Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of snow albedo parameterization when modelling Svalbard glaciers SVALI DELIVERABLE D 3 . 2 ‐ 8 Report

Albedo is of great importance in the surface energy balance. It affects surface skin temperature which in turn affects the atmospheric stratification and the temperature in the snow. For the snow, this affects the melting process and the snow mass balance. The snow metamorphosis may in turn affect the albedo to some degree and especially the timing of glacier surface exposure is of great importance. There is mainly a decrease in the albedo as the snow is aging after a snowfall. On longer time scale the summer melt increases the concentration of impurities and growth of algae further decreasing the albedo. In this report simulations with the atmospheric model WRF are presented and analysed. First, the sensitivity and effect on albedo of changing snow grain shape in a high-order snow scheme is investigated. Second, the effect on snow and air temperature of fitting the albedo in a lower-order snow scheme to observations is studied. The observations include in-situ measurements at three sites on Svalbard glaciers with different surface mass balance properties as well as remote sensing data from MODIS satellite observations. It is shown that different MODIS products give different albedos at the sites. The 1-day albedo was an average closer to the in-situ observations except on Lomonosovfonna where the 16-day albedo was closer. The effect of grain shape on albedo is small but still significant on 2-m air temperature and snow temperature. It is not evident if an albedo treatment based on spherical snow grains or non-spherical grains (an optimized habit combination; OHC) results in better agreement with observations. In the higher-order scheme the surface snow is heated by penetrating short wave radiation that is absorbed by the snow. This is probably why the summer snow temperature is much higher in this scheme and why the lower-order snow scheme requires a high snow thermal conductivity to account for the sub-surface heating in the summer. Further work is required to capture the effect of snow aging on the albedo.
Chapter 2 Diagnosis Using Support Vector Machines ( SVM )

Diagnosis of functional failures at the board level is critical for improving product yield and reducing manufacturing cost. State-of-the-art board-level diagnostic software is unable to cope with high complexity and ever-increasing clock frequencies, and the identification of the root cause of failure on a board is a major problem today. Ambiguous or incorrect repair suggestions lead to long debug times and even wrong repair actions, which significantly increase the repair cost and adversely impacts yield. In this chapter, we introduce a machine learning-based intelligent diagnosis system, which can automatically learn debug knowledge from empirical data and identify the most likely root cause of a new failed board. Using such a diagnosis system eliminates the difficulties involved in traditional knowledge acquisition. Fine-grained fault syndromes extracted from failure logs and the corresponding repair actions are used to train the system. Support vector machines (SVMs) have been used in boardlevel diagnosis to provide accurate root cause isolation. An SVM-based diagnosis system can be rapidly trained and is scalable to large datasets. However, the SVM method used in prior work [1] was simplistic, relying on an arbitrarily chosen kernel function, and it was not adaptive to the availability of new data or test cases. We propose a diagnosis system based on multi-kernel support vector machines (MK-SVMs) and incremental learning, which are used to tune the diagnosis system in an automatic manner. The MK-SVM method leverages a linear combination of single kernels to achieve accurate faulty component classification based on the errors observed. The MK-SVMs thus generated can also be updated based on incremental learning, which allows the diagnosis system to quickly adapt to new error observations and provide even more accurate fault diagnosis. The remainder of this chapter is organized as follows. Section 2.1 reviews the background and prior work. Section 2.2 reviews basic concepts in support vector machines. Section 2.3 introduces multi-kernel-based SVMs, and describes how MKSVMs can be extended for incremental learning, namely iMK-SVMs. Section 2.4 presents experimental results on diagnosis accuracy and training time for two industry
A New Color Image Watermarking Algorithm using 3-level Discrete Wavelet Transform

n this paper, a new color image watermarking based on discrete wavelet transform algorithm is presented, so that this scheme can raise the perceptual quality of watermarked image. The scheme is used the discrete wavelet transform (DWT) and the biorthogonal filter banks to decompose the LH sub-bands of main image up to three levels. The proposed watermarking algorithm is invisible and Non-blind. These algorithms has been tested with different images in the size of 256 × 256 , 512 × 512 and 1024 × 1024 . To measure the robustness and perceptual quality, the proposed watermarking scheme have been checked with method decomposing different sub-bands and the previous method. Experimental results demonstrate that this method has a higher perceptual quality than previous methods. Also the proposed watermarking scheme is robustness than previous methods. The watermarked image is under different attacks such as the noisy, compress JPEG, rotate, crop and Median filter. The proposed algorithm is robust against different attacks.
Persistent-hold Consensus Control of First-order Multi-agent Systems with Intermittent Communication

