Miracle-dz/UniFAR-FTUs huggingface dataset:
Curve Fit: a pixel‐level raster regression tool for mapping spatial patterns

Despite the fact that pixels (i.e. picture elements) are the basic sampling units of maps, we are aware of no software package or tool that allows users to model changes that may occur at such fine spatial resolutions over broad geographic extents. Curve Fit is an extension to the application ArcMap that allows users to conduct linear or nonlinear regression analysis on the range of values found within input raster data sets (geo‐referenced images), independently for each pixel. Outputs consist of raster surfaces of regression model parameter estimates, standard errors, goodness‐of‐fit estimates and multimodel inference measures. Curve fit outputs characterize continuous spatial or temporal change across a series of raster data sets.
A TIME-DEPENDENT SCHEDULING PROBLEM TO MINIMIZE THE SUM OF THE TOTAL WEIGHTED TARDINESS AMONG TWO AGENTS

The problem of scheduling with time-dependent processing times has been studied for more than two decades and significant advances have been made over the years. However, most work has paid more attention to the single-criterion models. Furthermore, most heuristics are constructed for the time-dependent scheduling problems in a step-by-step way. Motivated by the observations, this paper studies a two-agent scheduling model with increasing linear deterioration jobs in which the processing time of a job is modeled as an increasing linear function of its starting time. The objective function is to minimize the sum of the maximum weighted tardiness of the jobs of the first agent and the total weighted tardiness of the jobs of the second agent. This problem is known to be strongly NP-hard. Thus, as an alternative, the branch-and-bound, ant colony algorithm and simulated annealing algorithms are developed for the problem. Computational results are also presented to determine the performance of the proposed algorithms.
Petri Net Modeling and Decomposition Method for Solving Production Scheduling Problems

Considering the need to develop general scheduling problem solver, the recent integration of Petri Nets as modeling tools into effective optimization methods for scheduling problems is very promising. The paper addresses a Petri Net modeling and decomposition method for solving a wide variety of scheduling problems. The scheduling problems are represented as the optimal transition firing sequence problems for timed Petri Nets. The Petri Net is decomposed into several subnets in which each subproblem can be easily solved by Dijkstra' algorithm. The approach is applied to a flowshop scheduling problem. The performance of the proposed algorithm is compared with that of a simulated annealing method.
Routing Protocols under different Mobility Models, Node Density and Speed

Mobile Ad hoc NETwork (MANET) is self-organized and decentralized network. Due to these properties, they are usable in the field of disaster management, war scenarios, vehicular network etc. To send packets from source to destination, route(s) are required; to achieve this task routing protocols are designed. Routing protocols for MANET have been grouped into four types on the basis of single channel classification. These are Proactive, Reactive, Hybrid and Location-based/ Geographic. The performance of these protocols depends on various factors including mobility pattern/model, speed of node, density of node, area, transmission range etc. In this paper the factors taken into account are mobility models, speed of node and density of node. The performance metrics includes throughput, end to end delay and packet loss. The protocols simulated are DSR, LAR, OLSR and ZRP, choosing one from each group discussed above. The mobility models employed in this study are Random Way Point, Gauss Markov, Reference Point Group Mobility and Manhattan Grid. From the results it is evident that with the change in mobility pattern, speed and density of the nodes the performance varies. Furthermore it has been observed that location based routing protocols (LAR) and OLSR shows good performance with these variations.
R & GIS: Geospatial Plotting

Abstract Examples of spatial data within the R environment and the combination of R with data sets, spatial tools, libraries and other software products, which are common in real life environments, are provided in this paper. Beginning with the setup of a new project using Git and an online repository, a document build by Sweave - a combination of R and L a TEX - is explained. Once the fundamental setup is working, the import of data and (geo-) spatial information from different sources is shown. Finally some examples of spatial plots using (among others) the sp package of R are included. Additionally some tools like “integrated development environments” (IDE), which may be supportive during the daily work and help newcomers learning the presented techniques, are mentioned and specific programs are recommended. This paper itself is build using the described technologies and therefore illustrates all applied methods.
Isourea-mediated preparation of dehydro amino acids

Carbodiimide wie Diisopropylcarbodiimid (I) reagieren mit Alkoholen (II) zu den Isoharnstoffethern (III), die mit den geschutzten Serinderivaten (IV) verestert und dann mit weiterem Carbodiimid zu den Alaninderivaten (VI) dehydratisiert werden.
Content-based image retrieval using colour n-grams

