A model of octopus epidermis pattern mimicry mechanisms using inverse operation of the Turing reaction model

Many cephalopods such as octopi and squid can purposefully and rapidly change their skin color. Furthermore, it is widely known that some octopi have the ability to rapidly change the color and unevenness of their skin to mimic their surroundings. However, there has been little research published on the mechanisms by which an octopus recognizes its surrounding landscape and changes its skin pattern. We are unaware of any hypothetical model that explains this mimicry mechanism to date. In this study, the mechanism of octopus skin pattern change was assumed to be based on the Turing pattern model. Here, pattern formation using the Turing model was realized using an equivalent filter calculation model and a cellular automaton instead of directly solving the differential equations. It was shown that this model can create various patterns using two feature parameters. Furthermore, for visual recognition where two features are extracted from the Turing pattern image, a method that requires minimal calculation using the characteristics of the cellular Turing pattern model is proposed. These two calculations can be expressed in the same mathematical frame based on the cellular automaton model using a convolution filter. As a result, a model that is capable of extracting features from patterns and reconstructing those patterns rapidly can be created. This represents a basic model of the mimicry mechanism of octopi. Further, this study demonstrates the potential for creating a model with minimal learning calculation for application to machine learning.

Vision-Guided Recognition Method for Working State of Robot Arm based on Machine Learning

To realize the capture of the target in any position within the working range of the robot arm, a vision-guided target recognition and positioning control method based on machine learning was proposed to improve the accuracy of the working state recognition. The system grabs four different contours of workpieces (triangle, pentagon, round, square and place in the designated area as the task, and by using the MATLAB to process the image information, and all the connected domains are marked bythe neighborhood areamarking algorithm. Later, the logarithmic coordinates-Fourier transform template matchingmethod is adopted to identify workpiece types and extract their centroid as the positioning reference coordinates.Combined with the 3-DOF robot arm, the standard D-H parametric method is usedto establishthe robot arm kinematics model, and through the inverse operation of the robot armand according to the position of the workpiece coordinates,the joint angle of each robot arm can be obtained. Later, it is sent to the lower microcontroller Arduino through a serial port, and then the control instruction is completed by the Arduino torealize the capture and placement of the workpiece and complete the work state recognition task.The experimental results show that the working state recognition system can meet the design requirements.

Fault Attacks on CCA-secure Lattice KEMs

NIST’s post-quantum standardization effort very recently entered its final round. This makes studying the implementation-security aspect of the remaining candidates an increasingly important task, as such analyses can aid in the final selection process and enable appropriately secure wider deployment after standardization. However, lattice-based key-encapsulation mechanisms (KEMs), which are prominently represented among the finalists, have thus far received little attention when it comes to fault attacks.Interestingly, many of these KEMs exhibit structural similarities. They can be seen as variants of the encryption scheme of Lyubashevsky, Peikert, and Rosen, and employ the Fujisaki-Okamoto transform (FO) to achieve CCA2 security. The latter involves re-encrypting a decrypted plaintext and testing the ciphertexts for equivalence. This corresponds to the classic countermeasure of computing the inverse operation and hence prevents many fault attacks.In this work, we show that despite this inherent protection, practical fault attacks are still possible. We present an attack that requires a single instruction-skipping fault in the decoding process, which is run as part of the decapsulation. After observing if this fault actually changed the outcome (effective fault) or if the correct result is still returned (ineffective fault), we can set up a linear inequality involving the key coefficients. After gathering enough of these inequalities by faulting many decapsulations, we can solve for the key using a bespoke statistical solving approach. As our attack only requires distinguishing effective from ineffective faults, various detection-based countermeasures, including many forms of double execution, can be bypassed.We apply this attack to Kyber and NewHope, both of which belong to the aforementioned class of schemes. Using fault simulations, we show that, e.g., 6,500 faulty decapsulations are required for full key recovery on Kyber512. To demonstrate practicality, we use clock glitches to attack Kyber running on a Cortex M4. As we argue that other schemes of this class, such as Saber, might also be susceptible, the presented attack clearly shows that one cannot rely on the FO transform’s fault deterrence and that proper countermeasures are still needed.

Determination of the hydrophone phase-frequency response by its amplitude-frequency response

One of the tasks of the COOMET 786/RU/19 pilot comparisons is to check the correctness of the hydrophone model proposed in VNIIFTRI, consisting of an advance line and a minimum-phase part, including the effect of sound diffraction and resonance properties of the active element. This model makes it possible to use the Hilbert transform to obtain the phase-frequency response from the amplitude-frequency response as well as for inverse operation. The results of measuring experiments performed using facilities of the State Primary Standard GET 55-2017 are presented. For many practical tasks, it is not necessary to obtain the phase-frequency response for an acoustic center of the receiver. It is enough to determine the shape of the phase-frequency response using much less laborious methods. The question of which of the characteristics is expedient to determine during calibration - for an acoustic center, or for a point on the surface of an active element, deserves a discussion among specialists performing acoustic measurements.

