Input Convex Neural Networks - implementation -

(update: there is now a version 2)
Input Convex Neural Networks by Brandon Amos, Lei Xu, J. Zico Kolter

This paper presents the input convex neural network architecture. These are scalar-valued (potentially deep) neural networks with constraints on the network parameters such that the output of the network is a convex function of (some of) the inputs. The networks allow for efficient inference via optimization over some inputs to the network given others, and can be applied to settings including structured prediction, data imputation, reinforcement learning, and others. In this paper we lay the basic groundwork for these models, proposing methods for inference, optimization and learning, and analyze their representational power. We show that many existing neural network architectures can be made input-convex with only minor modification, and develop specialized optimization algorithms tailored to this setting. Finally, we highlight the performance of the methods on multi-label prediction, image completion, and reinforcement learning problems, where we show improvement over the existing state of the art in many cases.

 The implementation is here.

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Approximate Sparse Linear Regression

Some new results for the sparse regression problem and some extension of it:

Approximate Sparse Linear Regression by Sariel Har-Peled, Piotr Indyk, Sepideh Mahabadi

In the Sparse Linear Regression (SLR) problem, given a d×n matrix M and a d-dimensional vector q, we want to compute a k-sparse vector τ such that the error ||Mτ−q|| is minimized. In this paper, we present algorithms and conditional lower bounds for several variants of this problem. In particular, we consider (i) the Affine SLR where we add the constraint that ∑iτi=1 and (ii) the Convex SLR where we further add the constraint that τ≥0. Furthermore, we consider (i) the batched (offline) setting, where the matrix M and the vector q are given as inputs in advance, and (ii) the query(online) setting, where an algorithm preprocesses the matrix M to quickly answer such queries. All of the aforementioned variants have been well-studied and have many applications in statistics, machine learning and sparse recovery.
We consider the approximate variants of these problems in the "low sparsity regime" where the value of the sparsity bound k is low. In particular, we show that the online variant of all three problems can be solved with query time O~(nk−1). This provides non-trivial improvements over the naive algorithm that exhaustively searches all (nk) subsets B. We also show that solving the offline variant of all three problems, would require an exponential dependence of the form Ω~(nk/2/ek), under a natural complexity-theoretic conjecture. Improving this lower bound for the case of k=4 would imply a nontrivial lower bound for the famous Hopcroft's problem. Moreover, solving the offline variant of affine SLR in o(nk−1) would imply an upper bound of o(nd) for the problem of testing whether a given set of n points in a d-dimensional space is degenerate. However, this is conjectured to require Ω(nd) time.

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Course: Stat212b: Topics Course on Deep Learning by Joan Bruna, UC Berkeley, Stats Department. Spring 2016.

Joan Bruna released the detailed syllabus and lectures of his recent Stat212b: Topics Course on Deep Learning that he did at UC Berkeley in the Statistics Department during the Spring 2016. From the page:

 

Topics in Deep Learning

This topics course aims to present the mathematical, statistical and computational challenges of building stable representations for high-dimensional data, such as images, text and audio. We will delve into selected topics of Deep Learning, discussing recent models from both supervised and unsupervised learning. Special emphasis will be on convolutional architectures, invariance learning, unsupervised learning and non-convex optimization.

Detailed Syllabus and Lectures

• Lec1: Intro and Logistics
• Lec2: Representations for Recognition : stability, variability. Kernel approaches / Feature extraction.
  
  • Elements of Statistical Learning, chapt. 12, Hastie, Tibshirani, Friedman.
• Lec3: Groups, Invariants and Filters.
  
  • Learning Stable Group Invariant Representations with Convolutional Networks
  • Understanding Deep Convolutional Networks, S. Mallat.
  • A Wavelet Tour of Signal Processing, chapt 2-5,7, S. Mallat.
• Lec4: Scattering Convolutional Networks.
  
  • Invariant Scattering Convolutional Networks
  further reading
  
  • Group Invariant Scattering, S. Mallat
  • Scattering Representations for Recognition
• Lec5: Further Scattering: Properties and Extensions.
  
  • Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination, Sifre & Mallat.
• Lec6: Convolutional Neural Networks: Geometry and first Properties.
  
