Miracle-dz/UniFAR-FTUs huggingface dataset:
Nudging healthcare professionals to improve treatment of COVID-19: a narrative review

© Author(s) (or their employer(s)) 2021. Reuse permitted under CC BYNC. No commercial reuse. See rights and permissions. Published by BMJ. INTRODUCTION The COVID19 pandemic has placed unprecedented stress on healthcare systems worldwide and disrupted routine care. Healthcare professionals (HCPs) have had to quickly improvise and adapt to cope with a novel deadly virus with uncertain treatment options, lack of evidence, and a shortage of medical supply and personal protective equipment. In an ideal world, clinicians would base their decisions on scientific evidence and best practices. In practice, however, behavioural science shows that decisions are affected by emotional and cognitive biases, especially under stress. 2 Understanding the behavioural nature of decisionmaking is essential for designing processes that mitigate risks and improve the quality of care. This is particularly important because of the fastpaced developments characterising the COVID19 pandemic. One effective strategy in improving decisionmaking is nudging: subtle changes to the design of the environment or framing choices without restrictions to encourage a given behaviour. These strategies are often easy to scale and implement at a low cost, making nudging a practical approach to behaviour change. Nudging interventions have been widely effective, but often patient directed, and less attention has been given to the use of behavioural insights to support HCPs in making appropriate medical decisions. In this review, we synthesise the available literature on how nudging techniques can be used to affect the behaviour of HCPs in clinical settings in order to see if these can be useful in the prevention and treatment of COVID19. The results are intended to guide and inspire quick implementation of costeffective solutions that can improve the quality of care. Our objective was to identify interventions using nudge theory to affect the behaviour of HCPs in clinical settings, focusing on target groups, nudging techniques, delivery systems and empirical evidence.
Empirical Evaluation of the Emotiv EPOC BCI Headset for the Detection of Mental Actions

This study evaluated the detection accuracy of one of the first brain-computer interfaces intended for personal use by normal, healthy users: the Emotiv EPOC. This system allows the user to directly interact with computer software through thoughts alone. The system was evaluated on its ability to accurately detect and classify six sets of paired mental actions over three evaluation phases. Results found the system to perform significantly better than chance for all mental actions, and improve over time with additional training data. The system detected all actions with equivalent accuracy. Additional investigation of individual differences revealed that a user’s gender, age, handedness, attentional control, vividness of visual imagery, and mental rotation ability all had no bearing on the detection accuracy of the system. These results indicate the Emotiv EPOC system performs its function as a brain-computer interface with an acceptable level of accuracy, yielding many new possibilities for human-computer interaction.
Combining a CAR and a chimeric costimulatory receptor enhances T cell sensitivity to low antigen density and promotes persistence

