Miracle-dz/UniFAR-FTUs huggingface dataset:
Perfect signaling among three parties violating predefined causal order

The paradigmatic view where information is seen as a more fundamental concept than the laws of physics leads to a different understanding of spacetime, where the causal order of events emerges from correlations between random variables representing physical quantities. In particular, such an information-theoretic approach does not enforce a global spacetime structure. By following this path, we conclude that perfect signaling correlations among three parties are possible which do not obey the restrictions imposed by global spacetime. We show this using a recent framework based on the sole assumptions that locally, quantum theory is valid and random variables can be described by probability distributions. Our result is of zero-error type and can be seen as an analog to a three-party appearance of quantum non-locality which manifests itself by satisfying a condition with certainty, whereas the same is impossible for any local theory.
Ages of globular clusters: a new approach

A B ST R A C T W ehaveapplied a new m ethod to analyzethehorizontalbranch (HB)m orphology in relationtothedistributionofstarsneartheredgiantbranch(RGB)tipfortheglobular clustersM 22,M 5,M 68,M 107,M 72,M 92,M 3 and 47 Tuc.Thisnew m ethod perm its determ ination ofthe cluster ages to greater accuracy than conventionalisochrone �tting.Using them ethod in conjunction with ournew high-quality photom etricdata
Follow up of MRI bone marrow edema in the treated diabetic Charcot foot – a review of patient charts

ABSTRACT Background: Ill-defined areas of water-like signal on bone magnetic resonance imaging (MRI), characterized as bone marrow edema or edema-equivalent signal-changes (EESC), is a hallmark of active-stage pedal neuro-osteoarthropathy (Charcot foot) in painless diabetic neuropathy, and is accompanied by local soft-tissue edema and hyperthermia. The longitudinal effects on EESC of treating the foot in a walking cast were elucidated by reviewing consecutive cases of a diabetic foot clinic. Study design: Retrospective observational study, chart review Material and methods: Cases with active-stage Charcot foot were considered, in whom written reports on baseline and follow-up MRI studies were available for assessment. Only cases without concomitant infection or skin ulcer were chosen, in whom both was documented, onset of symptomatic foot swelling and patient compliance with cast treatment. Results: From 1994 to 2017, 45 consecutive cases in 37 patients were retrieved, with 95 MRI follow-up studies (1–6 per case, average interval between studies 13 weeks). Decreasing EESC was documented in 66/95 (69%) follow-up studies. However, 29/95 (31%) studies revealed temporarily increasing, migrating or stagnating EESC. Conclusion: EESC on MRI disappear in response to prolonged offloading and immobilizing treatment; however, physiologic as well as pathologic fluctuations of posttraumatic EESC have to be considered when interpreting the MR images. Conventional MRI is useful for surveillance of active-stage Charcot foot recovery.
Asynchronous Evolution for Fully‐Implicit and Semi‐Implicit Time Integration

We propose a series of techniques for hybridizing implicit and semi‐implicit time integration methods in a manner that retains much of the speed of the implicit method without sacrificing all of the higher quality vibrations one obtains with methods that handle elastic forces explicitly. We propose our scheme in the context of asynchronous methods, where different parts of the mesh are evolved at different time steps. Whereas traditional asynchronous methods evolve each element independently, we partition all of our elements into two groups: one group evolved at the frame rate using a fully implicit scheme, and another group which takes a number of substeps per frame using a scheme that is implicit on damping forces and explicit on the elastic forces. This allows for a straightforward coupling between the implicit and semi‐implicit methods at frame boundaries for added stability. As has been stressed by various authors, asynchronous schemes take some of the pressure off of mesh generation, allowing time evolution to remain efficient even in the face of sliver elements. Finally, we propose a force distributing projection method which allows one to redistribute the forces felt on boundaries between implicit and semi‐implicit regions of the mesh in a manner that yields improved visual quality.
Near – Simultaneous Footfalls Lend Stability to Multi – Legged Gaits

Parsimonious dynamical models for legged locomotion are piecewise–defined. The state flows in continuous–time according to an ordinary differential equation (ODE) until a touchdown or liftoff event occurs, triggering an instantaneous reset at a discrete time instant [1]. We’ve shown that models for periodic gaits with footfalls isolated in time (e.g. bipedal walk or run, quadrupedal walk or gallop) reduce to classical dynamical systems—smooth ODEs on smooth manifolds [2]. However, footfalls are not isolated for general behaviors.
Extraction of Total Nucleic Acids from Ticks for the Detection of Bacterial and Viral Pathogens