  • Deep Learning Y. LeCun, Bengio & Hinton.
  • Understanding Deep Convolutional Networks, S. Mallat.
• Lec7: Properties of learnt CNN representations: Covariance and Invariance, redundancy, invertibility.
  
  • Deep Neural Networks with Random Gaussian Weights: A universal Classification Strategy?, R. Giryes, G. Sapiro, A. Bronstein.
  • Intriguing Properties of Neural Networks C. Szegedy et al.
  • Geodesics of Learnt Representations O. Henaff & E. Simoncelli.
  • Inverting Visual Representations with Convolutional Networks, A. Dosovitskiy, T. Brox.
  • Visualizing and Understanding Convolutional Networks M. Zeiler, R. Fergus.
• Lec8: Connections with other models (Dict. Learning, Random Forests)
  
  • Proximal Splitting Methods in Signal Processing Combettes & Pesquet.
  • A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems Beck & Teboulle
  • Learning Fast Approximations of Sparse Coding K. Gregor & Y. LeCun
  • Task Driven Dictionary Learning J. Mairal, F. Bach, J. Ponce
  • Exploiting Generative Models in Discriminative Classifiers T. Jaakkola & D. Haussler
  • Improving the Fisher Kernel for Large-Scale Image Classification F. Perronnin et al.
  • NetVLAD R. Arandjelovic et al.
• Lec9: Other high level tasks: localization, regression, embedding, inverse problems.
  
  • Object Detection with Discriminatively Trained Deformable Parts Model Felzenswalb, Girshick, McAllester and Ramanan, PAMI'10
  • Deformable Parts Models are Convolutional Neural Networks, Girshick, Iandola, Darrel and Malik, CVPR'15.
  • Rich Feature Hierarchies for accurate object detection and semantic segmentation Girshick, Donahue, Darrel and Malik, PAMI'14.
  • Graphical Models, message-passing algorithms and convex optimization M. Wainwright.
  • Conditional Random Fields as Recurrent Neural Networks Zheng et al, ICCV'15
  • Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation Tompson, Jain, LeCun and Bregler, NIPS'14.
• Lec10: Extensions to non-Euclidean domain. Representations of stationary processes. Properties.
  
  • Dimensionality Reduction by Learning an Invariant Mapping Hadsell, Chopra, LeCun,'06.
  • Deep Metric Learning via Lifted Structured Feature Embedding Oh Song, Xiang, Jegelka, Savarese,'15.
  • Spectral Networks and Locally Connected Networks on Graphs Bruna, Szlam, Zaremba, LeCun,'14.
  • Spatial Transformer Networks Jaderberg, Simonyan, Zisserman, Kavukcuoglu,'15.
  • Intermittent Process Analysis with Scattering Moments Bruna, Mallat, Bacry, Muzy,'14.
• Lec11: Guest Lecture ( W. Zaremba, OpenAI ) Discrete Neural Turing Machines.
• Lec12: Representations of Stationary Processes (contd). Sequential Data: Recurrent Neural Networks.
  
  • Intermittent Process Analysis with Scattering Moments J.B., Mallat, Bacry and Muzy, Annals of Statistics,'13.
  • A mathematical motivation for complex-valued convolutional networks Tygert et al., Neural Computation'16.
  • Texture Synthesis Using Convolutional Neural Networks Gatys, Ecker, Betghe, NIPS'15.
  • A Neural Algorithm of Artistic Style, Gatys, Ecker, Betghe, '15.
  • Time Series Analysis and its Applications Shumway, Stoffer, Chapter 6.
  • Deep Learning Goodfellow, Bengio, Courville,'16. Chapter 10.
• Lec13: Recurrent Neural Networks (contd). Long Short Term Memory. Applications.
  
  • Deep Learning Goodfellow, Bengio, Courville,'16. Chapter 10.
  • Generating Sequences with Recurrent Neural Networks A. Graves.
  • The Unreasonable Effectiveness of Recurrent Neural Networks A. Karpathy
  • The Unreasonable effectiveness of Character-level Language Models Y. Goldberg
• Lec14: Unsupervised Learning: Curse of dimensionality, Density estimation. Graphical Models, Latent Variable models.
  
