Miracle-dz/UniFAR-FTUs huggingface dataset:
Attenuation of an Ultrasound Contrast Agent Estimated from Transient Solution of Linearized Rayleigh-Plesset Equation

The attenuation of low-intensity acoustic waves in the suspension of ultrasound contrast agents (UCAs, microbubbles) is determined by the oscillation of the microbubbles in the medium. This bubble-induced attenuation is a linear phenomenon and can be estimated via a linearized Rayleigh-Plesset equation (RPE). In the material characterization, theoretical attenuation is estimated from steady state oscillation of an UCA and immediately compared with experimental attenuation data that are usually measured by shot-pulse ultrasound. However, discrepancy could exist in the characterization if the UCA does not ring up to steady state oscillation. In this article, we investigate the situation where the transient solution of the RPE is not negligible and discuss its impact on the modeling of the shell parameters of an UCA. We provide a formula for attenuation estimation considering the contribution due to transient oscillation.
The Complementary Information Principle of Quantum Mechanics

The uncertainty principle bounds the uncertainties about incompatible measurements, clearly setting quantum theory apart from the classical world. Its mathematical formulation via uncertainty relations, plays an irreplaceable role in quantum technologies. However, neither the uncertainty principle nor uncertainty relations can fully describe the complementarity between quantum measurements. As an attempt to advance the efforts of complementarity in quantum theories, we formally propose a complementary information principle, significantly extending the one introduced by Heisenberg. First, we build a framework of black box testing consisting of pre- and post-testing with two incompatible measurements, introducing a rigorous mathematical expression of complementarity with definite information causality. Second, we provide majorization lower and upper bounds for the complementary information by utilizing the tool of semidefinite programming. In particular, we prove that our bounds are optimal under majorization due to the completeness of the majorization lattice. Finally, as applications to our framework, we present a general method to outer-approximating all uncertainty regions and also establish fundamental limits for all qualified joint uncertainties.
User Diverse Preference Modeling by Multimodal Attentive Metric Learning

Most existing recommender systems represent a user's preference with a feature vector, which is assumed to be fixed when predicting this user's preferences for different items. However, the same vector cannot accurately capture a user's varying preferences on all items, especially when considering the diverse characteristics of various items. To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items. In particular, for each user-item pair, we propose an attention neural network, which exploits the item's multimodal features to estimate the user's special attention to different aspects of this item. The obtained attention is then integrated into a metric-based learning method to predict the user preference on this item. The advantage of metric learning is that it can naturally overcome the problem of dot product similarity, which is adopted by matrix factorization (MF) based recommendation models but does not satisfy the triangle inequality property. In addition, it is worth mentioning that the attention mechanism cannot only help model user's diverse preferences towards different items, but also overcome the geometrically restrictive problem caused by collaborative metric learning. Extensive experiments on large-scale real-world datasets show that our model can substantially outperform the state-of-the-art baselines, demonstrating the potential of modeling user diverse preference for recommendation.
Non-negative Sparse and Collaborative Representation for Pattern Classification

Sparse representation (SR) and collaborative representation (CR) have been successfully applied in many pattern classification tasks such as face recognition. In this paper, we propose a novel Non-negative Sparse and Collaborative Representation (NSCR) for pattern classification. The NSCR representation of each test sample is obtained by seeking a non-negative sparse and collaborative representation vector that represents the test sample as a linear combination of training samples. We observe that the non-negativity can make the SR and CR more discriminative and effective for pattern classification. Based on the proposed NSCR, we propose a NSCR based classifier for pattern classification. Extensive experiments on benchmark datasets demonstrate that the proposed NSCR based classifier outperforms the previous SR or CR based approach, as well as state-of-the-art deep approaches, on diverse challenging pattern classification tasks.
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data

