Miracle-dz/UniFAR-FTUs huggingface dataset:
Image and Video Compression Standards: Algorithms and Architectures

From the Publisher: Image and Video Compression Standards: Algorithms and Architectures, Second Edition presents an introduction to thealgorithms and architectures that form the underpinnings of the imageand video compressions standards, including JPEG (compression ofstill-images), H.261 and H.263 (video teleconferencing), and MPEG-1and MPEG-2 (video storage and broadcasting). The next generation ofaudiovisual coding standards, such as MPEG-4 and MPEG-7, are alsobriefly described. In addition, the book covers the MPEG and DolbyAC-3 audio coding standards and emerging techniques for image andvideo compression, such as those based on wavelets and vectorquantization. Image and Video Compression Standards: Algorithms andArchitectures, Second Edition emphasizes the foundations ofthese standards; namely, techniques such as predictive coding,transform-based coding such as the discrete cosine transform (DCT),motion estimation, motion compensation, and entropy coding, as well ashow they are applied in the standards. The implementation details ofeach standard are avoided; however, the book provides all the materialnecessary to understand the workings of each of the compressionstandards, including information that can be used by the reader toevaluate the efficiency of various software and hardwareimplementations conforming to these standards. Particular emphasis isplaced on those algorithms and architectures that have been found tobe useful in practical software or hardware implementations. Image and Video Compression Standards: Algorithms andArchitectures, Second Edition uniquely covers all majorstandards (JPEG, MPEG-1, MPEG-2, MPEG-4, H.261, H.263) in asimple andtutorial manner, while fully addressing the architecturalconsiderations involved when implementing these standards. As such, itserves as a valuable reference for the graduate student, researcher orengineer. The book is also used frequently as a text for courses onthe subject, in both academic and professional settings.
A new hierarchical architecture based on SVM for Persian license plate character recognition

Each license plate recognition system is composed of three main parts, namely, license plate detection, character segmentation and character recognition. In this paper, we focus on the improvement and innovation of the character recognition step. For this purpose, a new hierarchical architecture based on Support Vector Machines (SVMs) is suggested for Persian license plate characters recognition. Clustering characters in the hierarchical architecture is proposed based on a new criterion using a confusion ma been used for clustering in this way. The hierarchical architecture precision is 97/4% and recognizes the entire characters of a license plate in approximately 60ms. All evaluations and comparisons methods and the proposed method in this paper have been done in the same hardware and software test bed. The dataset conditions such as, day, night, different distances and angles
CRAMm -- Memories for Robots Performing Everyday Manipulation Activities

Agents that learn from experience can profit immensely from memorizing what they have done, why, how, and what happened. For autonomous robots performing complex manipulation tasks, these memories include low level data, such as perceptual snapshots of relevant scenes that influenced decision making, detailed complex motions the robot performed, and effects of these motions. They also include high level representations of the intended actions and the belief-dependent descisions that led to the chosen course of action. In this paper, we propose CRAMm, a memory management system that can record very comprehensive and informative memories without slowing down the operation of the robot. CRAMm offers a query interface that allows the robot to retrieve the kinds of information stated above. This is done using a first-order logical language that provides predicates concerning the beliefs and intentions of the robot, its physical state, perceptual information, and action effects and their relations at various different levels of abstraction.
Neural networks for financial market prediction

The use of neural networks in financial market prediction presents a major challenge to the design of effective neural network predictors and classifiers. In this paper, the author examines several neural networks to evaluate their capability in prediction and in trend estimation which is treated as a classification problem. The networks considered are the backpropagation trained network (BPN), the general regression neural network (GRNN), the class-sensitive neural network (CSNN), and the conjugate gradient trained network (CGNN). It is concluded that CSNN is among the best performing networks in both prediction and trend estimation. All major indicators are evaluated by the neural networks. It is found that the use of good indicators like the rate of change, momentum, moving average, etc. can lead to about 5% improvement over the case that no indicator is used. Momentum computed from the previous 14 day data is the best single indicator. The complexity of the financial market probably explains why the large number of indicators cannot provide any significant improvement in network classification. <<ETX>>
Drainpipe network management information system design based on GIS and SCADA technique

Achieving urban drainpipe network integration of geographical information system (GIS) and supervisory control and data acquisition (SCADA) technology is described in this paper. The system design's plans are put forward, which have realized GIS and SCADA system supplementary in the technology and strengthened the model visible analysis ability. It is verified by practical cases that the system has more practical values and a good prospect.
Quantum Chromodynamics Monte Carlo TuningStudies in CMS