  • Describing Multimedia Content Using Attention-based Encoder-Decoder Networks K. Cho, A. Courville, Y. Bengio
  • Graphical Models, Exponential Families and Variational Inference M. Wainwright, M. Jordan.
• Lec15: Autoencoders. Variational Inference. Variational Autoencoders.
  
  • Graphical Models, Exponential Families and Variational Inference, chapter 3 M. Wainwright, M. Jordan.
  • Variational Inference with Stochastic Search J.Paisley, D. Blei, M.Jordan.
  • Stochastic Variational Inference M. Hoffman, D. Blei, Wang, Paisley.
  • Auto-Encoding Variational Bayes, Kingma & Welling.
  • Stochastic Backpropagation and variational inference in deep latent gaussian models D. Rezende, S. Mohamed, D. Wierstra.
• Lec16: Variational Autoencoders (contd). Normalizing Flows. Generative Adversarial Networks.
  
  • Semi-supervised learning with Deep generative models Kingma, Rezende, Mohamed, Welling.
  • Importance Weighted Autoencoders Burda, Grosse, Salakhutdinov.
  • Variational Inference with Normalizing Flows Rezende, Mohamed.
  • Unsupervised Learning using Nonequilibrium Thermodynamics Sohl-Dickstein et al.
  • Generative Adversarial Networks, Goodfellow et al.
• Lec17: Generative Adversarial Networks (contd).
  
  • Generative Adversarial Networks, Goodfellow et al.
  • Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks Denton, Chintala, Szlam, Fergus.
  • Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford, Metz, Chintala.
• Lec18: Maximum Entropy Distributions. Self-supervised models (analogies, video prediction, text, word2vec).
  
  • Graphical Models, Exponential Families and Variational Inference, chapter 3 M. Wainwright, M. Jordan.
  • An Introduction to MCMC for Machine Learning Andrieu, de Freitas, Doucet, Jordan.
  • Stochastic relaxation, Gibbs distributions and the Bayesian Restoration of Images Geman & Geman.
  • Distributed Representations of Words and Phrases and their compositionality Mikolov et al.
  • word2vec Explained: deriving Mikolov et al's negative-sampling embedding method Goldberg & Levy.
• Lec19: Self-supervised models (contd). Non-convex Optimization. Stochastic Optimization.
  
  • Pixel Recurrent Neural Networks A. van den Oord, N. Kalchbrenner, K. Kavukcuoglu.
  • The tradeoffs of Large Scale Learning Bottou, Bousquet.
  • Introduction to Statistical Learning Theory Bousquet, Boucheron, Lugosi.
• Lec20: Guest Lecture (S. Chintala, Facebook AI Research), "The Adversarial Network Nonsense".
• Lec21: Accelerated Gradient Descent, Regularization, Dropout.
  
  • Convex Optimization: Algorithms and Complexity S. Bubeck
  • Optimization, Simons Big Data Boot Camp B. Recht
  • The Zen of Gradient Descent M. Hardt.
  • Train Faster, Generalize Better: Stability of Stochastic Gradient Descent M. Hardt, B. Recht, Y. Singer.
  • Dropout: a simple way to prevent neural networks from Overfitting Srivastava, Hinton et al.
• Lec22: Dropout (contd). Batch Normalization, Tensor Decompositions.
  
  • Dropout Training as Adaptive Regularization Wager, Wang, Liang.
  • Batch Normalization: accelerating Deep Network Training by Reducing internal covariate shift Ioffe, Szegedy.
  • Global Optimality in Tensor Factorization, Deep Learning and Beyond Haefflele, Vidal.
• Lec23 Guest Lecture (Yann Dauphin, Facebook AI Research), "Optimizing Deep Nets".
• Lec24: Tensor Decompositions (contd), Spin Glasses.
  
  • On the expressive power of Deep Learning: a tensor analysis Cohen, Sharir, Shashua.
  • Beating the Perils of non-convexity: Guaranteed Training of Neural Networks using Tensor methods Janzamin, Sedghi, Anandkumar.
  • The Loss Surfaces of Multilayer Networks Choromaska, Henaff, Mathieu, Ben Arous, LeCun.