Description Combination of a CAR and a chimeric costimulatory receptor augments cytotoxicity and durability of T cells and elimination of antigen-low tumors. Chimeric costimulation Improving chimeric antigen receptor (CAR) T cell therapy is essential for avoiding relapses in patients with hematological cancers. Relapse is thought to be partially caused by both low antigen density and reduced persistence of CAR T cells. To address both of these mediators of relapse, Katsarou et al. developed chimeric costimulatory receptors (CCRs), which bind to a second tumor-associated antigen and provide intracellular signaling to support CAR T cell activation. Using a BCMA- or CD19-directed CAR and a CD38-directed CCR, the authors observed increased tumor cell killing in vitro and in vivo, even against tumor cells with very low antigen density. This was associated with improved CAR T cell persistence. These results suggest that combining CARs and CCRs may improve clinical outcomes for patients receiving CAR T cell treatments. Despite the high remission rates achieved using T cells bearing a chimeric antigen receptor (CAR) against hematogical malignancies, there is still a considerable proportion of patients who eventually experience tumor relapse. Clinical studies have established that mechanisms of treatment failure include the down-regulation of target antigen expression and the limited persistence of effective CAR T cells. We hypothesized that dual targeting mediated by a CAR and a chimeric costimulatory receptor (CCR) could simultaneously enhance T cell cytotoxicity and improve durability. Concomitant high-affinity engagement of a CD38-binding CCR enhanced the cytotoxicity of BCMA-CAR and CD19-CAR T cells by increasing their functional binding avidity. In comparison to second-generation BCMA-CAR or CD19-CAR T cells, double-targeted CAR + CD38-CCR T cells exhibited increased sensitivity to recognize and lyse tumor variants of multiple myeloma and acute lymphoblastic leukemia with low antigen density in vitro. In addition, complimentary costimulation by 4-1BB and CD28 endodomains provided by the CAR and CCR combination conferred increased cytokine secretion and expansion and improved persistence in vivo. The cumulatively improved properties of CAR + CCR T cells enabled the in vivo eradication of antigen-low tumor clones, which were otherwise resistant to treatment with conventional CAR T cells. Therefore, multiplexing targeting and costimulation through the combination of a CAR and a CCR is a powerful strategy to improve the clinical outcomes of CAR T cells by enhancing cytotoxic efficacy and persistence, thus preventing relapses of tumor clones with low target antigen density.
Retinal Organoids Long-Term Functional Characterization Using Two-Photon Fluorescence Lifetime and Hyperspectral Microscopy

Pluripotent stem cell-derived organoid technologies have opened avenues to preclinical basic science research, drug discovery, and transplantation therapy in organ systems. Stem cell-derived organoids follow a time course similar to species-specific organ gestation in vivo. However, heterogeneous tissue yields, and subjective tissue selection reduce the repeatability of organoid-based scientific experiments and clinical studies. To improve the quality control of organoids, we introduced a live imaging technique based on two-photon microscopy to non-invasively monitor and characterize retinal organoids’ (RtOgs’) long-term development. Fluorescence lifetime imaging microscopy (FLIM) was used to monitor the metabolic trajectory, and hyperspectral imaging was applied to characterize structural and molecular changes. We further validated the live imaging experimental results with endpoint biological tests, including quantitative polymerase chain reaction (qPCR), single-cell RNA sequencing, and immunohistochemistry. With FLIM results, we analyzed the free/bound nicotinamide adenine dinucleotide (f/b NADH) ratio of the imaged regions and found that there was a metabolic shift from glycolysis to oxidative phosphorylation. This shift occurred between the second and third months of differentiation. The total metabolic activity shifted slightly back toward glycolysis between the third and fourth months and stayed relatively stable between the fourth and sixth months. Consistency in organoid development among cell lines and production lots was examined. Molecular analysis showed that retinal progenitor genes were expressed in all groups between days 51 and 159. Photoreceptor gene expression emerged around the second month of differentiation, which corresponded to the shift in the f/b NADH ratio. RtOgs between 3 and 6 months of differentiation exhibited photoreceptor gene expression levels that were between the native human fetal and adult retina gene expression levels. The occurrence of cone opsin expression (OPN1 SW and OPN1 LW) indicated the maturation of photoreceptors in the fourth month of differentiation, which was consistent with the stabilized level of f/b NADH ratio starting from 4 months. Endpoint single-cell RNA and immunohistology data showed that the cellular compositions and lamination of RtOgs at different developmental stages followed those in vivo.
Altered Dynamic Functional Connectivity in Subcortical Ischemic Vascular Disease With Cognitive Impairment

