IA Scholar Query: Experimental Comparison of Continuous and Discrete Tangent Estimators Along Digital Curves.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgMon, 28 Nov 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help14402017
https://scholar.archive.org/work/uowfgqp7hzh2lltvflrcjwdtsm
Fresnel and Fraunhoffer diffraction-Polarization methods for the production of polarized light. Einstein's coefficients (expression for energy density). Requisites of a Laser system. Condition for laser action. Principle, Construction and working of He-Ne laser Holography-Principle of Recording and reconstruction of images. Propagation mechanism in optical fibers. Angle of acceptance. Numerical aperture. Types of optical fibers and modes of propagation. Attenuation, Block diagram discussion of point to point communication, applications. Module -4 10 hours Crystal Structure: Space lattice, Bravais lattice-Unit cell, primitive cell. Lattice parameters. Crystal systems. Direction and planes in a crystal. Miller indices. Expression for interplanar spacing. Coordination number. Atomic packing factors (SC, FCC, BCC). Bragg's law, Determination of crystal structure using Bragg's X-ray diffractometer. Polymorphism and Allotropy. Crystal Structure of Diamond. Module -5 10 hours ELEMENTS OF ELECTRONICS ENGINEERING Subject Code 17SEC13/23 IA Marks 50 Number of lecture hours/week 04 Exam Marks 50 Total number of lecture hours 50 Credits 04 Course Objectives: 1. To provide basic concepts D.C circuits and circuit analysis techniques 2. To provide knowledge on A.C circuit fundamental techniques 3. To understand construction and operation of BJT and Junction FET 4. Explain the different modes of communications from wired to wireless and the computing involved. 5. To provide fundamental knowledge of Digital Logic. Course Outcomes: CO1: Understand concepts of electrical circuits and elements. CO2: Apply basic electric laws in solving circuit problems. CO3: Analyse simple circuits containing transistors CO4: Understand concept of cellular wireless networks. CO5: Understand Number systems and design basic digital circuits.BTECH.MECHwork_uowfgqp7hzh2lltvflrcjwdtsmMon, 28 Nov 2022 00:00:00 GMT2019
https://scholar.archive.org/work/wcy47hfvvvdwvfgnwx2cuak4ze
On completion of this course, students will have knowledge in: • CO1.Basics of electrochemistry. Classical & modern batteries and fuel cells. CO2. Causes & effects of corrosion of metals and control of corrosion. Modification of surface properties of metals to develop resistance to corrosion, wear, tear, impact etc. by electroplating and electroless plating. CO3. Production & consumption of energy for industrialization of country and living standards of people. Utilization of solar energy for different useful forms of energy. CO4. Understanding Phase rule and instrumental techniques and its applications. CO5.Over viewing of synthesis, properties and applications of nanomaterials.BTECH.CSwork_wcy47hfvvvdwvfgnwx2cuak4zeMon, 28 Nov 2022 00:00:00 GMT2021
https://scholar.archive.org/work/ggji2kgovvhtlh6nq7bk7mukh4
Module 2 Interaction of radiation with matter -Absorption-Spontaneous emission -Stimulated emission-Einstein's coefficients (expression for energy density). Requisites of a Laser system. Condition for laser action. Principle, Construction and working of He-Ne laser. Propagation mechanism in optical fibers. Angle of acceptance. Numerical aperture. Types of optical fibers-Step index and Graded index fiber. Modes of propagation-Single mode and Multimode fibers. Attenuation-Attenuation mechanisms. Teaching Methodology: Chalk and talk method: Interaction of radiation with matter -Absorption-Spontaneous emission -Stimulated emission-Einstein's coefficients (expression for energy density). Requisites of a Laser system. Condition for laser action. Propagation mechanism in optical fibers. Angle of acceptance. Numerical aperture. Powerpoint presentation: Types of optical fibers-Step index and Graded index fiber. Modes of propagation-Single mode and Multimode fibers. Video: Construction and working of He-Ne laser. Self-study material: Attenuation-Attenuation mechanisms. 9 Hours Module 3 Temperature dependence of resistivity in metals and superconducting materials. Effect of magnetic field (Meissner effect). Isotope effect -Type I and Type II superconductors-Temperature dependence of critical field. BCS theory (qualitative). High temperature superconductors-Josephson effect -SQUID-Applications of superconductors-Maglev vehicles (qualitative). Magnetic dipole-dipole moment-flux density-magnetic field intensity-Intensity of magnetization-magnetic permeability-susceptibility-relation between permeability and susceptibility. Classification of magnetic materials-Dia, Para, Ferromagnetism. Hysteresis-soft and hard magnetic materials. Teaching Methodology: Chalk and talk method: Temperature dependence of resistivity in metals and superconducting materials. Effect of magnetic field (Meissner effect). Isotope effect -Type I and Type II superconductors-Temperature dependence of critical field. BCS theory (qualitative). High temperature superconductors-Powerpoint presentation: Josephson effect -SQUID-Applications of superconductors. Magnetic