Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilizing Simulation for Reinforcement Learning and Curiosity Driven Exploration in Robotics

Reinforcement learning (RL) is powerful when one does not have any, or sparsely labeled data to guide the learning of relevant behaviour. RL does, however, require a lot of training time, and for robotic training real operation for extended time is often impractical or impossible. The natural alternative to physical run time is to do training in simulation. However, for many tasks simulation accuracy can be lacking both in terms of the robotic model and the model of its environment, and a system trained on a faulty model will not in general perform well in the real world. A solution to this problem is to train the robot in simulation while varying parameterization, domain randomization [2], to make the system learn a robotic behaviour that is robust to noise and systematic variations in the physical behaviour of both the robot itself and the environment it interacts with. Further issues that are common to reinforcement learning are producing a good reward signal and efficiently exploring the state space. In constrained physical or simulated environments these problems could be alleviated by using curiosity driven exploration for a robotic agent [3], in order to let it find its own capabilities and the properties of the environment on its own. This could be a productive way to develop basic robotic skills, without an explicit reward signal, that could then be applied to more complex tasks. The curiosity driven approach could be complemented by or combined with reinforcement learning for open ended tasks like ”build as high as possible with the objects you can reach”. The hypothesis is that such tasks always give some reward, and even simple things like tearing down or constructing a pile will give some reward related feedback from which the system can learn and improve its behaviour.
Design of an ultra-compact lowpass filter with ultra-sharp roll-off for UHF application

This letter proposes a UHF lowpass filter based on the image parameter method. This study designed, fabricated and assembled a 0.48GHz semilumped lowpass filter using low cost FR4 substrate. It adopted a new layout concept for the air wound coils to achieve the purposes of compact size and good performances. The proposed UHF lowpass filter has the best performances for the roll-off rate and circuit size compared to previous works, and it also achieves the best figure of merit 973,369.
Bayesian neural network priors at the level of units

We investigate deep Bayesian neural networks with Gaussian priors on the weights and ReLU-like nonlinearities, shedding light on novel sparsity-inducing mechanisms at the level of the units of the network. Bayesian neural networks with Gaussian priors are well known to induce the weight decay penalty on the weights. In contrast, our result indicates a more elaborate regularization scheme at the level of the units, ranging from convex penalties for the first two layers-L 2 regularization for the first and Lasso for the second-to non convex penalties for deeper layers. Thus, although weight decay does not allow for the weights to be set exactly to zero, sparse solutions tend to be selected for the units from the second layer onward. This result provides new theoretical insight on deep Bayesian neural networks, underpinning their natural shrinkage properties and practical potential.
Emulation and Uncertainty Quantification for Models with Functional Response Using Bayesian Adaptive Splines

Author(s): Francom, Devin | Advisor(s): Sanso, Bruno | Abstract: When a computer code is used to simulate a complex system, a fundamental task is to assess the uncertainty of the simulator. In the case of computationally expensive simulators, this is often accomplished via a surrogate statistical model, a statistical output emulator. An effective emulator is one that provides good approximations to the computer code output for wide ranges of input values. In addition, an emulator should be able to handle large dimensional simulation output for a relevant number of inputs; it should flexibly capture heterogeneities in the variability of the response surface; it should be fast to evaluate for arbitrary combinations of input parameters; and it should provide an accurate quantification of the emulation uncertainty. In this work, we develop Bayesian adaptive spline methods for emulation of computer models that output functions. We introduce modifications to traditional Bayesian adaptive spline approaches that allow for fitting large amounts of data and allow for more efficient Markov chain Monte Carlo sampling. We develop a functional approach to sensitivity analysis that can be performed using this emulator. We present a sensitivity analysis of a computer model of the deformation of a protective plate used in pressure driven experiments. This example serves as an illustration of the ability of Bayesian adaptive spline emulators to fulfill all the necessities of computability, flexibility and reliable calculation on relevant measures of sensitivity.We extend the methods to emulation of an atmospheric dispersion simulator that outputs a plume in space and time based on inputs detailing the characteristics of the release, some of which are categorical. We achieve accurate emulation using Bayesian adaptive splines to model weights on empirical orthogonal functions. We extend the adaptive spline methodology to allow for categorical inputs. We use this emulator as well as appropriately identifiable simulator discrepancy and observational error models to calibrate the simulator using a dataset from an experimental release of particles from the Diablo Canyon Nuclear Power Plant in Central California. Since the release was controlled, these characteristics are known, allowing us to compare our findings to the truth.We further extend the methods to emulate a computer model that outputs misaligned functional data. We do this by modeling the aligned, or warped, data as well as the warping functions, using separate Bayesian adaptive spline models. We explore inference methods that treat these models jointly and separately, and establish methods to ensure that the warping functions are non-decreasing. These methods are applied to a high-energy-density physics model that outputs a curve representing energy as a function of time.
A Neural Network Based Classifier for a Segmented Facial Expression Recognition System Based on Haar Wavelet Transform

