Representing the nation through food based on real life stories

This thesis examines the fast growing new design field of food design, in order to identify whether it is a new legitimate profession or just a trend. In order to define food design, the study observes the realm of food, and its related design practices. The study follows a human-centered design approach to broaden the notion of food design and suggests various new roles of the designer as facilitator, storyteller, educator and toolmaker in the food domain. Participation in the 2nd International Conference on Food Design in New York City (2015) enhances the understanding of food design. The analysis validates the potential and possibility of food design as a concrete profession. In addition, the study continues to explore the role of education in defining the new design field and the key elements to cement food design as a new profession, reflecting my internship at Studio Marije Vogelzang in 2016. The effort of defining the relationship between food and design culminated in the food design practice within a multidisciplinary design project. The Design Department of Aalto University conducted a design exhibition titled “Nakuna,” or naked in Finnish. This special showcase was intended to celebrate Finland’s centennial independence anniversary during Milan Design Week, 2017. My project, “Laavu,” meaning a shelter in the forest in Finnish, was one of three projects created for the exhibition. This interactive food installation served approximately 1,200 visitors with three wild food products. The ingredients were collected from the Finnish public’s freezers by a participatory design process including interviews and roleplay. Laavu was exhibited in Circolo Filologico Milanese at Milan Design Week 2017 in Italy. Nakuna was nominated as one of the top 40 exhibitions in the Milano Design Award competition among approximately 1,700 exhibitions during Milan Design Week. The food project was acknowledged as one of the top 3 memorable food experience in Fuorisalone, 2017

Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models

Assessing the fidelity and diversity of the generative model is a difficult but important issue for technological advancement. So, recent papers have introduced k-Nearest Neighbor ($k$NN) based precision-recall metrics to break down the statistical distance into fidelity and diversity. While they provide an intuitive method, we thoroughly analyze these metrics and identify oversimplified assumptions and undesirable properties of kNN that result in unreliable evaluation, such as susceptibility to outliers and insensitivity to distributional changes. Thus, we propose novel metrics, P-precision and P-recall (PP\&PR), based on a probabilistic approach that address the problems. Through extensive investigations on toy experiments and state-of-the-art generative models, we show that our PP\&PR provide more reliable estimates for comparing fidelity and diversity than the existing metrics. The codes are available at \url{https://github.com/kdst-team/Probablistic_precision_recall}

Global Saccadic Eye Movements Characterise Artists’ Visual Attention While Drawing

Previous research has shown that artists employ flexible attentional strategies during offline perceptual tasks (Chamberlain et al., 2018; Chamberlain & Wagemans, 2015). The current study explored visual processing online, by tracking the eye movements of artists and non-artists (n=65) while they produced representational drawings of photographic stimuli. The findings revealed that it is possible to differentiate artists from non-artists on the basis of the relative amount of global-to-local saccadic eye movements they make when looking at the target stimulus while drawing, but not in a preparatory free viewing phase. Results indicated that these differences in eye movements are not specifically related to representational drawing ability, and may be a feature of artistic ability more broadly. This eye movement analysis technique may be used in future research to characterise the dynamics of attentional shifts in eye movements while artists are carrying out a range of artistic tasks

NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency

We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data. In NaturalInversion, we propose: (1) a Feature Transfer Pyramid which uses enhanced image prior of the original data by combining the multi-scale feature maps extracted from the pre-trained classifier, (2) a one-to-one approach generative model where only one batch of images are synthesized by one generator to bring the non-linearity to optimization and to ease the overall optimizing process, (3) learnable Adaptive Channel Scaling parameters which are end-to-end trained to scale the output image channel to utilize the original image prior further. With our NaturalInversion, we synthesize images from classifiers trained on CIFAR-10/100 and show that our images are more consistent with original data distribution than prior works by visualization and additional analysis. Furthermore, our synthesized images outperform prior works on various applications such as knowledge distillation and pruning, demonstrating the effectiveness of our proposed method.Comment: Published at AAAI 202

Parameter-Free Algorithms for Performative Regret Minimization under Decision-Dependent Distributions