dipole-dipole moment-flux density-magnetic field intensity-Intensity of magnetization-magnetic permeability-susceptibility-relation between permeability and susceptibility. Hysteresis-soft and hard magnetic materials. Video: Maglev vehicles (qualitative). Self-study material: Classification of magnetic materials-Dia, Para, Ferromagnetism 9 Hours Module 4 Amorphous and crystalline materials-Space lattice, Bravais lattice-Unit cell, primitive cell. Lattice parameters. Crystal systems. Direction and planes in a crystal. Miller indices -Determination of Miller indices of a plane. Expression for inter -planar spacing. Atoms per unit cell -Co-ordination number. Relation between atomic radius and lattice constant -Atomic packing factors (SC, FCC, BCC). Bragg's law. Determination of crystal structure using Bragg's X-ray diffractometer -X-ray spectrum. Teaching Methodology: Chalk and talk method: Direction and planes in a crystal. Miller indices -Determination of Miller indices of a plane. Powerpoint presentation: Atoms per unit cell -Co-ordination number. Relation between atomic radius and lattice constant -Atomic packing factors (SC, FCC, BCC). Bragg's law. Determination of crystal structure using Bragg's X-ray diffractometer -X-ray spectrum. Self-study material: Amorphous and crystalline materials-Space lattice, Bravais lattice-Unit cell, primitive cell. Lattice parameters. Crystal systems. 9 Hours Module 5 Interference of light -Superposition of two coherent waves-Constructive and destructive interference. Interference in thin films -Wedge shaped thin film-Air wedge -Application to find the diameter of a thin wire. Newton's rings -Application to find the refractive index of a liquid. Diffraction of light -Classes of diffraction -Fresnel and Fraunhofer diffraction. Fresnel theory of half period zone -Zone plate. Diffraction grating -Grating element -Grating equation -Construction of grating-Reflection and transmission grating. Teaching Methodology: Chalk and talk method: Interference of light -Superposition of two coherent waves-Constructive and destructive interference. Powerpoint presentation: Interference in thin films -Wedge shaped thin film-Air wedge -Application to find the diameter of a thin wire. Newton's rings -Application to find the refractive index of a liquid. Fresnel theory of half period zone -Zone plate. Diffraction grating -Grating element -Grating equation -Construction of grating-Reflection and transmission grating. Self-study material: Diffraction of light -Classes of diffraction -Fresnel and Fraunhofer diffraction. 9 Hours C PROGRAMMING Subject Code 21SCS12 IA Marks 50 Number of Lecture Hours/Week 2 (L) + 2 (T) Exam Marks 50 Total Number of Lecture Hours 45 Total Marks 100 Credits 03 Exam Hours 2 Course Objectives: 1. To understand the various steps in program development. 2. To learn the syntax and semantics of C programming language. 3. To learn the usage of structured programming approach in solving problems. Course Outcomes: CO1: On completion of this course students will be able to write algorithms and to draw flowcharts for solving problems. CO2: On completion of this course students will be able to convert the algorithms/flowcharts to C programs. CO3: Students will be able to code and test a given logic in C programming language. CO4: Students will be able to decompose a problem into functions and to develop modular reusable code. CO5: Students will be able to use arrays, pointers, strings and structures to write C programs. Module 1 Introduction to Algorithms: Steps to solve logical and numerical problems. Representation of Algorithm, Flowchart/Pseudo code with examples, Program design and structured programming Introduction to C Programming Language: variables, Syntax and Logical Errors in compilation, object and executable code, Operators, expressions and precedence, Expression evaluation, Storage classes, type conversion, The main method and command line arguments. Bitwise operations: Bitwise AND, OR, XOR and NOT operators. Conditional Branching and Loops: Writing and evaluation of conditionals and consequent branching with if, if-else, switch-case, ternary operator, goto, Iteration with for, while, do-while loops I/O: Simple input and output with scanf and printf, formatted I/O, Introduction to stdin, stdout and stderr. Command line arguments. Teaching Methodology: Chalk and talk using PPT and Demo to explain the concept. 9 Hours Module 2 Arrays, Strings, Structures and Pointers: Arrays: one and two-dimensional arrays, creating, accessing and manipulating elements of arrays. Strings: Introduction to strings, handling strings as array of characters, basic string functions available in C (strlen, strcat, strcpy, strstr etc.), arrays of strings. Structures: Defining structures, initializing structures, unions, Array of structures. Pointers: Idea of pointers, Defining pointers, Pointers to Arrays and Structures, Use of Pointers in self referential structures, usage of self referential structures in linked list (no implementation) Enumeration data type. Teaching Methodology: Chalk and talk using PPT and Demo to explain the concept. Module 3 9 Hours Preprocessor and File handling in C: Preprocessor: Commonly used Preprocessor commands like include, define, undef, if, ifdef, ifndef Files: Text and Binary files, Creating and Reading and writing text and binary files, Appending data to existing files, Writing and reading structures using binary files, Random access using fseek, ftell and rewind functions. Teaching Methodology: Chalk and talk using PPT and Demo to explain the concept. 