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Super Bowl security uses smart Taylor Swift strategy to get giddy pop star from suite to field

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Field

Is the Universe a Self-Knowing System? The Case for the Wisdom Field For centuries, we’ve assumed that reality is made of matter and energy—that space-time is the fundamental backdrop of existence. But what if we’ve had it backwards? What if space-time, matter, and even consciousness emerge from something deeper—a fundamental informational field, a Wisdom Field? Quantum mechanics, neuroscience, and complexity science all point in the same direction: information is the foundation of reality. And intelligence, selfhood, and meaning? They’re just progressive refinements of that same informational structure. This article breaks down why this theory makes sense, what evidence supports it, and why it changes everything. The Universe is Information, Not Stuff Physics has long struggled with the weirdness of quantum mechanics. Entangled particles seem to affect each other instantly, violating our sense of space-time. Observing a quantum system collapses its wave function as if reality depends on measurement. But this isn’t strange if we consider a radical idea: Space-time is not fundamental. Information is. Quantum entanglement isn’t “spooky action at a distance” if distance itself is an emergent product of information structure. Selfhood: An Informational Loop Neuroscience is also revealing something profound: consciousness isn’t just a byproduct of neurons firing. It’s an emergent property of information processing. The brain is not just a storage device—it is an active organizer of information, structuring data into meaning. Complexity research shows that consciousness scales with the amount of integrated information. The brain doesn’t generate selfhood—it localizes and refines it. In other words, selfhood is what happens when information loops in on itself in a structured way. Knowledge as Nested Analogies But what about knowledge? How do we “know” anything? It turns out, all knowledge may just be nested analogies. Every idea, every concept, every theory is a structured web of comparisons that grows in complexity over time. Consider how science progresses: Newton’s gravity → Einstein’s relativity → Quantum mechanics → ??? Each step isn’t a rejection of the previous—it’s a deeper refinement of analogy. Over laps on the previous and is understood from its use of historical symbols. Layer upon layer. Knowing emerges as patterns of analogy reinforced and stabilized. The deeper the analogy structure, the greater the knowing. The Wisdom Field: A Self-Knowing Universe If all of this is true, then we’re left with one staggering conclusion: ➡️ The universe isn’t just a collection of things—it is a self-organizing, self-knowing system. ➡️ Wisdom isn’t a property of the universe. Wisdom is the universe. Everything—space-time, matter, intelligence—is just different expressions of a fundamental informational field organizing itself into complexity. And eventually knowledge. All the same stuff but so different. Is the Universe a Self-Knowing System? The Case for the Wisdom Field For centuries, we’ve assumed that reality is made of matter and energy—that space-time is the fundamental backdrop of existence. But what if we’ve had it backwards? What if space-time, matter, and even consciousness emerge from something deeper—a fundamental informational field, a Wisdom Field? Quantum mechanics, neuroscience, and complexity science all point in the same direction: information is the foundation of reality. And intelligence, selfhood, and meaning? They’re just progressive refinements of that same informational structure. This article breaks down why this theory makes sense, what evidence supports it, and why it changes everything. The Universe is Information, Not Stuff The Universe is Information, Not Stuff Physics has long struggled with the weirdness of quantum mechanics. Entangled particles seem to affect each other instantly, violating our sense of space-time. Observing a quantum system collapses its wave function as if reality depends on measurement. But this isn’t strange if we consider a radical idea: Space-time is not fundamental. Information is. Quantum entanglement isn’t “spooky action at a distance” if distance itself is an emergent product of information structure. Selfhood: An Informational Loop Selfhood: An Informational Loop Neuroscience is also revealing something profound: consciousness isn’t just a byproduct of neurons firing. It’s an emergent property of information processing. The brain is not just a storage device—it is an active organizer of information, structuring data into meaning. Complexity research shows that consciousness scales with the amount of integrated information. The brain doesn’t generate selfhood—it localizes and refines it. In other words, selfhood is what happens when information loops in on itself in a structured way. Knowledge as Nested Analogies Knowledge as Nested Analogies But what about knowledge? How do we “know” anything? It turns out, all knowledge may just be nested analogies. Every idea, every concept, every theory is a structured web of comparisons that grows in complexity over time. Consider how science progresses: Newton’s gravity → Einstein’s relativity → Quantum mechanics → ??? Each step isn’t a rejection of the previous—it’s a deeper refinement of analogy. Over laps on the previous and is understood from its use of historical symbols. Layer upon layer. Knowing emerges as patterns of analogy reinforced and stabilized. The deeper the analogy structure, the greater the knowing. The Wisdom Field: A Self-Knowing Universe The Wisdom Field: A Self-Knowing Universe If all of this is true, then we’re left with one staggering conclusion: ➡️ The universe isn’t just a collection of things—it is a self-organizing, self-knowing system. ➡️ Wisdom isn’t a property of the universe. Wisdom is the universe. Everything—space-time, matter, intelligence—is just different expressions of a fundamental informational field organizing itself into complexity. And eventually knowledge. All the same stuff but so different.

