1) The document describes a neural network called SciNet that is designed to learn physical concepts and representations from experimental observations.
2) SciNet compresses observations into a latent representation, then uses the representation to make predictions about future observations when given a question.
3) The experiments show that SciNet is able to learn fundamental physical concepts like energy, momentum, angular momentum from different example systems and use these concepts to accurately predict future system behavior.
towards Quantum Machine Learning
Machine Learning (ML) gained a lot of momentum in the last ten years, mostly thanks to the advancements in non-linear patterns discovery, and more specifically, in Deep Learning (DL). But those who think that DL is going to address all possible problems might be terribly wrong. DL and ML tasks, in general, are categorized as Non-Polynomial problems, which means that the number of possible solutions for a given problem can grow exponentially, making it intractable using the classical algorithmic approach. Here, Quantum Computing (QC) techniques have the potential to address these issues and help ML methods to solve problems faster and sometimes better than the classical counterpart. The conjunction of these two disciplines resulted in a new exciting research direction to explore: Quantum Machine Learning (QML).
Shor's algorithm is for quantum computer. Using this algorithm any arbitrarily large number can be factored in polynomial time. which is not possible in classical computer
towards Quantum Machine Learning
Machine Learning (ML) gained a lot of momentum in the last ten years, mostly thanks to the advancements in non-linear patterns discovery, and more specifically, in Deep Learning (DL). But those who think that DL is going to address all possible problems might be terribly wrong. DL and ML tasks, in general, are categorized as Non-Polynomial problems, which means that the number of possible solutions for a given problem can grow exponentially, making it intractable using the classical algorithmic approach. Here, Quantum Computing (QC) techniques have the potential to address these issues and help ML methods to solve problems faster and sometimes better than the classical counterpart. The conjunction of these two disciplines resulted in a new exciting research direction to explore: Quantum Machine Learning (QML).
Shor's algorithm is for quantum computer. Using this algorithm any arbitrarily large number can be factored in polynomial time. which is not possible in classical computer
I am Keziah D. I am a Mechanical Engineering Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. Matlab, University of North Carolina, USA. I have been helping students with their homework for the past 8 years. I solve assignments related to Mechanical Engineering.
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Calculating transition amplitudes by variational quantum eigensolversQunaSys
This is our poster planned to be presented at APS March.
We proposed a method to calculate transition amplitudes between two orthogonal states on NISQ devices.
This work is a joint research between QunaSys and Mitsubishi Chemical Corporation.
Neutral Electronic Excitations: a Many-body approach to the optical absorptio...Claudio Attaccalite
Neutral Electronic Excitations: a Many-body approach to the optical absorption spectra.
Introduction to Bethe-Salpeter equation and linear response theory.
Calculating transition amplitudes by variational quantum eigensolversTenninYan
This is our poster planned to presented at APS March.
We proposed a method to calculate transition amplitudes between two orthogonal states on NISQ devices.
I am Samantha K. I am a Statistical Physics Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from, McGill University, Canada
I have been helping students with their homework for the past 8 years. I solve assignments related to Statistics.
Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Statistical Physics Assignments.
Presentation of third- and fifth-order optical nonlinearities measurement using the D4Sigma-Z-scan Method. I present a resolution of propagation equation in general case (with third- and fifth-order nonlinearities) and a numerical inversion.
This presentation is conclude with experimental results.
(If visualization is slow, please try downloading the file.)
Part 2 of a tutorial given in the Brazilian Physical Society meeting, ENFMC. Abstract: Density-functional theory (DFT) was developed 50 years ago, connecting fundamental quantum methods from early days of quantum mechanics to our days of computer-powered science. Today DFT is the most widely used method in electronic structure calculations. It helps moving forward materials sciences from a single atom to nanoclusters and biomolecules, connecting solid-state, quantum chemistry, atomic and molecular physics, biophysics and beyond. In this tutorial, I will try to clarify this pathway under a historical view, presenting the DFT pillars and its building blocks, namely, the Hohenberg-Kohn theorem, the Kohn-Sham scheme, the local density approximation (LDA) and generalized gradient approximation (GGA). I would like to open the black box misconception of the method, and present a more pedagogical and solid perspective on DFT.
I am Keziah D. I am a Mechanical Engineering Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. Matlab, University of North Carolina, USA. I have been helping students with their homework for the past 8 years. I solve assignments related to Mechanical Engineering.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Mechanical Engineering Assignments.
