401 research outputs found
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
- …