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On the undecidability of the panopticon detection problem

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arXiv 2021年

作者： Liagkou, V. Nastou, P.E. Spirakis, P. Stamatiou, Y.C. Computer Technology Institute and Press - "Diophantus" University of Patras Campus 26504 Greece Department of Computer Science University of Liverpool UK and Computer Engineering and Informatics Department University of Patras 26504 Greece Department of Business Administration University of Patras 26504 Greece University of Ioannina Department of Informatics and Telecommunications Koatakioi Arta47100 Greece Department of Mathematics University of the Aegean Applied Mathematics and Mathematical Modeling Laboratory Samos Greece

The Panopticon (which means "watcher of everything") is a well-known structure of continuous surveillance and discipline proposed by Bentham in 1785. This device was, later, used by Foucault and other philosophers as a paradigm and metaphor for the study of constitutional power and knowledge as well as a model of individuals’ deprivation of freedom. Nowadays, technological achievements have given rise to new, non-physical (unlike prisons), means of constant surveillance that transcend physical boundaries. This, combined with the confession of some governmental institutions that they actually collaborate with these Internet giants to collect or deduce information about people, creates a worrisome situation of several co-existing Panopticons that can act separately or in close collaboration. Thus, they can only be detected and identified through the expense of (perhaps considerable) effort. In this paper we provide a theoretical framework for studying the detectability status of Panopticons that fall under two theoretical, but not unrealistic, definitions. We show, using Oracle Turing Machines, that detecting modern day, ICT-based, Panopticons is an undecidable problem. Furthermore, we show that for each sufficiently expressive formal system, we can effectively construct a Turing Machine for which it is impossible to prove, within the formal system, either that it is a Panopticon or it is not a Panopticon. © 2021, CC BY.

关键词： Turing machines

Model for Analysis of High-Frequency Modulated Random Signals

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Model for Analysis of High-Frequency Modulated Random Signal...

International Conference on Advanced computer Information Technologies (ACIT)

作者： Ihor Javorskyj Roman Yuzefovych Oleh Lychak Pavlo Semenov Roman Sliepko Yulia Romanyshyn Department of Methods and Facilities for Acquisition and Processing Diagnostic Signals Karpenko Physico-mechanical Institute of NAS of Ukraine Lviv Ukraine Institute of Telecommunication and Computer Sciences Bydgoszcz University of Science and Technology Bydgoszcz Poland Department of Applied Mathematics Lviv Polytechnic National University Lviv Ukraine Department of Hoisting and Transport Machines and Engineering of Port Technological Equipment Odesa National Maritime University Odesa Ukraine Department of Records Management and Information Activity Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine

The use of the Hilbert transform for the analysis of periodically non-stationary random signals (PNRSs), whose carrier harmonics are modulated by jointly stationary high-frequency random processes is discussed. A representation of the signal in the form of a superposition of high-frequency components is obtained and it is shown that these components are jointly periodically non-stationary random processes. The properties of the band-pass filtered signals are examined, and it is shown that band-pass filtering can reduce both the number of signal variance cyclic harmonics and their amplitudes. We show that it is possible to extract the quadratures of narrow-band high-frequency modulation processes using the Hilbert transform.

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Stacked filter bank based descriptor for human action recognition from depth sequences

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Stacked filter bank based descriptor for human action recogn...

2019 IEEE Conference on Information and Communication Technology, CICT 2019

作者： Rathnakara Shetty, P. Shekar, B.H. Mestetsky, Leonid Manju Prasad, M. Mangalore University Department of Computer Science Karnataka India Lomonosov Moscow State University Department of Computational Mathematics Moscow Russia

ISBN: (纸本)9781728153988

Registering the motion cues from a video to produce a compact representation is a crucial stage in video based Human Action Recognition (HAR). Exploiting the most prominent features using an efficient descriptor from such a representation also plays equally significant role in the performance of recognition models. In this work, we present a concise Depth Motion Map with striding which registers the motion cues from depth sequences on a video and a novel Filter Bank based descriptor, wherein a Taylor Series Expansion (TSE) filter, a Riesz filter and a gradient filter are stacked together to extract the prominent features. We empirically evaluate the feasibility of our method on MSR Action 3D dataset under standard protocols, achieving state-of-the-art results. © 2019 IEEE.

关键词： Filter banks

Pseudo-Bayesian DIP Denoising as a Preprocessing Step for Kinetic Modelling in Dynamic PET

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Pseudo-Bayesian DIP Denoising as a Preprocessing Step for Ki...