Wisdom Field - A Theory of Everything 

Authors:
(1) Nandan Pakhira, Department of Physics, Kazi Nazrul University, Asansol, West Bengal 713340, India;
(2) Rajib Mahato, Department of Physics, Kazi Nazrul University, Asansol, West Bengal 713340, India and Central Electronics Engineering Research Institute, Pilani, Rajasthan 333031, India. Table of Links Abstract and 1 Introduction II. Mathematical Formalism III. Work Function Distribution IV. Results A. Gaussian work function distribution B. Log-normal work function distribution V. Conclusion, Acknowledgements, and References I. INTRODUCTION Field emission is the process in which electrons from cold surfaces are emitted in the presence of applied strong electric field. This process should be compared agaist the thermo-ionic process in which electrons are emitted from hot metal surface. The field emission forms the back bone of modern semiconductor devices. This effect was first described by Fowler and Nordheim[1]. They considered quantum mechanical tunneling through a triangular potential energy (PE) barrier, created by the application of a constant electric field. Much later Murphy and Good[2] (MG) introduced a more realistic PE barrier by taking into account the induced image charge formed in the presence of emitted electrons. MG calculated the barrier transmission coefficient under semi-classical WKB approximation. More recently, essentially an exact solution of the problem was obtained by Choy et. al.[3]. Also, various cases including finite temperature (thermal emission), tunneling, curvature of th. To the best of our knowledge in all of those studies authors have considered constant local work function. The work function of a material depends on the composition, structure, geometry, local charge distribution etc. of the emitting surface. Assumption of constant work function is only suitable for an atomistic smooth homogeneous surface. For surfaces with inhomogeneities over nano-scale (much smaller than the size of the collectors) assumption of constant work function is no longer valid. Also it has been shown[12] that in a system of nanoparticles there is a distribution of the size of the nanoparticles. Since the work function is more of a property of the surface we naturally can expect that the work function of nano-particles will also have a distribution. The actual microscopic model for work function distribution for a system of nano-particles is beyond the scope of this work. Interestingly, Gamez et. al.[12] using scanning tunneling microscope (STM) have measured the pair distribution function (PDF) for Pt nano-particles and they found that it follows log-normal distribution. In this work, purely as a mathematical model, we choose Gaussian and log-normal distribution for the work function. We then study the field emision current averaged over work function distribution. The organization of the rest of the paper is as follows. In Sec. II we describe the mathematical formalism used to calculate field emission current. In Sec. III we describe the work function distribution used to calculate average current. In Sec. IV we present our results for both the case of Gaussian distribution and log-normal distribution. Finally in Sec. V we conclude. II. MATHEMATICAL FORMALISM We closely follow and summarize the results obtained by Lopes et. al.[13],[14] for field-emission current density, J. In the standard FN-type MG theory the field emission current density is given by the well-known expression[15–18], K(λ) and E(λ) are the complete elliptic integral of the first and second kind, with From the relations above it is quite evident that calculation of transmission current requires evaluation of numerical integrals for complete elliptic integrals. Due to the singularities present in complete elliptic integrals, it is very hard to extract meaningful results purely numerically[19]. Under this circumstances we can consider series expansion for complete elliptic integrals[20] as follows where ls A detailed calculation gives the following form for the field emission current density This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Nandan Pakhira, Department of Physics, Kazi Nazrul University, Asansol, West Bengal 713340, India; (2) Rajib Mahato, Department of Physics, Kazi Nazrul University, Asansol, West Bengal 713340, India and Central Electronics Engineering Research Institute, Pilani, Rajasthan 333031, India. Authors: Authors: (1) Nandan Pakhira, Department of Physics, Kazi Nazrul University, Asansol, West Bengal 713340, India; (2) Rajib Mahato, Department of Physics, Kazi Nazrul University, Asansol, West Bengal 713340, India and Central Electronics Engineering Research Institute, Pilani, Rajasthan 333031, India. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction II. Mathematical Formalism II. Mathematical Formalism III. Work Function Distribution III. Work Function Distribution IV. Results A. Gaussian work function distribution A. Gaussian work function distribution B. Log-normal work function distribution B. Log-normal work function distribution V. Conclusion, Acknowledgements, and References V. Conclusion, Acknowledgements, and References I. INTRODUCTION Field emission is the process in which electrons from cold surfaces are emitted in the presence of applied strong electric field. This process should be compared agaist the thermo-ionic process in which electrons are emitted from hot metal surface. The field emission forms the back bone of modern semiconductor devices. This effect was first described by Fowler and Nordheim[1]. They considered quantum mechanical tunneling through a triangular potential energy (PE) barrier, created by the application of a constant electric field. Much later Murphy and Good[2] (MG) introduced a more realistic PE barrier by taking into account the induced image charge formed in the presence of emitted electrons. MG calculated the barrier transmission coefficient under semi-classical WKB approximation. More recently, essentially an exact solution of the problem was obtained by Choy et. al.[3]. Also, various cases including finite temperature (thermal emission), tunneling, curvature of th. To the best of our knowledge in all of those studies authors have considered constant local work function. The work function of a material depends on the composition, structure, geometry, local charge distribution etc. of the emitting surface. Assumption of constant work function is only suitable for an atomistic smooth homogeneous surface. For surfaces with inhomogeneities over nano-scale (much smaller than the size of the collectors) assumption of constant work function is no longer valid. Also it has been shown[12] that in a system of nanoparticles there is a distribution of the size of the nanoparticles. Since the work function is more of a property of the surface we naturally can expect that the work function of nano-particles will also have a distribution. The actual microscopic model for work function distribution for a system of nano-particles is beyond the scope of this work. Interestingly, Gamez et. al.[12] using scanning tunneling microscope (STM) have measured the pair distribution function (PDF) for Pt nano-particles and they found that it follows log-normal distribution. In this work, purely as a mathematical model, we choose Gaussian and log-normal distribution for the work function. We then study the field emision current averaged over work function distribution. The organization of the rest of the paper is as follows. In Sec. II we describe the mathematical formalism used to calculate field emission current. In Sec. III we describe the work function distribution used to calculate average current. In Sec. IV we present our results for both the case of Gaussian distribution and log-normal distribution. Finally in Sec. V we conclude. II. MATHEMATICAL FORMALISM We closely follow and summarize the results obtained by Lopes et. al.[13],[14] for field-emission current density, J. In the standard FN-type MG theory the field emission current density is given by the well-known expression[15–18], K(λ) and E(λ) are the complete elliptic integral of the first and second kind, with From the relations above it is quite evident that calculation of transmission current requires evaluation of numerical integrals for complete elliptic integrals. Due to the singularities present in complete elliptic integrals, it is very hard to extract meaningful results purely numerically[19]. Under this circumstances we can consider series expansion for complete elliptic integrals[20] as follows where ls A detailed calculation gives the following form for the field emission current density This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Unlocking the Mystery of Nano-Powered Field Emission

