Miracle-dz/UniFAR-FTUs huggingface dataset:
A Greedy Algorithm for Building Compact Binary Activated Neural Networks

We study binary activated neural networks in the context of regression tasks, provide guarantees on the expressiveness of these particular networks and propose a greedy algorithm for building such networks. Aiming for predictors having small resources needs, the greedy approach does not need to ﬁx in advance an architecture for the network: this one is built one layer at a time, one neuron at a time, leading to predictors that aren’t needlessly wide and deep for a given task. Similarly to boosting algorithms, our approach guarantees a training loss reduction every time a neuron is added to a layer. This greatly differs from most binary activated neural networks training schemes that rely on stochastic gradient descent (circumventing the 0-almost-everywhere derivative problem of the binary activation function by surrogates such as the straight through estimator or continuous binarization). We show that our method provides compact and sparse predictors while obtaining similar performances to state-of-the-art methods for training binary activated networks.
Diagnostic Ultrasound Quality Assurance by Means of Machine Output Parameters

The out-put parameters in terms of average acoustic power and intensity levels from a diagnostic ultrasound machine located at General Hospital Gboko were measured using ultrasound power meter UPM-DT-10AV. The ultrasound power meter measures the out-put acoustic power from the ultrasound beams which were converted to intensity levels, three set of readings were taken for each of the pre-timed durations of 3,6,9,12,15,18,21,24,27 and 30 minutes, the average values were the found. The work has assed ultrasound dosimetry compliance in this Hospital and provide database for diagnostic ultrasound quality control for the Hospital and provide database for diagnostic ultrasound quality control for the Hospital. The machine has a minimum average intensity value of 0.04 W/cm 2 at an exposed scanning duration of 3 minutes for both pulse wave and continuous wave propagation modes respectively. From the results, it can be seen that beyond scanning duration of 9 minutes the machine have out-put intensities beyond the maximum safe limit of 0.09 W/cm 2 for fetal imaging and other sensitive organs, also 0.017 W/cm 2 for ophthalmic scan, but within the maximum safe limit of 0.43 W/cm 2 for cardiac scan and 0.72 W/cm 2 for the scanning of the peripheral vessels as recommended by World Federal of Ultrasound in Medicine and Biology (WFUMB) (1998) and Food and Drug Administration (FDA) (1993)
Cutting Throughput with the Edge: App-Aware Placement in Fog Computing

Fog computing extends cloud computing technology to the edge of the infrastructure to support dynamic computation for IoT applications. Reduced latency and location awareness in objects' data access is attained by displacing workloads from the central cloud to edge devices. Doing so, it reduces raw data transfers from target objects to the central cloud, thus overcoming communication bottlenecks. This is a key step towards the pervasive uptake of next generation IoT-based services. In this work we study efficient orchestration of applications in fog computing, where a fog application is the cascade of a cloud module and a fog module. The problem results into a mixed integer non linear optimisation. It involves multiple constraints due to computation and communication demands of fog applications, available infrastructure resources and it accounts also the location of target IoT objects. We show that it is possible to reduce the complexity of the original problem with a related placement formulation, which is further solved using a greedy algorithm. This algorithm is the core placement logic of FogAtlas, a fog computing platform based on existing virtualization technologies. Extensive numerical results validate the model and the scalability of the proposed algorithm, showing performance close to the optimal solution with respect to the number of served applications.
Language model and acoustic model information in probabilistic speech recognition

The authors propose an approach to the estimation of the performance of the language model and the acoustic model in probabilistic speech recognition that tries to take into account the interaction between the two. It consists of a new measure, called speech decoder entropy (SDE), of joint acoustic-context information. Some results are presented for a 20000-word vocabulary recognizer. The authors discuss some limitations of the work and some suggestions for future developments. <<ETX>>
Measuring the Accuracy of Information Retrieval Based Bug Localization Techniques

Bug localization involves using information about a bug to locate affected code sections. Several automated bug localization techniques based on information retrieval (IR) models have been constructed recently. The "gold standard" of measuring an IR technique's accuracy considers the technique's ability to locate a "first relevant method. " However, the question remains -- does finding this single method enable the location of a complete set of affected methods? Previous arguments assume this to be true, however, few analyses of this assumption have been performed. In this paper, we perform a case study to test the reliability of this "gold standard" assumption. To further measure IR accuracy in the context of bug localization, we analyze the relevance of the IR model's "first method returned. " We use various structural analysis techniques to extend relevant methods located by IR techniques and determine accuracy and reliability of these assumptions.
Refrigeration of the western Cordilleran lithosphere during Laramide shallow-angle subduction

