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This chapter introduces the fundamental concepts and rules of quantum computing. In parallel, it develops an initial, easy-to-understand Python code base for building and simulating small-scale quantum circuits and algorithms. The chapter details single qubits, superposition, quantum states with many qubits, operators, including a sizable set of important single-qubit gates and controlled gates. The Bloch sphere and the quantum circuit notation are introduced. Entanglement follows, that fascinating “spooky action at a distance,” as Einstein called it. The chapter then discusses maximally entangled Bell states, the no-cloning and no-deleting theorems, local and global phases, and uncomputation. The quantum postulates are discussed briefly as preparation for the discussion on measurements.
Traditionally, linear cryptanalysis exploits linear approximations with atypically high absolute correlation. In this chapter, we discuss instead how linear approximations with correlation zero can be used. This variant of linear cryptanalysis is called zero-correlation linear cryptanalysis.
If more than one good linear approximation is available, then it is natural to try to exploit all of them simultaneously. This is called multiple linear cryptanalysis. The first part of this chapter discusses multiple linear cryptanalysis in general. The second part focuses on the special case with a set of masks that forms a vector space, which is called multidimensional linear cryptanalysis.
Finding linear trails with high absolute correlation quickly becomes tedious work, especially for ciphers with a more complicated structure than the example that we have worked with so far. Since the total number of trails is finite, finding linear trails with a maximal absolute correlation is an example of a combinatorial optimization problem. This chapter discusses three commonly used optimization methods: Matsui’s branch and bound method, mixed-integer linear programming, and satisfiability or satisfiability modulo theories. At the same time, the chapter introduces two additional example ciphers that follow a different design strategy.
Lyon uses the COVID epidemic to think about the instrumentalizing role of surveillance capitalism in digital society. He argues that the tech solutionism proffered by tech companies during the pandemic too often implied that democratic practices and social justice are at least temporarily dispensable for some greater good, with disastrous consequences for human flourishing. As a counterpoint, Lyon uses the notion of an ethics of care as a way to refocus on the importance of articulating the conditions that will enable the humans who live in datafied societies to live meaningful lives. He then offers Eric Stoddart’s notion of the “common gaze” to begin to imagine what those conditions might be. From this perspective, surveillance can be conceptualized as a gaze for the common good with a “preferential optic” focused on the conditions that will alleviate the suffering of the marginalized.
Murakami Wood makes both an empirical and a theoretical contribution by analysing the discourses contained in smart city marketing materials to create a detailed description of the kind of human that smart city developers and promoters envision as smart city residents. The resulting portrait of the “platform human” – a being whose entrepreneurial and libertarian needs are seamlessly enabled by technology built into the lived environment – is informed by a technologically-enabled notion of class, a particular and specific political identity of smart citizens as property-owning, entrepreneurial, and libertarian, and a generic environmental ‘goodness’ associated with smart platforms. The combination of these three elements resonates strongly with transhumanist speciation where humans are imagined as data-driven, surveillant, and robotic.
This paper presents the development of a graph autoencoder architecture capable of performing projection-based model-order reduction (PMOR) using a nonlinear manifold least-squares Petrov–Galerkin (LSPG) projection scheme. The architecture is particularly useful for advection-dominated flows modeled by unstructured meshes, as it provides a robust nonlinear mapping that can be leveraged in a PMOR setting. The presented graph autoencoder is constructed with a two-part process that consists of (1) generating a hierarchy of reduced graphs to emulate the compressive abilities of convolutional neural networks (CNNs) and (2) training a message passing operation at each step in the hierarchy of reduced graphs to emulate the filtering process of a CNN. The resulting framework provides improved flexibility over traditional CNN-based autoencoders because it is readily extendable to unstructured meshes. We provide an analysis of the interpretability of the graph autoencoder’s latent state variables, where we find that the Jacobian of the decoder for the proposed graph autoencoder provides interpretable mode shapes akin to traditional proper orthogonal decomposition modes. To highlight the capabilities of the proposed framework, which is named geometric deep least-squares Petrov–Galerkin (GD-LSPG), we benchmark the method on a one-dimensional Burgers’ model with a structured mesh and demonstrate the flexibility of GD-LSPG by deploying it on two test cases for two-dimensional Euler equations that use an unstructured mesh. The proposed framework is more flexible than using a traditional CNN-based autoencoder and provides considerable improvement in accuracy for very low-dimensional latent spaces in comparison with traditional affine projections.