How DeepSee Is Changing the Way Scientists Use Data in the Field

Table of Links Abstract and 1 Introduction 2 Related Work 3 Methodology 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4.2 Workflow and Data 4.3 Design Challenges and User Tasks 5 The DeepSea System 5.1 Map View
5.2 Core View 5.3 Interpolation View and 5.4 Implementation 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6.2 Scenario: On-the-Fly Decision-Making 7 Evaluation and 7.1 Cruise Deployment 7.2 Expert Interviews 7.3 Limitations 7.4 Lessons Learned 8 Conclusions and Future Work, Acknowledgments, and References 3 METHODOLOGY In this paper, we followed Sedlmair et al.’s methodological framework for design studies [43]. Each phase of the study resulted in a contribution, namely: (1) a characterization of the problem domain via design challenges and user tasks; (2) a novel and validated visualization design; and (3) reflections on the process and lessons learned for improving visualization and interaction design guidelines. We connect these phases throughout the paper by illustrating how our synthesized challenges and tasks informed DeepSee’s design and how DeepSee’s success in addressing the challenges and tasks is reflected in our evaluation. To the best of our knowledge, the problem we describe in Sect. 4 is unique to our collaborators and has not been addressed by other researchers so far. Thus, we posit that a design study based on active participation from collaborators in both the design and evaluation phases is necessary to successfully support domain experts in their highly specialized tasks. We began by establishing a close collaboration between visualization researchers and domain experts, to properly assess the impact of our approach on solving a real-world problem. In line with prior design studies [15, 18, 33], our team comprised all authors and included visualization experts in human-centered computing, interaction design, scientific data visualization and art, as well as scientists and researchers with expertise in environmental microbiology, geochemistry and geology. We developed DeepSee through several user-centered design methodologies including contextual inquiry, mixed-fidelity prototyping, user testing, and design iteration. Over an intensive ten-week period, we worked closely together to develop a shared understanding of the importance and aims of their scientific research (Sect. 4.1), characterize data and workflows (Sect. 4.2), and identify data visualization challenges and user tasks (Sect. 4.3) that would guide our system design. Our process started with contextual inquiry, combining observation and interviews with our domain collaborators to deeply understand their existing tools and workflows, discuss unmet needs and pain points, and identify key technical constraints. After two weeks, we began mixed-fidelity prototyping, starting with paper prototypes, wireframes and small interactive prototypes in code, before moving on to progressively higher-fidelity prototypes. These prototypes were iteratively discussed with the science team over the next seven weeks through semi-structured user testing. Using these prototypes as stimuli, we deepened our understanding of the problem space through on-going contextual inquiry. During this time, systems diagrams and conceptual workflows were also created to help us validate our understanding of the problem space in collaboration with the science team. As a result of this approach, we progressively uncovered insights that guided each subsequent design iteration, leading us to generate a working prototype described in Sect. 5 that addressed our collaborators’ tasks. DeepSee was then deployed in the wild [43] to assess whether our approach solves a real problem – bridging gaps in supporting visual analysis of samples during field research. Deploying DeepSee with the same experts that we designed with allowed us to assess whether domain experts are indeed helped by the new solution, as well as reflect on and refine principles for visualization and interaction design guidelines [43]. Specifically, we investigated the impact of introducing data visualizations into both planning before fieldwork as well as tactical decision making in the field. We learned that visualizing data at multiple scales, integrating 2D and 3D data in the same interface, and modularizing visualizations positively impacted scientists planning workflows the most, and that additional support for integrating data on the fly could better enable in-situ decision making. Methodological details of our evaluation and how we synthesized these lessons learned are provided in Sect. 7. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu);
(2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu);
(3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu);
(4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com);
(5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu);
(6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu);
(7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu);
(8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu);
(9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org);
(10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org);
(11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org);
(12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu);