IEEE Symposium on Nuclear science (NSS/MIC)

作者： Alexander C. Whitehead Kjell Erlandsson Ander Biguri Scott D. Wollenweber Jamie R. McClelland Kris Thielemans Institute of Nuclear Medicine University College London London UK Centre for Medical Image Computing University College London London UK Department of Computer Science University College London London UK Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge UK Molecular Imaging and Computed Tomography Engineering GE Healthcare Waukesha USA

Noise (among other artefacts) could be considered to be the bane of PET. Many methods have been proposed to alleviate the worst annoyances of noise, however, not many take into account the temporal nature of dynamically acquired PET. Here, we propose an adaption of a method, which has seen increasing attention in more traditional imaging denoising circles. Deep Image Prior exploits the initialisation of a carefully designed neural network, so as to treat it as a bank of custom filters, which are to be trained and used afresh on each new image, independently. Deep Image Prior has seen adaptation to PET previously (including dynamic PET), however, many of these adaptations do not take into account the large memory requirements of the method. Additionally, most previous work does not address the main weakness of the Deep Image Prior, its stopping criteria. Here, we propose a method which is both memory efficient, and includes a smoothing regularisation. In addition, we provide uncertainty estimates by incorporating a Bayesian approximation (using dropout), and prototype a training scheme by which the model is fit on all data simultaneously. The denoised images are then used as input for kinetic modelling. To evaluate the method, dynamic XCAT simulations have been produced, with a field of view of the lung and liver. The results of the new methods (along with total variation and the old Deep Image Prior) have been compared by; a visual analysis, SSIM, and K i values. Results indicate that the new methods potentially outperform the old methods, without increasing computation time, while reducing system requirements.

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Probing black hole in Starobinsky-Bel-Robinson gravity with thermodynamical analysis, effective force and gravitational weak lensing

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arXiv 2024年

作者： Mustafa, G. Ditta, Allah Javed, Faisal Atamurotov, Farruh Hussain, Ibrar Ahmedov, Bobomurat Department of Physics Zhejiang Normal University Jinhua321004 China Institute of Fundamental and Applied Research National Research University TIIAME Kori Niyoziy 39 Tashkent100000 Uzbekistan Department of Mathematics Shanghai University Newtouch Center for Mathematics of Shanghai University Shanghai200444 China New Uzbekistan University Movarounnahr street 1 Tashkent100000 Uzbekistan Central Asian University Milliy Bog' Street 264 Tashkent111221 Uzbekistan University of Tashkent for Applied Sciences Str. Gavhar 1 Tashkent100149 Uzbekistan School of Electrical Engineering and Computer Science National University of Sciences and Technology H-12 Islamabad Pakistan Ulugh Beg Astronomical Institute Astronomy St 33 Tashkent100052 Uzbekistan Institute of Theoretical Physics National University of Uzbekistan Tashkent100174 Uzbekistan

In this work, we investigate the effects of plasma and the coupling parameter β > 0, on the thermodynamic properties and weak gravitational lensing by the Schwarzschild-like black hole in the Starobinsky-Bel-Robinson gravity (SBRG).We observe that the horizon radius and the corrected entropy of the Schwarzschild-like black hole in the SBRG are not much sensitive to the parameter β. On contrary the energy emission rate of the Schwarzschild-like black hole in the SBRG is sensitive to the parameter β and decreases with increase in the values of the parameter β. We see that the Schwarzschild-like black hoe in the SBRG is stable as the thermodynamicl temperature is positive for different values of the parameter β. Moreover we observe that the deflection angle of photon beam by the black hole in uniform plasma, nonuniform self-interacting scalar plasma and non-singular isothermal gas sphere reduces with the parameter β, against the impact parameter b. We see that the deflection angle enhances with increase in the concentration of the plasma fields for all the three types of plasma media. Further we find that the magnification of the image due to lensing increases in a higher concentration of plasma field. It is interesting to notice that the image magnification in uniform plasma is much higher as compared to the one in nonuniform plasma field. We compare our results with those for the Schwarzschild black hole of General Relativity. Further, effective force is also calculated for the current analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： Thermodynamics

Counting cycles in planar triangulations

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arXiv 2022年

作者： Lo, On-Hei Solomon Zamfirescu, Carol T. Département d'Informatique et de Recherche Opérationnelle Université de Montréal Pavillon André-Aisenstadt 2920 chemin de la Tour MontréalQCH3T 1J4 Canada Department of Applied Mathematics Computer Science and Statistics Ghent University Krijgslaan 281 - S9 Ghent9000 Belgium Department of Mathematics Babeş-Bolyai University Cluj-Napoca Romania

We investigate the minimum number of cycles of specified lengths in planar n-vertex triangulations G. It is proven that this number is Ω(n) for any cycle length at most 3 + max{rad(G∗), ⌈(n−3/2)log32⌉}, where rad(G∗) denotes the radius of the triangulation's dual, which is at least logarithmic but can be linear in the order of the triangulation. We also show that there exist planar hamiltonian n-vertex triangulations containing O(n) many k-cycles for any k ∈ {⌈n −5 √n⌉, ..., n}. Furthermore, we prove that planar 4-connected n-vertex triangulations contain Ω(n) many k-cycles for every k ∈ {3, ..., n}, and that, under certain additional conditions, they contain Ω(n2) k-cycles for many values of k, including n. © 2022, CC BY.

