Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerated Life Cycling of Additively Printed Flexible Linear Charging Circuits and its Effect on Evolution of Line Resistance and Charging Current

The rising demand and consumer interest in wearable devices, fitness accessories, foldable smartphones, and biomedical equipment has led to a research impetus in the field of flexible electronics. Apart from the feature of device flexibility, incorporation of flexible electronics in a device also leads to weight and bulk reduction resulting in more sleek and thin devices. Thus, in the past few years, there have been studies focusing on the inclusion of traditional electronics components like resistors, capacitors, LEDs, sensors, etc. on flexible substrates like polyimide. In our previous study, we also demonstrated the lamination of thin-flexible Li-ion batteries, which are an integral component of modern consumer electronics products. However, apart from just the battery, the battery charging circuit is also an important component, which needs to be transferred from the rigid PCB format to the flexible format. This study focuses on printing of a battery charging circuit on a flexible polyimide substrate using electrically conductive ink. Electronically conductive adhesives (ECAs) will be used for component attachment to the circuit. Two distinct electronics printing technologies will be used and compared for this task, namely aerosol jet printing and direct ink write printing. A linear battery charging topology will be used to develop the additively printed charging circuit. After its preparation, the flexible charging circuit will be subjected to accelerated life cycling for 50 charging cycles to study its effect on the line resistance of the circuit and the change in battery charging current. To study the effect of the constant current (CC) charge current of the battery on the degradation in circuit line resistance, circuits with varying sense resistors have been fabricated to provide two different CC charge currents, i.e. 1C and 2C. Finally, the reliability of the additively printed charging circuit will be compared with that of an identical circuit printed on a rigid PCB and subjected to the same accelerated life-cycling test.
Damper Placement for Spaceborne Interferometers Using ?&-Norm Optimization

A general methodology for damper placement in spaceborne interferometers is introduced. The process consists of combined structural/optical/damper modeling, 'Hm cost selection, and genetic-algorithm or simulated annealing optimization. Combined structural/optical modeling allows the ability to quantitatively predict the effect of mechanical disturbances on optical performance metrics. 3tm cost allows for the consideration of multiple dissimilar disturbance inputs and multiple dissimilar performance outputs, as well as consideration of worst-case analysis. Genetic-algorithms and simulated annealing allow for an efficient search of a highdimensional optimization space. A numerical example is shown to demonstrate the methodology. A comparison of placement of a finite-number of passive dampers using genetic algorithms, simulated annealing, and exhaustive search is considered.
Compile-Time Information in Software Components

Component programming techniques help programmers manage the intrinsic complexity of large software systems by supporting their modular construction from collections of smaller, independent pieces. Each component presents to the other components an abstraction of its functionality with an explicitly specified interface. This dissertation presents two component systems based on the unit model of components, and it demonstrates how both systems support flexible placement of component-based abstraction boundaries by allowing component authors to specify compile-time information in interface specifications. It also emphasizes the importance of supporting a simple module system, alongside the component system, for the management of source-code namespaces and compilation. The first component system is embedded in a model of an ML-like typed functional programming language that includes a Haskell-style module construct. I formalize a type system that supports more flexible specification of type imports and exports than previous unit models in similar settings. These specifications are similar to those found in the imports and exports of ML's functors, and I investigate the connections between units and functors, showing formally how a simplified model of functors can be translated into the component system. The second component system is for languages that can be extended through the use of macros, and I describe its implementation for the PLT Scheme dialect of the Scheme programming language. By allowing definitions of macros to appear inside of component interface definitions, I preserve both compile-time expansion of macros and component independence while allowing different components to encapsulate features that share compile-time information. To accommodate new forms of compile-time information that can accompany new language extensions, the specification language for component interfaces can itself be extended with macros.
A Virtual Board Approach for Prototyping and Teaching Digital Design

This paper presents a novel approach for prototyping digital circuits and teaching digital design. The approach uses a client-server architecture and aims to reduce the use of computing resources and software maintenance by the student. The reduction of computing resources and software maintenance occurs because the system emulates digital circuits on the server-side while the student interacts with the system through a graphical user interface containing a virtual printed circuit board on the client-side. Preliminary results of the proposed architecture have emulated designs of up to 8,000 two-input NAND gates at 1Hz system clock without loss of responsiveness.
Understanding Children’s Trust Development through Repeated Interactions with a Virtual Social Robot

Studies in Child-Robot Interaction have shown that children form first impressions of a robot’s trustworthiness that might influence how they interact with social robots in long-term interactions. However, how children’s trust in robots evolves and how it relates to relationship formation is not well understood. This study investigates the effects of repeated encounters with a virtual social robot on children’s social and competency trust in social robots and their relationship formation. We developed an online storytelling game with the Furhat robot, where 25 children (9-12 years old) played with the robot over two sessions with seven days of zero exposure in between. Results show that children’s competency trust improved with time. We also found empirical evidence that children felt closer to the robot in the second encounter. This work enriches the scientific understanding of children’s trust development in social robots over extended periods of time in child-robot collaborative interactions.
A Graph Convolutional Stacked Temporal Attention Neural Network for Traffic Flow Forecasting

As the foundation of route planning and applications for intelligent transportation systems, accurate spatio-temporal traffic forecasting plays an essential role in improving both road utilization and traffic safety. Recently, the graph convolution network (GCN), recurrent neural network (RNN) and many other deep-learning based methods have been adopted for traffic flow forecasting and performed much better than the conventional statistical approaches. However, some node information may be lost during the propagation in graph convolutional layers, and the existing methods are insufficient to model the temporal dependencies especially for long-range sequences. In order to address these deficiencies, we innovatively come up with a graph convolutional stacked temporal attention neural network (GSTA), which can simultaneously extract the spatial and temporal features to forecast the traffic flow with higher accuracy. Specifically, our proposed framework uses a mix-hop GCN to better capture the spatial dependencies by preserving more useful information compared with the traditional GCN. Moreover, to identify the relations among traffic flow data over different time steps, we adopt an attention mechanism and introduce the temporal feature through embedding technology to capture the temporal regularity. We evaluate the proposed GSTA on two real-world traffic datasets, and the experimental results demonstrate the performance of our proposed model is significantly superior to several existing methods.
Analyzing Data Augmentation Methods for Convolutional Neural Network-based Brain-Computer Interfaces

Brain-computer interfaces (BCI) are systems that use brain signals to communicate with and control devices, with applications ranging over multiple domains. In healthcare, one of the major applications of BCIs is neurorehabilitation. For example, BCIs help stroke patients recover motor abilities by providing sensory feedback based on imagined movement. Convolutional neural networks (CNN) can be used to classify such motor imagery electroencephalogram (EEG) signals and provide this kind of feedback. However, since these signals are usually noisy and can differ significantly over time and among people, it is frequently necessary to collect a large amount of data to train these models. This process can be time-consuming and fatiguing for the user, impairing the quality of neurorehabilitation treatments and other applications. This paper investigates how data augmentation can mitigate this problem by reducing the need for data and increasing feedback accuracy. We analyze five data augmentation methods from the literature on two motor imagery datasets. We apply data augmentation to a few-parameter CNN in varying settings of EEG electrodes, motor imagery tasks, and number of training samples. Our results show that data augmentation can reduce the amount of original data needed, leading to superior accuracy with 33.33 % fewer training samples in some instances. They also show that combining different data augmentation methods can further improve accuracy.
Theory and implementation of UDDI

Universal description, discovery and integration protocol is a set of standard implementation specifications for the web-based, distributed registry center providing information for web service. It also provides the implementation standard for the registry of the web services provided by the enterprise and the discovery of the web services. The creation of UDDI registry center is to promote the devel- opment and discovery of web services provided by enterprises. Therefore, UDDI registry center is an important part of the web service. After analyzing of the UDDI specification, a simple UDDI system is designed and implemented.
Magnetic-field-induced reduction of the low-temperature superfluid density in cuprate superconductors

The weak magnetic field induced reduction of the low-temperature superfluid density in cuprate superconductors is studied based on the kinetic energy driven superconducting mechanism. The electromagnetic response kernel is evaluated by considering both couplings of the electron charge and electron magnetic momentum with a weak magnetic field and employed to calculate the superfluid density, then the main features of the weak magnetic field induced reduction of the low-temperature superfluid density are well reproduced. The theory also shows that the striking behavior of the weak magnetic field induced reduction of the low-temperature superfluid density is intriguingly related to both depairing due to the Pauli spin polarization and nonlocal response in the vicinity of the d-wave gap nodes on the Fermi surface to a weak magnetic field.
Renormalization of six-dimensional Yang-Mills theory in a background gauge field