ABSTRACT Ticks harbor numerous bacterial, protozoal, and viral pathogens that can cause serious infections in humans and domestic animals. Active surveillance of the tick vector can provide insight into the frequency and distribution of important pathogens in the environment. Nucleic-acid based detection of tick-borne bacterial, protozoan, and viral pathogens requires the extraction of both DNA and RNA (total nucleic acids) from ticks. Traditional methods for nucleic acid extraction are limited to extraction of either DNA or the RNA from a sample. Here we present a simple bead-beating based protocol for extraction of DNA and RNA from a single tick and show detection of Borrelia burgdorferi and Powassan virus from individual, infected Ixodes scapularis ticks. We determined expected yields for total nucleic acids by this protocol for a variety of adult tick species. The method is applicable to a variety of arthropod vectors, including fleas and mosquitoes, and was partially automated on a liquid handling robot.
Active perception for object manipulation

The active perception makes an important step towards the integration of the sensing in the planning phase of the object manipulation. Closing the perception-planning loop iteratively, this helps to reduce the uncertainty in the sensing and planning offering better scene parsing and object manipulation applications. In this paper we show the active perception concept using an industrial like scene with different pipes which are sensed with a depth camera. The pose information from the camera is used for a 7 degree of freedom robotic arm mounted on a mobile base in order to perform the planning for object manipulation purposes. For the whole active perception pipeline we have successfully implemented and tested the scene parsing using object part decoupling and the planning with the robot arm.
Sensor Fusion for Roll and Pitch Estimation Improvement of an Agricultural Sprayer Vehicle

Sensor fusion technique has been commonly used for improving the navigation of autonomous agricultural vehicles by means of combining complimentary sensors mounted on such vehicles for the position and attitude angle measurements. In this research, sensor fusion via an Extended Kalman Filter (EKF) was used to integrate the attitude angle estimates from the Digital Elevation Models (DEMs) and Terrain Compensation Module (TCM) sensor to improve the roll and pitch angle measurements of a self propelled sprayer. The fusion algorithm was also developed to improve the three-dimensional positioning of the sprayer, in particular the elevation measurements of a GPS receiver mounted on the sprayer. Vehicle attitude and field elevation were measured at two speeds, 5.6 km/h and 9.6 km/h, using a set of onboard sensors including a real-time kinematic-differential GPS receiver (RTK-DGPS), a TCM sensor and an Inertial Measurement Unit (IMU). A second order auto-regressive (AR) model was developed to model the TCM roll and GPS-based pitch errors. The derived error states were incorporated into the EKF algorithm and the measurement noise covariance was estimated from the AR model, which limited the fine tuning of noise covariance to the process noise covariance only. The EKF estimations were compared with the IMU measurements to validate the performance of the developed fusion algorithm. For the slow speed test data, the mean and standard deviation of the errors of roll (Mean: -0.2244o, Std. Dev.:1.471o) and pitch (Mean: 0.0597o, Std. Dev.: 0.6621o) from the EKF estimates were reduced considerably compared to that of the errors of roll (Mean: 0.2157o, Std. Dev.: 2.4610o) and pitch (Mean: 0.0473o, Std. Dev.: 1.3230o) from DEM. Medium speed test data also showed considerable improvement in the attitude angles estimated using the developed EKF algorithm. The fusion algorithm for improving the elevation measurement of the GPS also showed promising results. Thus, the fusion algorithm was effective in improving attitude and the navigational accuracy of the self-propelled agricultural sprayer, which in turn will also facilitate the automatic control of the implements that interact with the soil surface on undulated topographic surfaces.
How Foreign Function Integration Conquers Conquers Heterogeneous Query Processing

With the emergence of application systems which encapsulate databases and related application components, pure data integration using, for example, a federated database system is not possible anymore. Instead, access via predefined functions is the only way to get data from an application system. As a result, retrieval of such heterogeneous and encapsulated data sources needs the combination of generic query as well as predefined function access. In this paper, we present a middleware approach supporting such a novel and extended kind of integration. Starting with the overall architecture, we explain the functionality and cooperation of its core components: a federated database system and a workflow management system connected via a wrapper. Afterwards, we concentrate on essential aspects of query processing across these heterogeneous components focusing on the impact of the functions included. We discuss the operations the wrapper should provide in order to extend the workflow system’s native functionality. In addition to selection and projection, these operations could include aggregation and the support of subqueries. Moreover, we point out modifications to the traditional cost model needed to consider the cost estimates for the function calls as well.
Combining TF-IDF Text Retrieval with an Inverted Index over Symbol Pairs in Math Expressions: The Tangent Math Search Engine at NTCIR 2014