关键词： Triangulation

Interpretability by design using computer vision for behavioral sensing in child and adolescent psychiatry

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arXiv 2022年

作者： Frumosu, Flavia Dalia Lønfeldt, Nicole N. Mora-Jensen, A.-R. Cecilie Das, Sneha Lund, Nicklas Leander Pagsberg, A. Katrine Clemmensen, Line K.H. Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Child and Adolescent Mental Health Center Copenhagen University Hospital Mental Health Services CPH Hellerup Denmark Department of Clinical Medicine Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Department of Clinical Biochemistry Hospital Glostrup University Hospital Glostrup Denmark

Observation is an essential tool for understanding and studying human behavior and mental states. However, coding human behavior is a time-consuming, expensive task, in which reliability can be difficult to achieve and bias is a risk. Machine learning (ML) methods offer ways to improve reliability, decrease cost, and scale up behavioral coding for application in clinical and research settings. Here, we use computer vision to derive behavioral codes or concepts of a gold standard behavioral rating system, offering familiar interpretation for mental health professionals. Features were extracted from videos of clinical diagnostic interviews of children and adolescents with and without obsessive-compulsive disorder. Our computationally-derived ratings were comparable to human expert ratings for negative emotions, activity-level/arousal and anxiety. For the attention and positive affect concepts, our ML ratings performed reasonably. However, results for gaze and vocalization indicate a need for improved data quality or additional data modalities. © 2022, CC BY.

关键词： computer vision

Coupling streaming AI and HPC ensembles to achieve 100-1000× faster biomolecular simulations

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arXiv 2021年

作者： Brace, Alexander Yakushin, Igor Ma, Heng Trifan, Anda Munson, Todd Foster, Ian Ramanathan, Arvind Lee, Hyungro Turilli, Matteo Jha, Shantenu Data Science and Learning Division Mathematics and Computer Science Division Argonne National Laboratory Lemont United States Department of Computer Science University of Chicago Chicago United States Center For Biophysics University of Illinois Urbana-Champaign Champaign United States RADICAL-Lab Rutgers University New Brunswick United States Computational Science Initiative Brookhaven National Laboratory Upton United States

Machine learning (ML)-based steering can improve the performance of ensemble-based simulations by allowing for online selection of more scientifically meaningful computations. We present DeepDriveMD, a framework for ML-driven steering of scientific simulations that we have used to achieve orders-of-magnitude improvements in molecular dynamics (MD) performance via effective coupling of ML and HPC on large parallel computers. We discuss the design of DeepDriveMD and characterize its performance. We demonstrate that DeepDriveMD can achieve between 100–1000× acceleration for protein folding simulations relative to other methods, as measured by the amount of simulated time performed, while covering the same conformational landscape as quantified by the states sampled during a simulation. Experiments are performed on leadership-class platforms on up to 1020 nodes. The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity. © 2021, CC BY.

关键词： Molecular dynamics

Selecting the number of components in PCA via random signflips

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arXiv 2020年

作者： Hong, David Sheng, Yue Dobriban, Edgar Department of Electrical and Computer Engineering University of Delaware United States Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania United States Department of Statistics and Data Science University of Pennsylvania United States

Principal component analysis (PCA) is a foundational tool in modern data analysis, and a crucial step in PCA is selecting the number of components to keep. However, classical selection methods (e.g., scree plots, parallel analysis, etc.) lack statistical guarantees in the increasingly common setting of large-dimensional data with heterogeneous noise, i.e., where each entry may have a different noise variance. Moreover, it turns out that these methods, which are highly effective for homogeneous noise, can fail dramatically for data with heterogeneous noise. This paper proposes a new method called signflip parallel analysis (FlipPA) for the setting of approximately symmetric noise: it compares the data singular values to those of "empirical null" matrices generated by flipping the sign of each entry randomly with probability one-half. We develop a rigorous theory for FlipPA, showing that it has nonasymptotic type I error control and that it consistently selects the correct rank for signals rising above the noise floor in the large-dimensional limit (even when the noise is heterogeneous). We also rigorously explain why classical permutation-based parallel analysis degrades under heterogeneous noise. Finally, we illustrate that FlipPA compares favorably to state-of-the art methods via numerical simulations and an illustration on data coming from astronomy. Copyright © 2020, The Authors. All rights reserved.

关键词： Principal component analysis