Adjective Constructions is a speculative drawing project and a creative re-appropriation of the distance that has defined our decade-long remote collaboration across North America. Each resulting drawing – or ‘adjective construction’ – explores the question of what it means to draw together remotely, by enlisting architectural tools to push, probe, and mine the tension between the visceral materiality of the close-at-hand and its projection across the distance between us. In this paper we reflect on the process that underpins our drawing practice, one that is rooted in dialogue (between us, with the work, and with our tools). This practice foregrounds the emergent and generative capacity of play – as described by Miguel Sicart and theorised by Hans-Georg Gadamer – as the primary motive for the work. Following Gadamer’s understanding of play as essential to a dialectic approach to uncovering understanding, this paper eschews a definitive explanation of our drawing process, and instead sets up a conversation between this process and three thought-worlds which reach across disciplines and animate our designerly imagination: poet and Greek scholar Anne Carson’s presentation of the ‘adjective’ as a seemingly superfluous appendage, but one that can anchor a work in specificity; landscape architect Cornelia Hahn Oberlander’s instructions for adventure playgrounds; and garden designer Henk Gerritsen’s dialectical approach to (un)natural gardening and whim topiary. While each of these practices share meaningful resonances with our own process, they are not presented as direct metaphorical correspondents. They are brought together to set up a constellation of thought-worlds and sensibilities that vibrate and cross-pollinate into expanded possibilities, while maintaining the work’s ambiguity and openness, creating opportunities for further interpretations, subsequent re-appropriations, more play. Read the full article online at: https://drawingon.org/Issue-04-06-Adjective-Constructions
Durgesh Haribhau Salunkhe, Guillaume Michel, Shivesh Kumar
et al.
This work presents the development of a parallel manipulator used for otological surgery from the perspective of co-design. Co-design refers to the simultaneous involvement of the end-users (surgeons), stakeholders (designers, ergonomic experts, manufacturers), and experts from the fields of optimization and mechanisms. The role of each member is discussed in detail and the interactions between the stakeholders are presented. Co-design facilitates a reduction in the parameter space considered during mechanism optimization, leading to a more efficient design process. Additionally, the co-design principles help avoid unforeseen errors and help in quicker adaptation of the proposed solution.
Color is an indispensable element in illustration design. As a visual art, illustration expresses specific themes and artistic conception through images and colors. Regional color refers to the unique use of color in a specific regional cultural background. In illustration design, regional color, as a symbol of regional culture, can evoke people’s memory, association and emotional identification of a specific region. Based on the relevant theories of color geography and the guidance of color planning and design methods, this paper analyzes and studies the regional color culture and characteristics of Beijing. By collecting the information and values of regional colors in Beijing, the total color spectrum is established to form the color scheme for the illustration of “Qiongdao Chunyin”, which is finally applied to the illustration design practice of “Qiongdao Chunyin”. This paper discusses the design ideas and methods of regional colors in landscape theme illustration design, and promotes the inheritance and innovation of regional traditional culture.
The fundamental problem of weighted sampling involves sampling of satisfying assignments of Boolean formulas, which specify sampling sets, and according to distributions defined by pre-specified weight functions to weight functions. The tight integration of sampling routines in various applications has highlighted the need for samplers to be incremental, i.e., samplers are expected to handle updates to weight functions. The primary contribution of this work is an efficient knowledge compilation-based weighted sampler, INC, designed for incremental sampling. INC builds on top of the recently proposed knowledge compilation language, OBDD[AND], and is accompanied by rigorous theoretical guarantees. Our extensive experiments demonstrate that INC is faster than state-of-the-art approach for majority of the evaluation. In particular, we observed a median of 1.69X runtime improvement over the prior state-of-the-art approach.
Sara Di Bartolomeo, Giorgio Severi, Victor Schetinger
et al.