Colour histograms have been shown to be very effective in representing images for content-based retrieval systems. They are compact, robust and amenable to computer analysis. However, such histograms convey only global image properties and do not embody local colour information which is so important when comparing and contrasting images. We present a novel image coding scheme which captures some of this locally correlated colour information and improves the selectivity of the retrieval mechanism-an important issue for very large databases. The technique uses a histogram of features which represent local combinations of colour artifacts within an image, called colour n-grams. We outline the thrust of our approach and discuss the factors affecting the efficacy of the retrieval mechanism using a database of colour images. (6 pages)
Method of Redundant Features Eliminating Based on K-Means Clustering

When diagnosing mechanical faults using multi signal features, in order to keep the most efficient signal features and eliminate redundant ones, and make the mechanical fault diagnosis achieve a balance between computational complexity and diagnostic accuracy, a redundant features eliminating method based on k-means clustering was proposed. By optimizing the result of k-means clustering we obtained weights of all the inputted features, after setting appropriate threshold, compare it with all feature weights, eliminate those features whose weight is smaller than the threshold. After compared this method with some advanced methods, it shows that when using the SVM multi-classifier for mechanical fault diagnosis, this method produces a higher accuracy; and since eliminating part of features, it makes the time needed for diagnosis shorter.
Comprehensive Evaluation of Antioxidant Activity of Various Herb Teas Using Radar Chart

Antioxidant activities of 14 commercially available herb teas have been evaluated. Scavenging effects against hypochlorite ion and hydroxyl radical were determined with a method previously proposed by the authors, and expressed as myoglobin protective ratio. The antioxidant activities of the samples were also measured by the widely used DPPH method. In order to characterize the properties of the samples comprehensively, the antioxidant activities against three different radicals were compared in a radar chart. The patterns of the radar charts were similar to that to polyphenols, and the strength of the activity was rated as three ranks. Rose red, peppermint, lemon balm showed strong activity, and chamomile, linden, rosemary, hibiscus, rose hip, and raspberry leaf showed mild antioxidant activity. On the other hand, lemon grass, thyme, lavender, jasmine and erica did not show activity. Although the antioxidant activity of herb tea is strongly depend on the various factors such as origin, preparation methods, their characteristics can be successfully evaluated by the proposed radar chart in quantitative way.
Network and Discrete Location: Models, Algorithms, and Applications

Thoroughly updated and revised, Network and Discrete Location: Models, Algorithms, and Applications, Second Edition remains the go-to guide on facility location modeling. The book offers a unique introduction to methodological tools for solving location models and provides insight into when each approach is useful and what information can be obtained. The Second Edition focuses on real-world extensions of the basic models used in locating facilities, including production and distribution systems, location-inventory models, and defender-interdictor problems. A unique taxonomy of location problems and models is also presented. Featuring examples using the author’s own software—SITATION, MOD-DIST, and MENU-OKF—as well as Microsoft Office Excel, the book provides: • A theoretical and applied perspective on location models and algorithms• An intuitive presentation of the uses and limits of modeling techniques• An introduction to integrated location-inventory modeling and defender-interdictor models for the design of reliable facility location systems• A full range of exercises to equip readers with an understanding of the basic facility location model types Network and Discrete Location: Models, Algorithms, and Applications, Second Edition is an essential resource for practitioners in applied and discrete mathematics, operations research, industrial engineering, and quantitative geography. The book is also a useful textbook for upper-level undergraduate, graduate, and MBA courses.
Study of Rushing Attack in MANET

A mobile ad-hoc network or MANET incorporates mobile nodes that forward information or packets from node to node without a wired connection. This is the topology changes rapidly and unproductively, there is no central control for routing of packets. The communication is on mutual trust. There are many proposed routing protocol, on-demand routing is most preferable among all as its overhead is very low. This significantly attention has been paid on developing a secure reactive protocol against various attacks. In this paper a survey and study about rushing attack is provided. This attack results in denial-of-services and is effectively damaging as it can also be performed by weak attacker. Thus a Rushing attack prevention (RAP); generic rushing attack prevention mechanism introduced for the reactive protocols. General Terms Mobile Ad-hoc networks(MANET).
Fusion of horizons: Co-designing with Cambodian children who have prosthetic legs, using generative design tools