Under intermittent communication, a persistenthold consensus algorithm is proposed to solve the consensus problem of first-order multi-agent systems. Based on matrix theory and graph theory, consensus conditions are obtained for the agents converging to the asymptotic consensus under fixed topology and switching topologies, respectively. Numerical simulations show the correctness of theoretical results.
Real-time collaborative editing using CRDTs

Real-time collaborative editors such as Google Docs allow users to edit a shared document simultaneously and see each others changes in real-time. This thesis investigates how conflict-free replicated data types (CRDTs) can be used to implement a general purpose data store that supports real-time collaborative editing of semi-structured data. The purpose of the data store is that it can be used by application developers to easily add collaborative behaviour to any application. The performance of the implemented data store is evaluated and the results show that using CRDTs comes with a performance and memory penalty. However, replication over the internet is very efficient and concurrent updates is handled in a predictable way in most cases.
Robust Link Weight Optimization Under Demand Uncertainty

With the explosive growth of the Internet and the incredible development of network applications, the variation in traffic volume has become one of the most important problems faced by network operators. Designing a network using a single “busy hour” traffic matrix strains credibility due to the high volatility of traffic patterns. Thus, there is a need to incorporate demand uncertainty into the network design problems explicitly. This paper investigates a practical implementation of a network design model considering traffic demand uncertainty. More precisely, we focus on the problem of link weight optimization in IP networks where the traffic is routed along shortest paths according to the link metrics (OSPF and IS-IS-based networks). We propose a local search based algorithm for weight optimization under demand uncertainty. This algorithm can be used either in single-start mode, for incremental weight optimization, as well as in multi-start mode, for global weight optimization. Extensive computational results show that this algorithm yields IP routings which are close to the optimal routings within low computing times with respect to known weight optimization algorithms.
Musical Instrument Identiﬁcation with Supervised Learning

—In this paper, the classiﬁcation of musical instruments using supervised learning is studied. A combined feature set including psychoacoustically relevant spectral features is used. A fairly small dataset consisting of 496 sound examples from 4 instruments - violin, clarinet, saxophone and bassoon is used. A very high classiﬁcation accuracy is achieved on the test set with multi-class logistic regression and SVM with RBF kernel.
Event Searching in the Real World

We describe a system that, in an environment in which a sensor network constantly collects data produced by sensors attached to physical objects, searches for data segments corresponding to real-world events using natural language words in a query. The system translates each query into a physical quantity representation, searches for a sensor data segment that satisfies the representation’s description, and replies to the query by giving the event’s occurrence time and place or objects related to it. It is constructed as an application for the “s-room”, a project that aims to search for or retrieve specific events that occurred in the real world. Event Searching in the Real World † NTT Communication Science Laboratories Soraku-gun, 619-0237 Japan Email: houmi@idea.brl. ntt.co.jp * Ground: This term is used here, by analogy to electrical grounding, to denote giving the correspondence between a word and its
Visualising Lifelogging Data in Spatio-Temporal Virtual Reality Environments

The goal of this research was to extend an existing spatial visualisation of lifelogging data in virtual reality with the visualisation of temporal information. For this, we created two novel spatio-temporal visualisations of lifelogging data in virtual reality. A qualitative (n = 12) study was performed to find the effect of the temporal aspects of the visualisations on the enjoyment, usability, effectiveness, and nauseousness of the user. The continuous visualisation we created outperformed the other ones on all evaluation points and was therefore considered to be the most promising of the three. Furthermore, we demonstrated that the temporal aspects of the visualisations helped de-cluster images that were spatially too close to each other on the map, making the data more approachable for users.
Mobile Edge Computing ( MEC )-Enabled Wireless Blockchain Networks Menting

Blockchain technology has been applied in a variety of fields due to its capability of establishing trust in a decentralized fashion. However, the application of blockchain in wireless mobile networks is hindered by a major challenge brought by the proof-of-work (PoW) puzzle during the mining process, which sets a high demand for the computational capability and storage availability in mobile devices. To address this problem, we propose a novel mobile edge computing (MEC) enabled wireless blockchain framework where the computation-intensive mining tasks can be offloaded to nearby edge computing nodes and the cryptographic hashes of blocks can be cached in the MEC server. Particularly, two offloading modes are considered, i.e., offloaded to the nearby access point (AP) or a group of nearby users. First, we conduct the performance analysis of each mode with stochastic geometry methods. Then, the joint offloading decision and caching strategy is formulated as an optimization problem. Furthermore, an alternating direction method of multipliers (ADMM) based algorithm is utilized to solve the problem in a distributed manner. Finally, simulation results demonstrate the effectiveness of our proposed scheme.
Ressourcenverwalter Client Application Server 2 PC Transaction Monitor Service Method Impl