Continuing advances in neural interfaces have enabled simultaneous monitoring of spiking activity from hundreds to thousands of neurons. To interpret these large-scale data, several methods have been proposed to infer latent dynamic structure from high-dimensional datasets. One recent line of work uses recurrent neural networks in a sequential autoencoder (SAE) framework to uncover dynamics. SAEs are an appealing option for modeling nonlinear dynamical systems, and enable a precise link between neural activity and behavior on a single-trial basis. However, the very large parameter count and complexity of SAEs relative to other models has caused concern that SAEs may only perform well on very large training sets. We hypothesized that with a method to systematically optimize hyperparameters (HPs), SAEs might perform well even in cases of limited training data. Such a breakthrough would greatly extend their applicability. However, we find that SAEs applied to spiking neural data are prone to a particular form of overfitting that cannot be detected using standard validation metrics, which prevents standard HP searches. We develop and test two potential solutions: an alternate validation method ("sample validation") and a novel regularization method ("coordinated dropout"). These innovations prevent overfitting quite effectively, and allow us to test whether SAEs can achieve good performance on limited data through large-scale HP optimization. When applied to data from motor cortex recorded while monkeys made reaches in various directions, large-scale HP optimization allowed SAEs to better maintain performance for small dataset sizes. Our results should greatly extend the applicability of SAEs in extracting latent dynamics from sparse, multidimensional data, such as neural population spiking activity.
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors

In recent years, convolutional networks have demonstrated unprecedented performance in the image restoration task of super-resolution (SR). SR entails the upscaling of a single low-resolution image in order to meet application-specific image quality demands and plays a key role in mobile devices. To comply with privacy regulations and reduce the overhead of cloud computing, executing SR models locally on-device constitutes a key alternative approach. Nevertheless, the excessive compute and memory requirements of SR workloads pose a challenge in mapping SR networks on resource-constrained mobile platforms. This work presents MobiSR, a novel framework for performing efficient super-resolution on-device. Given a target mobile platform, the proposed framework considers popular model compression techniques and traverses the design space to reach the highest performing trade-off between image quality and processing speed. At run time, a novel scheduler dispatches incoming image patches to the appropriate model-engine pair based on the patch's estimated upscaling difficulty in order to meet the required image quality with minimum processing latency. Quantitative evaluation shows that the proposed framework yields on-device SR designs that achieve an average speedup of 2.13x over highly-optimized parallel difficulty-unaware mappings and 4.79x over highly-optimized single compute engine implementations.
First clear detection of the CCS Zeeman splitting toward the pre-stellar core, Taurus Molecular Cloud 1

We report the first clear detection of the Zeeman splitting of a CCS emission line at 45 GHz toward the nearby pre-stellar dense filament, Taurus Molecular Cloud 1 (TMC-1). We observed HC$_3$N non-Zeeman lines simultaneously with the CCS line, and did not detect any significant splitting of the HC$_3$N lines. Thus, we conclude that our detection of CCS Zeeman splitting is robust. The derived line-of-sight magnetic field strength is about $117 \pm 21 \, \mu$G, which corresponds to a normalized mass-to-magnetic flux ratio of 2.2 if we adopt an inclination angle of 45$^\circ$. Thus, we conclude that the TMC-1 filament is magnetically supercritical. Recent radiative transfer calculations of the CCS and HC$_3$N lines along the line of sight suggest that the filament is collapsing with a speed of $\sim$0.6 km s$^{-1}$, which is comparable to three times the isothermal sound speed. This infall velocity appears to be consistent with the evolution of a gravitationally infalling core.
Inertial migration of neutrally buoyant particles in superhydrophobic channels

At finite Reynolds numbers particles migrate across flow streamlines to their equilibrium positions in microchannels. Such a migration is attributed to an inertial lift force, and it is well-known that the equilibrium location of neutrally-buoyant particles is determined only by their size and the channel Reynolds number. Here we demonstrate that the decoration of a bottom wall of the channel by superhydrophobic grooves provides additional possibilities for manipulation of neutrally-buoyant particles. It is shown that the effective anisotropic hydrodynamic slip of such a bottom wall can be readily used to alter the equilibrium positions of particles and to generate their motion transverse to the pressure gradient. These results may guide the design of novel inertial microfluidic devices for efficient sorting of neutrally-buoyant microparticles by their size.
Jigsaw: Scalable software-defined caches

Shared last-level caches, widely used in chip-multi-processors (CMPs), face two fundamental limitations. First, the latency and energy of shared caches degrade as the system scales up. Second, when multiple workloads share the CMP, they suffer from interference in shared cache accesses. Unfortunately, prior research addressing one issue either ignores or worsens the other: NUCA techniques reduce access latency but are prone to hotspots and interference, and cache partitioning techniques only provide isolation but do not reduce access latency.
Fall detection using wavelet transform and support vector machine

THAI). I ABSTRACT (ENGLISH). III ACKNOWLEDGEMETS. V TABLE OF CONTENTS. VI LIST OF TABLE. VIII LIST OF FIGURES. IX SYMBOLS AND ABBREVIATIONS. XII CHAPTER
Walking Through Twitter: Sampling a Language-Based Follow Network of Influential Twitter Accounts