Automatic recognition of facial expressions is a vital component of natural human-machine interfaces. Facial expressions convey information about one's emotional state and helps regulate our social norms by helping detect and interpret a scene. In this paper, we propose a novel face expression recognition scheme based on Haar discrete wavelet transform and a neural network classifier. First, the sample image undergoes preprocessing where noise is removed using binary image processing techniques. Then feature vectors are extracted using DWT from corresponding pixels in the image. The extracted image pixel data are used as the input to the neural network. We demonstrate experimentally that when wavelet coefficients are fed into a back-propagation based neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. Based on our experimental results, the proposed scheme gives satisfactory results.
Contourlet-1.3 texture image retrieval system

Focusing on the low retrieval rate of contourlet texture image retrieval system,an anti-aliasing Contourlet-1.3 transform based texture image retrieval system is proposed.In the system,the Contourlet-1.3 transform is constructed by anti-aliasing critical subsampled Laplacian pyramid cascaded by critical subsampled directional filter banks,sub-bands energy and standard deviations in contourlet domain are cascaded to form feature vectors,and the similarity metric is Canberra distance.Experimental results show that anti-aliasing contourlet transform based image retrieval system is superior to those of the original contourlet transform,nun-subsampled contourlet system and Contourlet-2.3 under the same system structure with almost same length of feature vectors,retrieval time and memory needed;and anti-aliasing contourlet decomposition structure parameter can make significant effects on retrieval rates.
A study of the loops control for reconfigurable computing with OpenCL in the LABS local search problem

In this article, we study the steepest descent local search (SDLS) algorithm that is used as the improvement step in the memetic algorithms for the search of low autocorrelation binary sequences (LABS). We address the method of reconfigurable computing, as the algorithm is of the field programmable gate array (FPGA) type as it features the integer operations, bit-wise data representation, regular execution flow, and huge computational complexity. It contains four levels of nested loops, but its direct parallel implementation as a custom processor leads to typical problems because the loops expose a dynamic range and too many iterations. This inhibits a simple parallel data path that is typically produced by the method of the loop unrolling. We have examined the four architectures that mitigate the found obstacles, and we provide the results of their implementation. The solutions take advantages of the loop pipelining, reordering of the loops, and dynamic reconfiguration. The recently available development tool was involved in our study as we have used the OpenCL (OCL) platform for FPGAs to draw practical conclusions. The given proposals are characterized by their performance and capacity for a problem size. Consequently, the speed/size trade-off is highlighted, as an FPGA size is a design constraint. The performance of the FPGA-based solutions is compared to the CPU speed, and the maximum reported speed-up is 750. Readers can further develop and/or use the presented OCL solutions for efficient LABS discovery as we provide the corresponding software repository.
Stability Analysis of Company Co-Mention Network and Market Graph Over Time Using Graph Similarity Measures

The aim of the paper is to provide an analysis of news and financial data using their network representation. The formation of network structures from data sources is carried out using two different approaches: by building the so-called market graph in which nodes represent financial assets (e.g., stocks) and the edges between nodes stand for the correlation between the corresponding assets, by constructing a company co-mention network in which any two companies are connected by an edge if a news item mentioning both companies has been published in a certain period of time. Topological changes of the networks over the period 2005–2010 are investigated using the sliding window of six-month duration. We study the stability of the market graph and the company co-mention network over time and establish which of the two networks was more stable during the period. In addition, we examine the impact of the crisis of 2008 on the stability of the market graph as well as the company co-mention network. The networks that are considered in this paper and that are the objects of our study (the market graph and the company co-mention network) have a non-changing set of nodes (companies), and can change over time by adding/removing links between these nodes. Different graph similarity measures are used to evaluate these changes. If a network is stable over time, a measure of similarity between two graphs constructed for two different time windows should be close to zero. If there was a sharp change between the graphs constructed for two adjacent periods, then this should lead to a sharp increase in the value of the similarity measure between these two graphs. This paper uses the graph similarity measures which were proposed relatively recently. In addition, to estimate how the networks evolve over time we exploit QAP (Quadratic Assignment Procedure). While there is a sufficient amount of works studying the dynamics of graphs (including the use of graph similarity metrics), in this paper the company co-mention network dynamics is examined both individually and in comparison with the dynamics of market graphs for the first time.
An Improved Algorithm for TDOA Trajectory Tracking

