Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Driven Routing Using a Genetic Algorithm

In the physical design process for very large scale integrated (VLSI) circuits the logical structure of a circuit is transformed into its physical layout. Detailed routing is one of the tasks in this process. A detailed router connects pins of signal nets in a rectangular region in accordance with a set of routing constraints, such as the number of layers, the minimal space between wires, minimum wire width, number of vias, crosstalk and the net length. The results of this detailed routing has a strong influence on the fabrication yield and production costs of the circuit [1].
Transitions to palliative care in acute hospitals in England: qualitative study

Objective To explore how transitions to a palliative care approach are perceived to be managed in acute hospital settings in England. Design Qualitative study. Setting Secondary or primary care settings in two contrasting areas of England. Participants 58 health professionals involved in the provision of palliative care in secondary or primary care. Results Participants identified that a structured transition to a palliative care approach of the type advocated in UK policy guidance is seldom evident in acute hospital settings. In particular they reported that prognosis is not routinely discussed with inpatients. Achieving consensus among the clinical team about transition to palliative care was seen as fundamental to the transition being effected; however, this was thought to be insufficiently achieved in practice. Secondary care professionals reported that discussions about adopting a palliative care approach to patient management were not often held with patients; primary care professionals confirmed that patients were often discharged from hospital with “false hope” of cure because this information had not been conveyed. Key barriers to ensuring a smooth transition to palliative care included the difficulty of “standing back” in an acute hospital situation, professional hierarchies that limited the ability of junior medical and nursing staff to input into decisions on care, and poor communication. Conclusion Significant barriers to implementing a policy of structured transitions to palliative care in acute hospitals were identified by health professionals in both primary and secondary care. These need to be addressed if current UK policy on management of palliative care in acute hospitals is to be established.
Using Motion Planning for Knot Untangling

In this paper we investigate the application of motion planning techniques to the untangling of mathematical knots. Knot untangling can be viewed as a high-dimensional planning problem in reparametrizable configuration spaces. In the past, simulated annealing and other energy minimization methods have been used to find knot untangling paths. We have developed a probabilistic planner that is capable of untangling knots described by over 400 variables. We have tested on known difficult benchmarks in this area and untangled them more quickly than has been achieved with minimization in the literature. In this work, the use of motion planning techniques is critical for the untangling. Our planner defines local goals and makes combined use of energy minimization and randomized tree-based planning. We also show how to produce candidates with a minimal number of segments for a given knot. The planner developed in this work is novel in that it is used to study issues arising in practical motion planning for high-dimensional and reparametrizable geometry. The use of energy methods, local goals and tree-based expansion is also novel and may suggest solutions in other planning applications. Finally, we discuss some possible applications of our untangling planner in computational topology, in the study of DNA rings and protein folding and for planning with flexible robots.
DANCE TYPE CLASSIFICATION IN IRISH AND SCANDINAVIAN FOLK MUSIC

For centuries, western cultures have written folk songs down. In the 21st century, this has resulted in large databases of music from all around the world. We have built, trained, and tested classifiers on Irish and Scandinavian dance music using songs encoded in symbolic representation (ABC format), downloaded from John Chamber’s online folk-song database. These tunes were sorted by dance type: Reel, Jig, Hambo, Pols, and Hornpipe. Raw data was preprocessed to be in the same key, have no ornaments, and no abbreviations. Features extracted were standardized to have zero mean and unit variance. Extra trees classification and variance thresholding allowed us to reduce feature dimensionality from 65 to 23. We classified our data using Support Vector Machines (SVM), Logistic Regression, Naïve Bayes, Gradient Descent, and K-means Clustering. We evaluated these classification algorithms using leaveone-out cross-validation, trained on 80 percent of the data, and tested on 20 percent. Our SVM classification methods turned out to be the most accurate with less than 0.10 training and testing error.
An accurate lecture video segmentation method by using sift and adaptive threshold

Much research has been done in the past for segmenting lecture videos by detecting slide transitions. However, they do not perform well on certain kinds of videos recorded under non-stationary settings: the changes of a camera position or focus during a lecture. Since such non-stationary settings greatly affect visual properties of slides, the existing approaches utilizing global features and a global threshold, often have trouble in computing similarities between slides. In this paper, we propose a highly accurate method for lecture video segmentation by using SIFT and an adaptive threshold. By using SIFT, we can reliably match two slides whose contents are the same but are visually different. We also propose an adaptive threshold selection algorithm that detects slide transitions accurately by considering characteristics of features. Through various experiments that use real lecture videos, we show that our method provides 30% improvement in the average F1-score over other existing methods.
Employing Solid Phase Microextraction as Extraction Tool for Pesticide Residues in Traditional Medicinal Plants