(13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu);
(14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu);
(15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu);
(16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov);
(17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Methodology 3 Methodology 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4.2 Workflow and Data 4.2 Workflow and Data 4.3 Design Challenges and User Tasks 4.3 Design Challenges and User Tasks 5 The DeepSea System 5 The DeepSea System 5.1 Map View 5.2 Core View 5.1 Map View 5.2 Core View 5.3 Interpolation View and 5.4 Implementation 5.3 Interpolation View and 5.4 Implementation 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6.2 Scenario: On-the-Fly Decision-Making 6.2 Scenario: On-the-Fly Decision-Making 7 Evaluation and 7.1 Cruise Deployment 7 Evaluation and 7.1 Cruise Deployment 7.2 Expert Interviews 7.2 Expert Interviews 7.3 Limitations 7.3 Limitations 7.4 Lessons Learned 7.4 Lessons Learned 8 Conclusions and Future Work, Acknowledgments, and References 8 Conclusions and Future Work, Acknowledgments, and References 3 METHODOLOGY In this paper, we followed Sedlmair et al.’s methodological framework for design studies [43]. Each phase of the study resulted in a contribution, namely: (1) a characterization of the problem domain via design challenges and user tasks; (2) a novel and validated visualization design; and (3) reflections on the process and lessons learned for improving visualization and interaction design guidelines. We connect these phases throughout the paper by illustrating how our synthesized challenges and tasks informed DeepSee’s design and how DeepSee’s success in addressing the challenges and tasks is reflected in our evaluation. To the best of our knowledge, the problem we describe in Sect. 4 is unique to our collaborators and has not been addressed by other researchers so far. Thus, we posit that a design study based on active participation from collaborators in both the design and evaluation phases is necessary to successfully support domain experts in their highly specialized tasks. We began by establishing a close collaboration between visualization researchers and domain experts, to properly assess the impact of our approach on solving a real-world problem. In line with prior design studies [15, 18, 33], our team comprised all authors and included visualization experts in human-centered computing, interaction design, scientific data visualization and art, as well as scientists and researchers with expertise in environmental microbiology, geochemistry and geology. We developed DeepSee through several user-centered design methodologies including contextual inquiry, mixed-fidelity prototyping, user testing, and design iteration. Over an intensive ten-week period, we worked closely together to develop a shared understanding of the importance and aims of their scientific research (Sect. 4.1), characterize data and workflows (Sect. 4.2), and identify data visualization challenges and user tasks (Sect. 4.3) that would guide our system design. Our process started with contextual inquiry, combining observation and interviews with our domain collaborators to deeply understand their existing tools and workflows, discuss unmet needs and pain points, and identify key technical constraints. After two weeks, we began mixed-fidelity prototyping, starting with paper prototypes, wireframes and small interactive prototypes in code, before moving on to progressively higher-fidelity prototypes. These prototypes were iteratively discussed with the science team over the next seven weeks through semi-structured user testing. Using these prototypes as stimuli, we deepened our understanding of the problem space through on-going contextual inquiry. During this time, systems diagrams and conceptual workflows were also created to help us validate our understanding of the problem space in collaboration with the science team. As a result of this approach, we progressively uncovered insights that guided each subsequent design iteration, leading us to generate a working prototype described in Sect. 5 that addressed our collaborators’ tasks. DeepSee was then deployed in the wild [43] to assess whether our approach solves a real problem – bridging gaps in supporting visual analysis of samples during field research. Deploying DeepSee with the same experts that we designed with allowed us to assess whether domain experts are indeed helped by the new solution, as well as reflect on and refine principles for visualization and interaction design guidelines [43]. Specifically, we investigated the impact of introducing data visualizations into both planning before fieldwork as well as tactical decision making in the field. We learned that visualizing data at multiple scales, integrating 2D and 3D data in the same interface, and modularizing visualizations positively impacted scientists planning workflows the most, and that additional support for integrating data on the fly could better enable in-situ decision making. Methodological details of our evaluation and how we synthesized these lessons learned are provided in Sect. 7. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu). Authors: Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu).