Stat212b: Topics Course on Deep Learning is maintained by joanbruna. 

 

 
 
 
 
 
 
 

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The Famine of Forte: Few Search Problems Greatly Favor Your Algorithm

From the conclusion of the paper:

The search framework we propose is general enough to be applied to many problem areas, such as machine learning, evolutionary search, security and hyperparameter optimization. The results are not just of theoretical importance, but help explain real-world phenomena, such as the need for exploitable dependence in machine learning and the empirical diffi culty of automated learning [12]. Our results help us understand the growing popularity of deep learning methods and unavoidable interest in automated hyperparameter tuning methods. Extending the framework to continuous settings and other problem areas (such as active learning and regression) is the focus of ongoing research.

 

 

 

The Famine of Forte: Few Search Problems Greatly Favor Your Algorithm by George D. Montanez

No Free Lunch theorems show that the average performance across any closed-under-permutation set of problems is fixed for all algorithms, under appropriate conditions. Extending these results, we demonstrate that the proportion of favorable problems is itself strictly bounded, such that no single algorithm can perform well over a large fraction of possible problems. Our results explain why we must either continue to develop new learning methods year after year or move towards highly parameterized models that are both flexible and sensitive to their hyperparameters.

 
 
 
 
 
 

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Universal microbial diagnostics using random DNA probes

Using compressive sensing for pathogen detection.

Universal microbial diagnostics using random DNA probes by Amirali Aghazadeh, Adam Y. Lin, Mona A. Sheikh, Allen L. Chen, Lisa M. Atkins, Coreen L. Johnson, Joseph F. Petrosino, Rebekah A. Drezek and  Richard G. Baraniuk

Early identification of pathogens is essential for limiting development of therapy-resistant pathogens and mitigating infectious disease outbreaks. Most bacterial detection schemes use target-specific probes to differentiate pathogen species, creating time and cost inefficiencies in identifying newly discovered organisms. We present a novel universal microbial diagnostics (UMD) platform to screen for microbial organisms in an infectious sample, using a small number of random DNA probes that are agnostic to the target DNA sequences. Our platform leverages the theory of sparse signal recovery (compressive sensing) to identify the composition of a microbial sample that potentially contains novel or mutant species. We validated the UMD platform in vitro using five random probes to recover 11 pathogenic bacteria. We further demonstrated in silico that UMD can be generalized to screen for common human pathogens in different taxonomy levels. UMD’s unorthodox sensing approach opens the door to more efficient and universal molecular diagnostics.

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Thesis: Faster algorithms for convex and combinatorial optimization by Yin Tat Lee.

Congratulations Dr. Lee !

Here is some amazing work: Faster algorithms for convex and combinatorial optimization  by Yin Tat Lee.

In this thesis, we revisit three algorithmic techniques: sparsification, cutting and collapsing. We use them to obtain the following results on convex and combinatorial optimization: 

• Linear Programming: We obtain the first improvement to the running time for linear programming in 25 years. The convergence rate of this randomized algorithm nearly matches the universal barrier for interior point methods. As a corollary, we obtain the first ... time randomized algorithm for solving the maximum flow problem on directed graphs with m edges and n vertices. This improves upon the previous fastest running time of achieved over 15 years ago by Goldberg and Rao. 

• Maximum Flow Problem: We obtain one of the first almost-linear time randomized algorithms for approximating the maximum flow in undirected graphs. As a corollary, we improve the running time of a wide range of algorithms that use the computation of maximum flows as a subroutine. 

• Non-Smooth Convex Optimization: We obtain the first nearly-cubic time randomized algorithm for convex problems under the black box model. As a corollary, this implies a polynomially faster algorithm for three fundamental problems in computer science: submodular function minimization, matroid intersection, and semidefinite programming.

• Graph Sparsification: We obtain the first almost-linear time randomized algorithm for spectrally approximating any graph by one with just a linear number of edges. This sparse graph approximately preserves all cut values of the original graph and is useful for solving a wide range of combinatorial problems. This algorithm improves all previous linear-sized constructions, which required at least quadratic time. 