We report on the system design and NTCIR-Math-2 task results for the Tangent math-aware search engine. Tangent uses a federated search over two indices: 1) a TF-IDF textual search engine (Lucene), and 2) a query-by-expression engine. Query-by-expression is performed using a bag-ofwords approach where expressions are represented by pairs of symbols computed from symbol layout trees (e.g. as expressed in LTEX or Presentation MathML). Extensions to support matrices and prefix subscripts and superscripts are described. Our system produced the highest highly + partially relevant Precision@5 result for the main text/math query task (92%), and the highest Top-1 specific-item recall for the Wikipedia query-by-expression subtask (68%). The current implementation is slow and produces large indices for large corpora, but we believe this can be ameliorated. Source code for our system is publicly available.
Low power VCO using Gm-Boosting technique for optical pulse trains application

This letter presents a 0.7V low power Voltage Control Oscillator (VCO) is designed and implemented in a 0. 18µm COMS 1P6M process. This circuit is using Gm-Boosting technique that can reduce power consumption. At the supply voltage 0.7V, the output frequency is 2.228GHz and the phase noise is −121.56 dBc/Hz at 1MHz offset. Tuning range is about 113MHz(4.9%) between 2.228 to 2.341GHz while control voltage was tuned from 0 to 2V. Excluding output buffers the VCO consumes the power 0.7 mW under a standard supply of 0.7 V for optical pulse trains application.
Geographical Information Science to Enhance Location Coverage Modeling

The use of points to represent facilities, demand or a region of interest in location modeling may not effectively address the geometrically complex shape of abstracted spatial features resulting in certain measurement errors. While point-based representations facilitate model construction, simplify data requirements and enable more efficient problem solution, advances in GIScience and spatial data acquisition highlight deficiencies in using overly rudimentary feature depictions, like points, in certain modeling instances. In this paper other representations, such as lines and polygons, are investigated in covering location problems. The evaluation of warning siren siting in the city of Dublin, Ohio is used to discuss nuances of point representation and other geometric shapes in location modeling. We illustrate how geometric sophistication and reality can be addressed in location coverage modeling through the use of GIS. Application results show that complex geometric shapes can be considered in site/service selection, and doing so in a way that remains computationally feasible.
Exact Folding Dynamics of RNA Secondary Structures

Barrier trees consisting of local minima and their connecting saddle points imply a natural coarse-graining for the description of the energy landscape of RNA secondary structures. Here we show that, based on this approach, it is possible to predict the folding behavior of RNA molecules by numerical integration. Comparison with stochastic folding simulations shows reasonable agreement of the resulting folding dynamics and a drastic increase in computational efficiency that makes it possible to investigate the folding dynamics of RNA of at least tRNA size. Our approach is readily applicable to bistable RNA molecules and promises to facilitate studies on the dynamic behavior of RNA switches. § To whom correspondence should be addressed. Email: ivo@tbi.univie. ac.at, Phone: **43 1 4277 52736, Fax: **43 1 4277 52793 Dynamics of RNA Folding 2
Impact of Temporal and Operational Detail in Energy-System Planning Models

To limit the computational cost, bottom-up long-term (LT) energy-system planning models typically have a stylized temporal representation and do not consider techno-economic operational constraints of power plants. The aim of this work is to analyze the impact of these model simplifications in the context of an increasing penetration of intermittent renewable energy sources (IRES), and identify opportunities for model improvements. While the focus in the literature lies primarily on the impact of the temporal resolution, this work more fundamentally considers and assesses the different approaches of dealing with the temporal dimension. We find that the temporal representation typically used in large-scale energy-system planning models does not sufficiently account for the variability of IRES, resulting in a technology bias towards intermittent and inflexible technologies. Depending on the computational resources available, suggestions are made for alternative temporal representations which better account for the variability of IRES generation. Furthermore, the impact of the temporal representation and the level of techno-economic operational detail are quantified simultaneously, allowing to make the trade-off between improving planning models by extending the operational time dimension, and/or aiming for a better technical representation. As such, this paper presents new findings and goes beyond the existing literature. We show that the gains obtained by improving the temporal representation outweigh the gains obtained by incorporating detailed techno-economic constraints. Moreover, a temporal representation that preserves chronology and has a sufficiently high resolution is a prerequisite for detailed modeling of techno-economic operational constraints. For these reasons, we suggest to prioritize improving the temporal representation.
Multi-agent formation control based on artificial force with exponential form

Playing an important role in security patrol, invader hunting and space exploration, formation control is the typical problem of multi-agent cooperation control system. In order to solve the problem of fixed position assignment and long time to reach the target in traditional artificial force model, a formation control method is proposed based on artificial force with exponential form. Agents' positions are assigned using the global objective function which considers the position, velocity and angular velocity and maneuver procedures are controlled by artificial force with exponential form. Meanwhile, in order to overcome the problem of local minima in obstacle circumstance, an obstacle avoidance model with tangential force is proposed which considers the target goal. Simulation results show, applying the control algorithm of artificial force model with exponential form the agent assigns position naturally and consumes short time to reach the target goal.
The Glasgow Raspberry Pi Cloud: A Scale Model for Cloud Computing Infrastructures