HS-SPME was optimised using blank plant sample for analysis of organochlorine pesticides (OCPs) of varying polarities in selected medicinal plants obtained from northern part of Botswana, where OCPs such as DDT and endosulfan have been historically applied to control disease carrying vectors (mosquitos and tsetse fly). The optimised SPME parameters were used to isolate analytes from root samples of five medicinal plants obtained from Maun and Kasane, Botswana. The final analytes determination was done with a gas chromatograph equipped with GC-ECD and analyte was confirmed using electron ionisation mass spectrometer (GC-MS). Dieldrin was the only pesticide detected and confirmed with MS in the Terminalia sericea sample obtained from Kasane. The method was validated and the analyte recoveries ranged from 69.58 ± 7.20 to 113 ± 15.44%, with RSDs ranging from 1.19 to 17.97%. The method indicated good linearity (R 2 > 0.9900) in the range of 2 to 100 ng g−1. The method also proved to be sensitive with low limits of detection (LODs) ranging from 0.48 ± 0.16 to 1.50 ± 0.50 ng g−1. It can be concluded that SPME was successfully utilized as a sampling and extraction tool for pesticides of diverse polarities in root samples of medicinal plants.
Total variability factors combination for speaker verification

Intersession compensations in the total variability space have become the mainstream approach in the field of speaker verification. However, this space training through the standard algorithm needs lots of time and memory. An effective algorithm is proposed to obtain the total variability loading matrix in this paper. We orthogonally concatenate the speaker and the channel loading matrices from joint factor analysis (JFA) system, which can save much more time and memory. Furthermore, if the total variability factors from the standard algorithm and the proposed algorithm are combined, better recognition result can be obtained. In the telephone data of the core condition trials of the NIST SRE 2008, compared with the baseline system, we yield 9.94% and 5.70% relative equal error rate (EER) reduction for the male and female respectively.
Mining for Co-Changes in the Context of Web Localization

An approach for mining repositories of Web-based user documentation for patterns of evolutionary change in the context of internationalization and localization is presented. Sets of documents that are changed together during the translation process are uncovered and documented to support future evolution of the system. A sequential-pattern mining technique is used to uncover the patterns from Subversion repositories. The approach is applied to the open source KDE system. KDE maintains documentation for over fifty different natural languages and presents a prime example of the problem. Characteristics of the uncovered patterns such as size, frequency, and occurrences within a single language or across multiple languages are discussed. Such patterns help provide insight as to the effort required in retranslation due to a change in the documentation and help user communities estimated the progress of documentation in their respective languages
An Improved Parameter Computation Method for a MIMO V2V Rayleigh Fading Channel Simulator Under Non-Isotropic Scattering Environments

For simulations of multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) Rayleigh fading channels under more realistic non-isotropic scattering scenarios, we propose a new parameter computation method for the design of sum-of-sinusoids (SoS) simulation models. Compared with the existing relevant methods, the proposed method has the same simulation efficiency but provides a better approximation to the desired statistical properties of the theoretical reference model.
Evaluation of Feature Space Transforms for Czech Sign-Language Recognition

In the paper we give a brief introduction into sign language recognition and present a particular research task, where the access to MetaCentrum computing facilities was highly beneficial. Although the problem of signed speech recognition is currently being researched into by many research institutions all around the world, it lacks of a generally accepted baseline parametrization method. Our team introduced a parametrization method based on skin-color detection and object tracking. Because of the relatively high amount of information that is produced during the parametrization process, a method that reduces the unnecessary information while keeping the necessary information is required. Such methods are called feature space dimension reduction methods. We used the MetaCentrum facilities to evaluate several methods used widely in the field of acoustic speech recognition and their influence on recognition score of a Czech Sign-Language recognizer.
Flexible Process-Based Applications in Hybrid Clouds

Cloud applications target large costumer groups to leverage economies of scale. To increase the number of customers, a flexible application design is of major importance. It enables customers to adjust the application to their individual needs in a self-service manner. In this paper, we classify the required variability of these flexible applications: data variability -- changes to handled data structures, functional variability -- changes to the processes that the application supports, user interface variability -- changes to the appearance of the application, provisioning variability -- the ability of the application to be deployed in different runtime environments. Existing and new technologies and tools are leveraged to realize these classes of variability. Further, we cover architectural principles to follow during the design of flexible cloud applications and we introduce an abstract architectural pattern to enable data variability.
An Area Optimized 2.5-V 10-b 200-MS/s 200-μA CMOS DAC