Recent QCD Monte Carlo tuning studies done in the CMS Collaboration are presented. Jet kinematics, jet substructure, and underlying event measurements in top quark pair events are discussed. New CMS PYTHIA 8 event tunes are presented, exploiting Monte Carlo configurations with consistent parton distribution functions and strong coupling parameter values in the matrix element and the parton shower, at leading order (LO), next-to-leading order (NLO) and next-to-next-to-leading order (NNLO). Predictions from PYTHIA8 obtained with tunes based on NLO or NNLO PDFs are shown to reliably describe minimum-bias and underlying-event data with a similar level of agreement to predictions from tunes using LO PDF sets. The tunes are validated with a range of different measurements and matrix element-parton shower merged configurations.
On Performance Stability in LSM-based Storage Systems

The Log-Structured Merge-Tree (LSM-tree) has been widely adopted for use in modern NoSQL systems for its superior write performance. Despite the popularity of LSM-trees, they have been criticized for suffering from write stalls and large performance variances due to the inherent mismatch between their fast in-memory writes and slow background I/O operations. In this paper, we use a simple yet effective two-phase experimental approach to evaluate write stalls for various LSM-tree designs. We further identify and explore the design choices of LSM merge schedulers to minimize write stalls given an I/O bandwidth budget. We have conducted extensive experiments in the context of the Apache AsterixDB system and we present the results here.
Adaptive Precision CNN Accelerator Using Radix-X Parallel Connected Memristor Crossbars

Neural processor development is reducing our reliance on remote server access to process deep learning operations in an increasingly edge-driven world. By employing in-memory processing, parallelization techniques, and algorithm-hardware co-design, memristor crossbar arrays are known to efficiently compute large scale matrix-vector multiplications. However, state-of-the-art implementations of negative weights require duplicative column wires, and high precision weights using single-bit memristors further distributes computations. These constraints dramatically increase chip area and resistive losses, which lead to increased power consumption and reduced accuracy. In this paper, we develop an adaptive precision method by varying the number of memristors at each crosspoint. We also present a weight mapping algorithm designed for implementation on our crossbar array. This novel algorithm-hardware solution is described as the radix-X Convolutional Neural Network Crossbar Array, and demonstrate how to efficiently represent negative weights using a single column line, rather than double the number of additional columns. Using both simulation and experimental results, we verify that our radix-5 CNN array achieves a validation accuracy of 90.5% on the CIFAR-10 dataset, a 4.5% improvement over binarized neural networks whilst simultaneously reducing crossbar area by 46% over conventional arrays by removing the need for duplicate columns to represent signed weights.
Who said that?: Audio-visual speaker diarisation of real-world meetings

The goal of this work is to determine 'who spoke when' in real-world meetings. The method takes surround-view video and single or multi-channel audio as inputs, and generates robust diarisation outputs. To achieve this, we propose a novel iterative approach that first enrolls speaker models using audio-visual correspondence, then uses the enrolled models together with the visual information to determine the active speaker. We show strong quantitative and qualitative performance on a dataset of real-world meetings. The method is also evaluated on the public AMI meeting corpus, on which we demonstrate results that exceed all comparable methods. We also show that beamforming can be used together with the video to further improve the performance when multi-channel audio is available.
Multi-task Learning for Aggregated Data using Gaussian Processes

Aggregated data is commonplace in areas such as epidemiology and demography. For example, census data for a population is usually given as averages defined over time periods or spatial resolutions (cities, regions or countries). In this paper, we present a novel multi-task learning model based on Gaussian processes for joint learning of variables that have been aggregated at different input scales. Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task. We are then able to compute the cross-covariance between the different tasks either analytically or numerically. We also allow each task to have a potentially different likelihood model and provide a variational lower bound that can be optimised in a stochastic fashion making our model suitable for larger datasets. We show examples of the model in a synthetic example, a fertility dataset, and an air pollution prediction application.
The Cost of a Reductions Approach to Private Fair Optimization

Through the lens of information-theoretic reductions, we examine a reductions approach to fair optimization and learning where a black-box optimizer is used to learn a fair model for classification or regression. Quantifying the complexity, both statistically and computationally, of making such models satisfy the rigorous definition of differential privacy is our end goal. We resolve a few open questions and show applicability to fair machine learning, hypothesis testing, and to optimizing non-standard measures of classification loss. Furthermore, our sample complexity bounds are tight amongst all strategies that jointly minimize a composition of functions. The reductions approach to fair optimization can be abstracted as the constrained group-objective optimization problem where we aim to optimize an objective that is a function of losses of individual groups, subject to some constraints. We give the first polynomial-time algorithms to solve the problem with $(\epsilon, 0)$ or $(\epsilon, \delta)$ differential privacy guarantees when defined on a convex decision set (for example, the $\ell_P$ unit ball) with convex constraints and losses. Accompanying information-theoretic lower bounds for the problem are presented. In addition, compared to a previous method for ensuring differential privacy subject to a relaxed form of the equalized odds fairness constraint, the $(\epsilon, \delta)$-differentially private algorithm we present provides asymptotically better sample complexity guarantees, resulting in an exponential improvement in certain parameter regimes. We introduce a class of bounded divergence linear optimizers, which could be of independent interest, and specialize to pure and approximate differential privacy.
Conical Metrics on Riemann Surfaces, II: Spherical Metrics