Data Centers (DC) used to support Cloud services often consist of tens of thousands of networked machines under a single roof. The significant capital outlay required to replicate such infrastructures constitutes a major obstacle to practical implementation and evaluation of research in this domain. Currently, most research into Cloud computing relies on either limited software simulation, or the use of a testbed environments with a handful of machines. The recent introduction of the Raspberry Pi, a low-cost, low-power single-board computer, has made the construction of a miniature Cloud DCs more affordable. In this paper, we present the Glasgow Raspberry Pi Cloud (PiCloud), a scale model of a DC composed of clusters of Raspberry Pi devices. The PiCloud emulates every layer of a Cloud stack, ranging from resource virtualisation to network behaviour, providing a full-featured Cloud Computing research and educational environment.
Argus: An Orthogonal Defense Framework to Protect Public Infrastructure against Cyber-Physical Attacks

Argus, a framework for defending a public utility against cyber-physical attacks, contains intelligent checkers that use invariants derived from the physical and chemical interactions among various components and products of a utility. An Argus implementation is independent of the traditional layered defense that employs firewalls and other network-based logic to prevent intrusions into control systems, and hence is referred to as orthogonal defense. Portions of Argus have been implemented and tested in an operational water treatment testbed and found effective in detecting a number of single and complex multicomponent deception attacks.
Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors

In the long-term, crude oil prices may impact the economic stability and sustainability of many countries, especially those depending on oil imports. This study thus suggests an alternative model for accurately forecasting oil prices while reflecting structural changes in the oil market by using a Bayesian approach. The prior information is derived from the recent and expected structure of the oil market, using a subjective approach, and then updated with available market data. The model includes as independent variables factors affecting oil prices, such as world oil demand and supply, the financial situation, upstream costs, and geopolitical events. To test the model’s forecasting performance, it is compared with other models, including a linear ordinary least squares model and a neural network model. The proposed model outperforms on the forecasting performance test even though the neural network model shows the best results on a goodness-of-fit test. The results show that the crude oil price is estimated to increase to $169.3/Bbl by 2040.
Study on Subpixel Registration Methods based on Wavelet Analysis

Image registration is an important task in the field of computer vision and pattern recognition. And the applied values is also reflected in the study of remote sensing, medical imaging and the object indentifying of multi-sensor fusion. In this paper, a new subpixel registration methods which based on wavelet analysis was proposed by improving polynomial subdivision algorithm and pixel level registration. Furthermore, the precision of the method was also analysed after the process. The simulation results shows that the method in this paper achieves good effects and reaches the precision of sub-pixel.
Context Aware Keypoint Extraction for Robust Image Representation

We introduce a context-aware keypoint extractor, coined as CAKE, aimed at capturing the most informative image content. We find this algorithm particularly useful in tasks such as image retrieval, scene classification, and object (class) recognition, in which local features are mainly used to provide a robust and efficient image representation. We are motivated by the fact that the majority of local feature extractors are designed to respond to a reduced number of structures. Furthermore, we observe that the existent complementarity among feature sets is often neglected. Our context-aware algorithm is designed to respond to complementary features as long as they are informative. In the particular case of images with different types of structures, one can expect a high complementarity among the features retrieved by a context-aware extractor. By contrast, images with repetitive patterns will inhibit our method from retrieving a clear summarised description of the image content. Nonetheless, the extracted set of features can be complemented with a counterpart that retrieves the repetitive elements in the image. These two cases are depicted in Figure 1. The upper image shows a context-aware keypoint extraction on a well-structured scene, which retrieves the 100 most informative keypoints. This small number of features is sufficient to provide a good coverage of the content, which includes different types of structures. The lower image illustrates the advantages of combining context-aware keypoints with strictly local ones (SFOP keypoints [2]) to obtain a better coverage of images with repetitive patterns. An information theoretic framework is used to formulate our contextaware keypoint extraction. A keypoint will correspond to a certain image location within a structure with a low probability of occurrence (high information content). For each image location x, we consider w(x) ∈RD, any viable local representation (e.g, the Hessian matrix or the structure tensor matrix) as a “codeword” that represents the neighbourhood of x. To define the saliency measure, we regard the image codewords as samples of a multivariate probability density function. We compute the probability of a codeword w(y) using a Kernel Density Estimator [4] in which the kernel is a multidimensional Gaussian function with zero mean and standard deviation σk: