Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and analysis of Phase Change Materials for efficient thermal management

Direct placement of Phase Change Materials (PCMs) on the chip has been recently explored as a passive temperature management solution. PCMs provide the ability to store large amounts of heat at a close-to-constant temperature during the phase change (solid to liquid and vice versa). This latent heat capacity can be used to provide higher performance while reducing hot spots. Detailed modeling of the phase change behavior is essential for the design and evaluation of systems with PCM. This paper proposes an accurate phase change model that is integrated into the commonly used thermal simulation tool, HotSpot. It also provides validation of the proposed model by carrying out computational fluid dynamics (CFD) simulations using COMSOL Multiphysics®. This paper also explores the impact of PCM properties on the thermal profile of a processor, and demonstrates that PCM material choices can affect peak temperatures by up to 20.1°C. Experimental results show that dynamic policy decisions change dramatically when using the proposed detailed phase change model, as prior simpler PCM models can substantially over/under-estimate temperature and PCM melting duration. The proposed model helps design more effective dynamic management policies and enables realistic evaluation of systems with PCM.
The significance of fundamental granular materials research to space exploration

Granular materials are ubiquitous on Earth and in space. Because condensed planets and their satellites are chiefly made up of solids, any mission landing on their surface must establish how to deal with the technological obstacles they present [1]. Sample return missions to distant objects require a strategy for extracting solids, and possibly processing them to liberate valuable components such as hydrogen, helium-3, or oxygen, so the spacecraft musters enough fuel to return to Earth within a reasonable time. Human exploration implies landing large crafts on a bed of dispersed solids, resulting in a violent supersonic upsurge of solids, called rocket blast [2], toward the craft or its neighborhood. The protection of astronauts from cosmic radiation and the establishment of a colony require construction of berms, tunnels and spaceports, all involving massive solids handling. Because bulk properties of granular materials, such as mechanical strength, depend on such microscopic details as packing, chemical composition and size distribution, they are largely unpredictable without access to a sample. On Earth, science-based progress in powder technology benefits from availability of the actual solids. For example, the industrial processing of granular materials is mainly refined by trial and error. Mining of ores, chemical processing, agriculture and infrastructure construction all derive practices from centuries of experience that is only beginning to emerge in planetary exploration, for which complicating physics, such as massive electrostatic buildup in dry atmospheres or in vacuo, present unforeseeable factors [3]. Minimizing astronaut risk makes it likely that robots will always precede human exploration for advance reconnaissance, habitat construction, and harvesting/processing resources prior to astronauts’ arrival. However, delays in signal transmission require robotic autonomy, thus requiring that on-board software incorporates realistic models of soil and ore behavior. Granular materials also concern planetary geophysicists. For instance, an understanding of Martian slides [4] requires better insight of the dynamic behavior
Skåne Emergency Department Assessment of Patient Load (SEAL)—A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments

Objectives Emergency department (ED) crowding is an increasing problem in many countries. The purpose of this study was to develop a quantitative model that estimates the degree of crowding based on workload in Swedish EDs. Methods At five different EDs, the head nurse and physician assessed the workload on a scale from 1 to 6 at randomized time points during a three week period in 2013. Based on these assessments, a regression model was created using data from the computerized patient log system to estimate the level of crowding based on workload. The final model was prospectively validated at the two EDs with the largest census. Results Workload assessments and data on 14 variables in the patient log system were collected at 233 time points. The variables Patient hours, Occupancy, Time waiting for the physician and Fraction of high priority (acuity) patients all correlated significantly with the workload assessments. A regression model based on these four variables correlated well with the assessed workload in the initial dataset (r2 = 0.509, p < 0.001) and with the assessments in both EDs during validation (r2 = 0.641; p < 0.001 and r2 = 0.624; p < 0.001). Conclusions It is possible to estimate the level of crowding based on workload in Swedish EDs using data from the patient log system. Our model may be applicable to EDs with different sizes and characteristics, and may be used for continuous monitoring of ED workload. Before widespread use, additional validation of the model is needed.
Parallel Cluster Identi cation for MultidimensionalLatticesStephen

The cluster identiication problem is a variant of connected component labeling that arises in cluster algorithms for spin models in statistical physics. We present a multidimensional version of Belkhale and Banerjee's Quad algorithm for connected component labeling on distributed memory parallel computers. Our extension abstracts away extraneous spatial con-nectivity information in more than two dimensions, simplifying implementation for higher di-mensionality. We identify two types of locality present in cluster conngurations, and present optimizations to exploit locality for better performance. Performance results from 2D, 3D, and 4D Ising model simulations with Swendson-Wang dynamics show that the optimizations improve performance by 20-80%.
Does culture affect the adoption of advanced mobile services?: a comparative study of young adults' perceptions in Denmark and the UK