Subcortical ischemic vascular disease (SIVD) can cause cognitive impairment and affect the static functional connectivity of resting functional magnetic resonance imaging (fMRI). Numerous previous studies have demonstrated that functional connectivities (FCs) fluctuate dynamically over time. However, little is known about the impact of cognitive impairment on brain dynamic functional connectivity (DFC) in SIVD patients with MCI. In the present study, the DFC analysis method was applied to the resting functional magnetic resonance imaging (fMRI) data of 37 SIVD controls (SIVD-Control) without cognitive impairment, 34 SIVD patients with amnestic MCI (SIVD-aMCI) and 30 SIVD patients with nonamnestic MCI (SIVD-naMCI). The results indicated that the cognitive impairment of SIVD mainly reduced the mean dwell time of State 3 with overall strong positive connections. The reduction degree of SIVD-aMCI was larger than that of SIVD-naMCI. The memory/execution function impairment of SIVD also changed the relationship between the mean dwell time of State 3 and the behavioral performance of the memory/execution task from significant to non-significant correlation. Moreover, SIVD-aMCI showed significantly lower system segregation of FC states than SIVD-Control and SIVD-naMCI. The system segregation of State 5 with overall weak connections was significantly positive correlated with the memory performance. The results may suggest that the mean dwell time of State 3 and the system segregation of State 5 may be used as important neural measures of cognitive impairments of SIVD.
Design of high-order elliptic filter from a versatile mode generic OTA-C structure

A new synthesis methodology for high-order versatile mode programmable Operational transconductance amplifier and capacitor (OTA-C) generic filter structure is proposed. The structure fulfills the three main criteria of high frequency operation i.e it uses (1) less number of components (2) only single ended input OTAs (3) only grounded capacitors. Any nth order transfer function can be realised from it. Elliptic filter is designed from the generic structure using optimisation technique to reduce the number of OTAs. SPICE simulation with BSIM level 53 model and 0.13 μm process confirms the theoretical analysis. Frequency response of third-order and fourth-order elliptic filter is shown as representative set of simulated result. Sensitivity and non-ideal effect of the designed filter are studied.
Neural frame interpolation for rendered content

The demand for creating rendered content continues to drastically grow. As it often is extremely computationally expensive and thus costly to render high-quality computer-generated images, there is a high incentive to reduce this computational burden. Recent advances in learning-based frame interpolation methods have shown exciting progress but still have not achieved the production-level quality which would be required to render fewer pixels and achieve savings in rendering times and costs. Therefore, in this paper we propose a method specifically targeted to achieve high-quality frame interpolation for rendered content. In this setting, we assume that we have full input for every n-th frame in addition to auxiliary feature buffers that are cheap to evaluate (e.g. depth, normals, albedo) for every frame. We propose solutions for leveraging such auxiliary features to obtain better motion estimates, more accurate occlusion handling, and to correctly reconstruct non-linear motion between keyframes. With this, our method is able to significantly push the state-of-the-art in frame interpolation for rendered content and we are able to obtain production-level quality results.
VR social copresence with light field displays

As virtual reality (VR) devices become increasingly commonplace, asymmetric interactions between people with and without headsets are becoming more frequent. Existing video pass-through VR headsets solve one side of these asymmetric interactions by showing the user a live reconstruction of the outside world. This paper further advocates for reverse pass-through VR, wherein a three-dimensional view of the user's face and eyes is presented to any number of outside viewers in a perspective-correct manner using a light field display. Tying together research in social telepresence and copresence, autostereoscopic displays, and facial capture, reverse pass-through VR enables natural eye contact and other important non-verbal cues in a wider range of interaction scenarios, providing a path to potentially increase the utility and social acceptability of VR headsets in shared and public spaces.
Motion recommendation for online character control

Reinforcement learning (RL) has been proven effective in many scenarios, including environment exploration and motion planning. However, its application in data-driven character control has produced relatively simple motion results compared to recent approaches that have used large complex motion data without RL. In this paper, we provide a real-time motion control method that can generate high-quality and complex motion results from various sets of unstructured data while retaining the advantage of using RL, which is the discovery of optimal behaviors by trial and error. We demonstrate the results for a character achieving different tasks, from simple direction control to complex avoidance of moving obstacles. Our system works equally well on biped/quadruped characters, with motion data ranging from 1 to 48 minutes, without any manual intervention. To achieve this, we exploit a finite set of discrete actions, where each action represents full-body future motion features. We first define a subset of actions that can be selected in each state and store these pieces of information in databases during the preprocessing step. The use of this subset of actions enables the effective learning of control policy even from a large set of motion data. To achieve interactive performance at run-time, we adopt a proposal network and a k-nearest neighbor action sampler.
The Application of “Human Systems Integration” to Safety-Critical-Systems Design

