285 research outputs found
Animals are friends, not food: anthropomorphism leads to less favorable attitudes toward meat consumption by inducing feelings of anticipatory guilt
Why do people befriend animals, yet don't feel conflicted about eating some of them? Previous research on the “meat paradox” suggests that the dehumanization of meat animals plays a crucial role in attenuating the negative affective states that consumers may experience when consuming meat. However, relatively little is known about how the converse process, namely anthropomorphism, influences meat consumption. The current research provides evidence that anthropomorphizing meat animals through the friendship metaphor, “animals are friends”, can alter (omnivorous) consumers' attitudes and behavioral intentions toward meat eating, and induce feelings of guilt. More specifically, our experimental findings reveal that anthropomorphism has a negative effect on consumers' attitudes toward the food served in a restaurant and their intentions to patronize it when (pork) meat is on offer. This effect holds whether consumers are invited to consider themselves (Study 1a) or staff members (Study 1b) as taking part in a friendly human-animal interaction. We also demonstrate a similar effect of anthropomorphism on attitudes toward a (pork) meat product and their intentions to buy it, when consumers consider animal-animal friendship or human-animal friendship (Study 2). Last, we show that the negative effect of anthropomorphism on consumers' attitudes and behavioral intentions toward (pork) meat consumption is mediated by increased feelings of anticipatory guilt (Studies 3a and 3c). Nevertheless, no such effect was found with another kind of meat (beef), which indicates that anthropomorphizing meat animals through the friendship metaphor cannot be successfully applied to all commonly eaten species (Study 3b). Implications of these results for meat consumption are discussed
For the love of money and the planet: experimental evidence on co-benefits framing and food waste reduction intentions
This randomized control trial examines the effect of informational nudges highlighting the monetary or environmental benefits, or co-benefits, of food-saving behaviors on intentions to reduce food waste within the framework of the Extended Theory of Planned Behavior (ETPB). A representative sample of Spanish participants (N = 1008) were exposed to control, monetary, environmental, or co-benefits conditions and asked to indicate their intentions to reduce household fruit and vegetable waste. Psychological, behavioral, and situational ETPB factors affecting food waste behavior were also measured. Only co-benefits framing was found to have a significant effect. Participants who were highly concerned about the environmental impact of wasting food were more strongly influenced by the co-benefits and monetary framings. Further, perceived behavioral control and food waste habits were positively associated with food-saving intentions. Thus, co-benefits framing in informational nudges can strengthen consumer intention to reduce fruit and vegetable waste, especially among consumers with higher levels of environmental concern
An embodied approach to informational interventions: using conceptual metaphors to promote sustainable healthy diets
Poor diet quality and environmental degradation are two major challenges of our times. Unhealthy and unsustainable dietary practices, such as the overconsumption of meat and consumer food waste behaviour, contribute greatly to both issues. Across seventeen online and field experiments, in two different cultures (US and China), this thesis investigates if the embodied cognition approach, and more specifically, research on conceptual metaphors, can be used to develop interventions to promote sustainable healthy diets. Interventions relying on conceptual metaphors have been shown to stimulate attitudinal and behavioural changes in other fields (e.g., marketing and political communications), but are rarely adopted to encourage sustainable healthy diets. To fill in this gap in the literature, I conducted five sets of experimental studies examining the effects of different metaphors on specific sustainable healthy dietary practices, each of which forms an independent empirical paper (Chapters 2-6 of the thesis). After introducing the current perspectives on embodied cognition and conceptual metaphors in the context of this research (Chapter 1), Chapter 2 looks into the conceptual metaphor “Healthy is Up”, demonstrating that US people implicitly associate healthiness with verticality, and offering recommendations for healthy eating guidelines. Chapter 3 extends this research to Chinese samples and partially replicates the results. Chapter 4 shows that the anthropomorphic metaphor “Animals are Friends” discourages meat consumption by inducing anticipatory guilt among US omnivores, whereas Chapter 5 reveals that Chinese omnivores are more responsive to another anthropomorphic metaphor, namely, “Animals are Family”. Bringing lab insights 6 to the real world, Chapter 6 demonstrates with a longitudinal field experiment that anthropomorphic metaphors together with environmental feedback result in a higher reduction in food waste as compared to other feedback interventions. The strengths, limitations and implications of those empirical papers are discussed in the conclusive part of the thesis
Graph Attention for Automated Audio Captioning
State-of-the-art audio captioning methods typically use the encoder-decoder
structure with pretrained audio neural networks (PANNs) as encoders for feature
extraction. However, the convolution operation used in PANNs is limited in
capturing the long-time dependencies within an audio signal, thereby leading to
potential performance degradation in audio captioning. This letter presents a
novel method using graph attention (GraphAC) for encoder-decoder based audio
captioning. In the encoder, a graph attention module is introduced after the
PANNs to learn contextual association (i.e. the dependency among the audio
features over different time frames) through an adjacency graph, and a top-k
mask is used to mitigate the interference from noisy nodes. The learnt
contextual association leads to a more effective feature representation with
feature node aggregation. As a result, the decoder can predict important
semantic information about the acoustic scene and events based on the
contextual associations learned from the audio signal. Experimental results
show that GraphAC outperforms the state-of-the-art methods with PANNs as the
encoders, thanks to the incorporation of the graph attention module into the
encoder for capturing the long-time dependencies within the audio signal. The
source code is available at https://github.com/LittleFlyingSheep/GraphAC.Comment: Accepted by IEEE Signal Processing Letter