The Laramide orogeny has generally been attributed to a shift from normal-angle to shallow-angle subduction beneath the western margin of the North American plate. In addition to important mechanical effects, this shift may have had important thermal effects on the lithosphere in the western Cordillera. Before the Laramide, geothermal gradients in the western Cordillera were probably normal to high because of the presence below of a hot asthenospheric wedge. With the shift to shallow subduction, the wedge was probably expelled and replaced by a cold subducting slab that extended just below the Cordilleran lithosphere. This would shift the western Cordillera into a cold, forearc-like thermal setting dominated by the refrigeration effects of the subducting slab. Thermochronologic data from the Sierra Nevada, Great Basin, Mojave Desert, western Arizona, and perhaps the Colorado Plateau record latest Cretaceous-early Tertiary cooling that may be evidence of such refrigeration. If regional refrigeration occurred, it would have several important implications: (1) metamorphism in the western Cordillera would have waned with the start of the Laramide orogeny; (2) the crust in the western Cordillera would have strengthened and become much more resistant to deformation (e.g., gravitational collapse and extension of thickened crust in the Sevier hinterland would have been impeded); and (3) latest Cretaceous-early Tertiary isotopic cooling ages may not be valid indicators of a period of major regional uplift and unroofng.
Mining metabolic pathways through gene expression

Motivation: An observed metabolic response is the result of the coordinated activation and interaction between multiple genetic pathways. However, the complex structure of metabolism has meant that a compete understanding of which pathways are required to produce an observed metabolic response is not fully understood. In this article, we propose an approach that can identify the genetic pathways which dictate the response of metabolic network to specific experimental conditions. Results: Our approach is a combination of probabilistic models for pathway ranking, clustering and classification. First, we use a non-parametric pathway extraction method to identify the most highly correlated paths through the metabolic network. We then extract the defining structure within these top-ranked pathways using both Markov clustering and classification algorithms. Furthermore, we define detailed node and edge annotations, which enable us to track each pathway, not only with respect to its genetic dependencies, but also allow for an analysis of the interacting reactions, compounds and KEGG sub-networks. We show that our approach identifies biologically meaningful pathways within two microarray expression datasets using entire KEGG metabolic networks. Availability and implementation: An R package containing a full implementation of our proposed method is currently available from http://www.bic. kyoto-u.ac. jp/pathway/timhancock Contact: timhancock@kuicr.kyoto-u.ac. jp Supplementary information: Supplementary data are available at Bioinformatics online.
Policy Recognition in the Abstract Hidden Markov Model

In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.
APAN: Asynchronous Propagate Attention Network for Real-time Temporal Graph Embedding

Limited by the time complexity of querying k-hop neighbors in a graph database, most graph algorithms cannot be deployed online and execute millisecond-level inference. This problem dramatically limits the potential of applying graph algorithms in certain areas, such as financial fraud detection. Therefore, we propose Asynchronous Propagation Attention Network, an asynchronous continuous time dynamic graph algorithm for real-time temporal graph embedding. Traditional graph models usually execute two serial operations: first graph computation and then model inference. We decouple model inference and graph computation step so that the heavy graph query operations will not damage the speed of model inference. Extensive experiments demonstrate that the proposed method can achieve competitive performance and 8.7 times inference speed improvement in the meantime.
Hunter-gatherer: Applying Constraint Satisfaction, Branch-and-bound and Solution Synthesis to Computational Semantics

This work 1 integrates three related AI search techniques and applies the result to processing computational semantics, both in the analysis of source text to discover underlying semantics, as well as in the planning of target text using input semantics. We summarize the approach a s \Hunter-Gatherer:" Branch-and-Bound and Constraint Satisfaction allow u s t o \ h unt d o wn" non-optimal and impossible solutions and prune them from the search space. Solution Synthesis methods then \gather together" all optimal solutions while avoiding exponential complexity. Each of these three general AI techniques will be described. We will look at how h o w they have been used to solve a v ariety of problems. These general techniques were extended or used in novel ways in this project. We will describe these extensions in detail and give examples of how they were applied to computational semantic processing. A major contribution of this work will also be in showing how and why Natural Language is a prime candidate for applying these methods, and how they can enable near-linear time processing. As part of this discussion, we will demonstrate the important result that by c o n verting Text Planning to a constraint satisfaction problem, Means-End type planning can be replaced by an ecient constraint-based search through a complex tree. Finally, w e will examine the results in the light of the Mikrokosmos Machine Translation project. This project is a large-scale Spanish-English MT system implemented at New Mexico State University. We w i l l b e a b l e t o e v aluate the control mechanism presented here against a large corpus of sample texts. In particular, we will show t h a t a s e a r c h space in the billions (or in some cases ga-zillions) can be reduced to a few thousand or less, with a corresponding decrease in run-time.
Analytical Frequency Nadir Prediction Considering Inverter-Based Fast Frequency Response

—This letter develops an analytical frequency nadir prediction method that allows for the consideration of three potential forms of fast frequency response (FFR) provided by inverter-based resources. The proposed method provides fast and accurate frequency nadir estimation after N-1 generation tripping contingencies. Our method is grounded on the closed-form solution for the frequency nadir, which is solved from the second-order system frequency response model considering the governor dynamics and three types of FFR. The simulation results in the IEEE 39-bus system with different types of FFR demonstrate that the proposed method provides accurate and fast prediction for the frequency nadir under various disturbances.
Quality of Service Parameters for Usage with Diameter

This document defines a number of Quality of Service (QoS) parameters that can be reused for conveying QoS information within Diameter. The defined QoS information includes data traffic parameters for describing a token bucket filter, a bandwidth parameter, and a per-hop behavior class object. [STANDARDS-TRACK]
MANAGEMENT OF FURROW IRRIGATION TO IMPROVE WATER USE EFFICIENCY AND SUSTAIN THE GROUNDWATER RESOURCE—PRELIMINARY RESULTS FROM A CASE STUDY IN THE BURDEKIN DELTA

The Burdekin delta region depends on groundwater for irrigation and urban supply. Therefore, any adverse impacts on the groundwater resource are undesirable. Field studies were implemented to improve knowledge and understanding of impacts of current and modified irrigation management practices on crop water use, productivity and long-term sustainability of the Burdekin delta groundwater system. Field trials were established on six sites representing different soil types of the delta comparing conventional (CONV) and best management practice (BMP) irrigation systems. The BMP applied was site specific, and was determined by consultation with BSES extension officers and the individual grower. Sites were instrumented to measure irrigation application, crop water use and the quality of water draining beneath sugarcane crops. Preliminary results highlight that BMP irrigation reduced the amount of water applied to each treatment by an average 15%. BMP irrigation practice increased yields on three of the six sites by about 6%. Three of the six sites have nitratenitrogen values above the ANZECC long-term trigger value of 5mg/L. There were differing trends in nitrate-nitrogen movement throughout the season, with evidence that nitrate-nitrogen movement is greatest shortly after fertiliser application.
Relationship between structures, stress and seismicity in the Charlevoix seismic zone revealed by 3‐D geomechanical models: Implications for the seismotectonics of continental interiors

[1] The Charlevoix seismic zone in the St. Lawrence valley of Quebec is the most active in eastern Canada. The structurally complex region comprises a series of subparallel steeply dipping Iapetan rift faults, superimposed by a 350 Ma meteorite impact structure, resulting in a heavily faulted volume. The elongate seismic zone runs through the crater parallel to the rift. Most large events localize outside the crater and are consistent with slip along the rift faults, whereas background seismicity primarily occurs within the volume of rock bounded by the rift faults within and beneath the crater. The interaction between rift and crater faults is explored using the three-dimensional stress analysis code FLAC3D. The rift faults are represented by frictional discontinuities, and the crater is represented by a bowl-shaped elastic volume of reduced modulus. Differential stresses are slowly built up from boundary displacements similar to tectonic loading. Results indicate that weakening the rift faults produces a stress increase in the region of the crater bounded by the faults. This causes a decrease in stability of optimally oriented faults and may explain the localization of low-level seismicity. Additionally, slip distribution along the rift faults shows that large events localize at the perimeter of the crater and produce focal mechanisms with P axes oblique to the applied stress field, consistent with historic large earthquakes. It is speculated that similar systematic rotation of focal mechanism P axes may be expected along other intraplate rift zones, raising a potential caveat for the use of focal mechanisms for stress estimation in continental interiors.
A new formulation of garnet–clinopyroxene geothermometer based on accumulation and statistical analysis of a large experimental data set

