When a cognitive system modifies its own functioning, what exactly does it modify: a low-level rule, a control rule, or the norm that evaluates its own revisions? Cognitive science describes executive control, metacognition, and hierarchical learning with precision, but lacks a formal framework distinguishing these targets of transformation. Contemporary artificial intelligence likewise exhibits self-modification without common criteria for comparison with biological cognition. We show that the question of what counts as a self-modifying system entails a minimal structure: a hierarchy of rules, a fixed core, and a distinction between effective rules, represented rules, and causally accessible rules. Four regimes are identified: (1) action without modification, (2) low-level modification, (3) structural modification, and (4) teleological revision. Each regime is anchored in a cognitive phenomenon and a corresponding artificial system. Applied to humans, the framework yields a central result: a crossing of opacities. Humans have self-representation and causal power concentrated at upper hierarchical levels, while operational levels remain largely opaque. Reflexive artificial systems
We attempt to modify the time-convolutionless master equation (TCL-ME) to be more resistant to breakdown. We remove the standard assumption that a portion of the generator is invertible by instead taking the Moore-Penrose inverse. We rederive the perturbative expansion using Israel and Charnes' result, and test the equation up to sixth and fifth orders on the Jaynes-Cummings and Ising models, respectively. We find that in both cases, the modified equation fails to capture the dynamics of the exact solution compared to the standard TCL due to the terms of the modified equation scaling exponentially with the dimension of the bath, and connect this failure to a loss of convergence of the perturbative expansion.
Existing face stylization methods always acquire the presence of the target (style) domain during the translation process, which violates privacy regulations and limits their applicability in real-world systems. To address this issue, we propose a new method called MODel-drIven Face stYlization (MODIFY), which relies on the generative model to bypass the dependence of the target images. Briefly, MODIFY first trains a generative model in the target domain and then translates a source input to the target domain via the provided style model. To preserve the multimodal style information, MODIFY further introduces an additional remapping network, mapping a known continuous distribution into the encoder's embedding space. During translation in the source domain, MODIFY fine-tunes the encoder module within the target style-persevering model to capture the content of the source input as precisely as possible. Our method is extremely simple and satisfies versatile training modes for face stylization. Experimental results on several different datasets validate the effectiveness of MODIFY for unsupervised face stylization.
This paper is a prelude and elaboration on Proofs that Modify Proofs. Here we present an ordinal analysis of a fragment of the $μ$-calculus around the strength of parameter-free $Π^1_2$-comprehension using the same approach as that paper, interpreting functions on proofs as proofs in an expanded system. We build up the ordinal analysis in several stages, beginning by illustrating the method systems at the strength of paremeter-free $Π^1_1$-comprehension and full $Π^1_1$-comprehension.
A Self modifying code is code that modifies its own instructions during execution time. It is nowadays widely used, especially in malware to make the code hard to analyse and to detect by anti-viruses. Thus, the analysis of such self modifying programs is a big challenge. Pushdown systems (PDSs) is a natural model that is extensively used for the analysis of sequential programs because they allow to accurately model procedure calls and mimic the program's stack. In this work, we propose to extend the PushDown System model with self-modifying rules. We call the new model Self-Modifying PushDown System (SM-PDS). A SM-PDS is a PDS that can modify its own set of transitions during execution. We show how SM-PDSs can be used to naturally represent self-modifying programs and provide efficient algorithms to compute the backward and forward reachable configurations of SM-PDSs. We implemented our techniques in a tool and obtained encouraging results. In particular, we successfully applied our tool for the detection of self-modifying malware.
Self modifying code is code that can modify its own instructions during the execution of the program. It is extensively used by malware writers to obfuscate their malicious code. Thus, analysing self modifying code is nowadays a big challenge. In this paper, we consider the LTL model-checking problem of self modifying code. We model such programs using self-modifying pushdown systems (SM-PDS), an extension of pushdown systems that can modify its own set of transitions during execution. We reduce the LTL model-checking problem to the emptiness problem of self-modifying Büchi pushdown systems (SM-BPDS). We implemented our techniques in a tool that we successfully applied for the detection of several self-modifying malware. Our tool was also able to detect several malwares that well-known antiviruses such as BitDefender, Kinsoft, Avira, eScan, Kaspersky, Qihoo-360, Baidu, Avast, and Symantec failed to detect.