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining
Existing contrastive learning methods for anomalous sound detection refine
the audio representation of each audio sample by using the contrast between the
samples' augmentations (e.g., with time or frequency masking). However, they
might be biased by the augmented data, due to the lack of physical properties
of machine sound, thereby limiting the detection performance. This paper uses
contrastive learning to refine audio representations for each machine ID,
rather than for each audio sample. The proposed two-stage method uses
contrastive learning to pretrain the audio representation model by
incorporating machine ID and a self-supervised ID classifier to fine-tune the
learnt model, while enhancing the relation between audio features from the same
ID. Experiments show that our method outperforms the state-of-the-art methods
using contrastive learning or self-supervised classification in overall anomaly
detection performance and stability on DCASE 2020 Challenge Task2 dataset.Comment: To appear in IEEE International Conference on Acoustics, Speech, and
Signal Processing (ICASSP 2023
Platelet Distribution Width Levels Can Be a Predictor in the Diagnosis of Persistent Organ Failure in Acute Pancreatitis
Purpose. The change of serum platelet indices such as platelet distribution width (PDW) has been reported in a series of inflammatory reaction and clinical diseases. However, the relationship between PDW and the incidence of persistent organ failure (POF) in acute pancreatitis (AP) has not been elucidated so far. Materials and Methods. A total of 135 patients with AP admitted within 72 hours from symptom onset of AP at our center between December 2014 and January 2016 were included in this retrospective study. Demographic parameters on admission, organ failure assessment, laboratory data, and in-hospital mortality were compared between patients with and without POF. Multivariable logistic regression analyses were utilized to evaluate the predictive value of serum PDW for POF. Results. 30 patients were diagnosed with POF. Compared to patients without POF, patients with POF showed a significantly higher value of serum PDW on admission (14.88 ± 2.24 versus 17.60 ± 1.96%, P<0.001). After multivariable analysis, high PDW level remained a risk factor for POF (odds ratio 39.42, 95% CI: 8.64–179.77; P<0.001). A PDW value of 16.45% predicted POF with an area under the curve (AUC) of 0.870, a sensitivity with 0.867, and a specificity with 0.771, respectively. Conclusions. Our results indicate that serum PDW on admission could be a predictive factor in AP with POF and may serve as a potential prognostic factor
Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces
Continual graph learning routinely finds its role in a variety of real-world
applications where the graph data with different tasks come sequentially.
Despite the success of prior works, it still faces great challenges. On the one
hand, existing methods work with the zero-curvature Euclidean space, and
largely ignore the fact that curvature varies over the coming graph sequence.
On the other hand, continual learners in the literature rely on abundant
labels, but labeling graph in practice is particularly hard especially for the
continuously emerging graphs on-the-fly. To address the aforementioned
challenges, we propose to explore a challenging yet practical problem, the
self-supervised continual graph learning in adaptive Riemannian spaces. In this
paper, we propose a novel self-supervised Riemannian Graph Continual Learner
(RieGrace). In RieGrace, we first design an Adaptive Riemannian GCN (AdaRGCN),
a unified GCN coupled with a neural curvature adapter, so that Riemannian space
is shaped by the learnt curvature adaptive to each graph. Then, we present a
Label-free Lorentz Distillation approach, in which we create teacher-student
AdaRGCN for the graph sequence. The student successively performs
intra-distillation from itself and inter-distillation from the teacher so as to
consolidate knowledge without catastrophic forgetting. In particular, we
propose a theoretically grounded Generalized Lorentz Projection for the
contrastive distillation in Riemannian space. Extensive experiments on the
benchmark datasets show the superiority of RieGrace, and additionally, we
investigate on how curvature changes over the graph sequence.Comment: Accepted by AAAI 2023 (Main Track), 9 pages, 4 figure
Integrated Sensing and Communication for Network-Assisted Full-Duplex Cell-Free Distributed Massive MIMO Systems
In this paper, we combine the network-assisted full-duplex (NAFD) technology
and distributed radar sensing to implement integrated sensing and communication
(ISAC). The ISAC system features both uplink and downlink remote radio units
(RRUs) equipped with communication and sensing capabilities. We evaluate the
communication and sensing performance of the system using the sum communication
rates and the Cramer-Rao lower bound (CRLB), respectively. We compare the
performance of the proposed scheme with other ISAC schemes, the result shows
that the proposed scheme can provide more stable sensing and better
communication performance. Furthermore, we propose two power allocation
algorithms to optimize the communication and sensing performance jointly. One
algorithm is based on the deep Q-network (DQN) and the other one is based on
the non-dominated sorting genetic algorithm II (NSGA-II). The proposed
algorithms provide more feasible solutions and achieve better system
performance than the equal power allocation algorithm.Comment: 14 pages, 7 figures,submit to China Communication February 28, 2023,
date of major revision July 09, 202
Plan4MC: Skill Reinforcement Learning and Planning for Open-World Minecraft Tasks
We study building a multi-task agent in Minecraft. Without human
demonstrations, solving long-horizon tasks in this open-ended environment with
reinforcement learning (RL) is extremely sample inefficient. To tackle the
challenge, we decompose solving Minecraft tasks into learning basic skills and
planning over the skills. We propose three types of fine-grained basic skills
in Minecraft, and use RL with intrinsic rewards to accomplish basic skills with
high success rates. For skill planning, we use Large Language Models to find
the relationships between skills and build a skill graph in advance. When the
agent is solving a task, our skill search algorithm walks on the skill graph
and generates the proper skill plans for the agent. In experiments, our method
accomplishes 24 diverse Minecraft tasks, where many tasks require sequentially
executing for more than 10 skills. Our method outperforms baselines in most
tasks by a large margin. The project's website and code can be found at
https://sites.google.com/view/plan4mc.Comment: 19 page
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