Using the background field method, we study in a general covariant gauge the renormalization of the 6-dimensional Yang-Mills theory. This requires background gauge invariant counterterms, some of which do not vanish on shell. Such counterterms occur, even off-shell, with gauge-independent coefficients. The analysis is done at one loop order and the extension to higher orders is discussed by means of the BRST identities. We examine the behaviour of the beta function, which implies that this theory is not asymptotically free.
Automatic seamless image mosaic method based on SIFT features

An automatic seamless image mosaic method based on SIFT features is proposed. First a scale-invariant feature extracting algorithm SIFT is used for feature extraction and matching, which gains sub-pixel precision for features extraction. Then, the transforming matrix H is computed with improved PROSAC algorithm , compared with RANSAC algorithm, the calculate efficiency is advanced, and the number of the inliers are more. Then the transforming matrix H is purify with LM algorithm. And finally image mosaic is completed with smoothing algorithm. The method implements automatically and avoids the disadvantages of traditional image mosaic method under different scale and illumination conditions. Experimental results show the image mosaic effect is wonderful and the algorithm is stable very much. It is high valuable in practice.
Using Long-term Millisecond Pulsar Timing to Obtain Physical Characteristics of the Bulge Globular Cluster Terzan 5

Over the past decade, the discovery of three unique stellar populations and a large number of confirmed pulsars within the globular cluster Terzan 5 has raised questions over its classification. Using the long-term radio pulsar timing of ms pulsars in the cluster core, we provide new measurements of key physical properties of the system. As Terzan 5 is located within the galactic bulge, stellar crowding and reddening make optical and near-infrared observations difficult. Pulsar accelerations, however, allow us to study the intrinsic characteristics of the cluster independent of reddening and stellar crowding and probe the mass density profile without needing to quantify the mass-to-light ratio. Relating the spin and orbital periods of each pulsar to the acceleration predicted by a King model, we find a core density of × 106 pc−3, a core radius of pc, a pulsar density profile of , and a total mass of ( pc) ≃ 3.0 × 105 , assuming a cluster distance of 5.9 kpc. Using this information, we argue against Terzan 5 being a disrupted dwarf galaxy and discuss the possibility of it being a fragment of the Milky Way’s proto-bulge. We also discuss whether low-mass pulsars were formed via electron-capture supernovae or exist in a core full of heavy white dwarfs and hard binaries. Finally, we provide an upper limit for the mass of a possible black hole at the core of the cluster of .
hg19K: addressing a significant lacuna in hg19‐based variant calling

The hg19 assembly of the human genome is the most heavily annotated and most commonly used reference to make variant calls for individual genomes. Based on the phase 3 report of the 1000 genomes project (1000G), it is now well known that many positions in the hg19 genome represent minor alleles. Since commonly used variant call methods are developed under the assumption that hg19 reference harbors major alleles at all the ~3 billion positions, these methods mask the calls whenever an individual is homozygous to the minor allele at the respective positions. Hence, it is important to address the extent and impact of these minor alleles in hg19 from the point of view of individual genomes.
"Off with their heads". Profiling TAM in ICE corpora

The main aim of our chapter is a methodological one, that of comparing a largely data-driven approach to regional variation in world Englishes and a corpus-based approach. As a case study, we examine tense, aspect and modality (TAM) differences between five varieties. Our investigation uses frequency differences in verb chunks and tags, based on syntactically annotated material from the International Corpus of English. Most of our results corroborate previous, corpus-based findings. The data-driven findings guide our qualitative investigation of the perfect tense, modal verbs and the progressive. While our approach is far from being fully automatic, only minimal manual interaction is needed for going through and filtering the top one or two dozen entries in ranked lists.
Single and Multi-Person Face Recognition Using the Enhanced Eigenfaces Method

This research studies and analyzes the possibility of single-person and multi-person face detection and recognition. Face detection is performed by the Viola-Jones face detection method and recognition is performed by the Eigenfaces method. Unchanged face detection and recognition methods are explained and tested to their limits. Improvement in face recognition is achieved by observing the flaws of the Eigenfaces method and their enhancement.
Multi-agent opportunism