I am Ben R. I am a Statistics Assignment Expert at statisticshomeworkhelper.com. I hold a Ph.D. in Statistics, from University of Denver, USA. I have been helping students with their homework for the past 5 years. I solve assignments related to Statistics.
Visit statisticshomeworkhelper.com or email info@statisticshomeworkhelper.com.
You can also call on +1 678 648 4277 for any assistance with Statistics Assignments.
Calculating transition amplitudes by variational quantum eigensolversQunaSys
This is our poster planned to be presented at APS March.
We proposed a method to calculate transition amplitudes between two orthogonal states on NISQ devices.
This work is a joint research between QunaSys and Mitsubishi Chemical Corporation.
Neutral Electronic Excitations: a Many-body approach to the optical absorptio...Claudio Attaccalite
Neutral Electronic Excitations: a Many-body approach to the optical absorption spectra.
Introduction to Bethe-Salpeter equation and linear response theory.
Calculating transition amplitudes by variational quantum eigensolversTenninYan
This is our poster planned to presented at APS March.
We proposed a method to calculate transition amplitudes between two orthogonal states on NISQ devices.
I am Samantha K. I am a Statistical Physics Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from, McGill University, Canada
I have been helping students with their homework for the past 8 years. I solve assignments related to Statistics.
Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Statistical Physics Assignments.
Presentation of third- and fifth-order optical nonlinearities measurement using the D4Sigma-Z-scan Method. I present a resolution of propagation equation in general case (with third- and fifth-order nonlinearities) and a numerical inversion.
This presentation is conclude with experimental results.
(If visualization is slow, please try downloading the file.)
Part 2 of a tutorial given in the Brazilian Physical Society meeting, ENFMC. Abstract: Density-functional theory (DFT) was developed 50 years ago, connecting fundamental quantum methods from early days of quantum mechanics to our days of computer-powered science. Today DFT is the most widely used method in electronic structure calculations. It helps moving forward materials sciences from a single atom to nanoclusters and biomolecules, connecting solid-state, quantum chemistry, atomic and molecular physics, biophysics and beyond. In this tutorial, I will try to clarify this pathway under a historical view, presenting the DFT pillars and its building blocks, namely, the Hohenberg-Kohn theorem, the Kohn-Sham scheme, the local density approximation (LDA) and generalized gradient approximation (GGA). I would like to open the black box misconception of the method, and present a more pedagogical and solid perspective on DFT.
Ill-posedness formulation of the emission source localization in the radio- d...Ahmed Ammar Rebai PhD
To contact the authors : tarek.salhi@gmail.com and ahmed.rebai2@gmail.com
In the field of radio detection in astroparticle physics, many studies have shown the strong dependence of the solution of the radio-transient sources localization problem (the radio-shower time of arrival on antennas) such solutions are purely numerical artifacts. Based on a detailed analysis of some already published results of radio-detection experiments like : CODALEMA 3 in France, AERA in Argentina and TREND in China, we demonstrate the ill-posed character of this problem in the sens of Hadamard. Two approaches have been used as the existence of solutions degeneration and the bad conditioning of the mathematical formulation problem. A comparison between experimental results and simulations have been made, to highlight the mathematical studies. Many properties of the non-linear least square function are discussed such as the configuration of the set of solutions and the bias.
INVERSIONOF MAGNETIC ANOMALIES DUE TO 2-D CYLINDRICAL STRUCTURES –BY AN ARTIF...ijsc
Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic
anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets
are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical
analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the
centre of the cylinder (Z), the inclination of magnetic vector(Ɵ)and the constant term (A)comprising the
radius(R)and the intensity of the magnetic field(I). The method of inversion is demonstrated over a
theoretical model with and without random noise in order to study the effect of noise on the technique and
then extended to real field data. It is noted that the method under discussion ensures fairly accurate results
even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana,
India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.The
statistics of the predicted parameters relative to the measured data, show lower sum error (<9.58%) and
higher correlation coefficient (R>91%) indicating that good matching and correlation is achieved between
the measured and predicted parameters.
INVERSIONOF MAGNETIC ANOMALIES DUE TO 2-D CYLINDRICAL STRUCTURES –BY AN ARTIF...ijsc
Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic
anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets
are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical
analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the
centre of the cylinder (Z), the inclination of magnetic vector(Ɵ)and the constant term (A)comprising the
radius(R)and the intensity of the magnetic field(I). The method of inversion is demonstrated over a
theoretical model with and without random noise in order to study the effect of noise on the technique and
then extended to real field data. It is noted that the method under discussion ensures fairly accurate results
even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana,
India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.The
statistics of the predicted parameters relative to the measured data, show lower sum error (<9.58%) and
higher correlation coefficient (R>91%) indicating that good matching and correlation is achieved between
the measured and predicted parameters.