Large language models (LLMs) have recently taken the world by storm. They can generate coherent text, hold meaningful conversations, and be taught concepts and basic sets of instructions - such as the steps of an algorithm. In this context, we are interested in exploring the application of LLMs to graph drawing algorithms by performing experiments on ChatGPT. These algorithms are used to improve the readability of graph visualizations. The probabilistic nature of LLMs presents challenges to implementing algorithms correctly, but we believe that LLMs' ability to learn from vast amounts of data and apply complex operations may lead to interesting graph drawing results. For example, we could enable users with limited coding backgrounds to use simple natural language to create effective graph visualizations. Natural language specification would make data visualization more accessible and user-friendly for a wider range of users. Exploring LLMs' capabilities for graph drawing can also help us better understand how to formulate complex algorithms for LLMs; a type of knowledge that could transfer to other areas of computer science. Overall, our goal is to shed light on the exciting possibilities of using LLMs for graph drawing while providing a balanced assessment of the challenges and opportunities they present. A free copy of this paper with all supplemental materials required to reproduce our results is available on https://osf.io/n5rxd/?view_only=f09cbc2621f44074810b7d843f1e12f9
The 2019/20 Black Summer bushfires in Australia demonstrated the brutal and disastrous consequences of changing the technological world without considering linkages with the biophysical, ecological or human worlds. An emerging more-than-human design philosophy encourages designers to consider such interrelations between humans and non-human entities. Yet, the design research community has focused on situated or embodied experiences for designers, rather than developing processes to legitimate the perspectives of non-human entities through participatory design. This paper explores how adopting the `intentional stance', a concept from philosophy, might provide a heuristic for more-than-human participatory design. Through experimentation with the intentional stance in the context of smart lighting systems, the paper demonstrates that the approach has potential for non-human entities from the ecological world, but less so for the biophysical world. The paper concludes by encouraging critique and evolution of the intentional stance, and of other approaches, to legitimate the perspectives of non-human entities in everyday design.
Participatory Design (PD) seeks political change to support people's democratic control over processes, solutions, and, in general, matters of concern to them. A particular challenge remains in supporting vulnerable groups to gain power and control when they are dependent on organizations and external structures. We reflect on our five-year engagement with survivors of sex trafficking in Nepal and an anti-trafficking organization that supports the survivors. Arguing that the prevalence of deficit perspective in the setting promotes dependency and robs the survivors' agency, we sought to bring change by exploring possibilities based on the survivors' existing assets. Three configurations illuminate how our design decisions and collective exploration operate to empower participation while attending to the substantial power implicitly and explicitly manifest in existing structures. We highlight the challenges we faced, uncovering actions that PD practitioners can take, including an emphasis on collaborative entanglements, attending to contingent factors, and encouraging provisional collectives.
Mural image inpainting is far less explored compared to its natural image counterpart and remains largely unsolved. Most existing image-inpainting methods tend to take the target image as the only input and directly repair the damage to generate a visually plausible result. These methods obtain high performance in restoration or completion of some pre-defined objects, e.g., human face, fabric texture, and printed texts, etc., however, are not suitable for repairing murals with varying subjects and large damaged areas. Moreover, due to discrete colors in paints, mural inpainting may suffer from apparent color bias. To this end, in this paper, we propose a line drawing guided progressive mural inpainting method. It divides the inpainting process into two steps: structure reconstruction and color correction, implemented by a structure reconstruction network (SRN) and a color correction network (CCN), respectively. In structure reconstruction, SRN utilizes the line drawing as an assistant to achieve large-scale content authenticity and structural stability. In color correction, CCN operates a local color adjustment for missing pixels which reduces the negative effects of color bias and edge jumping. The proposed approach is evaluated against the current state-of-the-art image inpainting methods. Qualitative and quantitative results demonstrate the superiority of the proposed method in mural image inpainting. The codes and data are available at https://github.com/qinnzou/mural-image-inpainting.
