Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of land surface temperatures: comparison of two climate models and satellite retrievals

Abstract. Recently there has been significant progress in the retrieval of land surface temperature from satellite observations. Satellite retrievals of surface temperature offer several advantages, including broad spatial coverage, and such data are potentially of great value in assessing general circulation models of the atmosphere. Here, retrievals of the land surface temperature over the contiguous United States are compared with simulations from two climate models. It is found that these two models generally simulate the diurnal range of surface temperature realistically, but show significant warm biases during the summer. The models' diurnal cycle of surface temperature is related to their surface flux budgets. Differences in the diurnal cycle of the surface flux budget between the models are found to be more pronounced than those in the diurnal cycle of surface temperature.
Self-adjoint S-parameter sensitivities for lossless homogeneous TLM problems: Research Articles

We present a novel efficient algorithm for the estimation of S-parameter sensitivities in homogeneous and lossless transmission line modelling (TLM) problems. Our approach estimates S-parameter adjoint-based sensitivities without actually carrying out any adjoint simulation. By applying a transformation to the original TLM simulation we establish an isomorphism between the original and the adjoint problem. The unique properties of the TLM node in a lossless and homogeneous problem are also exploited in establishing the isomorphism. For an electromagnetic structure with Np ports, only the Np original simulations utilized in evaluating the S-parameters are required to estimate their sensitivities as well. Our novel approach is illustrated through estimating S-parameter sensitivities with respect to waveguide discontinuities. Good match is obtained between our sensitivity estimates and those calculated using finite differences at the response level. Copyright © 2005 John Wiley & Sons, Ltd.
MODFIED GLITCH-FREE AND CASCADABLE ADIABATIC LOGIC CIRCUITS

Conventional CMOS logic circuit design approaches rely on charging the output capacitive nodes to the power supply Vdd or discharging it to ground.This design approach has been widely used.For Low power applications, although there are many techniques both at circuit level and system level, a very fundamental source of energy dissipation is the discharge of the capacitor to ground.Every time a capacitor is discharged to ground, an amount of energy = 1 2 CV 2 stored in the capacitor is lost.This loss of energy can be prevented if instead of discharging the capacitor to ground,the charge or energy stored can be recycled. Another way of reducing the power consumption is to design the circuit in such a way that the charging of the capacitive node takes place very slowly.It has been observed that slowly charging the capacitor requires lesser energy than abrupt charging.Adiabatic circuits use the above two methods viz. slow charging and discharging, and charge recycling to minimize the power consumed.Several Adiabatic designs have been proposed in literature.Most of them achieve significant power savings in comparision to conventional circuits.Drawbacks of these circuits include complex circuit design for achieving simple operations,requirement of multiple clocks and/or requirement of complimentary input signals for controlling the charging/discharging process.The Current work is based on an existing adiabatic logic style known as GFCAL (Glitch Free and Cascadable Adiabatic Logic )which is simple and doesnt require complimentary signals or complex clocking.The GFCAL has a drawback that it uses junction diodes for controlling the charging and discharging of output nodal capacitance.Junction diodes are difficult to fabricate in a CMOS process and they are responsible for power dissipation due to the cut-in voltage drop across them. This work improves the circuit by replacing the diodes with transistors that control the charging and discharging process.The efficiency has gone up from 50% for GFCAL to about 60% for the modified circuit in comparison to conventional CMOS logic circuits.The modified design is found to work satisfactorily, is cascadable and simple block level implementations like adders have been demonstrated and verified to work with lower power consumption than conventional CMOS circuits.
Mapping Bug Reports to Relevant Source Code Files Based on the Vector Space Model and Word Embedding

Although software bug localization in software maintenance and evolution is cumbersome and time-consuming, it is also very important, especially for large-scale software projects. To lighten the workload of developers, researchers have developed various information retrieval (IR)-based bug localization models for automated software support. In this paper, we propose a new method that reduces the time required for bug localization. First, the surface lexical similarity between a bug report and source code file is calculated based on the vector space model. Second, to address the lexical gap between the programming language and natural language, the word vector is used to calculate the semantic similarity between the bug report and source code file. Then, we use surface lexical and semantic similarity to calculate the total similarity for detecting buggy source code files. Our experimental word vectors are derived from Skip-gram and GloVe model training. We select an optimal 100 dimensional word vector for bug localization by evaluating it on four open source software examples. Finally, our experimental results show that our method outperforms classical IR-based methods in locating relevant source code files based on several indicators.
Power-aware compression scheme for multiple scan-chain