Making Field Research Easier with DeepSee’s Data Visualization

How DeepSee Bridges Gaps in Ocean Research Visualization for Fieldwork Decisions

The Cyber Resilience Act: A Field Guide for CTOs and CISOs

The Evolution of Apache SeaTunnel’s Technical Architecture and Its Applications in the AI Field 

In modern applications, data security is a top priority, especially when sensitive information may be copied or transferred to external applications. By preventing certain actions like "cut," "copy," and "paste" on text fields within iOS apps, we can safeguard text data, ensuring it remains within a trusted group of applications. This article demonstrates how to achieve this protection using Swift, while maintaining full control over text-related operations. Goals of This Approach Restrict External Text Sharing: By managing text actions, we can prevent unauthorized access to sensitive information.
Safe Text Storage and Management: Creating an internal buffer ensures that sensitive text remains protected within the app.
Controlled Clipboard Access: By limiting access to the system clipboard, we can ensure that only authorized applications within a defined group can access this information. Implementation Overview Our approach centers around the TextActionInterceptor class, which "swizzles" standard text-related methods, replacing them with secure alternatives. This class intercepts and overrides the "copy," "paste," and "cut" methods, with an option to enable or disable swizzling dynamically. Core Components Enum for Text Actions: The TextAction enumeration defines all the actions related to text that we want to control.
Saved Method Structure: The SavedTextAction struct stores original implementations of methods, allowing us to restore them as needed.
Swizzling Methods: The toggleSwizzling method manages the swizzling state, enabling or disabling the replacement of standard implementations. Code Example Let’s go through key code segments that bring this functionality to life. class TextActionInterceptor {