9 Hours Module 4 Function and Dynamic Memory Allocation: Functions: Designing structured programs, Declaring a function, Signature of a function, Parameters and return type of a function, passing parameters to functions, call by value, Passing arrays to functions, passing pointers to functions, idea of call by reference, Some C standard functions and libraries Recursion: Simple programs, such as Finding Factorial, Fibonacci series etc., Limitations of Recursive functions. Dynamic memory allocation: Allocating and freeing memory, Allocating memory for arrays of different data types. Teaching Methodology: Chalk and talk using PPT and Demo to explain the concept. 9 Hours Module 5 C PROGRAMMING LABORATORY Subject Code 21SCSL12 IA Marks 25 Number of Practical Hours/Week 1 (T) + 2 (L) Exam Marks 25 Total Number of Practical Hours 36 Total Marks 50 Credits 02 Exam Hours 3 Course Objectives: 1. To describe the basics of computer and understand the problem-solving aspect. 2. To demonstrate the algorithm and flow chart for the given problem. 3. To introduce students to the basic knowledge of programming fundamentals of C language. 4. To impart writing skill of C programming to the students and solving problems. 5. To impart the concepts like looping, array, functions, pointers, file, structure. Course Outcomes: CO1: Understand the problem solving to write efficient algorithms to solve real time problems. CO2: Understand and use various constructs of the programming language such as conditionals, iteration, and recursion. CO3: Implement your algorithms to build programs in the C programming language. CO4: Use data structures like arrays, linked lists, and stacks to solve various problems. CO5: Understand and use file handling in the C programming language. EXPERIMENTS: Implement the following programs with WINDOWS / LINUX platform using appropriate C compiler. Course Objectives: 1. To provide basic concepts D.C circuits and circuit analysis techniques 2. To provide knowledge on A.C circuit fundamental techniques 3. To understand construction and operation of BJT and Junction FET 4. Explain the different modes of communications from wired to wireless and the computing involved. 5. To provide fundamental knowledge of Digital Logic. Course Outcomes: CO1: Understand concepts of electrical circuits and elements. CO2: Apply basic electric laws in solving circuit problems. CO3: Analyze simple circuits containing transistors CO4: Understand concept of cellular wireless networks. CO5: Understand Number systems and design basic digital circuits.BTECH.MECHwork_ggji2kgovvhtlh6nq7bk7mukh4Mon, 28 Nov 2022 00:00:00 GMTTurbulence as Clebsch Confinement
https://scholar.archive.org/work/qrlmjshh65cfddfvw4x3lbhb44
We argue that in the strong turbulence phase, as opposed to the weak one, the Clebsch variables compactify to the sphere S_2 and are not observable as wave excitations. Various topologically nontrivial configurations of this confined Clebsch field are responsible for vortex sheets. Stability equations (CVS) for closed vortex surfaces (bubbles of Clebsch field) are derived and investigated. The exact non-compact solution for the stable vortex sheet family is presented. Compact solutions are proven not to exist by De Lellis and Brué. Asymptotic conservation of anomalous dissipation on stable vortex surfaces in the turbulent limit is discovered. We derive an exact formula for this anomalous dissipation as a surface integral of the square of velocity gap times the square root of minus local normal strain. Topologically stable time-dependent solutions, which we call Kelvinons, are introduced. They have a conserved velocity circulation around static loop; this makes them responsible for asymptotic PDF tails of velocity circulation, perfectly matching numerical simulations. The loop equation for circulation PDF as functional of the loop shape is derived and studied. This equation is exactly equivalent to the Schrödinger equation in loop space, with viscosity ν playing the role of Planck's constant. This equivalence opens the way for direct numerical simulation of turbulence on quantum computers. Kelvinons are fixed points of the loop equation at turbulent limit ν→ 0. Area law and the asymptotic scaling law for mean circulation at a large area are derived. The representation of the solution of the loop equation in terms of a singular stochastic equation for momentum loop trajectory is presented.Alexander Migdalwork_qrlmjshh65cfddfvw4x3lbhb44Mon, 28 Nov 2022 00:00:00 GMTDeepAngle: Fast calculation of contact angles in tomography images using deep learning
https://scholar.archive.org/work/mec6m7f65fbzrok2txczpwjozq
DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3--D needs to be done within the surface perpendicular to the angle planes, and it could become inaccurate when dealing with the discretized space of the image voxels. A computationally intensive solution is to correlate and vectorize all surfaces using an adaptable grid, and then measure the angles within the desired planes. On the contrary, the present study provides a rapid and low-cost technique powered by deep learning to estimate the interfacial angles directly from images. DeepAngle is tested on both synthetic and realistic images against the direct measurement technique and found to improve the r-squared by 5 to 16% while lowering the computational cost 20 times. This rapid method is especially applicable for processing large tomography data and time-resolved images, which is computationally intensive. The developed code and the dataset are available at an open repository on GitHub (https://www.github.com/ArashRabbani/DeepAngle).Arash Rabbani, Chenhao Sun, Masoud Babaei, Vahid J. Niasar, Ryan T. Armstrong, Peyman Mostaghimiwork_mec6m7f65fbzrok2txczpwjozqMon, 28 Nov 2022 00:00:00 GMT2019
https://scholar.archive.org/work/g6qfzbclcfe6pobzwry66zimou
On completion of this course, students will have knowledge in: • CO1.Basics of electrochemistry. Classical & modern batteries and fuel cells. CO2. Causes & effects of corrosion of metals and control of corrosion. Modification of surface properties of metals to develop resistance to corrosion, wear, tear, impact etc. by electroplating and electroless plating. CO3. Production & consumption of energy for industrialization of country and living standards of people. Utilization of solar energy for different useful forms of energy. CO4. Understanding Phase rule and instrumental techniques and its applications. CO5.Over viewing of synthesis, properties and applications of nanomaterials.BTECH.MECHwork_g6qfzbclcfe6pobzwry66zimouMon, 28 Nov 2022 00:00:00 GMTMotility Assessment of Ram Spermatozoa
https://scholar.archive.org/work/pjlmlu5qizcmngletz62pwxigu
For successful fertilisation to occur, spermatozoa need to successfully migrate through the female reproductive tract and penetrate the oocyte. Predictably, poor sperm motility has been associated with low rates of fertilisation in many mammalian species, including the ram. As such, motility is one of the most important parameters used for in vitro evaluation of ram sperm quality and function. This review aims to outline the mechanical and energetic processes which underpin sperm motility, describe changes in motility which occur as a result of differences in sperm structure and the surrounding microenvironment, and assess the effectiveness of the various methods used to assess sperm motility in rams. Methods of subjective motility estimation are convenient, inexpensive methods widely used in the livestock industries, however, the subjective nature of these methods can make them unreliable. Computer-assisted sperm analysis (CASA) technology accurately and objectively measures sperm motility via two-dimensional tracing of sperm head motion, making it a popular method for sperm quality assurance in domesticated animal production laboratories. Newly developed methods of motility assessment including flagellar tracing, three-dimensional sperm tracing, in vivo motility assessment, and molecular assays which quantify motility-associated biomarkers, enable analysis of a new range of sperm motion parameters with the potential to reveal new mechanistic insights and improve ram semen assessment. Experimental application of these technologies is required to fully understand their potential to improve semen quality assessment and prediction of reproductive success in ovine artificial breeding programs.Madeleine Van de Hoek, Jessica P. Rickard, Simon P. de de Graafwork_pjlmlu5qizcmngletz62pwxiguSat, 26 Nov 2022 00:00:00 GMTDeep Curvilinear Editing: Commutative and Nonlinear Image Manipulation for Pretrained Deep Generative Model
https://scholar.archive.org/work/ew5y7ajcqvfjrlo3m3tqxzrxqe
Semantic editing of images is the fundamental goal of computer vision. Although deep learning methods, such as generative adversarial networks (GANs), are capable of producing high-quality images, they often do not have an inherent way of editing generated images semantically. Recent studies have investigated a way of manipulating the latent variable to determine the images to be generated. However, methods that assume linear semantic arithmetic have certain limitations in terms of the quality of image editing, whereas methods that discover nonlinear semantic pathways provide non-commutative editing, which is inconsistent when applied in different orders. This study proposes a novel method called deep curvilinear editing (DeCurvEd) to determine semantic commuting vector fields on the latent space. We theoretically demonstrate that owing to commutativity, the editing of multiple attributes depends only on the quantities and not on the order. Furthermore, we experimentally demonstrate that compared to previous methods, the nonlinear and commutative nature of DeCurvEd facilitates the disentanglement of image attributes and provides higher-quality editing.Takehiro Aoshima, Takashi Matsubarawork_ew5y7ajcqvfjrlo3m3tqxzrxqeSat, 26 Nov 2022 00:00:00 GMT2019
https://scholar.archive.org/work/a6rcrhwkfbbhrgfmps5qbny5a4
2019- AI & MLAI & MLwork_a6rcrhwkfbbhrgfmps5qbny5a4Sat, 26 Nov 2022 00:00:00 GMTConditional Gradient Methods