Inversion of Magnetic Anomalies Due to 2-D Cylindrical Structures – By an Art...ijsc
Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the centre of the cylinder (Z), the inclination of magnetic vector(Ɵ)and the constant term (A)comprising the radius(R)and the intensity of the magnetic field(I). The method of inversion is demonstrated over a theoretical model with and without random noise in order to study the effect of noise on the technique and then extended to real field data. It is noted that the method under discussion ensures fairly accurate results even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana, India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.The statistics of the predicted parameters relative to the measured data, show lower sum error (<9.58%) and higher correlation coefficient (R>91%) indicating that good matching and correlation is achieved between the measured and predicted parameters.
A STDP RULE THAT FAVOURS CHAOTIC SPIKING OVER REGULAR SPIKING OF NEURONSijaia
We compare the number of states of a Spiking Neural Network (SNN) composed from chaotic spiking
neurons versus the number of states of a SNN composed from regular spiking neurons while both SNNs
implementing a Spike Timing Dependent Plasticity (STDP) rule that we created. We find out that this
STDP rule favors chaotic spiking since the number of states is larger in the chaotic SNN than the regular
SNN. This chaotic favorability is not general; it is exclusive to this STDP rule only. This research falls
under our long-term investigation of STDP and chaos theory.
Diagnosis of Faulty Sensors in Antenna Array using Hybrid Differential Evolut...IJECEIAES
In this work, differential evolution based compressive sensing technique for detection of faulty sensors in linear arrays has been presented. This algorithm starts from taking the linear measurements of the power pattern generated by the array under test. The difference between the collected compressive measurements and measured healthy array field pattern is minimized using a hybrid differential evolution (DE). In the proposed method, the slow convergence of DE based compressed sensing technique is accelerated with the help of parallel coordinate decent algorithm (PCD). The combination of DE with PCD makes the minimization faster and precise. Simulation results validate the performance to detect faulty sensors from a small number of measurements.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
P-Wave Onset Point Detection for Seismic Signal Using Bhattacharyya DistanceCSCJournals
In seismology Primary p-wave arrival identification is a fundamental problem for the geologist worldwide. Several numbers of algorithms that deal with p-wave onset detection and identification have already been proposed. Accurate p- wave picking is required for earthquake early warning system and determination of epicenter location etc. In this paper we have proposed a novel algorithm for p-wave detection using Bhattacharyya distance for seismic signals. In our study we have taken 50 numbers of real seismic signals (generated by earthquake) recorded by K-NET (Kyoshin network), Japan. Our results show maximum standard deviation of 1.76 sample from true picks which gives better accuracy with respect to ratio test method.
Similar to PR12-225 Discovering Physical Concepts With Neural Networks (20)
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
PR12-225 Discovering Physical Concepts With Neural Networks
1. Discovering physical concepts with neural networks
Raban Iten*, Tony Metger*, Henrik Wilming, Lídia del Rio, and Renato Renner
Presenter: Kyunghoon Jung
@ Integrated Quantum System Lab
in Seoul National University
→ Prediction (X)
→ Concept (O)
ex) Energy conservation
2. FIG. 1. Learning physical representations.
(a) Human learning. A physicist compresses experimental observations into a simple representation (encoding).
When later asked any question about the physical setting, the physicist should be able to produce a correct answer
using only the representation and not the original data.
We call the process of producing the answer from the representation “decoding.”
For example, the observations may be the first few seconds of the trajectory of a particle moving with constant speed;
the representation could be the parameters “speed v” and “initial position x0” and
the question could be “where will the particle be at a later time t0?”
(b) Neural network structure for SciNet. Observations are encoded as real parameters fed to an encoder which compresses
the data into a representation (latent representation). The question is also encoded in a number of real parameters, which,
together with the representation, are fed to the decoder network to produce an answer.
[Ref] beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Introduction
5. Figure S3: Damped pendulum. SciNet is fed a time series of the trajectory of a damped pendulum. It learns to store the two relevant physical parameters,
frequency and damping, in the representation, and makes correct predictions about the pendulum’s future position.