A 10-b 200-MSample/s digital-to-analog converter is optimized for low power and low area with plusmn25%, 0.1% incremental gain control. The converter uses a combined thermometer coded current-steering DAC and an R-2R load to achieve a compact layout and sub-muA LSB current. It provides more than 61dB spurious-free dynamic range over 50MHz bandwidth within -20degC to +95degC temperature range and plusmn10% supply variation from a dual 2.5/1.8-V supply. The differential and integral non-linearity of the converter are within plusmn0.1LSB. The 0.55mm times 0.47mm converter is implemented in a 0.35/0.18-mum CMOS technology and the DAC core dissipates only 200muA static current from a 2.5V analog supply
Reactive power and harmonic currents compensation in traction systems using active power filter with DFT-based current reference generator

This contribution deals with a shunt-type active power filter designed mainly for railway applications (50-Hz and 16.7-Hz electrification systems) which is able to compensate undesired harmonic currents as well as reactive power. Its control algorithm employs an optimized DFT-based fundamental current and voltage estimator generating an accurate control reference signal. The APF configuration, the current reference generator and the APF control algorithm are discussed in detail in this paper. Theoretical conclusions are verified beside simulations also by experiments on designed laboratory prototype of rated power of 5.75 kVA.
Optimal Downlink Beamforming Using Semidefinite Optimization

When using antenna arrays at the base station of a cellular system, one critical aspect is the transmission strategy. An optimal choice of beamformers for simultaneous transmission to several co-channel users must be solved jointly for all users and base stations in an area. We formulate an optimal transmit strategy and show how the solution can be calculated efficiently using interior point methods for semidefinite optimization. The algorithm minimizes the total transmitted power under certain constraints to guarantee a specific quality of service. The method provides large flexibility in the choice of constraints and can be extended to be robust to channel perturbations.
A load dependent metric for balancing Internet traffic in Wireless Mesh Networks

In wireless mesh networks (WMNs) traffic is routed from mobile clients through a multihop wireless backbone to and from Internet gateways (IGWs). Because of their limited number, IGWs become the major traffic bottlenecks. The purpose of this work is to explore the benefits of introducing load-dependent routing metrics to increase WMN capacity and performance. We use weighted shortest path routing and introduce LAETT a weight metric that captures both traffic load and link quality. We compare the scheme to ETT and MIC, two load independent metrics, and show in simulation its benefits for various network and traffic configurations.
Revision History

Distributed applications are making increasing use of bitmap structures to track state and control the operation of various of their features. A number of these bitmap structures are located on disk storage in order that they can survive the failure of one or more servers. While some of these bitmaps are related to the storage on which they reside (e.g. a ‘dirty” sector list or a mirror resynchronization scoreboard), many are related to general application features such as:
Minimizing size of decision trees for multi-label decision tables

We used decision tree as a model to discover the knowledge from multi-label decision tables where each row has a set of decisions attached to it and our goal is to find out one arbitrary decision from the set of decisions attached to a row. The size of the decision tree can be small as well as very large. We study here different greedy as well as dynamic programming algorithms to minimize the size of the decision trees. When we compare the optimal result from dynamic programming algorithm, we found some greedy algorithms produce results which are close to the optimal result for the minimization of number of nodes (at most 18.92% difference), number of nonterminal nodes (at most 20.76% difference), and number of terminal nodes (at most 18.71% difference).
Jumping without Using Legs: The Jump of the Click-Beetles (Elateridae) Is Morphologically Constrained

To return to their feet, inverted click-beetles (Elateridae) jump without using their legs. When a beetle is resting on its dorsal side, a hinge mechanism is locked to store elastic energy in the body and releases it abruptly to launch the beetle into the air. While the functional morphology of the jumping mechanism is well known, the level of control that the beetle has over this jumping technique and the mechanical constraints governing the jumps are not entirely clear. Here we show that while body rotations in air are highly variable, the jumps are morphologically constrained to a constant “takeoff” angle (79.9°±1.56°, n = 9 beetles) that directs 98% of the jumping force vertically against gravity. A physical-mathematical model of the jumping action, combined with measurements from live beetle, imply that the beetle may control the speed at takeoff but not the jumping angle. In addition, the model shows that very subtle changes in the exact point of contact with the ground can explain the vigorous rotations of the body seen while the beetle is airborne. These findings suggest that the evolution of this unique non-legged jumping mechanism resulted in a jumping technique that is capable of launching the body high into the air but it is too constrained and unstable to allow control of body orientation at landing.
Optimized classification of multiclass problems applied to EMG-control of hand prostheses