https://scholar.archive.org/work/b2imrksvmfclhaik7ghfh6bcte
The purpose of this survey is to serve both as a gentle introduction and a coherent overview of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for function minimization. These algorithms are especially useful in convex optimization when linear optimization is cheaper than projections. The selection of the material has been guided by the principle of highlighting crucial ideas as well as presenting new approaches that we believe might become important in the future, with ample citations even of old works imperative in the development of newer methods. Yet, our selection is sometimes biased, and need not reflect consensus of the research community, and we have certainly missed recent important contributions. After all the research area of Frank--Wolfe is very active, making it a moving target. We apologize sincerely in advance for any such distortions and we fully acknowledge: We stand on the shoulder of giants.Gábor Braun, Alejandro Carderera, Cyrille W. Combettes, Hamed Hassani, Amin Karbasi, Aryan Mokhtari, Sebastian Pokuttawork_b2imrksvmfclhaik7ghfh6bcteFri, 25 Nov 2022 00:00:00 GMTResponse of a large deep-seated gravitational slope deformation to meteorological, seismic, and deglaciation drivers as measured by InSAR
https://scholar.archive.org/work/4cz7bflxzvedfcbwuroju5gdiy
We analyze the sensitivity of a large (area extent ∼3 km2), deep-seated gravitational slope deformation (Fels slide, Alaska Range) to three specific drivers: (i) liquid surface water input from ERA-5 reanalysis snow melt and rainfall; (ii) locally projected seismic activity of Alaskan earthquakes; and (iii) lowering of Fels Glacier at the slide toe estimated from topographic data. A surface displacement map-series is derived from 1991 to 2016 spaceborne multi-sensor InSAR data (ERS, RADARSAT-1/2, ALOS, TerraSAR-X) using adaptive demodulation to unwrap interferograms of variable spatial resolution and quality. On this series we use independent component analysis (ICA) to uncover five displacement patterns that map to independently moving domains of the slide and then correlate the corresponding temporal pattern intensities with the suspected drivers. We find significant sub-annual correlation between displacement pattern intensities and seasonal water input variations. The correlation can be optimized, for each ICA pattern, by choosing appropriate values of temporal smoothing and lag to create depth-propagated versions of the water input driver. Lag time results ranging from one to 3 weeks relate to shallower and deeper propagations of water input, driving the different deformation patterns. For two of the deformation patterns, seasonal sensitivity to water input was strongly amplified by the 2002 Mw7.9 Denali earthquake. Sensitivity of these patterns remained high for 4 years until abruptly dropping to below pre-earthquake values, which suggests a highly non-linear modulation by the seismic driver. Other deformation patterns show a steady intensity increase that appears linked to the deglaciation driver. Despite these observations, the inter-annual variations in ICA pattern intensities show no clear predictability by individual drivers or driver combinations. This suggests that the mechanical and hydraulic evolution of the slide, especially after damaging events such as earthquakes or heavy rainfall, is a crucial factor not adequately modeled in our approach. Despite this limitation, our analysis provides the first direct evidence that the Fels slide comprises several independently moving domains that respond differently to the suspected drivers as is suggestive of a complex slope deformation.Bernhard Rabus, Jeanine Engelbrecht, John J. Clague, Davide Donati, Doug Stead, Mirko Francioniwork_4cz7bflxzvedfcbwuroju5gdiyWed, 23 Nov 2022 00:00:00 GMTAn Ensemble 1D-CNN-LSTM-GRU Model with Data Augmentation for Speech Emotion Recognition
https://scholar.archive.org/work/f67l6ckpcja7dih7rjphheqrri
In this paper, we propose an ensemble of deep neural networks along with data augmentation (DA) learned using effective speech-based features to recognize emotions from speech. Our ensemble model is built on three deep neural network-based models. These neural networks are built using the basic local feature acquiring blocks (LFAB) which are consecutive layers of dilated 1D Convolutional Neural networks followed by the max pooling and batch normalization layers. To acquire the long-term dependencies in speech signals further two variants are proposed by adding Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) layers respectively. All three network models have consecutive fully connected layers before the final softmax layer for classification. The ensemble model uses a weighted average to provide the final classification. We have utilized five standard benchmark datasets: TESS, EMO-DB, RAVDESS, SAVEE, and CREMA-D for evaluation. We have performed DA by injecting Additive White Gaussian Noise, pitch shifting, and stretching the signal level to generalize the models, and thus increasing the accuracy of the models and reducing the overfitting as well. We handcrafted five categories of