La pandemia del Covid-19 ha dado un vuelco a nuestras vidas, ha cambiado nuestros hábitos, destrozado nuestros planes y, de alguna forma, también ha influenciado nuestros proyectos de futuro a corto, medio e incluso largo plazo. Repasamos cúal ha sido la respuesta de algunas marcas durante estas primeras semanas de crisis y confinamiento; que aún suponiendo un breve lapso de tiempo, han puesto a prueba la creatividad de todos.
Generative systems have a significant potential to synthesize innovative design alternatives. Still, most of the common systems that have been adopted in design require the designer to explicitly define the specifications of the procedures and in some cases the design space. In contrast, a generative system could potentially learn both aspects through processing a database of existing solutions without the supervision of the designer. To explore this possibility, we review recent advancements of generative models in machine learning and current applications of learning techniques in design. Then, we describe the development of a data-driven generative system titled DeepCloud. It combines an autoencoder architecture for point clouds with a web-based interface and analog input devices to provide an intuitive experience for data-driven generation of design alternatives. We delineate the implementation of two prototypes of DeepCloud, their contributions, and potentials for generative design.
We provide an amendment to the first theorem of "Control Contraction Metrics: Convex and Intrinsic Criteria for Nonlinear Feedback Design" by Manchester & Slotine in the form of an additional technical condition required to show integrability of differential control signals. This technical condition is shown to be satisfied under the original assumptions if the input matrix is constant rank, and also if the strong conditions for a CCM hold. However a simple counterexample shows that if the input matrix drops rank, then the weaker conditions of the original theorem may not imply stabilizability of all trajectories. The remaining claims and illustrative examples of the paper are shown to remain valid with the new condition.
Developments in information and communication technologies have been greatly influential on the practices in all fields, and education is not an exception to this. To illustrate with, computers were first used in computer assisted education in order to increase the efficiency of teaching process. Recently, computer has contributed more to the field through interactive and smart class applications that are specially designed for classroom use. The aim of this study is to develop a low cost, portable and projection supported touchscreen to be used in educational environments by using FPGA technology and to test its usability. For the purposes of the study, the above mentioned system was developed by using the necessary hardware and software, and later it was tested in terms of usability. This usability test was administered to teachers, who were the target end users of this touchscreen writing / drawing system. The aim of this test was to determine user friendliness, subservientness and usability of the system. Several tools were used to obtain data from the users that participated in the study. The analysis and evaluation of the data collected revealed that the system has achieved its objectives successfully.
Jonathan X. Zheng, Samraat Pawar, Dan F. M. Goodman
A popular method of force-directed graph drawing is multidimensional scaling using graph-theoretic distances as input. We present an algorithm to minimize its energy function, known as stress, by using stochastic gradient descent (SGD) to move a single pair of vertices at a time. Our results show that SGD can reach lower stress levels faster and more consistently than majorization, without needing help from a good initialization. We then show how the unique properties of SGD make it easier to produce constrained layouts than previous approaches. We also show how SGD can be directly applied within the sparse stress approximation of Ortmann et al. [1], making the algorithm scalable up to large graphs.
Radoslav Fulek, Michael Pelsmajer, Marcus Schaefer
A drawing of a graph $G$ is radial if the vertices of $G$ are placed on concentric circles $C_1, \ldots, C_k$ with common center $c$, and edges are drawn radially: every edge intersects every circle centered at $c$ at most once. $G$ is radial planar if it has a radial embedding, that is, a crossing-free radial drawing. If the vertices of $G$ are ordered or partitioned into ordered levels (as they are for leveled graphs), we require that the assignment of vertices to circles corresponds to the given ordering or leveling. A pair of edges $e$ and $f$ in a graph is independent if $e$ and $f$ do not share a vertex. We show that a graph $G$ is radial planar if $G$ has a radial drawing in which every two independent edges cross an even number of times; the radial embedding has the same leveling as the radial drawing. In other words, we establish the strong Hanani-Tutte theorem for radial planarity. This characterization yields a very simple algorithm for radial planarity testing.
