to the mitigation procedure is only moderate., Comment: 11 pages, 5 figures, Proceedings of the 38th International Symposium on Lattice Field Theory, 26th-30th July 2021, Zoom/Gather@Massachusetts Institute of Technology

While we expect quantum computers to surpass their classical counterparts in the future, current devices are prone to high error rates and techniques to minimise the impact of these errors are indispensable. There already exists a variety of error mitigation methods addressing this quantum noise that differ in effectiveness, and scalability. But for a more systematic and comprehensible approach we propose the introduction of modelling, in particular for representing cause-effect relations as well as for evaluating methods or combinations thereof with respect to a selection of relevant criteria.

Explainable artificial intelligence (XAI) can help foster trust in and acceptance of intelligent and autonomous systems. Moreover, understanding the motivation for an agent's behavior results in better and more successful collaborations between robots and humans. However, not only can humans benefit from a robot's explanation but the robot itself can also benefit from explanations given to him. Currently, most attention is paid to explaining deep neural networks and black-box models. However, a lot of these approaches are not applicable to humanoid robots. Therefore, in this position paper, current problems with adapting XAI methods to explainable neurorobotics are described. Furthermore, NICO, an open-source humanoid robot platform, is introduced and how the interaction of intrinsic explanations by the robot itself and extrinsic explanations provided by the environment enable efficient robotic behavior., Comment: Fall Symposium AAAI 2020

Motivated by a recently proposed design for a DNA coded randomised algorithm that enables inference of the average generation of a collection of cells descendent from a common progenitor, here we establish strong convergence properties for the average generation of a super-critical Bellman-Harris process. We further extend those results to a two-type Bellman-Harris process where one type can give rise to the other, but not vice versa. These results further affirm the estimation method's potential utility by establishing its long run accuracy on individual sample-paths, and significantly expanding its remit to encompass cellular development that gives rise to differentiated offspring with distinct population dynamics.

Cellular barcoding is a significant, recently developed, biotechnology tool that enables the familial identification of progeny of individual cells in vivo. Most existing approaches rely on ex vivo viral transduction of cells with barcodes, followed by adoptive transfer into an animal, which works well for some systems, but precludes barcoding cells in their native environment, such as those inside solid tissues. With a view to overcoming this limitation, we propose a new design for a genetic barcoding construct based on the Cre Lox system that induces randomly created stable barcodes in cells in situ by exploiting inherent sequence distance constraints during site-specific recombination. Leveraging this previously unused feature, we identify the cassette with maximal code diversity. This proves to be orders of magnitude higher than what is attainable with previously considered Cre Lox barcoding approaches and is well suited for its intended applications as it exceeds the number of lymphocytes or hematopoietic progenitor cells in mice. Moreover, it can be built using established technology.

For proliferating cells subject to both division and death, how can one estimate the average generation number of the living population without continuous observation or a division-diluting dye? In this paper we provide a method for cell systems such that at each division there is an unlikely, heritable one-way label change that has no impact other than to serve as a distinguishing marker. If the probability of label change per cell generation can be determined and the proportion of labeled cells at a given time point can be measured, we establish that the average generation number of living cells can be estimated. Crucially, the estimator does not depend on knowledge of the statistics of cell cycle, death rates or total cell numbers. We validate the estimator and illustrate its features through comparison with published data and physiologically parameterized stochastic simulations, using it to suggest new experimental designs.

In a typical optical tweezers detection system, the position of a trapped object is determined from laser light impinging on a quadrant photodiode. When the laser is infrared and the photodiode is of silicon, they can act together as an unintended low-pass filter. This parasitic effect is due to the high transparency of silicon to near-infrared light. A simple model that accounts for this phenomenon (Berg-Sorensen et al., J. Appl. Phys., 93, 3167-3176 (2003)) is here solved for frequencies up to 100 kHz, and for laser wavelengths between 750 and 1064 nm. The solution is applied to experimental data in the same range, and is demonstrated to give this detection system of optical tweezers a bandwidth, accuracy, and precision that is limited only by the data acquisition board's band-width and bandpass ripples, here 96.7 kHz, resp. 0.005 dB., Comment: 12 pages, 8 figures. Submitted to Review of Scientific Instruments