Nowadays, tiered architectures are widely accepted for constructing large scale information systems. In this context application servers often form the bottleneck for a system’s efficiency. An application server exposes an object oriented interface consisting of set of methods which are accessed by potentially remote clients. The idea of method cachingis to store results of read-only method invocations with respect to the application server’s interface on the client side. If the client invokes the same method with the same arguments again, the corresponding result can be taken from the cache without contacting the server. It has been shown that this approach can considerably improve a real world system’s efficiency. This paper extends the concept of method caching by addressing the case where clients wrap related method invocations in ACID transactions. Demarcating sequences of method calls in this way is supported by many important application server standards. In this context the paper presents an architecture, a theory and an efficient protocol for maintaining full transactional consistency and in particular serializability when using a method cache on the client side. In order to create a protocol for scheduling cached method results, the paper extends a classical transaction formalism. Based on this extension, a recovery protocol and an optimistic serializability protocol are derived. The latter one differs from traditional transactional cache protocols in many essential ways. An efficiency experiment validates the approach: Using the cache a system’s performance and scalability are considerably improved.
3 D GaN nanoarchitecture for fi eld-e ff ect transistors

The three-dimensionality of 3D GaN field-effect transistors (FETs) provides them with unique advantages compared to their planar counterparts, introducing a promising path towards future FETs beyond Moore's law. Similar to today's Si processor technology, 3D GaN FETs offer multi-gate structures that provide excellent electrostatic control over the channel and enable very low subthreshold swing values close to the theoretical limit. Various concepts have been demonstrated, including both lateral and vertical devices with GaN nanowire (NW) or nanofin (NF) geometries. Outstanding transport properties were achieved with laterally contacted NWs that were grown in a bottom-up approach and transferred onto an insulating substrate. For higher power application, vertical FETs based on regular arrays of GaN nanostructures are particularly promising due to their parallel integration capability and large sidewall surfaces, which can be utilized as channel area. In this paper, we review the current status of 3D GaN FETs and discuss their concepts, fabrication techniques, and performances. In addition to the potential benefits, reliability issues and difficulties that may arise in complex 3D processing are discussed, which need to be tackled to pave the way for future switching applications.
Efficient and Secure Outsourcing of Large-Scale Linear System of Equations

Solving the large-scale linear system of equations is one of the most fundamental problems both in theory and practice. However this problem requires too much computational resource for most users to solve it. With the rapid development of cloud services, many users tend to outsource the expensive computing to the cloud server, which is regarded as an efficient way of solving such problem. Nevertheless, the cloud server can not protect the data privacy well, especially when the user’s linear system of equations contain private and sensitive data. There are many previous research works on secure outsourcing of systems of linear equations. In this paper we first analyze a privacy preserving CGM (conjugate gradient method) algorithm for secure outsourcing of large-scale systems of linear equations proposed in [1] . We find that the cloud server can recover the protected coefficient matrix of the linear system of equations from the message it receives, which makes the security method in this scheme fails. This is a serious problem, which makes the private and sensitive data of the user leak to the cloud server, and privacy preserving does not exist. To overcome this problem, we modified this algorithm to protect the message from leaking, which can protect the users’ privacy well. We also show the security of this new scheme and do experiments to show its efficiency.
A Survey of Fast Inter Mode Decision Algorithms for HEVC

The use of coding tree units instead of macroblocks in HEVC increases the coding efficiency while increasing computational complexity. Early termination for coding units at depth level and skipping computations at prediction units called fast mode decision provides a significant contribution to reducing computational complexity in HEVC. In this work, a detailed comparison process is performed by examining the fast inter-mode decision algorithms developed for HEVC.
Contribution MODELING OFMICROBUBBLE-ENHANCED HIGH-INTENSITY FOCUSED ULTRASOUND