The large temperature gradients in the solar transition region present a significant challenge to large scale numerical modelling of the Sun's atmosphere. In response, a variety of techniques have been developed which modify the thermodynamics of the system. This sacrifices accuracy in the transition region in favour of accurately tracking the coronal response to heating events. Invariably, the modification leads to an artificial broadening of the transition region. Meanwhile, many contemporary models of the solar atmosphere rely on tracking energy flux from the lower atmosphere, through the transition region and into the corona. In this article, we quantify how the thermodynamic modifications affect the rate of energy injection into the corona. We consider a series of one-dimensional models of atmospheric loops with different numerical resolutions and treatments of the thermodynamics. Then, using Alfvén waves as a proxy, we consider how energy injection rates are modified in each case. We find that the thermodynamic treatment and the numerical resolution significantly modify Alfvén travel times, the eigenfrequencies and eigenmodes of the system, and the rate at which energy is inj
With the rapid development of Virtual Reality (VR) technology, the research of User Interface (UI), especially menus, in the VR environment has attracted more and more attention. However, it is very tedious for researchers to develop UI from scratch or modify existing functions and there are no easy-to-use tools for efficient development. This paper aims to present VRMenuDesigner, a flexible and modular toolkit for automatically generating/modifying VR menus. This toolkit is provided as open-source library and easy to extend to adapt to various requirements. The main contribution of this work is to organize the menus and functions with object-oriented thinking, which makes the system very understandable and extensible. VRMenuDesigner includes two key tools: Creator and Modifier for quickly generating and modifying elements. Moreover, we developed several built-in menus and discussed their usability. After a brief review and taxonomy of 3D menus, the architecture and implementation of the toolbox are introduced.
Probabilistic programming languages and other machine learning applications often require samples to be generated from a categorical distribution where the probability of each one of $n$ categories is specified as a parameter. If the parameters are hyper-parameters then they need to be modified, however, current implementations of categorical distributions take $\mathcal{O}(n)$ time to modify a parameter. If $n$ is large and the parameters are being frequently modified, this can become prohibitive. Here we present the insight that a Huffman tree is an efficient data structure for representing categorical distributions and present algorithms to generate samples as well as add, delete and modify categories in $\mathcal{O}(\log(n))$ time. We demonstrate that the time to sample from the distribution remains, in practice, within a few percent of the theoretical optimal value. The same algorithm may also be useful in the context of adaptive Huffman coding where computational efficiency is important.
We introduce the standard model of elementary particles and discuss the reasons why we have to modify it. Emphasis is put on the indications from the neutrinos and on the role of the Higgs particle; some promising theoretical ideas, like quark-lepton symmetry, existence of super-heavy ``right-handed'' neutrinos, grand unification and supersymmetry at the weak scale, are introduced and shortly discussed.
Attention-based neural encoder-decoder frameworks have been widely used for image captioning. Many of these frameworks deploy their full focus on generating the caption from scratch by relying solely on the image features or the object detection regional features. In this paper, we introduce a novel framework that learns to modify existing captions from a given framework by modeling the residual information, where at each timestep the model learns what to keep, remove or add to the existing caption allowing the model to fully focus on "what to modify" rather than on "what to predict". We evaluate our method on the COCO dataset, trained on top of several image captioning frameworks and show that our model successfully modifies captions yielding better ones with better evaluation scores.
I briefly discuss the challenges presented by attempting to modify general relativity to obtain an explanation for the observed accelerated expansion of the universe. Foremost among these are the questions of theoretical consistency - the avoidance of ghosts in particular - and the constraints imposed by precision local tests of gravity within the solar system. For those models that clear these highly constraining hurdles, modern observational cosmology offers its own suite of tests, improving with upcoming datasets, that offer the possibility of ruling out modified gravity approaches or providing an intriguing hint of new infrared physics. In the second half of the talk, I discuss a recent approach to extracting cosmology from higher-dimensional induced gravity models.
Maxwell's equations are modified to incorporate a scalar field to account for the London's superconductivity. Assuming the electromagnetic field is described by the Klein-Gordon equation, London's equations of superconductivity are then derived, which are invariant under a new set of transformations. The invariance of the modified Maxwell's equations under these transformations requires the electromagnetic field and the scalar field to be scale-invariant. Relying on these transformations, a quantized Josephson-like current is derived. This current gives rise to a residual magnetic field. The spatial and temporal variations of the scalar field are linked to the electric polarization such that the polarization vector is curl-less.