Published experimental data including garnet and clinopyroxene as run products were used to develop a new formulation of the garnet–clinopyroxene geothermometer based on 333 garnet–clinopyroxene pairs. Only experiments with graphite capsules were selected because of difficulty in estimating the Fe3+ content of clinopyroxene. For the calibration, a published subregular‐solution model was adopted to express the non‐ideality of garnet. The magnitude of the Fe–Mg excess interaction parameter for clinopyroxene (WFeMgCpx), and differences in enthalpy and entropy of the Fe–Mg exchange reaction were regressed from the accumulated experimental data set. As a result, a markedly negative value was obtained for the Fe–Mg excess interaction parameter of clinopyroxene (WFeMgCpx = − 3843 J mol−1). The pressure correction is simply treated as linear, and the difference in volume of the Fe–Mg exchange reaction was calculated from a published thermodynamic data set and fixed to be −120.72 (J kbar−1 mol−1). The regressed and obtained thermometer formulation is as follows: where T = temperature, P = pressure (kbar), A = 0.5 Xgrs (Xprp − Xalm − Xsps), B = 0.5 Xgrs (Xprp − Xalm + Xsps), C = 0.5 (Xgrs + Xsps) (Xprp − Xalm), Xprp = Mg/(Fe2+ + Mn + Mg + Ca)Grt, Xalm = Fe/(Fe2+ + Mn + Mg + Ca)Grt, Xsps = Mn/(Fe2+ + Mn + Mg + Ca)Grt, Xgrs = Ca/(Fe2+ + Mn + Mg + Ca)Grt, XMgCpx = Mg/(Al + Fetotal + Mg)Cpx, XFeCpx = Fe2+/(Al + Fetotal + Mg)Cpx, KD = (Fe2+/Mg)Grt/(Fe2+/Mg)Cpx, Grt = garnet, Cpx = clinopyroxene. A test of this new formulation to the accumulated data gave results that are concordant with the experimental temperatures over the whole range of the experimental temperatures (800–1820 °C), with a standard deviation (1 sigma) of 74 °C. Previous formulations of the thermometer are inconsistent with the accumulated data set; they underestimate temperatures by about 100 °C at >1300 °C and overestimate by 100–200 °C at <1300 °C. In addition, they tend to overestimate temperatures for high‐Ca garnet (Xgrs ≈ 0.30–0.50). This new formulation has been tested against previous formulations of the thermometer by application to natural eclogites. This gave temperatures some 20–100 °C lower than previous formulations.
Application of Neural Networks in Power Systems ; A Review

The electric power industry is currently undergoing an unprecedented reform. One of the most exciting and potentially profitable recent developments is increasing usage of artificial intelligence techniques. The intention of this paper is to give an overview of using neural network (NN) techniques in power systems. According to the growth rate of NNs application in some power system subjects, this paper introduce a brief overview in fault diagnosis, security assessment, load forecasting, economic dispatch and harmonic analyzing . Advantages and disadvantages of using NNs in above mentioned subjects and the main challenges in these fields have been explained, too. Keywords—Neural network, power system, security assessment, fault diagnosis, load forecasting, economic dispatch,
Robocup Rescue - Virtual Robots Team STEEL (USA) MrCS - The Multirobot Control System

This paper describes the software system supporting the Carnegie Mellon/Univ. of Pittsburgh team of simulated search and rescue robots in the Robocup Rescue 2010 Virtual Robots competition. Building on the Machinetta agent software, robot command and control is decomposed into a hierarchy of subtasks managed by independent agents both on the robot and colocated with human operators. By encapsulating all robot and human operator interactions into interfaces to these agents, the system can perform with a high level of robustness and reusability. As in previous years, the entire code base is portable and platform-independent, running entirely in Java.
A Language-based Approach for Interoperability of IoT Platforms

— The Internet of Things (IoT) promotes the communication among heterogeneous entities, from small sensors to Cloud systems. However, this is realized using a wide range of communication media and data protocols, usually incompatible with each other. Thus, IoT systems tend to grow as homogeneous isolated platforms, which hardly interact. To achieve a higher degree of interoperability among disparate IoT platforms, we propose a language-based approach for communication technology integration. We build on the Jolie programming language, which allows programmers to easily make the same logic work over disparate communication stacks in a declarative, dynamic way. Jolie currently supports the main technologies from Service-Oriented Computing, such as TCP/IP, Bluetooth, and RMI at transport level, and HTTP and SOAP at application level. As technical result, we integrate in Jolie the two most adopted protocols for IoT communication, i.e., CoAP and MQTT. In this paper, we report our experience and we present high-level concepts valuable both for the general implementation of interoperable systems and for the development of other language-based solutions.