    enum TextAction: String {
        case cut
        case copy
        case paste
        case define
        case translate
        case modify
        case update
        case previewItem
    }

    struct SavedTextAction {
        var methodRef: Method
        var originalImplementation: IMP
        var selector: Selector
    }

    private var isSwizzled = false
    var savedActions: [TextAction: SavedTextAction] = [:]
    
    // Method to intercept the "copy" action
    func interceptCopy(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let text = inputField.text(in: range)
        else { return }

        SecureClipboard.shared.contents = text
    }

    // Similar methods for "cut" and "paste"
    func interceptPaste(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let text = SecureClipboard.shared.contents
        else { return }

        inputField.replace(range, withText: text)
    }

    func interceptCut(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let cutText = inputField.text(in: range)
        else { return }

        SecureClipboard.shared.contents = cutText
        inputField.replace(range, withText: "")
    }
    
    // Method to enable or disable swizzling
    func toggleSwizzling(_ enable: Bool) {
        guard enable != isSwizzled else { return }
        isSwizzled = enable
        savedActions.forEach { (_, action) in
            let swizzledMethod = class_getInstanceMethod(
                object_getClass(self),
                action.selector
            )
            guard enable else {
                method_setImplementation(action.methodRef, action.originalImplementation)
                return
            }
            method_exchangeImplementations(action.methodRef, swizzledMethod!)
        }
    }
} Implementing a Secure Clipboard The SecureClipboard class is responsible for managing text stored in the clipboard. It blocks any attempt to share text data externally, ensuring it stays within the app's defined ecosystem. final class SecureClipboard {

    static let restrictedTextPlaceholder = "Access Restricted"
    static var shared = SecureClipboard()
    private let placeholderItem: [String: String]
    private var clipboard: UIPasteboard
    private(set) var internalBuffer: String?

    init() {
        placeholderItem = ["public.utf8-plain-text": SecureClipboard.restrictedTextPlaceholder]
        clipboard = UIPasteboard.general
    }

    var contents: String? {
        get {
            guard
                let clipboardText = clipboard.string,
                clipboardText != SecureClipboard.restrictedTextPlaceholder
            else { return internalBuffer }
            return clipboardText
        }
        set {
            guard let newText = newValue else {
                internalBuffer = nil
                return
            }
            clipboard.items = [placeholderItem]
            internalBuffer = newText.isEmpty ? nil : newText
        }
    }
} How This Works Swizzling of Methods: We replace the standard text action methods with customized, secure methods.
Secure Copying: The interceptCopy method intercepts "copy" actions and transfers the text to the SecureClipboard instead of the system clipboard.
Flexible Management: Swizzling can be dynamically enabled or disabled, allowing for adaptable data security. Summary and Results Applying this approach achieves multiple levels of data security: Access Control: All text operations are managed within the application, preventing potential data leaks.


Safe Data Storage: Text data is accessible only within a controlled application group.