Mobile TV has been rapidly adopted in the Asian markets such as South Korea and Japan, whereas in Europe the diffusion has been slow and the services less popular. The present study explores the relationship between young adults' perceptions of value elements and their adoption intentions of mobile TV. Building on the theory of reason-based choice from the behavioural economics field, the study investigates how culture affects; 1) the perceived costs and the perceived benefits relationships with the adoption intentions, and 2) the preferences for particular mobile TV services. The data collection is based on a survey conducted in two different cultural contexts in Europe; Denmark and the UK. The results of the study show that culture moderates the relationship between perceived benefits and adoption intentions, whereas the relationship between perceived costs and adoption intentions is not affected by culture. Culture also affects the preferences for several entertainment services offered by mobile TV.
Incorporating Duration Information for Trajectory Classification

Trajectory classification has many useful applications. Existing works on trajectory classification do not consider the duration information of trajectory. In this paper, we extract duration-aware features from trajectories to build a classifier. Our method utilizes information theory to obtain regions where the trajectories have similar speeds and directions. Further, trajectories are summarized into a network based on the MDL principle that takes into account the duration difference among trajectories of different classes. A graph traversal is performed on this trajectory network to obtain the top-k covering path rules for each trajectory. Based on the discovered regions and top-k path rules, we build a classifier to predict the class labels of new trajectories. Experiment results on real-world datasets show that the proposed duration-aware classifier can obtain higher classification accuracy than the state-of-the-art trajectory classifier.
Is gold a safe haven for investors

This thesis uses a high-frequency intra-day dataset consisting of gold, S&P500 and U.S. Treasury Bond futures returns, together with precisely timed macroeconomic announcements, to examine the role of gold futures as a safe haven. We test the effects of macroeconomic announcements on the returns and return volatility of gold futures using least squares regressions and a Component-GARCH model. The comovement between gold, S&P500 and U.S. Treasury Bond futures is examined using a Dynamic Conditional Correlation model and copulas, with both methods incorporating the macroeconomic announcements. We find that many of the macroeconomic announcements have a significant effect on the return and volatility of gold futures, and that our univariate analysis supports the role of gold as a safe haven. The results of our multivariate analysis show that many macroeconomic announcements have a significant influence. Moreover, we find that the comovement between gold and S&P500 is very weak, but that there is some proof for the safe haven role of gold following from our copula estimations. For gold and U.S. Treasury Bond futures we find that both futures move in tandem, which supports gold's role as a safe haven as U.S. Treasury Bonds are considered a generally considered as a safe asset.
Data Science as a New Frontier for Design

The purpose of this paper is to contribute to the challenge of transferring know-how, theories and methods from design research to the design processes in information science and technologies. More specifically, we shall consider a domain, namely data-science, that is becoming rapidly a globally invested research and development axis with strong imperatives for innovation given the data deluge we are currently facing. We argue that, in order to rise to the data-related challenges that the society is facing, data-science initiatives should ensure a renewal of traditional research methodologies that are still largely based on trial-error processes depending on the talent and insights of a single (or a restricted group of) researchers. It is our claim that design theories and methods can provide, at least to some extent, the much-needed framework. We will use a worldwide data-science challenge organized to study a technical problem in physics, namely the detection of Higgs boson, as a use case to demonstrate some of the ways in which design theory and methods can help in analyzing and shaping the innovation dynamics in such projects.
Supplementary materials for “ A simplified neuro-mechanical model for insect locomotion : Feedforward stability and proprioceptive feedback ”

Here we provide details on the components of the hexapedal model described in the main paper, including mechanical and kinematic aspects of limb and body geometry, neural activation and muscle force generation, and reduced ion-channel descriptions of bursting neurons used in the CPG and motoneurons. Parameter values are taken from experimental studies of the death’s head cockroach Blaberus discoidalis.
Telling juxtapositions: Using repetition and alignable difference in diagram understanding