The topological properties of field configurations in gauge theory contain important data about the (generalized) global symmetries of the theory as well as potential inconsistencies in the form of gauge anomalies. In this work we modify the topological classes of Abelian $p$-form fields, generating new global variants of gauge theories. These modifications implement constraints directly on the classifying space of the gauge field and its cohomology classes via homotopy fiber construction. This general approach allows us to investigate the universal effects of the constraints on the conserved global charges encoded in gauge characteristic classes. We further demonstrate that this procedure generically leads to new topological sectors introducing additional global charges and anomalies in the modified gauge theories.
Many Generalized Uncertainty Principle (GUP) models modify the inner-product measure to ensure symmetric position or momentum operators. We show that an alternate approach to these GUPs is to symmetrize the operators rather than modifying the inner product. This preserves the standard momentum space allowing the eigenstates and maximally localized states of the modified position operator to have a standard position representation. We compare both approaches and highlight their merits.
We investigate how deviations from the Bekenstein-Hawking entropy modify black-hole spacetimes through the recently proposed entropy-geometry correspondence. For four representative modified entropies, namely Barrow, Rényi, Kaniadakis, and logarithmic, we derive the corresponding effective metrics and analyze their thermodynamic and topological classification using the off-shell free energy and winding numbers. We show that Barrow and Rényi entropies yield a single unstable sector with global charge $W=-1$, while logarithmic and Kaniadakis corrections produce canceling defects with $W=0$, revealing topological structures absent in the Schwarzschild case. Using the modified metrics, we further calculate the photon-sphere radius and shadow size, showing that each modified entropy relation induces characteristic optical shifts. Thus, by comparing with Event Horizon Telescope observations of Sgr A$^\ast$, we extract new bounds on all entropy-deformation parameters. Our results demonstrate that thermodynamic topology, together with photon-sphere phenomenology, offers a viable way to test generalized entropy frameworks and probe departures from the Bekenstein-Hawking area law.
Most of the potential physical effects of loop quantum gravity have been derived in effective models that modify the constraints of canonical general relativity in specific forms. Emergent modified gravity evaluates important conditions that ensure the existence of a compatible geometrical space-time interpretation of canonical solutions, as well as phase-space covariance under different choices of canonical variables. This setting, specialized to modifications suggested by loop quantum gravity, is therefore an important contribution to physical evaluations of this approach to quantum gravity. Here, it is shown that emergent modified gravity restricts several ambiguities that existed in previous formulations, rules out several specific candidates, and provides a unified treatment of different types of (holonomy) modifications that had been thought to be physically distinct.
We present a modification to the diffusion entropy analysis method for detecting temporal scaling. Diffusion entropy analysis detects temporal scaling in a data set by converting a time-series into a diffusion trajectory and using the entropy of that trajectory to measure the temporal scaling in the data. We modify this by performing an event detection step to construct the diffusion trajectory. The new modified diffusion entropy analysis offers substantial improvements over the original method, especially for noisy data. We describe the method's purpose, how it works step-by-step, its application, and future development.
Practically all the full-fledged MOND theories propounded to date are of the modified-gravity (MG) type: they modify only the Newtonian, Poisson action of the gravitational potential, or the general-relativistic Einstein-Hilbert action, leaving other terms (inertia) intact. Here, I discuss the interpretation of MOND as modified inertia (MI). My main aim is threefold: (a) to advocate exploring MOND theories beyond MG, and appreciating their idiosyncrasies, (b) to highlight the fact that secondary predictions of such theories can differ materially from those of MG theories, (c) to demonstrate some of this with specific MI models. I discuss some definitions and generalities concerning MI. I then present instances of MI in physics, and the lessons we can learn from them for MOND. I then concentrate on a specific class of nonrelativistic, MOND, MI models, and contrast their predictions with those of the two workhorse, MG theories -- AQUAL and QUMOND. The MI models predict possibly a stronger external-field effect -- e.g. on low acceleration systems in the solar neighborhood -- such as very wide binary stars -- and on vertical motions in disc galaxies. More generally, the workings of the