• Numerical Linear Algebra: Multigrid is an efficient method for solving large-scale linear systems which arise from graphs in low dimensions. It has been used extensively for 30 years in scientific computing. Unlike the previous approaches that make assumptions on the graphs, we give the first generalization of the multigrid that provably solves Laplacian systems of any graphs in nearly-linear expected time.

Yin Tat's publications are here.

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Fastfood Dictionary Learning for Periocular-Based Full Face Hallucination

Inpainting with FastFood. I put the faces reconstructed from the slides presentation because our discriminative human vision is far more informative at devising how good this technique is than any figure of merit.

  

Fastfood Dictionary Learning for Periocular-Based Full Face Hallucination by Felix Juefei-Xu and Marios Savvides

The kernel trick becomes a burden for some machine learning tasks such as dictionary learning, where a huge amount of training samples are needed, making the kernel matrix gigantic and infeasible to store or process. In this work, we propose to alleviate this problem and achieve Gaussian RBF kernel expansion explicitly for dictionary learning using Fastfood transform, which is an approximation of full kernel expansion. We have shown, in the context of missing data recovery through joint dictionary learning i.e. periocular-based full face hallucination, that the approximated kernel expansion using Fastfood transform for joint dictionary learning yields much better results than its image space counterparts. Also, explicit kernel expansion through Fastfood allows us to de-kernelize the reconstructed image in the feature space back to the image space, enabling applications that require reconstructive dictionaries such as cross-domain reconstruction, image super-resolution, missing data recovery, etc.

Slides are also available.

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CSjob redux: Postdoc, Signal processing & inverse problems for the future SKA, Nice, France

David just let me know that this awesome announcement for a Postdoc, Signal processing & inverse problems for the future SKA, Nice, France is still available as the candidate  who initally said yes, just bolted. Here is the postdoc ad.
 
 
 

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Nuit Blanche in Review (September 2016)

Since the last Nuit Blanche in Review (August 2016), Rosetta landed on a comet but we also had three Paris Machine Learning meetups, three reviews, two sunday morning insights, and many more in-depth entries. Enjoy !

Implementations

• Speeding up Bayesian Optimizations with Random Projections - implementations -
• Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
• gvnn: Neural Network Library for Geometric Computer Vision - implementation -

Sunday Morning Insights

• Sunday Morning Insight: "More F$*%(g Data"
• Predicting the Future: The Steamrollers and Machine Learning.

In-depth:

• L1-PCA, Online Sparse PCA and Discretization and Minimization of the L1 Norm on Manifolds
• A Randomized Tensor Singular Value Decomposition based on the t-product
• Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis
• Large-Scale Strategic Games and Adversarial Machine Learning
• Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
• Randomized single-view algorithms for low-rank matrix approximation
• Integrating multiple random sketches for singular value decomposition
• A Randomized Approach to Efficient Kernel Clustering
• Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases
• A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models
• Convexified Convolutional Neural Networks

Paris Machine Learning Meetups

• Paris Machine Learning Meetup, Hors série #1: Introduction to Bayesian Inference with Stan and R
• Paris Machine Learning Meetup, Hors série #2: Scalable Machine Learning with H2O
• Paris Machine Learning #1 Season 4: AlphaGo, Deep Learning & Global Biodiversity, DataScience Game
• Paris Machine Learning Meetup Newsletter September 2016 [mostly in French]

Book/reviews/courses

• Book: Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto 
• Low-Rank Tensor Networks for Dimensionality Reduction and Large-Scale Optimization Problems: Perspectives and Challenges PART 1
• Course: Data Science From data to knowledge (slides, codes, datasets...), Xavier Bresson

LightOn

• LightOn, Forward We Go.