The article proposes a new control scheme for a multifunctional myoelectric control of hand prostheses. Therefore, switch signals are introduced for movement selection. A finite state automaton changes to corresponding movement states after having analyzed the switch signal. This is done by data processing algorithms like MANOVA, discriminant analysis and maximum-likelihood estimation. However, implementations using recorded data are not able to discriminate all switch signals. Therefore, modifications have been developed to increase classification accuracy, using modified transformation matrices and hierarchical classifiers. These algorithms are tested and compared with data of two above elbow amputees and two below elbow amputees.
Comparison of Recalibrated Atmospheric Emitted Radiance Interferometer Observations with Line-by-Line Radiative Transfer Model Calculations

The comparison of recalibrated spectra from the atmospheric emitted radiance interferometer prototype (AERI-00) and lineby-line radiative transfer model (LBLRTM) calculations show much smaller differences in the 800-1200 cm atmospheric -1 window region under “clear conditions” compared with the preliminary calibrated data. Results indicate that the new AERI-00 data is in good agreement with similar comparisons using Spectral Radiance Experiment (SPECTRE) data. However, the recalibrated spectra have small changes in other spectral regions. Large observation-model differences still exist in the 550-630 cm region, especially for dry cases, -1 indicating a potential problem in arctic radiance and atmospheric cooling rate calculations.
White Teacher, Know Thyself: Improving Anti-Racist Praxis Through Racial Identity Development

This article asserts that White teachers in urban schools must turn their racialized focus away from implied deficits of students of Color in the “achievement gap” frame and toward the impact their racial identities have on their craft. Through empirical analysis of White teachers’ experiences, the article suggests six areas of self-work for developing positive, anti-racist White racial identities, an integral component in culturally responsive teaching. The authors draw upon Zeus Leonardo’s “third space” of navigating Whiteness and Janet Helms’s racial identity development framework to offer practical suggestions for building more anti-racist and effective pedagogy.
IMAGE RECOGNITION USING DISCRETE COSINE TRANSFORMS AS DIMENSIONALITY REDUCTION

Principal Component Analysis (PCA) approaches to image recognition are data dependent and computationally expensive. To classify unknown images they need to match the nearest neighbour in the stored database of extracted image features. In this paper, Discrete Cosine Transforms (DCTs) are used to reduce the dimensionality of image space by truncating high frequency DCT components. The remaining coefficients are fed into a neural network for classification. Because only a small number of low frequency DCT components are necessary to preserve the most important facial features such as hair outline, eyes and mouth, our DCT-based image recognition system is much faster than other approaches.
MacroBase: Analytic Monitoring for the Internet of Things

An increasing proportion of data today is generated by automated processes, sensors, and systems---collectively, the Internet of Things (IoT). A key challenge in IoT data management and a core aspect of many IoT applications (e.g., industrial diagnostics, predictive maintenance, and equipment auditing) is in identifying and highlighting unusual and surprising data (e.g., poor driving behavior, equipment failures, physical intrusion). We call this task, which is often statistical in nature and time-sensitive, analytic monitoring. To facilitate rapid development and scalable deployment of analytic monitoring queries, we have developed MacroBase, a data analytics engine that performs analytic monitoring of IoT data streams. MacroBase implements a customizable pipeline of outlier detection, summarization, and ranking operators. For efficient and accurate execution, MacroBase implements several cross-layer optimizations across robust estimation, pattern mining, and sketching procedures, allowing order-of-magnitude speedups. As a result, MacroBase can analyze up to 1M events per second on a single core. MacroBase has already delivered meaningful analytic monitoring results in production at a medium-scale IoT startup.
Community-Based Network Study of Protein-Carbohydrate Interactions in Plant Lectins Using Glycan Array Data

Lectins play major roles in biological processes such as immune recognition and regulation, inflammatory responses, cytokine signaling, and cell adhesion. Recently, glycan microarrays have shown to play key roles in understanding glycobiology, allowing us to study the relationship between the specificities of glycan binding proteins and their natural ligands at the omics scale. However, one of the drawbacks in utilizing glycan microarray data is the lack of systematic analysis tools to extract information. In this work, we attempt to group various lectins and their interacting carbohydrates by using community-based analysis of a lectin-carbohydrate network. The network consists of 1119 nodes and 16769 edges and we have identified 3 lectins having large degrees of connectivity playing the roles of hubs. The community based network analysis provides an easy way to obtain a general picture of the lectin-glycan interaction and many statistically significant functional groups.