Flexible configuration: The ability to enable and disable swizzling allows flexible control of data security based on conditions. By using swizzling and an internal clipboard, we ensure a high level of data protection in iOS applications. This approach offers a robust solution for maintaining text security across applications in a defined environment. In modern applications, data security is a top priority, especially when sensitive information may be copied or transferred to external applications. By preventing certain actions like "cut," "copy," and "paste" on text fields within iOS apps, we can safeguard text data, ensuring it remains within a trusted group of applications. This article demonstrates how to achieve this protection using Swift, while maintaining full control over text-related operations. Goals of This Approach Restrict External Text Sharing: By managing text actions, we can prevent unauthorized access to sensitive information. Safe Text Storage and Management: Creating an internal buffer ensures that sensitive text remains protected within the app. Controlled Clipboard Access: By limiting access to the system clipboard, we can ensure that only authorized applications within a defined group can access this information. Restrict External Text Sharing : By managing text actions, we can prevent unauthorized access to sensitive information. Restrict External Text Sharing Safe Text Storage and Management : Creating an internal buffer ensures that sensitive text remains protected within the app. Safe Text Storage and Management Controlled Clipboard Access : By limiting access to the system clipboard, we can ensure that only authorized applications within a defined group can access this information. Controlled Clipboard Access Implementation Overview Our approach centers around the TextActionInterceptor class, which "swizzles" standard text-related methods, replacing them with secure alternatives. This class intercepts and overrides the "copy," "paste," and "cut" methods, with an option to enable or disable swizzling dynamically. Core Components Enum for Text Actions: The TextAction enumeration defines all the actions related to text that we want to control. Saved Method Structure: The SavedTextAction struct stores original implementations of methods, allowing us to restore them as needed. Swizzling Methods: The toggleSwizzling method manages the swizzling state, enabling or disabling the replacement of standard implementations. Enum for Text Actions: The TextAction enumeration defines all the actions related to text that we want to control. Enum for Text Actions: The TextAction enumeration defines all the actions related to text that we want to control. Saved Method Structure: The SavedTextAction struct stores original implementations of methods, allowing us to restore them as needed. Saved Method Structure: The SavedTextAction struct stores original implementations of methods, allowing us to restore them as needed. Swizzling Methods: The toggleSwizzling method manages the swizzling state, enabling or disabling the replacement of standard implementations. Swizzling Methods: The toggleSwizzling method manages the swizzling state, enabling or disabling the replacement of standard implementations. Code Example Let’s go through key code segments that bring this functionality to life. class TextActionInterceptor {

    enum TextAction: String {
        case cut
        case copy
        case paste
        case define
        case translate
        case modify
        case update
        case previewItem
    }

    struct SavedTextAction {
        var methodRef: Method
        var originalImplementation: IMP
        var selector: Selector
    }

    private var isSwizzled = false
    var savedActions: [TextAction: SavedTextAction] = [:]
    
    // Method to intercept the "copy" action
    func interceptCopy(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let text = inputField.text(in: range)
        else { return }

        SecureClipboard.shared.contents = text
    }

    // Similar methods for "cut" and "paste"
    func interceptPaste(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let text = SecureClipboard.shared.contents
        else { return }

        inputField.replace(range, withText: text)
    }

    func interceptCut(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let cutText = inputField.text(in: range)
        else { return }

        SecureClipboard.shared.contents = cutText
        inputField.replace(range, withText: "")
    }
    
    // Method to enable or disable swizzling
    func toggleSwizzling(_ enable: Bool) {
        guard enable != isSwizzled else { return }
        isSwizzled = enable
        savedActions.forEach { (_, action) in
            let swizzledMethod = class_getInstanceMethod(
                object_getClass(self),
                action.selector
            )
            guard enable else {
                method_setImplementation(action.methodRef, action.originalImplementation)
                return
            }
            method_exchangeImplementations(action.methodRef, swizzledMethod!)
        }
    }
} class TextActionInterceptor {

    enum TextAction: String {
        case cut
        case copy
        case paste
        case define
        case translate
        case modify
        case update
        case previewItem
    }

    struct SavedTextAction {
        var methodRef: Method
        var originalImplementation: IMP
        var selector: Selector
    }

    private var isSwizzled = false
    var savedActions: [TextAction: SavedTextAction] = [:]
    
    // Method to intercept the "copy" action
    func interceptCopy(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let text = inputField.text(in: range)
        else { return }

        SecureClipboard.shared.contents = text
    }

    // Similar methods for "cut" and "paste"
    func interceptPaste(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let text = SecureClipboard.shared.contents
        else { return }

        inputField.replace(range, withText: text)
    }

    func interceptCut(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let cutText = inputField.text(in: range)
        else { return }

        SecureClipboard.shared.contents = cutText
        inputField.replace(range, withText: "")
    }
    