This paper presents a field study exploring the challenges and implications of applying a participatory design process through the use of generative design tools with children using prosthetic legs in rural Cambodia in order to facilitate their involvement in the design process. First, it reviews the main research paradigms in which user research is conducted, including positivism, critical theory and interpretivism, and compares the paradigms in terms of the motives they carry for involving users in the design process. The case is then positioned in the interpretivistic research paradigm using philosophical hermeneutics as a guide. As the first application of generative design tools with children in developing countries who have special needs, the case revealed many challenges and pointed to the need to go well beyond the contextual domains typically addressed in user-centred or product-centred design today. The paper concludes by presenting the pyramid model, which designers can use to obtain an overview of the knowledge they need for developing a relevant solution, and to reflect upon how their understanding develops in this process.
ATLAS offline data quality monitoring

The ATLAS experiment at the Large Hadron Collider reads out 100 Million electronic channels at a rate of 200 Hz. Before the data are shipped to storage and analysis centres across the world, they have to be checked to be free from irregularities which render them scientifically useless. Data quality offline monitoring provides prompt feedback from full first-pass event reconstruction at the Tier-0 computing centre and can unveil problems in the detector hardware and in the data processing chain. Detector information and reconstructed proton-proton collision event characteristics are distilled into a few key histograms and numbers which are automatically compared with a reference. The results of the comparisons are saved as status flags in a database and are published together with the histograms on a web server. They are inspected by a 24/7 shift crew who can notify on-call experts in case of problems and in extreme cases signal data taking abort.
Improvements and applications for key word recognition using hidden Markov modeling techniques

A hidden Markov model based key wordspotting algorithm developed previously can recognize key words from a predefined vocabulary list spoken in an unconstrained fashion. Improvements in the feature analysis used to represent the speech signal and modeling techniques used to train the system are explored. The authors discuss several task domain issues which influence evaluation criteria. They present results from extensive evaluations on three speaker independent databases: the 20 word vocabulary Stonehenge Road Rally database, distributed by the National Security Agency, a five word vocabulary used to automate operator-assisted calls, and a three word Spanish vocabulary that is currently being tested in Spain's telephone network. Currently, recognition accuracies range from 99.9% on the Spanish database to 74% (with 8.8 FA/H/W) on the Stonehenge task. <<ETX>>
Hierarchical semiautomatic video object segmentation for multimedia applications

In this paper, an efficient tool to extract video objects from video sequences is presented. With this tool, it is possible to segment video content in a user-friendly manner to provide easy manipulation of video content. The tool is comprised of two stages. Firstly, the initial object extraction is performed using the Recursive Shortest Spanning Tree (RSST) algorithm and the Binary Partition Tree (BPT) technique. Secondly, automatic object tracking is performed using a single frame forward region tracking method. In the first stage, an initial partition is created using the RSST algorithm which allows the user to specify the initial number of regions. This process is followed by progressive binary merging of these regions to create the BPT. The purpose of creating the BPT is to allow the user to browse the content of the scene in a hierarchical manner. This merging step creates the binary tree with nearly double the user-specified number of homogenous regions. User interaction then allows grouping particular regions into objects. In the second stage, each subsequent frame is segmented using the RSST and corresponding regions are identified using a forward region tracking method.
Wavelet-cellular neural network architecture and learning algorithm

Cellular Neural Networks (CNN) provides fast parallel computational capability for image processing applications. The behavior of the CNN is defined by two template matrices. In this paper, adjustment of these template-matrix coefficients have been realized using supervised learning algorithm based on back-propagation technique and wavelet function. Back-propagation algorithm has been modified for dynamic behavior of CNN. Wavelet function is utilized to provide the activation function derivation in this learning algorithm. The supervised learning algorithm is then executed to obtain a compact CNN architecture, called as Wave-CNN. The proposed new learning algorithm and Wave-CNN architecture performance have been tested for 2D image processing applications.
Towards network admissible optimal dispatch of flexible loads in distribution networks

This paper presents a computationally tractable convex, optimal power flow (OPF) formulation that is admissible with respect to voltage and current limits. This is achieved by considering bounds on the non linear terms in the power flow equations. The resulting OPF solution respects all the network constraints and is hence robust against modeling simplifications. An convex, inner approximation of the OPF problem is presented with a computational solve time similar to that of linearized OPF formulations. However, unlike linear formulations, this formulation provides certain feasibility guarantees for dispatching flexible loads over multiple time-steps. Simulation results on IEEE test networks validate that the formulation is network admissible.
Auglabel: Exploiting Word Representations to Augment Labels for Face Attribute Classification