The real world is a complex place, rife with uncertainty; and prone to rapid change. Agents operating in a real-world domain need to be capable of dealing with the unexpected events that will occur as they carry out their tasks. While unexpected events are often related to failures in an agent's plan, or inaccurate knowledge in an agent's memory, they can also be opportunities for the agent. For example, an unexpected event may present the opportunity to achieve a goal that was previously unattainable. Similarly, real-world multi-agent systems (MASs) can benefit from the ability to exploit opportunities. These benefits include the ability for the MAS itself to better adapt to its changing environment, the ability to ensure agents obtain critical information in a timely fashion, and improvements in the overall performance of the system. In this dissertation we present a framework for multi-agent opportunism that is applicable to open systems of heterogeneous planning agents. The contributions of our research are both theoretical and practical. On the theoretical side, we provide an analysis of the critical issues that must be addressed in order to successfully exploit opportunities in a multi-agent system. This analysis can provide MAS designers and developers important guidance to incorporate multi-agent opportunism into their own systems. It also provides the fundamental underpinnings of our own specific approach to multi-agent opportunism. On the practical side, we have developed, implemented, and evaluated a specific approach to multi-agent opportunism for a particular class of multi-agent system. Our evaluation demonstrates that multi-agent opportunism can indeed be effective in systems of heterogeneous agents even when the amount of knowledge the agents share is severely limited. Our evaluation also demonstrates that agents that are capable of exploiting opportunities for their own goals are also able, using the same mechanisms, to recognize and respond to potential opportunities for the goals of other agents. Further and perhaps more interesting, we show that under some circumstances, multi-agent opportunism can be effective even when the agents are not themselves capable of single-agent opportunism.
Research of patterns based on .NET distributed system

N-tiered architecture pattern based on.NET framework is proposed to solve the deficiency of traditional B/S and C/S architectures in distributed system.Using design patterns,service provider model is put forward to fit the new architecture's data access layer.This model is a design pattern method to improve the extensibility of software,its core character is to replace the core function of software without changing the source code.Finally,by using.NET reflection technology and factory method,the model is actualized according to implementation patterns to get a better extensibility in the field of data access.
On The Complexity Of The Process Shop

AbstractWe consider the problem of scheduling a two-stage processing system where each stage consists of a set of independent parallel processors. A job requires processing on a processor from each set and no-wait schedules are required. We show that this problem is hard for it generalizes jobshop and flowshop scheduling. This observation provides a new conceptual framework for process scheduling and allows us to identify several solvable special cases.
A principal component analysis of quasar UV spectra at z ~ 3

From a principal component analysis (PCA) of 78 z ∼ 3 high-quality quasar spectra in the SDSS-DR7 we derive the principal components that characterize the QSO continuum over the full available wavelength range. The shape of the mean continuum is similar to that measured at low-z (z ∼ 1), but the equivalent width of the emission lines is larger at low redshift. We calculate the correlation between fluxes at different wavelengths and find that the emission line fluxes in the red part of the spectrum are correlated with those in the blue part. We construct a projection matrix to predict the continuum in the Lyman-α forest from the red part of the spectrum. We apply this matrix to quasars in the SDSS-DR7 to derive the evolution with redshift of the mean flux in the Lyman-α forest caused by the absorption by the intergalactic neutral hydrogen. A change in the evolution of the mean flux is apparent around z ∼ 3 as a steeper decrease of the mean flux at higher redshifts. The same evolution is found when the continuum is estimated from the extrapolation of a power-law continuum fitted in the red part of the quasar spectrum if a correction derived from simple simulations is applied. Our findings are consistent with previous determinations using high spectral resolution data. We provide the PCA eigenvectors over the wavelength range 1020−2000 A and the distribution of their weights that can be used to simulate QSO mock spectra.
Adaptive learning algorithm for moving target detection based on Gaussian mixture model

Aiming at the problem of modal residual and ghosting for traditional Gaussian mixture model which has a fixed learning rate,an improved algorithm of moving target detection by using adaptive learning rate is presented.The variation characteristic of pixel on the image sequence and the model performance of controlling parameters are analyzed,model of the learning process can be divided into initial formation and background maintenance updates in two stages.To adopt different learning strategies in different stage,the initial formation stage,a bigger decreasing learning rate is adopted to accelerate the background modeling.The maintenance updates stage,according to the number of the pixel matching and mismatching as feedback to adjust and implement model of adaptive learning.Experimental results show that the improved algorithm can effectively improve the original model of the slow convergence rate lead to problems in terms of background model update not in time,which can more accurately in detecting moving target.It is characterized by good adaptability and robustness.