features: Mel-frequency cepstral coefficients, Log Mel-Scaled Spectrogram, Zero-Crossing Rate, Chromagram, and statistical Root Mean Square Energy value from each audio sample. These features are used as the input to the LFAB blocks that further extract the hidden local features which are then fed to either fully connected layers or to LSTM or GRU based on the model type to acquire the additional long-term contextual representations. LFAB followed by GRU or LSTM results in better performance compared to the baseline model. The ensemble model achieves the state-of-the-art weighted average accuracy in all the datasets.Md. Rayhan Ahmed, Salekul Islam, Ph. D, A. K. M. Muzahidul Islam, Ph. D, Swakkhar Shatabda, Ph. Dwork_f67l6ckpcja7dih7rjphheqrriTue, 22 Nov 2022 00:00:00 GMTThe Neural Process Family: Survey, Applications and Perspectives
https://scholar.archive.org/work/lpfyllkwovcqvjcz354tq4pnv4
The standard approaches to neural network implementation yield powerful function approximation capabilities but are limited in their abilities to learn meta representations and reason probabilistic uncertainties in their predictions. Gaussian processes, on the other hand, adopt the Bayesian learning scheme to estimate such uncertainties but are constrained by their efficiency and approximation capacity. The Neural Processes Family (NPF) intends to offer the best of both worlds by leveraging neural networks for meta-learning predictive uncertainties. Such potential has brought substantial research activity to the family in recent years. Therefore, a comprehensive survey of NPF models is needed to organize and relate their motivation, methodology, and experiments. This paper intends to address this gap while digging deeper into the formulation, research themes, and applications concerning the family members. We shed light on their potential to bring several recent advances in other deep learning domains under one umbrella. We then provide a rigorous taxonomy of the family and empirically demonstrate their capabilities for modeling data generating functions operating on 1-d, 2-d, and 3-d input domains. We conclude by discussing our perspectives on the promising directions that can fuel the research advances in the field. Code for our experiments will be made available at https://github.com/srvCodes/neural-processes-survey.Saurav Jha, Dong Gong, Xuesong Wang, Richard E. Turner, Lina Yaowork_lpfyllkwovcqvjcz354tq4pnv4Tue, 22 Nov 2022 00:00:00 GMTNovel Special Orthogonal Group Optimization for Coarse Alignment Method of SINS on a Rocking Base
https://scholar.archive.org/work/c4dxaup3mbfsva7jrfd3npj4dq
In order to solve the coarse alignment problem of the strapdown inertial navigation system on a rocking base, a fast coarse alignment method using the Special Orthogonal Group optimization has been proposed in this paper. In this method, based on the alignment idea of tracing gravitational apparent motion in inertial frame, the model of coarse alignment on a rocking base has been established using the Special Orthogonal Group directly. A new attitude error function has been proposed on the basis of the cosines between the measurement vector and predictive vector to describe the error between the estimated attitude and the true one. In order to directly reflect the change in the attitude error in the new innovation term and enable the attitude error to converge to zero as fast as possible, the gradient of the new attitude error function has been selected as the new innovation term to compensate for the attitude in the estimation process. Finally, the stability of the proposed optimization estimation method has been proved by employing the Lyapunov stability theory. Simulation and experiment results show that the method presented in this paper exhibits good performance in terms of alignment accuracy and time and can be applied to coarse alignment under a rocking base under different environments.Fujun Pei, Siyuan Li, Li Peng, Shunan Yin, Xingling Shaowork_c4dxaup3mbfsva7jrfd3npj4dqMon, 21 Nov 2022 00:00:00 GMTFundamental underpinnings of electromyography-based biofeedback for transtibial amputee gait rehabilitation
https://scholar.archive.org/work/63pxy5nxtbc5zibbzhyewmtbgi
Electromyography (EMG) feedback can deliver targeted information about the generation of movement patterns, with potential for daily living applications. Our understanding of how to implement EMG-based feedback within lower limb amputees is limited. This thesis investigated three fundamental features of EMG that require consideration prior to being incorporated into a biofeedback system. Gait temporal parameters, commonly derived from ground reaction forces and kinematic algorithms, identify key gait phases and permit analysis of variables in reference to the gait cycle. Identification of these parameters from EMG without the necessity for additional biomechanical measures is key for the application of an EMG-based biofeedback system outside of a clinical setting during daily living. Gait phases (stance and swing) were classified from three able-bodied lower limb muscles using a binary support vector machine (97.58 – 97.62% accuracy). The absolute error in heel strike and ground contact time were smaller than