Diagrams often use repetition to convey points and establish contrasts. This paper shows how MAGI, our model of repetition and symmetry detection, can model the cognitive processes humans use when reading repetition-based diagrams. MAGI, which is based on the Structure Mapping Engine, detects repetition by aligning both visual and conceptual relational structure. This lets visual regularity of form support an understanding of the conceptual regularity such forms often depict. We describe JUXTA, which uses this insight to critique a class of diagrams that juxtapose similar scenes to demonstrate physical laws.
Computing complex events in an event-driven and logic-based approach

In this paper we propose to demonstrate a logic-based and data-driven complex event processor. The event processor is called ETALIS (Event-driven Transaction Logic Inference System). ETALIS is based on decomposition of complex event patterns into patterns goals. Goals are asserted by declarative rules, which are executed in the backward chaining mode. A specific property of these rules is that they are event-driven, and allow reasoning over events relationships.
Non-linear prediction horizon time-discretization for model predictive control of linear sampled-data systems

Model predictive sampled-data control of linear continuous-time plants is considered. The time-discretization of the prediction horizon may be non-linear, in order to reduce the number of optimization variables for a given prediction horizon length. This is done for the purpose of allowing faster implementation. While the method is aimed at constrained systems, this paper focuses on the achievable performance of such control strategies for unconstrained systems. A general solution to the finite-horizon optimal control problem is derived for a prediction horizon of arbitrary time-discretization. The model predictive control strategy is consequently derived, and the optimal control input shown to be given by a time-invariant state feedback expression. Three non-linear prediction horizon time-discretization schemes are proposed, and their relative merits discussed. The benefit of employing the presented control strategy is demonstrated by a satellite attitude control case study. The same case study is further used to highlight limitations of and performance differences between the three proposed prediction horizon time-discretization schemes.
Role of ultrasound‐guided fine‐needle aspiration of indeterminate and suspicious axillary lymph nodes in the initial staging of breast carcinoma

Ultrasound (US) is more sensitive than physical examination alone in determining axillary lymph node involvement during preliminary staging of breast carcinoma. Due to occasional overlap of sonographic features of benign and indeterminate lymph nodes, fine‐needle aspiration (FNA) of sonographically indeterminate/suspicious lymph nodes can provide a more definitive diagnosis than US alone. This study was undertaken to determine the diagnostic accuracy of US‐guided FNA of indeterminate/suspicious/metastatic‐appearing axillary lymph nodes during the initial staging of breast carcinoma.
Quasi-Optimal Convergence Rate for an Adaptive Finite Element Method

We analyze the simplest and most standard adaptive finite element method (AFEM), with any polynomial degree, for general second order linear, symmetric elliptic operators. As is customary in practice, the AFEM marks exclusively according to the error estimator and performs a minimal element refinement without the interior node property. We prove that the AFEM is a contraction, for the sum of the energy error and the scaled error estimator, between two consecutive adaptive loops. This geometric decay is instrumental to derive the optimal cardinality of the AFEM. We show that the AFEM yields a decay rate of the energy error plus oscillation in terms of the number of degrees of freedom as dictated by the best approximation for this combined nonlinear quantity.
A Minimum Makespan Grid Workflow Scheduling algorithm

Grid computing is hardware and software infrastructure which offers a cheap, distributable, coordinated and reliable access to powerful computational capabilities. To achieve the promising potentials of tremendous distributed resources, effective and efficient scheduling algorithms are fundamentally important. Most of the applications in grid computing fall into interdependent task model called workflow application. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. It is well known that the complexity of a general scheduling problem is NP-Complete [1]. The grid workflow task scheduling problem is described by a Directed Acyclic Graph (DAG) or task graph. The graph represents the dependency among tasks, their computation time and communication time between them. In the management of workflow execution scheduling, the key issues that impact on the performance of the system is based on proper scheduling. In this paper, a new algorithm, named Minimum Makespan Grid Workflow Scheduling (MMGWS) is proposed to minimize makespan by reserving the resources in advance and schedule the task on priority. The proposed algorithm has been implemented for arbitrary task graphs in a simulated environment. Finally, the results are compared with the well known Min-Min and HEFT scheduling algorithms and showing that the proposed algorithm is yielding better results, that is, minimizing makespan.
Deployment of Services in a Cloud Subject to Memory and License Constraints

When deploying services in a cloud, a balance must be found between performance and capacity of the service, and the memory available on nodes. This is further complicated if the number of replicas of an application is limited, for instance by the available number of licenses. The analysis of interference between services must scale to large numbers of host nodes, applications, replicas of applications, and classes of users. This paper combines a multi-dimensional packing heuristic and network flow optimization to satisfy simultaneous constraints on throughputs, processor utilizations, memory availability and license availability, at a minimum cost and with a minimum of host processors.
Universal Features of Handwriting : Towards a Non-linear Model