Central to drawn representations of activism and memory are ideas of embodiment and trace. From DIY protest signs to craftivism, the articulation of protest and memory is connected to the handmade trace of a witnessing individual present in time and place. This is reflected in comics scholarship through the notion of the drawn line conveying subjective experience through the trace of the body.
This article will consider the relationship between witnessing, truth claims, autographic drawing, and memory at a moment when AI image-generation tools have called into question the connection of drawn traces to their origin in time, space, materiality, and the body.
Although a combination of critical AI theory and comics studies, this article will outline ways in which generative AI presents a challenge to these ideas. Through comparison of Joe Sacco’s graphic reportage with recent AI images of conflict and history, the article considers the truth claims of images that are the products of computational and algorithmic processes considered broadly.
Comics scholarship has been slow to critically respond to these new conditions, and the task of disentangling the human/non-human in ontologies of trace is now compounded by generative drawings, which represent the outcome of archival reappropriation defined by opaque algorithmic parameters. This article will explore theoretical assumptions around authenticity and truth claims in analogue, computational, algorithmic, and generative drawing practice and ask what kinds of theory and practice are appropriate if activist graphic memoir is to endure as documents of political memory.
This article examines the short-lived Marvel comic Misty (1985–1986), created by feminist cartoonist Trina Robbins, as a case study in how comics can invite and depend on reader participation. We draw on an archival collection of over 1,000 fan letters and fashion designs submitted to Misty, along with recent communications with former readers, to explore how children and young adults influenced both the published comic and its surrounding culture. We argue that readers’ contributions – ranging from clothing designs to story ideas – constituted a form of activism: they challenged corporate publishing practices, promoted new story directions, and built local fan communities. Highlighting the recent memories of Misty’s reader contributors, we show how engaging in the comic’s participatory culture could, in turn, have lasting effects on readers, shaping their confidence, career paths, and creative philosophies. By reframing Misty’s collective participatory culture as activism and placing it in conversation with readers’ personal memories, this study contributes to scholarship on comics, fandom, and memory: even small acts of reader engagement can transform both cultural texts and individual lives.
In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input–output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the suitability of outputs for their corresponding inputs, and trains a feedforward neural network to learn the mapping relationship using the selected data. Experimental results show that the learning cost is significantly reduced while maintaining competitive performance compared to two representative meshing algorithms.
We study exponentiable functors in the context of synthetic $\infty$-categories. We do this within the framework of simplicial homotopy type theory of Riehl and Shulman. Our main result characterizes exponentiable functors. In order to achieve this, we explore Segal type completions. Moreover, we verify that our result is semantically sound.
Design Science is the discipline that studies the creation of artifacts – products, services, and systems and their embedding in our physical, virtual, psychological, economic, and social environments. This editorial is a collective effort of the Design Science Journal’s editorial board members, past and present. The journal’s inaugural 2015 editorial, “Design Science: Why, What and How,” reflected the thoughts and vision of that first editorial board for the new journal and the discipline it represented. The present contribution offers the reflections of editors who served the journal in the past 10 years. The individual contributions were not primed and are presented here unedited for conformity or consistency. Differently from the 2015 editorial, there is no effort to synthesize the individual contributions, leaving the task to our readers, who can draw their own conclusions about the Design Science Journal and community accomplishments to date, and the challenges ahead.