In a precision positioning system, one important source of positioning error is the clock synchronization problem, which is caused by multiple base stations. Therefore, eliminating the clock synchronization problem in a no-line-of-sight (NLOS) environment plays an important role in reducing errors in a positioning system. To address this problem, this study designs a practical experimental environment and proposes the concepts of Time Difference of Arrival (TDOA) and non-line-of-sight-Time Difference of Arrival (N-TDOA). First, the improved TDOA algorithm is used to determine the tag’s position; second, the tag’s trajectory is drawn at different times; then, a map of the monitored area is loaded, and the tag trajectory is displayed in the actual experimental environment. The experimental results show that the N-TDOA algorithm synchronizes the base stations at the algorithmic layer; thus, deploying network cables or wires to achieve clock synchronization is unnecessary: wireless deployment can be used. In an NLOS environment, the N-TDOA method significantly improves the positioning accuracy compared with that of other algorithms, which facilitates further trajectory tracking research. Overall, the proposed approach improves both the accuracy and stability of trajectory tracking.
Smart Interactive Vessel Visualization in Radiology

Cardiovascular diseases occur with increasing frequency in our society. Their diagnosis often requires tailored visualization techniques, e.g., to examine the blood flow channel in case of luminal narrowing. Curved Planar Reformation (CPR) addresses this field by creating longitudinal sections along the centerline of blood vessels. With the possibility to rotate around an axis, the entire vessel can be assessed for possible vascular abnormalities (e.g., calcifications on the vessel wall, stenoses, and occlusions). In this thesis, we present a visualization technique, called Centerline Reformation (CR), that offers the possibility to investigate the interior of any blood vessel, regardless of its spatial orientation. Starting from the projected vessel centerlines, the lumen of any vessel is generated by employing wavefront propagation in image space. The vessel lumen can be optionally delineated by halos, to enhance spatial relationships when examining a dense vasculature. We present our method in a focus+context setup, by rendering a different kind of visualization around the lumen. We explain how to resolve correct visibility of multiple overlapping vessels in image space. Additionally, our visualization method allows the examination of a complex vasculature by means of interactive vessel filtering and subsequent visual querying. We propose an improved version of the Centerline Reformation (CR) technique, by generating a completely three-dimensional reformation of vascular structures using ray casting. We call this process Curved Surface Reformation (CSR). In this method, the cut surface is smoothly extended into the surrounding tissue of the blood vessels. Moreover, automatically generated cutaways reveal as much of the vessel lumen as possible, while still retaining correct visibility. This technique offers unrestricted navigation within the inspected vasculature and allows diagnosis of any tubular structure, regardless of its spatial orientation. The growing amount of data requires increasing knowledge from a user in order to select the appropriate visualization method for their analysis. In this thesis, we present an approach that externalizes the knowledge of domain experts in a human readable form and employs an inference system to provide only suitable visualization techniques for clinical diagnosis, namely Smart Super Views. We discuss the visual representation of such automatically suggested visualizations by encoding the respective relevance into shape and size of their view. By providing a smart spatial arrangement and integration, the image becomes the menu itself. Such a system offers a guided medical diagnosis by domain experts. After presenting the approach in a general setting, we describe an application scenario for diagnostic vascular visualization techniques. Since vascular structures usually consist of many vessels, we describe an anatomical layout for the investigation of the peripheral vasculature of the human lower extremities. By aggregating the volumetric information around the vessel
OSSIA: Towards a unified interface for scoring time and interaction

