Attainable and Relevant Moral Exemplars Are More Effective than Extraordinary Exemplars in Promoting Voluntary Service Engagement

The present study aimed to develop effective moral educational interventions based on social psychology by using stories of moral exemplars. We tested whether motivation to engage in voluntary service as a form of moral behavior was better promoted by attainable and relevant exemplars or by unattainable and irrelevant exemplars. First, experiment 1, conducted in a lab, showed that stories of attainable exemplars more effectively promoted voluntary service activity engagement among undergraduate students compared with stories of unattainable exemplars and non-moral stories. Second, experiment 2, a middle school classroom-level experiment with a quasi-experimental design, demonstrated that peer exemplars, who are perceived to be attainable and relevant to students, better promoted service engagement compared with historic figures in moral education classes

Interactive Text2Pickup Network for Natural Language based Human-Robot Collaboration

In this paper, we propose the Interactive Text2Pickup (IT2P) network for human-robot collaboration which enables an effective interaction with a human user despite the ambiguity in user's commands. We focus on the task where a robot is expected to pick up an object instructed by a human, and to interact with the human when the given instruction is vague. The proposed network understands the command from the human user and estimates the position of the desired object first. To handle the inherent ambiguity in human language commands, a suitable question which can resolve the ambiguity is generated. The user's answer to the question is combined with the initial command and given back to the network, resulting in more accurate estimation. The experiment results show that given unambiguous commands, the proposed method can estimate the position of the requested object with an accuracy of 98.49% based on our test dataset. Given ambiguous language commands, we show that the accuracy of the pick up task increases by 1.94 times after incorporating the information obtained from the interaction.Comment: 8 pages, 9 figure

Generative Autoregressive Networks for 3D Dancing Move Synthesis from Music

This paper proposes a framework which is able to generate a sequence of three-dimensional human dance poses for a given music. The proposed framework consists of three components: a music feature encoder, a pose generator, and a music genre classifier. We focus on integrating these components for generating a realistic 3D human dancing move from music, which can be applied to artificial agents and humanoid robots. The trained dance pose generator, which is a generative autoregressive model, is able to synthesize a dance sequence longer than 5,000 pose frames. Experimental results of generated dance sequences from various songs show how the proposed method generates human-like dancing move to a given music. In addition, a generated 3D dance sequence is applied to a humanoid robot, showing that the proposed framework can make a robot to dance just by listening to music.Comment: 8 pages, 10 figure

Gradient Surgery for One-shot Unlearning on Generative Model

Recent regulation on right-to-be-forgotten emerges tons of interest in unlearning pre-trained machine learning models. While approximating a straightforward yet expensive approach of retrain-from-scratch, recent machine unlearning methods unlearn a sample by updating weights to remove its influence on the weight parameters. In this paper, we introduce a simple yet effective approach to remove a data influence on the deep generative model. Inspired by works in multi-task learning, we propose to manipulate gradients to regularize the interplay of influence among samples by projecting gradients onto the normal plane of the gradients to be retained. Our work is agnostic to statistics of the removal samples, outperforming existing baselines while providing theoretical analysis for the first time in unlearning a generative model.Comment: ICML 2023 Workshop on Generative AI & La

Self-Supervised Motion Retargeting with Safety Guarantee

In this paper, we present self-supervised shared latent embedding (S3LE), a data-driven motion retargeting method that enables the generation of natural motions in humanoid robots from motion capture data or RGB videos. While it requires paired data consisting of human poses and their corresponding robot configurations, it significantly alleviates the necessity of time-consuming data-collection via novel paired data generating processes. Our self-supervised learning procedure consists of two steps: automatically generating paired data to bootstrap the motion retargeting, and learning a projection-invariant mapping to handle the different expressivity of humans and humanoid robots. Furthermore, our method guarantees that the generated robot pose is collision-free and satisfies position limits by utilizing nonparametric regression in the shared latent space. We demonstrate that our method can generate expressive robotic motions from both the CMU motion capture database and YouTube videos

Synergistic Effects of Hyaluronate - Epidermal Growth Factor Conjugate Patch on Chronic Wound Healing