We continue our study, initiated in [34], of Riemann surfaces with constant curvature and isolated conic singularities. Using the machinery developed in that earlier paper of extended configuration families of simple divisors, we study the existence and deformation theory for spherical conic metrics with some or all of the cone angles greater than $2\pi $. Deformations are obstructed precisely when the number $2$ lies in the spectrum of the Friedrichs extension of the Laplacian. Our main result is that, in this case, it is possible to find a smooth local moduli space of solutions by allowing the cone points to split. This analytic fact reflects geometric constructions in [37, 38].
Heterogeneous Active Messages (HAM): Implementing Lightweight Remote Procedure Calls in C++

We present HAM (Heterogeneous Active Messages), a C++-only active messaging solution for heterogeneous distributed systems. Combined with a communication protocol, HAM can be used as a generic Remote Procedure Call (RPC) mechanism. It has been used in HAM-Offload to implement a low-overhead offloading framework for inter- and intra-node offloading between different architectures including accelerators like the Intel Xeon Phi x100 series and the NEC SX-Aurora TSUBASA Vector Engine. HAM uses template meta-programming to implicitly generate active message types and their corresponding handler functions. Heterogeneity is enabled by providing an efficient address translation mechanism between the individual handler code addresses of processes running different binaries on different architectures, as well a hooks to inject serialisation and deserialisation code on a per-type basis. Implementing such a solution in modern C++ sheds some light on the shortcomings and grey areas of the C++ standard when it comes to distributed and heterogeneous environments.
Pose Estimation for Non-Cooperative Rendezvous Using Neural Networks

This work introduces the Spacecraft Pose Network (SPN) for on-board estimation of the pose, i.e., the relative position and attitude, of a known non-cooperative spacecraft using monocular vision. In contrast to other state-of-the-art pose estimation approaches for spaceborne applications, the SPN method does not require the formulation of hand-engineered features and only requires a single grayscale image to determine the pose of the spacecraft relative to the camera. The SPN method uses a Convolutional Neural Network (CNN) with three branches to solve for the pose. The first branch of the CNN bootstraps a state-of-the-art object detector to detect a 2D bounding box around the target spacecraft. The region inside the bounding box is then used by the other two branches of the CNN to determine the attitude by initially classifying the input region into discrete coarse attitude labels before regressing to a finer estimate. The SPN method then uses a novel Gauss-Newton algorithm to estimate the position by using the constraints imposed by the detected 2D bounding box and the estimated attitude. The secondary contribution of this work is the generation of the Spacecraft PosE Estimation Dataset (SPEED). SPEED consists of synthetic as well as actual camera images of a mock-up of the Tango spacecraft from the PRISMA mission. The synthetic images are created by fusing OpenGL-based renderings of the spacecraft's 3D model with actual images of the Earth captured by the Himawari-8 meteorological satellite. The actual camera images are created using a 7 degrees-of-freedom robotic arm, which positions and orients a vision-based sensor with respect to a full-scale mock-up of the Tango spacecraft. The SPN method, trained only on synthetic images, produces degree-level attitude error and cm-level position errors when evaluated on the actual camera images not used during training.
The non-tightness of the reconstruction threshold of a 4 states symmetric model with different in-block and out-block mutations

The tree reconstruction problem is to collect and analyze massive data at the $n$th level of the tree, to identify whether there is non-vanishing information of the root, as $n$ goes to infinity. Its connection to the clustering problem in the setting of the stochastic block model, which has wide applications in machine learning and data mining, has been well established. For the stochastic block model, an "information-theoretically-solvable-but-computationally-hard" region, or say "hybrid-hard phase", appears whenever the reconstruction bound is not tight of the corresponding reconstruction on the tree problem. Although it has been studied in numerous contexts, the existing literature with rigorous reconstruction thresholds established are very limited, and it becomes extremely challenging when the model under investigation has $4$ states (the stochastic block model with $4$ communities). In this paper, inspired by the newly proposed $q_1+q_2$ stochastic block model, we study a $4$ states symmetric model with different in-block and out-block transition probabilities, and rigorously give the conditions for the non-tightness of the reconstruction threshold.
Foolproof Cooperative Learning

This paper extends the notion of learning equilibrium in game theory from matrix games to stochastic games. We introduce Foolproof Cooperative Learning (FCL), an algorithm that converges to a Tit-for-Tat behavior. It allows cooperative strategies when played against itself while being not exploitable by selfish players. We prove that in repeated symmetric games, this algorithm is a learning equilibrium. We illustrate the behavior of FCL on symmetric matrix and grid games, and its robustness to selfish learners.
Abundances of sulphur molecules in the Horsehead nebula First NS+ detection in a photodissociation region.