Twitter continuously tightens the access to its data via the publicly accessible, cost-free standard APIs. This especially applies to the follow network. In light of this, we successfully modified a network sampling method to work efficiently with the Twitter standard API in order to retrieve the most central and influential accounts of a language-based Twitter follow network: the German Twittersphere. We provide evidence that the method is able to approximate a set of the top 1% to 10% of influential accounts in the German Twittersphere in terms of activity, follower numbers, coverage, and reach. Furthermore, we demonstrate the usefulness of these data by presenting the first overview of topical communities within the German Twittersphere and their network structure. The presented data mining method opens up further avenues of enquiry, such as the collection and comparison of language-based Twitterspheres other than the German one, its further development for the collection of follow networks around certain topics or accounts of interest, and its application to other online social networks and platforms in conjunction with concepts such as agenda setting and opinion leadership.
Planetary systems in a star cluster I: the Solar system scenario

Young stars are mostly found in dense stellar environments, and even our own Solar system may have formed in a star cluster. Here, we numerically explore the evolution of planetary systems similar to our own Solar system in star clusters. We investigate the evolution of planetary systems in star clusters. Most stellar encounters are tidal, hyperbolic, and adiabatic. A small fraction of the planetary systems escape from the star cluster within 50 Myr; those with low escape speeds often remain intact during and after the escape process. While most planetary systems inside the star cluster remain intact, a subset is strongly perturbed during the first 50 Myr. Over the course of time, $0.3\! -\! 5.3{{\ \rm per\ cent}}$ of the planets escape, sometimes up to tens of millions of years after a stellar encounter occurred. Survival rates are highest for Jupiter, while Uranus and Neptune have the highest escape rates. Unless directly affected by a stellar encounter itself, Jupiter frequently serves as a barrier that protects the terrestrial planets from perturbations in the outer planetary system. In low-density environments, Jupiter provides protection from perturbations in the outer planetary system, while in high-density environments, direct perturbations of Jupiter by neighbouring stars is disruptive to habitable-zone planets. The diversity amongst planetary systems that is present in the star clusters at 50 Myr, and amongst the escaping planetary systems, is high, which contributes to explaining the high diversity of observed exoplanet systems in star clusters and in the Galactic field.
The role of caching in next generation cellular networks: A survey and research outlook

Mobile applications and social networks tend to enhance the needs for high‐quality content access. To address the expeditious growing demand for data services in 5G cellular networks, it is important to develop distribution techniques and an efficient content caching, aiming to significantly reduce redundant data transmission and, thus, improve the efficiency of the networks. In modern communication systems, caching has emerged as a vital tool for reducing peak data rates. It is anticipated that energy harvesting and self‐powered small base stations are the fundamental part of next‐generation cellular networks. However, uncertainties in energy are the main reason to adopt energy efficient power control schemes to reduce SBS energy consumption and ensure the quality of services for users. Using the edge cooperative caching such as energy efficient design can also be achievable, which reduces the usage of the capacity limited SBSs backhaul and the energy consumption. To support the huge power demand of cellular network, renewable energy harvesting technologies can be leveraged. In addition to this, power supply to the infrastructures is the main challenge to the mobile network operators (MNOs) especially in terms of economic optimum, sustainability, and green energy in developing countries for the growth of cellular networks. Renewable energy–based solutions for MNOs not only reduce the overall carbon dioxide emissions but also provide numerous profits.
Ordering phenomena of spin trimers accompanied by a large geometrical Hall effect

Noncoplanar spin trimers are the basic units that define nonzero scalar spin chirality. They are the key ingredients to achieve the chirality-induced geometrical Hall effect. Here, through neutron diffraction, the authors reveal the emergence of noncoplanar Dy${}^{3+}$ spin-trimer units in metallic Dy${}_{3}$Ru${}_{4}$Al${}_{12}$. With a magnetic field, they can control the local spin chirality of the spin trimers as well as its ferroic/antiferroic order. In phases with nonzero total scalar spin chirality, a large geometrical Hall effect is observed. The study reveals that systems composed of tunable spin trimers can be a fertile field for the exploration of a large emergent electromagnetic response, arising from real-space topological magnetic order.
Factors Activating Big Data Adoption by Korean Firms