commonly implemented kinematic algorithms (absolute error in toe off was comparable). An able-bodied trained model (different muscles) was implemented within a lower limb amputee population, obtaining an accuracy of 92.39%. Prior warning of impending heel strikes could potentially help augment the diminished internal feedback from the prosthetic limb, however, the real time application was limited by the feature window length. To evaluate whether a feedback intervention has elicited a meaningful muscle activation adaptation beyond normal variation, knowledge of the signal's nature is required. Variability quantified how much motor patterns varied in magnitude at time points, and Lyapunov exponents quantified how much the motor pattern varied across a time series, providing information regarding control strategies (stability), with analysis requiring many consecutive gait cycles. The present thesis proposed a method to concatenate short kinematic time series (overground trials) to provide accurate estimates o [...]Natalie Eggintonwork_63pxy5nxtbc5zibbzhyewmtbgiMon, 21 Nov 2022 00:00:00 GMTNeural frames: A Tool for Studying the Tangent Bundles Underlying Image Datasets and How Deep Learning Models Process Them
https://scholar.archive.org/work/pzwjpjrbqjd27j56zyvnv2jacm
The assumption that many forms of high-dimensional data, such as images, actually live on low-dimensional manifolds, sometimes known as the manifold hypothesis, underlies much of our intuition for how and why deep learning works. Despite the central role that they play in our intuition, data manifolds are surprisingly hard to measure in the case of high-dimensional, sparsely sampled image datasets. This is particularly frustrating since the capability to measure data manifolds would provide a revealing window into the inner workings and dynamics of deep learning models. Motivated by this, we introduce neural frames, a novel and easy to use tool inspired by the notion of a frame from differential geometry. Neural frames can be used to explore the local neighborhoods of data manifolds as they pass through the hidden layers of neural networks even when one only has a single datapoint available. We present a mathematical framework for neural frames and explore some of their properties. We then use them to make a range of observations about how modern model architectures and training routines, such as heavy augmentation and adversarial training, affect the local behavior of a model.Henry Kvinge, Grayson Jorgenson, Davis Brown, Charles Godfrey, Tegan Emersonwork_pzwjpjrbqjd27j56zyvnv2jacmSat, 19 Nov 2022 00:00:00 GMTRobotic manipulators for in situ inspections of jet engines
https://scholar.archive.org/work/akn6hclqbnggrcnwxuhvh2oiby
Jet engines need to be inspected periodically and, in some instances, repaired. Currently, some of these maintenance operations require the engine to be removed from the wing and dismantled, which has a significant associated cost. The capability of performing some of these inspections and repairs while the engine is on-wing could lead to important cost savings. However, existing technology for on-wing operations is limited, and does not suffice to satisfy some of the needs. In this work, the problem of performing on-wing operations such as inspection and repair is analysed, and after an extensive literature review, a novel robotic system for the on-wing insertion and deployment of probes or other tools is proposed. The system consists of a fine-positioner, which is a miniature and dexterous robotic manipulator; a gross-positioner, which is a device to insert the fine-positioner to the engine region of interest; an end-effector, such as a probe; a deployment mechanism, which is a passive device to ensure correct contact between probe and component; and a feedback system that provides information about the robot state for control. The research and development work conducted to address the main challenges to create this robotic system is presented in this thesis. The work is focussed on the fine-positioner, as it is the most relevant and complex part of the system. After a literature review of relevant work, and as part of the exploration of potential robot concepts for the system, the kinematic capabilities of concentric tube robots (CTRs) are first investigated. The complete set of stable trajectories that can be traced in follow-the-leader motion is discovered. A case study involving simulations and an experiment is then presented to showcase and verify the work. The research findings indicate that CTRs are not suitable for the fine-positioner. However, they show that CTRs with non-annular cross section can be used for the gross-positioner. In addition, the new trajectories discovered show promise in minimally i [...]Arnau Garriga Casanovas, Ferdinando Rodriguez Y Baena, Engineering And Physical Sciences Research Council, Rolls-Royce Group Plc.work_akn6hclqbnggrcnwxuhvh2oibyFri, 18 Nov 2022 00:00:00 GMTOptimised Bayesian system identification in quantum devices
https://scholar.archive.org/work/ote7j7p6g5hsvg3c2po6biubsm