Context Sulphur is one of the most abundant elements in the Universe (S/H∼1.3×10 -5 ) and plays a crucial role in biological systems on Earth. The understanding of its chemistry is therefore of major importance. Aims Our goal is to complete the inventory of S-bearing molecules and their abundances in the prototypical photodissociation region (PDR) the Horsehead nebula to gain insight into sulphur chemistry in UV irradiated regions. Based on the WHISPER (Wide-band High-resolution Iram-30m Surveys at two positions with Emir Receivers) millimeter (mm) line survey, our goal is to provide an improved and more accurate description of sulphur species and their abundances towards the core and PDR positions in the Horsehead. Methods The Monte Carlo Markov Chain (MCMC) methodology and the molecular excitation and radiative transfer code RADEX were used to explore the parameter space and determine physical conditions and beam-averaged molecular abundances. Results A total of 13 S-bearing species (CS, SO, SO2, OCS, H2CS - both ortho and para - HDCS, C2S, HCS+, SO+, H2S, S2H, NS and NS+) have been detected in the two targeted positions. This is the first detection of SO+ in the Horsehead and the first detection of NS+ in any PDR. We find a differentiated chemical behaviour between C-S and O-S bearing species within the nebula. The C-S bearing species C2S and o-H2CS present fractional abundances a factor of > two higher in the core than in the PDR. In contrast, the O-S bearing molecules SO, SO2, and OCS present similar abundances towards both positions. A few molecules, SO+, NS, and NS+, are more abundant towards the PDR than towards the core, and could be considered as PDR tracers. Conclusions This is the first complete study of S-bearing species towards a PDR. Our study shows that CS, SO, and H2S are the most abundant S-bearing molecules in the PDR with abundances of ∼ a few 10-9. We recall that SH, SH+, S, and S+ are not observable at the wavelengths covered by the WHISPER survey. At the spatial scale of our observations, the total abundance of S atoms locked in the detected species is < 10-8, only ∼0.1% of the cosmic sulphur abundance.
Speaker verification in a time-feature space

The goal of this dissertation is to determine the relative importance of components of the modulation spectrum for automatic speaker verification and to use this knowledge to improve the performance of an automatic speaker verification system. It is proposed that the power spectrum of a time sequence of logarithmic energy, called the modulation spectrum, provide information that may be used to reduce the effects of adverse environments. The proposed strategy is to attenuate spectral components that are not particularly useful for speaker verification. The aim is to reduce system sensitivity to telephone handset variability without reducing verification accuracy. By computing the effect of carbon-button and electret microphone transducers on the modulation spectrum of telephone speech, it is found that handset transducer variability accounts for a substantial portion of the total variability at moderate to high modulation frequencies. This is shown to be the case also at very low modulation frequencies, where variability is ascribed to the effect of a convolutional channel. This result is substantiated with verification results on the Switchboard corpora as used in 1997–1998 NIST speaker recognition evaluations. The main conclusion is that components of the modulation spectrum between 0.1 Hz and 10 Hz contain the most useful information for speaker verification. To deal with adverse environments, it is proposed that the time sequences of logarithmic energy be lowpass filtered. When compared to other filtering techniques such as cepstral mean subtraction that may retain components up to 50 Hz or RASTA processing that retains components between 1 Hz and 13 Hz, lowpass filtering to 10 Hz is found to significantly reduce verification error in conditions where handset transducers differ between training and testing. It is furthermore proposed that the feature stream be sampled down from a 100 Hz sampling rate to as low as a 25 Hz sampling rate after lowpass filtering. Using this processing, a relative reduction in error of about 10% is shown for the 1997 and 1998 NIST speaker recognition evaluations. Additional contributions of the dissertation include the design and implementation of a modular, high-performance speaker recognition toolkit.
Towards a Blockchain based digital identity verification, record attestation and record sharing system

In this study, we investigated the utilization of Blockchain technology in identity systems. We reviewed the drawbacks and weaknesses of traditional identity systems, and discussed the potential benefits of employing Blockchain technology for a more efficient identity system. As a result, we proposed a Blockchain based digital identity verification, record attestation and record sharing system. When compared to traditional identity systems, it makes identity verification and identity based record sharing more efficient and secure, while respecting privacy of identity owners. By exploiting the trust fabric of Blockchain, a Blockchain based identity system eliminates the middle man and wait lines for authentication, authorization, and attestation. It allows individuals to decide what parts of their identity they want to share and with whom they want to share it.