ABSTRACT Big data is expected to drive the new digital transformation of society, government, and business. The proportion of firms implementing and utilizing big data, however, is not high. There are many challenges and obstacles related to firm adoption of big data. The purpose of this study is to identify the factors influencing the adoption of big data among Korean firms using the technology-organization-environment (TOE) framework. The results of the analytic hierarchy process (AHP) and regression analysis from data collected from 50 experts and 226 companies, respectively, are as follows: the benefits from big data, technological capabilities, financial investment competence, and data quality and integration are perceived by the experts and the firms as the strongest determinants of adoption. Management support for big data, security and privacy, and government support and policy are also influential factors of firm big data adoption.
Connection probabilities in the double-dimer model -- the case of two connectivity patterns

We apply the Grassmannian representation of the dimer model, an equivalent approach to Kasteleyn's solution to the close-packed dimer problem, to calculate the connection probabilities for the double-dimer model with wired/free/wired/free boundary conditions, on a rectangular subdomain of the square lattice with four marked boundary points at the corners. Using some series identities related to Schwarz-Christoffel transformations, we show that the continuum of the result is consistent with the corresponding one in the upper half-plane (previously obtained by Kenyon-Wilson), which is in turn identical to the connection probabilities for 4SLE$_4$ emanating from the boundary, or equivalently, to a conditioned version of CLE$_4$ with wired/free/wired/free boundary conditions in the context of conformal loop ensembles.
Downtime in the Automotive Industry Production Process – Cause Analysis

Purpose: The article presents an analysis of the application of selected quality management methods and tools in order to identify factors affecting downtimes in the production line, illustrated with an example of the automotive industry.Methodology/Approach: The paper contains an analysis of downtimes in the production process using selected methods and tools of quality management. The authors used a combination of tools and methods to carry out the analysis. In this concept, first, the 5WHY method and the Ishikawa diagram were used.Findings: The research results presented in the paper suggest that better results can be achieved by using a set of quality tools instead of one particular tool. The authors found that using a wide range of quality tools can be useful to reduce downtimes on the production line.Research limitation/implication: The major limitation of the paper is that it is based on one case of an organisation from the automotive industry. In the future, it will be necessary to conduct studies in more organisations so as to find out if the same result can be achieved.Originality/Value of paper: The article is concerned with the use of quality management methods and tools to analyse production line breakdowns. Until present, in subject literature, the causes of downtimes have been analysed without differentiating between planned and unplanned downitmes of the production line.
Usability Study for a Community College Library Website: A Methodology for Large-Scale Data Gathering

Abstract Usability studies (UX) are increasingly common in libraries. A UX methodology is a useful tool to support librarians’ decision-making processes during a Website (re)design. This article describes a UX process using a custom-built usability-testing environment and a data-collection tool, written in JavaScript and Python respectively. The resulting automated approach allowed us to collect usability data from a sample of 225 students. The methodology described in this study will be of interest to those considering or planning a UX study in their libraries.
Metal Abundances and Star Formation Rates of Emission-line Galaxies in and around the Boötes Void

We explore the possible dependencies of galaxy metal abundance and star formation rate (SFR) on local environment, focusing on the volume of space in and around the Boötes Void. Our sample of star-forming galaxies comes from the second catalog of the Hα-selected KPNO International Spectroscopic Survey (KISS), which overlaps the void. This sample represents a statistically complete, line-flux-limited ensemble of 820 star-forming galaxies, all of which possess metallicity and SFR estimates. We carry out two distinct analyses of the KISS galaxies: one that probes the properties of the entire sample as a function of local density, and a second that details the properties of 33 KISS star-forming galaxies located within the Boötes Void. In both cases, we find no evidence that either the metallicity of the KISS galaxies or their SFRs depend on the environments within which the galaxies are located. Our global analysis does show weak trends for decreasing stellar mass, decreasing metallicity, and decreasing SFRs with decreasing local densities. However, we argue that the metallicity and SFR trends are artifacts of the stellar mass—local density trend. In particular, the change in metallicity with density is precisely what one would predict from the mass–metallicity relation, given the observed drop in stellar mass with decreasing metallicity. Likewise, the SFR trend with density disappears when one instead considers the mass-normalized specific SFR. The KISS galaxies dwelling in the Boötes Void are found to have metallicity and SFR properties nearly identical to those of a matched comparison sample, despite the fact that the former are located in density environments that are, on average, more than 16 times lower.