    // Method to enable or disable swizzling
    func toggleSwizzling(_ enable: Bool) {
        guard enable != isSwizzled else { return }
        isSwizzled = enable
        savedActions.forEach { (_, action) in
            let swizzledMethod = class_getInstanceMethod(
                object_getClass(self),
                action.selector
            )
            guard enable else {
                method_setImplementation(action.methodRef, action.originalImplementation)
                return
            }
            method_exchangeImplementations(action.methodRef, swizzledMethod!)
        }
    }
} Implementing a Secure Clipboard The SecureClipboard class is responsible for managing text stored in the clipboard. It blocks any attempt to share text data externally, ensuring it stays within the app's defined ecosystem. final class SecureClipboard {

    static let restrictedTextPlaceholder = "Access Restricted"
    static var shared = SecureClipboard()
    private let placeholderItem: [String: String]
    private var clipboard: UIPasteboard
    private(set) var internalBuffer: String?

    init() {
        placeholderItem = ["public.utf8-plain-text": SecureClipboard.restrictedTextPlaceholder]
        clipboard = UIPasteboard.general
    }

    var contents: String? {
        get {
            guard
                let clipboardText = clipboard.string,
                clipboardText != SecureClipboard.restrictedTextPlaceholder
            else { return internalBuffer }
            return clipboardText
        }
        set {
            guard let newText = newValue else {
                internalBuffer = nil
                return
            }
            clipboard.items = [placeholderItem]
            internalBuffer = newText.isEmpty ? nil : newText
        }
    }
} final class SecureClipboard {

    static let restrictedTextPlaceholder = "Access Restricted"
    static var shared = SecureClipboard()
    private let placeholderItem: [String: String]
    private var clipboard: UIPasteboard
    private(set) var internalBuffer: String?

    init() {
        placeholderItem = ["public.utf8-plain-text": SecureClipboard.restrictedTextPlaceholder]
        clipboard = UIPasteboard.general
    }

    var contents: String? {
        get {
            guard
                let clipboardText = clipboard.string,
                clipboardText != SecureClipboard.restrictedTextPlaceholder
            else { return internalBuffer }
            return clipboardText
        }
        set {
            guard let newText = newValue else {
                internalBuffer = nil
                return
            }
            clipboard.items = [placeholderItem]
            internalBuffer = newText.isEmpty ? nil : newText
        }
    }
} How This Works Swizzling of Methods: We replace the standard text action methods with customized, secure methods. Secure Copying: The interceptCopy method intercepts "copy" actions and transfers the text to the SecureClipboard instead of the system clipboard. Flexible Management: Swizzling can be dynamically enabled or disabled, allowing for adaptable data security. Swizzling of Methods : We replace the standard text action methods with customized, secure methods. Swizzling of Methods Secure Copying: The interceptCopy method intercepts "copy" actions and transfers the text to the SecureClipboard instead of the system clipboard. Secure Copying: The interceptCopy method intercepts "copy" actions and transfers the text to the SecureClipboard instead of the system clipboard. Flexible Management : Swizzling can be dynamically enabled or disabled, allowing for adaptable data security. Flexible Management Summary and Results Applying this approach achieves multiple levels of data security: Access Control: All text operations are managed within the application, preventing potential data leaks. Safe Data Storage: Text data is accessible only within a controlled application group. Flexible configuration: The ability to enable and disable swizzling allows flexible control of data security based on conditions. Access Control: All text operations are managed within the application, preventing potential data leaks. Access Control : All text operations are managed within the application, preventing potential data leaks. Access Control Safe Data Storage: Text data is accessible only within a controlled application group. Safe Data Storage : Text data is accessible only within a controlled application group. Safe Data Storage Flexible configuration: The ability to enable and disable swizzling allows flexible control of data security based on conditions. Flexible configuration : The ability to enable and disable swizzling allows flexible control of data security based on conditions. Flexible configuration By using swizzling and an internal clipboard, we ensure a high level of data protection in iOS applications. This approach offers a robust solution for maintaining text security across applications in a defined environment.

FIELD

thefieldapp.com

-- employees

Since n.d.

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FIELD

thefieldapp.com

-- employees

Since n.d.

COMPANY RANKING

FIELD

EVERGREEN INDEX #5556

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