The proteolytic microenvironment in the wound area reduces the stability and the half-life of growth factors in vivo, making difficult the topical delivery of growth factors. Here, epidermal growth factor (EGF) was conjugated to hyaluronate (HA) to improve the long-term stability against enzymatic degradation and the therapeutic effect by enhancing the biological interaction with HA receptors on skin cells. After the synthesis of HA-EGF conjugates, they were incorporated into a patch-type formulation for the facile topical application and sustained release of EGF. According to ELISA, the HA-EGF conjugates showed a long-term stability compared with native EGF. Furthermore, HA-EGF conjugates appeared to interact with skin cells through two types of HA and EGF receptors, resulting in a synergistically improved healing effect. Taken together, we could confirm the feasibility of HA-EGF conjugates for the transdermal treatment of chronic wounds.11Ysciescopu

Suicide rate and social environment characteristics in South Korea: the roles of socioeconomic, demographic, urbanicity, general health behaviors, and other environmental factors on suicide rate

Abstract Background Suicide is a serious worldwide public health concern, and South Korea has shown the highest suicide rate among Organisation for Economic Co-operation and Development (OECD) countries since 2003. Nevertheless, most previous Korean studies on suicide had limitations in investigating various social environment factors using long-term nationwide data. Thus, this study examined how various social environment characteristics are related to the suicide rate at the district-level, using nationwide longitudinal data over 11years. Methods We used the district-level age-standardized suicide rate and a total of 12 annual social environment characteristics that represented socioeconomic, demographic, urbanicity, general health behaviors, and other environmental characteristics from 229 administrative districts in South Korea. A Bayesian hierarchical model with integrated Laplace approximations (INLA) was used to examine the spatiotemporal association between the rate of suicide and the social environment indicators selected for the study. Results In the total population, the indicators % of population aged 65 and older eligible for the basic pension, % vacant houses in the area, % divorce, % single elderly households, % detached houses, % current smokers, and % of population with obesity showed positive associations with the suicide rate. In contrast, % of people who regularly participated in religious activities showed negative associations with suicide rate. The associations between these social environment characteristics and suicide rate were generally more statistically significant in males and more urbanized areas, than in females and less urbanized areas; however, associations differed amongst age groups, depending on the social environment characteristic variable under study. Conclusions This study investigated the complex role of social environments on suicide rate in South Korea and revealed that higher suicide rates were associated with lower values of socioeconomic status, physical exercise, and religious activities, and with higher social isolation and smoking practice. Our results can be used in the development of targeted suicide prevention policies

The antifungal activity and membrane-disruptive action of dioscin extracted from Dioscorea nipponica

AbstractDioscin is a kind of steroidal saponin isolated from the root bark of wild yam Dioscorea nipponica. We investigated the antifungal effect of dioscin against different fungal strains and its antifungal mechanism(s) in Candida albicans cells. Using the propidium iodide assay and calcein-leakage measurement, we confirmed that dioscin caused fungal membrane damage. Furthermore, we evaluated the ability of dioscin to disrupt the plasma membrane potential, using 3,3′-dipropylthiadicarbocyanine iodide [DiSC3(5)] and bis-(1,3-dibarbituric acid)-trimethine oxanol [DiBAC4(3)]. Cells stained with the dyes had a significant increase in fluorescent intensity after exposure to dioscin, indicating that dioscin has an effect on the membrane potential. To visualize the effect of dioscin on the cell membrane, we synthesized rhodamine-labeled giant unilamellar vesicles (GUVs) mimicking the outer leaflet of the plasma membrane of C. albicans. As seen in the result, the membrane disruptive action of dioscin caused morphological change and rhodamine leakage of the GUVs. In three-dimensional contour-plot analysis using flow cytometry, we observed a decrease in cell size, which is in agreement with our result from the GUV assay. These results suggest that dioscin exerts a considerable antifungal activity by disrupting the structure in membrane after invading into the fungal membrane, resulting in fungal cell death

Visually Grounding Instruction for History-Dependent Manipulation

This paper emphasizes the importance of robot's ability to refer its task history, when it executes a series of pick-and-place manipulations by following text instructions given one by one. The advantage of referring the manipulation history can be categorized into two folds: (1) the instructions omitting details or using co-referential expressions can be interpreted, and (2) the visual information of objects occluded by previous manipulations can be inferred. For this challenge, we introduce the task of history-dependent manipulation which is to visually ground a series of text instructions for proper manipulations depending on the task history. We also suggest a relevant dataset and a methodology based on the deep neural network, and show that our network trained with a synthetic dataset can be applied to the real world based on images transferred into synthetic-style based on the CycleGAN.Comment: 8 pages, 6 figure