Although the awareness of human factors as a crucial factor in accidents and safety management systems has been raised, practical procedures and methodologies for integrating the human and the technical elements of a safety management system are still not established. The Human Systems Integration Case (HSI Case) offers a concise, stepwise approach starting with identifying critical human factors in systems design, through to implementing human-performance oriented solutions in systems engineering. Currently there are no mandatory requirements to investigate or assess human factors. The authors strongly promote the integration of human factors into the safety development processes. In this paper the authors present an HSI case model, which has been successfully applied in an air defence project.
A Future-Oriented Cache Management for Mobile Games

As a common but critical module, cache management plays more and more important roles in modern videos games, especially for the games on mobiles whose hardware is weaker than the desktop computers. Current cache management policies typically resort to heuristics designed for the common access patterns, which cannot fully utilize the information in mobile games and may fail on the complex access patterns in it. In this paper, we propose a future-oriented cache management (FOCM) to cope with the cache management problem in mobile games' typical scenarios. FOCM leverages a dual Markov model to bridge the cache management's decision-making process and the behavioural process of the players in games, resulting in modelling the potential access requests in the future more accurately. Based on this model, we propose two cache management methods based on imitation learning and reinforcement learning, with each one suitable for different types of games. The experimental results in both the cache simulation environment and the industrial massively multiplayer online role-playing game (MMORPG) demonstrate the effectiveness of the proposed methods.
Frequency analysis of multi-source single area power system using WCA algorithm

Automatic generation control (AGC) is becoming an essential part of complex power system in order to give good quality power to consumers. When there is load fluctuation happening in power system, it leads to frequency unbalance in power system and due to this complex power system become unstable. To avoid this kind of problem AGC and load frequency control method is used for balancing the power system frequency. In this paper a multi-source single area power system has been presented and is studied using the statistical analysis. In this work proportional-integral (PI) controller has been used as area controller. In order to optimize controller, WCA algorithm has been used. Load frequency control system was simulated on MATLAB with the load disturbances of 0.01 p.u. in all the areas. In this work multi-source single area power system LFC has been used for simulation purpose.
Models and Methods to Characterize Site Amplification from a Pair of Records

The paper presents a tutorial review of the models and methods that are used to characterize site amplification from the pairs of rock- and soil-site records, and introduces some new techniques with better theoretical foundations. The models and methods discussed include spectral and cross-spectral ratios, spectral ratios for downhole records, response spectral ratios, constant amplification factors, parametric models, physical models, and time-varying filters. An extensive analytical and numerical error analysis of spectral and cross-spectral ratios shows that probabilistically cross-spectral ratios give more reliable estimates of site amplification. Spectral ratios should not be used to determine site amplification from downhole-surface recording pairs because of the feedback in the downhole sensor. Response spectral ratios are appropriate for low frequencies, but overestimate the amplification at high frequencies. The best method to be used depends on how much precision is required in the estimates.
Intrinsic statistical techniques for robust pose estimation