The theory of interactive scores addresses the writing and execution of temporal constraints between musical objects, with the ability to describe the use of interactivity in the scores. In this paper, a notation for the use of conditional branching in interactive scores will be introduced. It is based on a high level formalism for the authoring of interactive scores developed during the course of the OS-SIA research project. This formalism is meant to be at the same time easily manipulated by composers, and translat-able to multiple formal methods used in interactive scores like Petri nets and timed automaton. An application programming interface that allows the interactive scores to be embedded in other software and the authoring software, I-SCORE, will be presented.
A Concise Review of Named Entity Recognition System: Methods and Features

Named Entity Recognition (NER) is an elementary tool for all application areas in Natural Language Processing (NLP) such as Automatic Summarization, Information Extraction, Information Retrieval, Text Mining, Machine Translation, Question Answering, and Genetics. NER is a task to discover and categorises the named entities (‘atomic elements’) in the text into predefined classes such as the names of persons, organizations, locations, terminologies of time, quantity and etc. Different languages may have different morphologies and thus involve dissimilar NER procedures. For example, an Arabic NER system cannot be practically used in processing Malay texts due to the different morphological features. The morphological features of every language are rich and complex and donates to the difficulties of implementing an actual method to develop the accurate NER system. In this paper, we review on three main techniques that commonly used to develop an NER system well-known as Rule-Based, Machine Learning, and Hybrid approach. This paper also highlights the features of each technique.
Secure Control Protocols for USB Mass Storage Devices

- USB based devices are very handy in nature due to their plug and play technology. With the advancement of technology their data carrying capacity is increasing day by day, which is imposing a threat of data theft. Presently most of the USB mass storage devices are not secured and therefore most of the companies restrict USB mass storage devices due to fear of confidential data loss. To avail the benefits of high speed data transmission and convenience of use, many research scholars suggested different secure protocols for establishing communication with USB mass storage devices. This study shows various features of existing secure control protocols for USB storage devices.
Ovarian structures segmentation using a neural network approach

The segmentation and detection of anatomical structures have many clinical applications in different medical fields. In particular, in the fields of gynecology and obstetrics, the segmentation and detection of ovarian structures is a task, which in clinical practice, becomes time-consuming, repetitive and error-prone when performed manually by the physicians. One of the areas of application of this task is Assisted Reproductive Technology, whose treatments have been reporting an increase in the demand. These treatments require a frequent analysis of ultrasounds images of the ovaries, regarding the count and size of ovarian follicles. Also, the area of investigation of ovarian cancer would benefit from an automatic method that delivered the segmentation and detection of ovarian structures. Taking into account the need for a method of automatic segmentation and detection of ovarian structures, scientific and technological investigation is working towards finding effective solutions for that end. This dissertation’s aim is the development of an algorithm based on Convolutional Neural Networks that performs this task. This document presents key concepts for the understanding of the proposed model, namely the concepts of ultrasound imaging and deep learning, as well as the state-of-the-art in the area of follicle segmentation in ovarian ultrasound images. The proposed model is developed based on a baseline model, to which modifications and additional steps are proposed in order to improve its performance. The baseline model uses Bmode images as input of a U-Net optimized using a loss function based on DSC, to obtain the prediction of the follicle or ovary segmentation. The proposed adaptations include alterations regarding 1) input data, namely the type of data (B-mode and BRF), and pre-processing methods, 2) input size and U-Net architecture parameters, 3) the implementation of a different loss function and 4) post-processing methods. The results of the final model showed that it is suitable to perform the proposed task but it presents some limitations that do not allow its implementation in clinical practice. Furthermore, the conclusions taken from this work are relevant for future investigation in the area. In the future, the proposed loss function can be refined and the US radio-frequency data can be explored using signal-processing algorithms instead of image-processing algorithms so that the model can utilize its complete information, without the data loss that the conversion from radiofrequency to B-mode entails. An increase in the size of the dataset and a more balanced variety of follicle size in the dataset would also improve the model’s performance.
R&I smart specialisation strategies: classification of EU regions priorities. Results from automatic text analysis

Building on automatic text analysis, this paper proposes an original categorization of Research and Innovation Smart Specialisation Strategy (RIS3) priorities and provides a common language (with detailed dictionaries) to classify priorities and then to associate EU regions to multiclass categories of priorities. This result is a powerful tool to interpret the current state of diversification across regions, with its potential of complementarities and synergies that might support territorial integrated development paths. It would also support regions in their future strategic programmes on RIS3. A case study on the Alpine macro-region shows innovation development paths to outline macroregion strategic planning