One Step in-Memory Solution of Inverse Algebraic Problems

Machine learning requires to process large amount of irregular data and extract meaningful information. Von-Neumann architecture is being challenged by such computation, in fact a physical separation between memory and processing unit limits the maximum speed in analyzing lots of data and the majority of time and energy are spent to make information travel from memory to the processor and back. In-memory computing executes operations directly within the memory without any information travelling. In particular, thanks to emerging memory technologies such as memristors, it is possible to program arbitrary real numbers directly in a single memory device in an analog fashion and at the array level, execute algebraic operation in-memory and in one step. In this chapter the latest results in accelerating inverse operation, such as the solution of linear systems, in-memory and in a single computational cycle will be presented.

On Application Oriented Fuzzy Numbers for Imprecise Investment Recommendations

The subtraction of fuzzy numbers (FNs) is not an inverse operator to FNs addition. The family of all oriented FNs (OFNs) may be considered as symmetrical closure of all the FNs family in that the subtraction is an inverse operation to addition. An imprecise present value is modelled by a trapezoidal oriented FN (TrOFN). Then, the expected discount factor (EDF) is a TrOFFN too. This factor may be applied as a premise for invest-making. Proposed decision strategies are dependent on a comparison of an oriented fuzzy profit index and the specific profitability threshold. This way we get an investment recommendation described as a fuzzy subset on the fixed rating scale. Risk premium measure is a special case of profit index. Further in the paper, the Sharpe’s ratio, the Jensen’s ratio, the Treynor’s ratio, the Sortino’s ratio, Roy’s criterion and the Modiglianis’ coefficient are generalised for the case when an EDF is given as a TrOFN. In this way, we get many different imprecise recommendations. For this reason, an imprecise recommendation management module is described. Obtained results show that the proposed theory can be used as a theoretical background for financial robo-advisers. All theoretical considerations are illustrated by means of a simple empirical case study.

Noise Suppression in Compressive Single-Pixel Imaging

Compressive single-pixel imaging (CSPI) is a novel imaging scheme that retrieves images with nonpixelated detection. It has been studied intensively for its minimum requirement of detector resolution and capacity to reconstruct image with underdetermined acquisition. In practice, CSPI is inevitably involved with noise. It is thus essential to understand how noise affects its imaging process, and more importantly, to develop effective strategies for noise compression. In this work, two ypes of noise classified as multiplicative and additive noises are discussed. A normalized compressive reconstruction scheme is firstly proposed to counteract multiplicative noise. For additive noise, two types of compressive algorithms are studied. We find that pseudo-inverse operation could render worse reconstructions with more samplings in compressive sensing. This problem is then solved by introducing zero-mean inverse measurement matrix. Both experiment and simulation results show that our proposed algorithms significantly surpass traditional methods. Our study is believed to be helpful in not only CSPI but also other denoising works when compressive sensing is applied.

Linear Twin Quadratic Surface Support Vector Regression

Twin support vector regression (TSVR) generates two nonparallel hyperplanes by solving a pair of smaller-sized problems instead of a single larger-sized problem in the standard SVR. Due to its efficiency, TSVR is frequently applied in various areas. In this paper, we propose a totally new version of TSVR named Linear Twin Quadratic Surface Support Vector Regression (LTQSSVR), which directly uses two quadratic surfaces in the original space for regression. It is worth noting that our new approach not only avoids the notoriously difficult and time-consuming task for searching a suitable kernel function and its corresponding parameters in the traditional SVR-based method but also achieves a better generalization performance. Besides, in order to make further improvement on the efficiency and robustness of the model, we introduce the 1-norm to measure the error. The linear programming structure of the new model skips the matrix inverse operation and makes it solvable for those huge-sized problems. As we know, the capability of handling large-sized problem is very important in this big data era. In addition, to verify the effectiveness and efficiency of our model, we compare it with some well-known methods. The numerical experiments on 2 artificial data sets and 12 benchmark data sets demonstrate the validity and applicability of our proposed method.

Reduction of term in scientific theory

The subject of this research is the concept of reduction in logics and methodology of science. On the one hand, reduction is understood as a relation between the term and its defining expression within the scientific theory; while on the other – it represents the relation between two theories. Since the extension of theory is possible through introduction to its vocabulary of new terms by means of nominal definitions, the reduction represents an inverse operation – removing the terms from the vocabulary of the theory. At the same time, the theory itself is defined in accordance with the theoretical-multiple approach as a class of sentences closed in relation to derivability. The scientific novelty consists in examination of semantic and epistemological aspects of the formal definition of reduction. Particularly, the explication of reduction relation between two theories leans in the concept of functional equivalence of the theories. It is established that the list of basic terms of the theory can be set only conventionally. All terms introduces by the means of nominal definitions turn out to be reducible. Therefore, a distinctive feature of theoretical terms is the possibility of its reduction.