(a) Trajectory prediction of SciNet. Here, the spring constant is κ = 5kg/s2 and the damping factor is b = 0.5kg/s. SciNet’s prediction is in excellent
agreement with the true time evolution.
(b) Representation learned by SciNet. The plots show the activations of the three latent neurons of SciNet as a function of the spring constant κ and the
damping factor b. The first two neurons store the damping factor and spring constant, respectively.
The activation of the third neuron is close to zero, suggesting that only two physical variables are required. On an abstract level, learning that one
activation can be set to a constant is encouraged by searching for uncorrelated latent variables, i.e., by minimizing the common information of
the latent neurons during training.
Key findings:
• SciNet predicts the positions
x(tpred) with a root mean square
error below 2% (with respect to the
amplitude A0 = 1m) (Figure S3a).
• SciNet stores κ and b in two of
the latent neurons, and does not
store any information in the third
latent neuron (Figure S3b).
Results1
7. Figure S4: Collision under conservation of angular momentum. In a classical mechanics scenario where the total angular momentum is conserved,
the neural network learns to store this quantity in the latent representation.
(a) Physical setting. A body of mass mrot is fixed on a rod of length r (and of negligible mass) and rotates around the origin with angular velocity ω.
A free particle with velocity vfree and mass mfree collides with the rotating body at position q = (0, r). After the collision, the angular velocity of
the rotating particle is ω’ and the free particle is deflected with velocity v’free.
(b) (b) Representation learned by SciNet. Activation of the latent neuron as a function of the total angular momentum. SciNet learns to store the
total angular momentum, a conserved quantity of the system.
Key findings:
• SciNet predicts the position of
the rotating particle with root
mean square prediction error
below 4% (with respect to the
radius r = 1m).
• SciNet is resistant to noise.
• SciNet stores the total angular
momentum in the latent neuron
Results2
8. 실험 조건이 주어졌을 때, 어떤 상태값
을 얻을 확률
minimum required dimension
to express n qubits
: 2(2 𝑛−1
− 1)
→1 qubit : 2
2 qubits: 6
Results3
9. Key findings:
• SciNet can be used to determine the
minimal number of parameters
necessary to describe the state ψ
without being provided with any prior
knowledge about quantum physics.
• SciNet distinguishes tomographically
complete and incomplete sets of
measurements.
Figure S5: Quantum tomography. SciNet is given tomographic data for one or two qubits and an operational description of a measurement as a question
input and has to predict the probabilities of outcomes for this measurement. We train SciNet with both tomographically complete and incomplete sets of
measurements, and find that, given tomographically complete data, SciNet can be used to find the minimal number of parameters needed to describe a
quantum state (two parameters for one qubit and six parameters for two qubits). Tomographically incomplete data can be recognized, since SciNet cannot
achieve perfect prediction accuracy in this case, and the prediction accuracy can serve as an estimate for the amount of information provided by the
tomographically incomplete set. The plots show the root mean square error of SciNet’s measurement predictions for test data as a function of the number
of latent neurons.
Results3
11. FIG. 3. Heliocentric model of the solar system. SciNet is given the angles of the Sun and Mars as seen from Earth at an initial time t0
and has to predict these angles for later times.
(a) Recurrent version of SciNet for time-dependent variables. Observations are encoded into a simple representation r(t0) at time t0.
Then, the representation is evolved in time to r(t1) and a decoder is used to predict a(t1), and so on.
In each (equally spaced) time step, the same time evolution network and decoder network are applied.
(b) Physical setting. The heliocentric angles ϕE and ϕM of the Earth and Mars are observed from the Sun; the angles θS and θM of the Sun
and Mars are observed from Earth. All angles are measured relative to the fixed star background.
(c) Representation learned by SciNet. The activations r1,2(t0) of the two latent neurons at time t0 [see Fig. 3(a)] are plotted as a function
of the heliocentric angles ϕE and ϕM. The plots show that the network stores and evolves parameters that are linear combinations
of the heliocentric angles.
(a)
Key findings:
• SciNet predicts the angles of Mars and
the Sun with a root mean square error
below 0.4% (with respect to 2π).
• SciNet stores the angles φE and φM of
the Earth and Mars as seen from the Sun
in the two latent neurons (see Figure 3c).
Results4
12. In this work, we have shown that SciNet can be used to recover physical variables from
experimental data in various physical toy settings.
The learned representations turned out to be the ones commonly used in physics textbooks,
under the assumption of uncorrelated sampling.
Conclusion