Drawing appropriate diagrams is a useful problem solving heuristic that can transform a given problem into a representation that is easier to exploit for solving it. A major focus while helping introductory physics students learn problem solving is to help them appreciate that drawing diagrams facilitates problem solution. We conducted an investigation in which 111 students in an algebra-based introductory physics course were subjected to two different interventions during recitation quizzes throughout the semester. They were either (1) asked to solve problems in which the diagrams were drawn for them or (2) explicitly told to draw a diagram. A comparison group was not given any instruction regarding diagrams. We developed a rubric to score the problem-solving performance of students in different intervention groups. Here, we present some surprising results for problems which involve considerations of initial and final conditions.
Syed Shakib Sarwar, Swagath Venkataramani, Anand Raghunathan
et al.
Large-scale artificial neural networks have shown significant promise in addressing a wide range of classification and recognition applications. However, their large computational requirements stretch the capabilities of computing platforms. The fundamental components of these neural networks are the neurons and its synapses. The core of a digital hardware neuron consists of multiplier, accumulator and activation function. Multipliers consume most of the processing energy in the digital neurons, and thereby in the hardware implementations of artificial neural networks. We propose an approximate multiplier that utilizes the notion of computation sharing and exploits error resilience of neural network applications to achieve improved energy consumption. We also propose Multiplier-less Artificial Neuron (MAN) for even larger improvement in energy consumption and adapt the training process to ensure minimal degradation in accuracy. We evaluated the proposed design on 5 recognition applications. The results show, 35% and 60% reduction in energy consumption, for neuron sizes of 8 bits and 12 bits, respectively, with a maximum of ~2.83% loss in network accuracy, compared to a conventional neuron implementation. We also achieve 37% and 62% reduction in area for a neuron size of 8 bits and 12 bits, respectively, under iso-speed conditions.
Gopalakrishnan Srinivasan, Parami Wijesinghe, Syed Shakib Sarwar
et al.
Multilayered artificial neural networks (ANN) have found widespread utility in classification and recognition applications. The scale and complexity of such networks together with the inadequacies of general purpose computing platforms have led to a significant interest in the development of efficient hardware implementations. In this work, we focus on designing energy efficient on-chip storage for the synaptic weights. In order to minimize the power consumption of typical digital CMOS implementations of such large-scale networks, the digital neurons could be operated reliably at scaled voltages by reducing the clock frequency. On the contrary, the on-chip synaptic storage designed using a conventional 6T SRAM is susceptible to bitcell failures at reduced voltages. However, the intrinsic error resiliency of NNs to small synaptic weight perturbations enables us to scale the operating voltage of the 6TSRAM. Our analysis on a widely used digit recognition dataset indicates that the voltage can be scaled by 200mV from the nominal operating voltage (950mV) for practically no loss (less than 0.5%) in accuracy (22nm predictive technology). Scaling beyond that causes substantial performance degradation owing to increased probability of failures in the MSBs of the synaptic weights. We, therefore propose a significance driven hybrid 8T-6T SRAM, wherein the sensitive MSBs are stored in 8T bitcells that are robust at scaled voltages due to decoupled read and write paths. In an effort to further minimize the area penalty, we present a synaptic-sensitivity driven hybrid memory architecture consisting of multiple 8T-6T SRAM banks. Our circuit to system-level simulation framework shows that the proposed synaptic-sensitivity driven architecture provides a 30.91% reduction in the memory access power with a 10.41% area overhead, for less than 1% loss in the classification accuracy.
A. Zavdoveev, Ya. Beygelzimer, E. Pashinska
et al.
The work considers the effect of deformation on the exhaustion of the plasticity resource of steel 45 after the drawing deformation. The results of theoretical and experimental studies of damage accumulation are listed. A possibility of employment of a scanning electron microscope to observe submicropores is demonstrated.