Augmenting data in image space (eg. flipping, cropping etc) and activation space (eg. dropout) are being widely used to regularise deep neural networks and have been successfully applied on several computer vision tasks. Unlike previous works, which are mostly focused on doing augmentation in the aforementioned domains, we propose to do augmentation in label space. In this paper, we present a simple, yet effective novel method to generate fixed dimensional labels with continuous values for images by exploiting the word2vec – semantic representations – of the existing categorical labels. We then append these representations to existing categorical labels and train the model. We validated our idea on two challenging face attribute classification data sets viz. CelebA and LFWA. Our extensive experiments show that the augmented labels improve the performance of the competitive deep learning baseline and also attains the state-of-the-art performance.
Global and local pointwise error estimates for finite element approximations to the Stokes problem on convex polyhedra

The main goal of the paper is to show new stability and localization results for the finite element solution of the Stokes system in $W^{1,\infty}$ and $L^{\infty}$ norms under standard assumptions on the finite element spaces on quasi-uniform meshes in two and three dimensions. Although interior error estimates are well-developed for the elliptic problem, they appear to be new for the Stokes system on unstructured meshes. To obtain these results we extend previously known stability estimates for the Stokes system using regularized Green's functions.
New design method for controlling power stages based on IGBT switching ferrite transformers: applied to an 8 kW small size light weight electric welding machine

To solve the problem of weight (/spl sim/50 kg) and size (/spl sim/60 cm/spl times/40 cm/spl times/20 cm) of power electric welding machines, a new one was achieved. Working at high frequencies (/spl sim/80 kHz) with IGBTs switching ferrite transformers, its specifications were chosen for industrial application (50 V 160 A output, size of a small case and 5 kg maximum weight). This paper focuses on one problem which remained unsolved: immediate IGBT destruction when the conceived transformer was included in the circuit. By working below 50% duty cycle during switching, we were able to prevent the destruction of the IGBT and control the soldering arc. An interpretation of IGBT behavior leading to its destruction was deduced: after conduction of the IGBT, a constant current crosses it limited by the transformer inductive circuit without load. At cutoff strong instantaneous power amplifies the temperature overtaking its value limits leading to component destruction.
Non-Fermi-liquid fixed point in multi-orbital Kondo impurity model relevant for Hund's metals

Due to the separation between the spin and the orbital screening scales, the normal state of Hund's metals at ambient temperature can be loosely characterized as a partially coherent state with fluctuating spins and quenched orbital moments. With the aim to characterize this situation more precisely, we investigate the Kondo-Kanamori impurity model that describes the low-energy local physics of three-orbital Hund's metals occupied by two or four electrons. Within this model one can diminish the mixed spin-orbital terms and thereby enhance the separation between the two screening scales, allowing a more precise investigation of the intermediate state. Using the numerical renormalization group we calculate the impurity entropy as well as the temperature and frequency dependence of the spin and the orbital susceptibilities. We uncover a non-Fermi-liquid two-channel overscreened SU(3) fixed point that controls the behavior in the intermediate regime. We discuss its fingerprints in the frequency dependence of local orbital susceptibility and the shape of the spectral function.
Threshold Logical Clocks for Asynchronous Distributed Coordination and Consensus

Consensus protocols for asynchronous networks are usually complex and inefficient, leading practical systems to rely on synchronous protocols. This paper attempts to simplify asynchronous consensus by building atop a novel threshold logical clock abstraction, which enables upper layers to operate as if on a synchronous network. This approach yields an asynchronous consensus protocol for fail-stop nodes that may be simpler and more robust than Paxos and its leader-based variants, requiring no common coins and achieving consensus in a constant expected number of rounds. The same approach can be strengthened against Byzantine failures by building on well-established techniques such as tamper-evident logging and gossip, accountable state machines, threshold signatures and witness cosigning, and verifiable secret sharing. This combination of existing abstractions and threshold logical clocks yields a modular, cleanly-layered approach to building practical and efficient Byzantine consensus, distributed key generation, time, timestamping, and randomness beacons, and other critical services.
Information processing constraints in travel behaviour modelling: A generative learning approach

Travel decisions tend to exhibit sensitivity to uncertainty and information processing constraints. These behavioural conditions can be characterized by a generative learning process. We propose a data-driven generative model version of rational inattention theory to emulate these behavioural representations. We outline the methodology of the generative model and the associated learning process as well as provide an intuitive explanation of how this process captures the value of prior information in the choice utility specification. We demonstrate the effects of information heterogeneity on a travel choice, analyze the econometric interpretation, and explore the properties of our generative model. Our findings indicate a strong correlation with rational inattention behaviour theory, which suggest that individuals may ignore certain exogenous variables and rely on prior information for evaluating decisions under uncertainty. Finally, the principles demonstrated in this study can be formulated as a generalized entropy and utility based multinomial logit model.