This paper studies performative risk minimization, a formulation of stochastic optimization under decision-dependent distributions. We consider the general case where the performative risk can be non-convex, for which we develop efficient parameter-free optimistic optimization-based methods. Our algorithms significantly improve upon the existing Lipschitz bandit-based method in many aspects. In particular, our framework does not require knowledge about the sensitivity parameter of the distribution map and the Lipshitz constant of the loss function. This makes our framework practically favorable, together with the efficient optimistic optimization-based tree-search mechanism. We provide experimental results that demonstrate the numerical superiority of our algorithms over the existing method and other black-box optimistic optimization methods

α-Actinin-4 promotes the progression of prostate cancer through the Akt/GSK-3β/β-catenin signaling pathway

The first-line treatment for prostate cancer (PCa) is androgen ablation therapy. However, prostate tumors generally recur and progress to androgen-independent PCa (AIPC) within 2-3 years. alpha-Actinin-4 (ACTN4) is an actin-binding protein that belongs to the spectrin gene superfamily and acts as an oncogene in various cancer types. Although ACTN4 is involved in tumorigenesis and the epithelial-mesenchymal transition of cervical cancer, the role of ACTN4 in PCa remains unknown. We found that the ACTN4 expression level increased during the transition from androgen-dependent PCa to AIPC. ACTN4 overexpression resulted in enhanced proliferation and motility of PCa cells. Increased beta-catenin due to ACTN4 promoted the transcription of genes involved in proliferation and metastasis such as CCND1 and ZEB1. ACTN4-overexpressing androgen-sensitive PCa cells were able to grow in charcoal-stripped media. In contrast, ACTN4 knockdown using si-ACTN4 and ACTN4 nanobody suppressed the proliferation, migration, and invasion of AIPC cells. Results of the xenograft experiment revealed that the mice injected with LNCaPACTN4 cells exhibited an increase in tumor mass compared with those injected with LNCaPMock cells. These results indicate that ACTN4 is involved in AIPC transition and promotes the progression of PCa

Time Series Analysis on the Conformational Change of c-Src Tyrosine Kinase

c-Src tyrosine kinase plays an important role in signal transduction pathways, where its activity is regulated by phosphorylation of the two tyrosine residues. We performed targeted molecular dynamics simulation to obtain trajectory of conformational change from inactive to active form. To investigate the conformational change of c-Src tyrosine kinase, we applied network analysis to time series of correlation among residues. The time series of correlation between residues during the conformational change generated by targeted molecular dynamic simulation. With centrality measures such as betweenness centrality, degree centrality, and closeness centrality, we observed a few important residues that significantly contribute to the conformational change of c-Src tyrosine kinase for the different time steps

Alone but not isolated: social presence and cognitive load in learning with 360 virtual reality videos

IntroductionThis study aimed to identify any differences in social presence and cognitive load among three types of 360 virtual reality (VR)-based videos lectures. We hypothesized that social presence would be higher when interactions among peers are visible in a 360 VR video lectures while the cognitive load would be also increased.MethodsA total of 48 college students were randomly assigned to one of the three study groups to view an assigned 360 VR video lecture. The three groups were: (1) an instructor-only video viewing group, (2) a classroom lecture video viewing group, and (3) a classroom lecture and activity video viewing group. The video lectures were differently designed depending on the levels of peer visibility and the interactions between the instructor and peers. The participants watched one of the three types of assigned video lecture and subsequently completed two sets of questionnaires regarding social presence and cognitive load. A multivariate analysis of variance (MANOVA) was conducted with a planned contrast analysis for the type of video lectures.ResultsWe found that, contrary to the hypotheses, students in the group 1 (instructor-only video) showed higher social presence scores than students in the groups 2 and 3. However, no significant differences were found in the cognitive load scores.DiscussionThe results show that 360 VR video lectures with an instructor-only are more effective at enhancing users’ social presence than 360 VR video lectures with both the instructor and class-peers. We suggest creating 360 VR video lectures with the presence of the course instructor to offer learners the sense of actually participating in a lecture