Rotterdam, a city in the Netherlands, experienced significant bombing in its city centre during the Second World War. Despite the trauma associated with this event, in 1948, the city adopted a new motto: ‘Sterker Door Strijd’, translating as ‘Stronger Through Struggle’. This motto remains visible today under the city’s coat of arms, symbolising the resilience and strength of its inhabitants as they rebuilt their city. ‘Sterker Door Strijd’ has become a central aspect of Rotterdam’s development, particularly in its architecture and urban planning. It showcases a shift in the city’s memory from pain to pride and hope for the future. The motto beautifully embodies Rigney’s ‘memory–activism nexus’ from a spatial perspective, reconstructing the city’s traumatic memory of destruction into a narrative of resistance. The motto is widely known and felt by every Rotterdammer, including foreigners who live and work in the city, like me. The visual essay ‘From Struggle to Strength’ poetically focuses on the city of Rotterdam and its motto. It intimately follows my personal artistic journey and my embodiment in the city. The story unfolds as I walk and draw around the city. Additionally, I interviewed inhabitants focusing on the challenges of social housing issues in the city, such as displacement and demolition and considering how the residents are actively resisting these issues. Through these interactions, the visual essay reflects on the transformative power of memory and activism in shaping the city’s past, present and future.
In this article, we identify the comics of the Real Cost of Prisons Project as graphic memory work that denaturalises ‘penal common sense’ and engages in graphic witnessing. To show how the United States’ ‘crime problem’ established a seemingly natural link between crime and incarceration, we first review the criminological aspects of American comics memory. Then, we demonstrate how The Real Cost of Prisons Comix reworks the historical and social dynamics of the American carceral regime through its abolitionist framework. We discuss the importance of the image–text form for abolitionist pedagogy by reflecting on the position of comics in carceral textual cultures and the use of these comics in activist education. Finally, we emphasise that the comics created by the Real Cost of Prisons Project should be understood as pedagogical tools in a broader abolitionist movement whereby the historical and social education initiated by memory work aims to ignite collaborative praxis. In this sense, we show that their activist memory work is a means to demystify the historical processes of carceral expansion, enabling its audience to develop historical consciousness.
This paper focuses on six-degree-of-freedom (six-DOF) spatial cable-suspended parallel robots with eight cables (8-6 CSPRs) because the redundantly actuated CSPRs are relevant in many applications, such as large-scale assembly and handling tasks, and pick-and-place operations. One of the main concerns for the 8-6 CSPRs is the stability because employing cables with strong flexibility and unidirectional restraint operates the end-effector of the robot under external disturbances. As a consequence, this paper attempts to address two key issues related to the 8-6 CSPRs: the force-pose stability measure method and the stability sensitivity analysis method. First, a force-pose stability measure model taking into account the poses of the end-effector and the cable tensions of the 8-6 CSPR is presented, in which two cable tension influencing factors and two position influencing factors are developed, while an attitude influence function representing the influence of the attitudes of the end-effector on the stability of the robot is constructed. And furthermore, a new type of workspace related to the force-pose stability of the 8-6 CSPRs is defined and generated in this paper. Second, a force-pose stability sensitivity analysis method for the 8-6 CSPRs is developed with the gray relational analysis method, where the relationship between the force-pose stability of the robot and the 14 influencing factors (the end-effector’s poses and cable tensions) is investigated to reveal the sequence of the 14 influencing factors on the force-pose stability of the robot. Finally, the proposed force-pose stability measure method and stability sensitivity analysis method for the 8-6 CSPRs are verified through simulations.
Low-dimensional representation and clustering of network data are tasks of great interest across various fields. Latent position models are routinely used for this purpose by assuming that each node has a location in a low-dimensional latent space and by enabling node clustering. However, these models fall short through their inability to simultaneously determine the latent space dimension and number of clusters. Here we introduce the latent shrinkage position cluster model (LSPCM), which addresses this limitation. The LSPCM posits an infinite-dimensional latent space and assumes a Bayesian nonparametric shrinkage prior on the latent positions’ variance parameters resulting in higher dimensions having increasingly smaller variances, aiding the identification of dimensions with non-negligible variance. Further, the LSPCM assumes the latent positions follow a sparse finite Gaussian mixture model, allowing for automatic inference on the number of clusters related to non-empty mixture components. As a result, the LSPCM simultaneously infers the effective dimension of the latent space and the number of clusters, eliminating the need to fit and compare multiple models. The performance of the LSPCM is assessed via simulation studies and demonstrated through application to two real Twitter network datasets from sporting and political contexts. Open-source software is available to facilitate widespread use of the LSPCM.