Identifying and calibrating quantitative dynamical models for physical quantum systems is important for a variety of applications. Here we present a closed-loop Bayesian learning algorithm for estimating multiple unknown parameters in a dynamical model, using optimised experimental "probe" controls and measurement. The estimation algorithm is based on a Bayesian particle filter, and is designed to autonomously choose informationally-optimised probe experiments with which to compare to model predictions. We demonstrate the performance of the algorithm in both simulated calibration tasks and in an experimental single-qubit ion-trap system. Experimentally, we find that with 60x fewer samples, we exceed the precision of conventional calibration methods, delivering an approximately 93x improvement in efficiency (as quantified by the reduction of measurements required to achieve a target residual uncertainty and multiplied by the increase in accuracy). In simulated and experimental demonstrations, we see that successively longer pulses are selected as the posterior uncertainty iteratively decreases, leading to an exponential improvement in the accuracy of model parameters with the number of experimental queries.Thomas M. Stace, Jiayin Chen, Li Li, Viktor S. Perunicic, Andre R. R. Carvalho, Michael R. Hush, Christophe H. Valahu, Ting Rei Tan, Michael J. Biercukwork_ote7j7p6g5hsvg3c2po6biubsmWed, 16 Nov 2022 00:00:00 GMTThe significance of cephalopod beaks as a research tool: An update
https://scholar.archive.org/work/kizwug2offgshfybssyaif4iye
The use of cephalopod beaks in ecological and population dynamics studies has allowed major advances of our knowledge on the role of cephalopods in marine ecosystems in the last 60 years. Since the 1960's, with the pioneering research by Malcolm Clarke and colleagues, cephalopod beaks (also named jaws or mandibles) have been described to species level and their measurements have been shown to be related to cephalopod body size and mass, which permitted important information to be obtained on numerous biological and ecological aspects of cephalopods in marine ecosystems. In the last decade, a range of new techniques has been applied to cephalopod beaks, permitting new kinds of insight into cephalopod biology and ecology. The workshop on cephalopod beaks of the Cephalopod International Advisory Council Conference (Sesimbra, Portugal) in 2022 aimed to review the most recent scientific developments in this field and to identify future challenges, particularly in relation to taxonomy, age, growth, chemical composition (i.e., DNA, proteomics, stable isotopes, trace elements) and physical (i.e., structural) analyses. In terms of taxonomy, new techniques (e.g., 3D geometric morphometrics) for identifying cephalopods from their beaks are being developed with promising results, although the need for experts and reference collections of cephalopod beaks will continue. The use of beak microstructure for age and growth studies has been validated. Stable isotope analyses on beaks have proven to be an excellent technique to get valuable information on the ecology of cephalopods (namely habitat and trophic position). Trace element analyses is also possible using beaks, where concentrations are significantly lower than in other tissues (e.g., muscle, digestive gland, gills). Extracting DNA from beaks was only possible in one study so far. Protein analyses can also be made using cephalopod beaks. Future challenges in research using cephalopod beaks are also discussed.José C. Xavier, Alexey V. Golikov, José P. Queirós, Catalina Perales-Raya, Rigoberto Rosas-Luis, José Abreu, Giambattista Bello, Paco Bustamante, Juan C. Capaz, Valerie H. Dimkovikj, Angel F. González, Hugo Guímaro, Airam Guerra-Marrero, José N. Gomes-Pereira, Tsunemi Kubodera, Vladimir Laptikhovsky, Evgenia Lefkaditou, Fedor Lishchenko, Amanda Luna, Bilin Liu, Graham J. Pierce, Vasco Pissarra, Elodie Reveillac, Evgeny V. Romanov, Rui Rosa, Marjorie Roscian, Lisa Rose-Mann, Isabelle Rouget, Pilar Sánchez, Antoni Sánchez-Márquez, Sónia Seixas, Louise Souquet, Jaquelino Varela, Erica A. G. Vidal, Yves Cherelwork_kizwug2offgshfybssyaif4iyeWed, 16 Nov 2022 00:00:00 GMTPulse shape discrimination using a convolutional neural network for organic liquid scintillator signals
https://scholar.archive.org/work/g2giefbxerhfxpuhrnztbyfd7u
A convolutional neural network (CNN) architecture is developed to improve the pulse shape discrimination (PSD) power of the gadolinium-loaded organic liquid scintillation detector to reduce the fast neutron background in the inverse beta decay candidate events of the NEOS-II data. A power spectrum of an event is constructed using a fast Fourier transform of the time domain raw waveforms and put into CNN. An early data set is evaluated by CNN after it is trained using low energy β and α events. The signal-to-background ratio averaged over 1-10 MeV visible energy range is enhanced by more than 20 result of the CNN method compared to that of an existing conventional PSD method, and the improvement is even higher in the low energy region.K. Y. Jung, B. Y. Han, E. J. Jeon, Y. Jeong, H. S. Jo, J. Y. Kim, J. G. Kim, Y. D. Kim, Y. J. Ko, M. H. Lee, J. Lee, C. S. Moon, Y. M. Oh, H. K. Park, S. H. Seo, D. W. Seol, K. Siyeon, G. M. Sun, Y. S. Yoon, I. Yuwork_g2giefbxerhfxpuhrnztbyfd7uTue, 15 Nov 2022 00:00:00 GMT