Gijs Dubbelman richt zich in zijn onderzoek op intelligente autonome mobiele voertuigen. Deze kunnen potentieel toegepast worden om taken die te gevaarlijk, te tijdrovend of te ingewikkeld zijn voor mensen toch efficient, veilig en kosteneffectief te verrichten. Voor hun gebruik is het bepalen van de geometrische structuur van hun omgeving en hun eigen positie en orientatie (‘pose’) cruciaal. Dubbelman richt zich in het bijzonder op methodologieen binnen computerbeeldverwerking om de positie en stand van een bewegende camera op het voertuig ten opzichte van de opgenomen beelddata te berekenen. Hij presenteert fundamentele uitgangspunten die de nauwkeurigheid, stabiliteit en efficiency van gangbare state of the art-oplossingen beperken. Dubbelman laat zien dat nauwkeuriger en efficientere methodes ontwikkeld kunnen worden door gebruik te maken van de mathematische structuur van ‘pose-ruimtes’ binnen statistische berekeningen. Hij presenteert drie van dergelijke nieuwe algoritmes. Zijn resultaten evalueerde hij met gesimuleerde data en met echte beeldgegevens. De verbeterde nauwkeurigheid en efficientie die Dubbelman met zijn methoden verkreeg, brengen de werkelijke toepassing van intelligente mobiele voertuigen dichterbij. Hij deed zijn onderzoek bij TNO Defence, Security and Safety.
High Order Countermeasures for Elliptic-Curve Implementations with Noisy Leakage Security

Elliptic-curve implementations protected with state-of-the-art countermeasures against side-channel attacks might still be vulnerable to advanced attacks that recover secret information from a single leakage trace. The effectiveness of these attacks is boosted by the emergence of deep learning techniques for side-channel analysis which relax the control or knowledge an adversary must have on the target implementation. In this paper, we provide generic countermeasures to withstand these attacks for a wide range of regular elliptic-curve implementations. We first introduce a framework to formally model a regular algebraic program which consists in a sequence of algebraic operations indexed by key-dependent values. We then introduce a generic counter-measure to protect these types of programs against advanced single-trace side-channel attacks. Our scheme achieves provable security in the noisy leakage model under a formal assumption on the leakage of randomized variables. To demonstrate the applicability of our solution, we provide concrete examples on several widely deployed scalar multiplication algorithms and report some benchmarks for a protected implementation on a smart card.
Dynamic Adjustment of Scheduling Period in Mobile Networks Based on C-RAN

The cloud radio access network (C-RAN) is considered as one of the compelling architectures to meet requirements of the future mobile networks in term of delivering cutting-edge applications and increasing the network flexibility. However, one of the key challenges to cope with in the C-RAN is the fronthaul delay between the baseband unit (BBU) and remote radio head (RRH). The fronthaul delay impacts negatively on various radio resource management techniques including scheduling. The scheduling suffers from the fronthaul due to an additional delay between the time when inputs for the scheduling are provided by the users and the time when the new scheduling is applied. In this paper, we propose a dynamic adjustment of the centralized scheduling period aiming to suppress the negative impact of the fronthaul delay and to increase the network throughput. To this end, we propose two algorithms estimating the scheduling period for individual users: i) from previous channel quality information, and ii) via a prediction of the future channel quality. Simulation results show that both proposed solutions not only provide a network throughput close to the theoretical upper bound but also outperform existing approaches.
Performance Comparisons between Reconfigurable Intelligent Surface and Full/Half-duplex Relays

Reconfigurable intelligent surface (RIS) is an emerging technique employing metasurface to reflect the signal from the source node to the destination node. Essentially, RIS can be considered as a passive relay between the source and destination node. By contrast, relay node in a traditional network has to be active, consuming energy when it is relaying the signal or information. It is worth investigating the differences between RIS and relay. In this paper, we compare the performances between RIS and active relay for a general multiple-input multiple-output (MIMO) system. To make the comparison fair and comprehensive, both the performances of RIS and active relay are optimized with best-effort, by formulating a non-convex optimization problem respectively. In case of RIS, transmit beamforming and reflecting coefficients are jointly optimized. In case of active relay including half-duplex relay and full-duplex relay, transmit beamforming for both source and relay are optimized. The original nonconvex optimization problems are transformed into to a weighted mean-square error (MSE) minimization problem and solved by the proposed alternating optimization algorithm, respectively. Numerical results are presented to demonstrate the validity of the proposed algorithm.