In motor control studies alone, handwriting has been studied from many points of view, from models integrating cognitive and motor processes to algorithms simulating handwriting trajectories. In research fields as diverse as Developmental Psychology, Neuropsychology, or Computer Science, basic units can be words, letters, or strokes. Thus, a unified view is hard to come by. Whereas all studies strive to explain how recorded handwritten traces are produced, very few propose lawful principles predicting observed changes in handwriting, whether due to maturation, learning, fatigue, or pathology. Following authors who showed that one can produce cursive letters through manipulation of the amplitude and phase of two harmonic oscillators moving orthogonally, we propose that handwriting, alike all rhythmic behaviors, can be modeled as the dynamics of non-linear coupled oscillators. Our recent studies show that, firstly, only a small number of shapes can be produced stably and, secondly, these stable shapes persist differently, but lawfully, under varying constraints. Such a model could provide a unifying base to study phenomena ranging from acquisition to pathological decay of handwriting. Its ability to predict lawful losses of stability could help improve handwriting recognition algorithms, which tend to become less successful when fatigue or speed deteriorates handwriting.
A distributed SPICE-model of a solar cell

In this paper, a distributed SPICE-model for a solar cell is worked out. Special attention is paid to the problems of nonhomogeneous current distribution and the effective series resistance. Elaborate experimental techniques have been used to determine the model parameters. They are based on the I-V characteristics, the small signal impedance, and the open-circuit voltage decay measurements. The SPICE simulation results are compared with the measured static and dynamic characteristics of a solar cell. We found satisfactory agreement concerning the static characteristics, whereas there is discrepancy in the dynamic characteristics. This is because the SPICE-model of a p-n junction diode contains only one time constant. It is found that two time constants are necessary for accurate description of the dynamic performance. The techniques and methodologies developed here are applicable to other junction diodes operating at high frequencies or under transient conditions.
Combining digital access and parallel partition for quicksort and quickselect

In this paper, we combine digital access to strings with parallel partition to enhance parallel quicksort and quickselect implementations. Previously, digital access had only been combined with sequential comparison-based algorithms and data structures. We present broadly our approach, not only algorithmically but from the design and implementation point of view. Finally, we give some experimental results that indicate its effectiveness in practice.
A Naive Bayes approach to improve histogram matching quality

Histogram Matching (HM) is a well known technique for matching the colors of a source image to those of a reference image. A major application is High Dynamic Range Imaging, where images with different exposures have to be color matched. HM works best when the images are aligned and the color difference is small. However, due to camera or object motion, the images may not be aligned, which can result in color artifacts. Furthermore, HM typically involves brightening dark regions of the source image, which increases the image noise. As a result, there is a need for detecting and correcting color artifacts and increased image noise. In this paper we present a novel post-processing method for improving the quality of histogram matched images using statistical properties of the reference image. The proposed method is based on ensembles of neighboring pixels. We learn the statistics of such ensembles within the reference image (target image), which exhibits content difference in comparison to the source image. Based on these statistics, we detect and reconstruct faulty intensities, by following a naive Bayes approach. In addition to its relative easiness of implementation, experimental results show considerable subjective (visual) as well as objective (PSNR) improvements over the original HM. Moreover, promising results are obtained on comparisons of our method with Non-Local-Means denoising based post-processing, which is a state-of-the-art denoising method.
Lessons From the Evolution of the Strategy Paradigm

Abstract A review of the strategy literature suggests there have been five phases in the evolution of the field since the Second World War. The present paper discusses each of these phases, drawing on developments in each decade to determine the key lessons for managers and researchers. In particular, it is suggested that ongoing concerns with the effectiveness of strategy implementation in the 1980s and 1990s have led the field to pay greater attention to managers' thought processes, the use of technology, organisational learning, organisational politics, organisational culture and group dynamics. It also appears that a debate has evolved as to whether strategy should be practiced as art, a science or a combination of both.
FLIER: A Novel Sensor Fusion Algorithm

This paper proposes a novel sensor fusion algorithm to obtain instantaneous position and attitude estimates, which can either be used for aerial navigation or can be utilized to construct state feedbacks for camera stabilization. A divergence control strategy has also been formulated and the algorithm was embedded in real-time hardware. A comparative study between the proposed and conventional algorithm illustrates its efficacy.