Hamming's time

• It's Friday, it's Hamming's time: Call for deep learning research problems from your "problem surplus" stack

Meetings

• ITWIST 2016: Slides and Posters, International Traveling Workshop on Interaction between Sparse models and Technology 2016

CfP:

• CfP: ESANN2017 Special sessions, 26 - 28 April 2017
• CfP: SPARS 2017 Lisbon, Portugal, June 5-8, 2017

Videos

• Saturday Morning Videos: Plenary Panel: Is Deep Learning the New 42? at KDD 2016
• Saturday Morning Videos: Learning to learn and compositionality with deep recurrent neural networks, Nando de Freitas. KDD 2016
• Sunday Morning Video: Bay Area Deep Learning School Day 2 Live streaming
• Sunday Morning Videos: HORSE2016, On “Horses” and “Potemkin Villages” in Applied Machine Learning
• Saturday Morning Video: Bay Area Deep Learning School Day 1 Live streaming
• Saturday Morning Videos: Other plenary videos KDD2016 by Joe Hellerstein, Whitfield Diffie and Jennifer Chayes.
• Saturday Morning Video: A VC View of Investing in ML by Greg Papadopoulos, NEA (KDD 2016)

Jobs:

• CSjobs: Lecturers (Assistant Professors) in: Machine Learning & Computer Vision; Robot Vision & Autonomous Systems
• CSjobs: Two Ph.D. positions at TU Wien (Austria) and Aalto University (Finland)
• CSjobs: Several Post-Doctoral Research Associate Signal and Image Processing Institute, University of Southern California

credit: ESA, Roman Tkachenko

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CfP: ESANN2017 Special sessions, 26 - 28 April 2017

 

 

Rosetta's last image,

 

 

Matthieu just sent me the following:

 

 

Dear Igor, 

I hope you are doing well. I am writing you because I would really appreciate if you could share some information which should interest your blog readers.

Gilles Delmaire, Gilles Roussel, and myself are organizing a special session on Environmental Signal Processing for the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN https://www.elen.ucl.ac.be/esann/) but it would be fairer to promote all the special sessions (https://www.elen.ucl.ac.be/esann/index.php?pg=specsess), as all of them may interest the Nuit Blanche readers. I think it will really be worth attending ESANN in April 2017, in the magnificent city of Brugge!
All the special session organizers sent submission invitations to potential participants but, at least in our case, if we forget a Nuit Blanche reader who wish to submit a paper in our session, we would appreciate to be firstly contacted.
Thank you in advance for your help.
Best regards,
Matthieu
-- Matthieu PUIGT, Ph.D.

 

Thanks Matthieu ! according to the main page, prospective authors are invited to submit their contributions before 19 November 2016.

 The following special sessions will be organized at ESANN2017:

• Environmental signal processing: new trends and applications
  Organized by Gilles Delmaire, Matthieu Puigt, Gilles Roussel (Université du Littoral Côte d'Opale, France)
• Biomedical data analysis in translational research: integration of expert knowledge and interpretable models
  Organized by Gyan Bhanot (Rutgers University, New Jersey, USA), Michael Biehl (University of Groningen, The Netherlands), Thomas Villmann (Univ. of Applied Sciences Mittweida, Germany), Dietlind Zühlke (Seven Principles, Germany)
• Processing, Mining and Visualizing Massive Urban Data
  Organized by Etienne Côme (UPE-Ifsttar, France), Pierre Borgnat (ENS Lyon, France), Latifa Oukhellou (UPE-Ifsttar, France)
• Randomized Machine Learning approaches: analysis and developments
  Organized by Claudio Gallicchio (University of Pisa, Italy), José D. Martín-Guerrero (University of Valencia, Spain), Alessio Micheli (University of Pisa, Italy), Emilio Soria (University of Valencia, Spain)
• Deep and kernel methods: best of two worlds
  Organized by Lluís A. Belanche, Marta R. Costa-jussà (Universitat Politècnica de Catalunya, Barcelona, Spain)
• Algorithmic Challenges in Big Data Analytics
  Organized by: Veronica Bolon-Canedo, Amparo Alonso-Betanzos (University of A Coruña, Spain), Beatriz Remeseiro (University of Barcelona, Spain), David Martinez-Rego (University College London, UK), Konstantinos Sechidis (University of Manchester, UK)
• Machine Learning in Biomorphic Robots
  Organized by Nigel Crook, Matthias Rolf, Tjeerd olde Scheper (Oxford Brookes Univ., UK)

 
 
 Photo Copyright ESA/Rosetta/MPS for OSIRIS Team MPS/UPD/LAM/IAA/SSO/INTA/UPM/DASP/IDA
 

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