A Inc., 10 aswin@ Abstract—Heat enhancement at the target in a high intensity focused ultrasound (HIFU) field is investigated by considering the effects of the injection of microbubbles in the vicinity of the tumor to be ablated. The interaction between the bubble cloud and the HIFU field is investigated using a 3-D numerical model. The propagation of non-linear ultrasonic waves in the tissue or in a phantom medium is modeled using the compressible Navier Stokes equations on a fixed Eulerian grid, while the microbubbles dynamics and motion are modeled as discrete singularities, which are tracked in a Lagrangian framework. These two models are coupled to each other such that both the acoustic field and the bubbles influence each other. The resulting temperature rise in the field is calculated by solving a heat transfer equation applied over a much longer time scale. The compressible continuum part of the model is validated by conducting axisymmetric HIFU simulations without microbubbles and comparing the pressure and temperature fields against available experiments. The coupled Eulerian Lagrangian approach is then validated against existing experiments conducted with a phantom tissue. The bubbles are distributed randomly in a 3-D fashion inside a cylindrical volume, while the background acoustic field is assumed axisymmetric. The presence of microbubbles modifies the ultrasound field in the focal region and significantly enhances heat deposition. The various mechanisms through which heat deposition is increased are then examined. Among these effects, viscous damping of the bubble oscillations is found to be the main contributor to the enhanced heat deposition. The effects of the initial void fraction in the cloud are then sought by considering the changes in the attenuation of the primary ultrasonic wave and the modifications of the enhanced heat deposition in the focal region. It is observed that although high bubble void fractions lead to increased heat deposition, they also cause significant pre-focal heating because of acoustic shielding. The effects of the microbubble cloud size and its location in the focal region are studied, and the effects of these parameters in altering the temperature rise and the location of the temperature peak are discussed. It is found that concentrating the bubbles adjacent to the focus and farther away from the acoustic source leads to effective heat deposition. Finally, the presence of a shell at the bubble surface, as in contrast agents, is seen to reduce heat deposition by restraining bubble oscillations. (E-mail: aswin@dynaflow-inc.com) © 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.
Visual Analogy Graph Late Fusion Graph Embedding Embedded Regression

Humans are far better learners who can learn a new concept very fast with only a few samples compared with machines. The plausible mystery making the difference is two fundamental learning mechanisms: learning to learn and learning by analogy. In this paper, we attempt to investigate a new human-like learning method by organically combining these two mechanisms. In particular, we study how to generalize the classification parameters from previously learned concepts to a new concept. we first propose a novel Visual Analogy Graph Embedded Regression (VAGER) model to jointly learn a low-dimensional embedding space and a linear mapping function from the embedding space to classification parameters for base classes. We then propose an out-of-sample embedding method to learn the embedding of a new class represented by a few samples through its visual analogy with base classes and derive the classification parameters for the new class. We conduct extensive experiments on ImageNet dataset and the results show that our method could consistently and significantly outperform state-of-the-art baselines.
A Comparative Study of Statistical Analysis Techniques

Due to the continuous progress of science and technology, big data has also been rapidly developed, and people's access to resources and information is becoming more and more diverse. This situation has brought unprecedented challenges to the statistical work of grassroots data in the past. It has not only ushered in new challenges, but also accordingly ushered in new opportunities. Therefore, the author studies the comparison of big data statistical analysis techniques. In the context of big data, the development of statistics should take a proactive approach, including designing more complex methods to collect data and design more unique methods to analyze data. Research shows that statistical analysis needs to combine big data ideas, which is not only the need of government service work, but also marks a new height in the transformation of government departments' work methods.
Optimization Task Scheduling Techniques on Load Balancing in Cloud Using Intelligent Bee Colony Algorithm

Cloud computing is one of the talented field in the advanced web based technology. It is a platform for providing collection of large number of resources and virtualization and is pay on demand based model. Cloud provides shared resources over internet in an innovative way for different purposes. The usage of cloud data accessing are increasing daily. Usually clouds data centers handle large number of users every moments and it is a major issues faced in the area of cloud. Cloud which uses more than one node and dynamically distribute workload to all nodes and ensure that no single node is overloaded. In load balance mainly focus is to boost the performance of the system and reduce less carbon emission by resource utilization and optimum usage of resources. Many researchers are done lot of algorithm to do load balancing and good scheduling task. Here we have observed and studied existing various load balancing algorithms and contrast them based on based on many arguments like throughput, reliability, power saving, performance utilization, scalability, stability etc.
A Survey on Event-B Decomposition Thai

Model decomposition is a powerful tool to scale the design of large and complex systems. It enables developers to separate components development from the concerns of their integration and orchestration. Event-B is a refinement-based formal method, equipped with three decomposition styles that come with solid semantic foundations and strong tool supports. This paper intends to give some useful insights and modelling guidelines for using these decomposition styles, illustrated by an actual development of a master data updating system.