As test data continues to grow quickly, test cost also increases. For the sake of decreasing the test cost, this article presents a new data dependency compression scheme for large circuit which is based on multiple scan chains. We propose new compression architecture with fixed length for running tests. In results, when the complexity of a VLSI circuit is growing, the number of input pins for testing is very low. Since test data in power aware is not changed frequently, we use a selector to filter the unnecessary status and buffers to hold the back data. We also propose a new algorithm to assign multiple scan chains and an improved linear dependency compute method to find the hidden dependency between scan chains. Experimental results show that the proposed method can reduce both test data volume and shift-in power.
GRID Services as Learning Agents: Steps Towards Induction of Communication Protocols

In this article, we outline some issues in agent interaction on the Web, which is the center point of supporting the needs of fully-realized learning GRID in the future. Of particular importance is conversation support, with its core element, communication protocols. We propose to construct communication protocols through concept learning of services generated on the Web. The proposed approach incorporates a machine learning model into a conversation environment for the induction of communication protocols.
Change in Age Pattern of Persons with Dengue , Northeastern Brazil

1. Queenan AM, Bush K. Carbapenemases: the versatile β-lactamases. Clin Microbiol Rev. 2007;20:440–58. DOI: 10.1128/ CMR.00001-07 2. Clinical and Laboratory Standards Institute. Performance standards for antimicrobial susceptibility testing: 20th informational supplement (update 2010 Jun). Document M100–S20-U. Wayne (PA): The Institute; 2010. 3. Yong D, Toleman MA, Giske CG, Cho HS, Sundman K, Lee K, et al. Characterization of a new metallo-β-lactamase gene, blaNDM-1, and a novel erythromycin esterase gene carried on a unique genetic structure in Klebsiella pneumoniae sequence type 14 from India. Antimicrob Agents Chemother. 2009;53:5046–54. DOI: 10.1128/AAC.00774-09 4. Jacoby GA. AmpC β-lactamases. Clin Microbiol Rev. 2009;22:161–81. DOI: 10.1128/CMR.00036-08 5. Chu YW, Afzal-Shah M, Houang ET, Palepou MI, Lyon DJ, Woodford N, et al. IMP-4, a novel metallo-beta-lactamase from nosocomial Acinetobacter spp. collected in Hong Kong between 1994 and 1998. Antimicrob Agents Chemother. 2001;45:710–4. DOI: 10.1128/ AAC.45. 3.710-714.2001 6. Aubron C, Poirel L, Ash RJ, Nordmann P. Carbapenemase-producing Enterobacteriaceae, US rivers. Emerg Infect Dis. 2005;11:260–4. 7. Yu YS, Du XX, Zhou ZH, Chen YG, Li LJ. First Isolation of blaIMI-2 in an Enterobacter cloacae clinical isolate from China. Antimicrob Agents Chemother. 2006;50:1610–1. DOI: 10.1128/ AAC.50. 4.1610-1611.2006 8. Poirel L, Lagrutta E, Taylor P, Pham J, Nordmann P. Emergence of metallobeta-lactamase NDM-1-producing multidrug resistant Escherichia coli in Australia. Antimicrob Agents Chemother. 2010;54:4914–6. DOI: 10.1128/AAC. 00878-10 9. Kumarasamy KK, Toleman MA, Walsh TR, Bagaria J, Butt F, Balakrishnan R, et al. Emergence of a new antibiotic resistance mechanism in India, Pakistan, and the UK: a molecular, biological, and epidemiological study. Lancet Infect Dis. 2010;10:597–602. DOI: 10.1016/S14733099(10)70143-2 10. Bilavsky E, Schwaber MJ, Carmeli Y. How to stem the tide of carbapenemaseproducing Enterobacteriaceae? : proactive versus reactive strategies. Curr Opin Infect Dis. 2010;23:327–31. DOI: 10.1097/ QCO.0b013e32833b3571
Statistical Analysis of Depot-wise PMPML Data

The study deals with the Depot-wise assessment of buses for the months of 2017 using the data available on the Pune Municipal Corporation Open Data Portal. Data consists of statistical report of 13 depots on various bus transport related parameters. We rank the bus depots on the basis of efficiency, performance and earnings per bus per day. Further, we also compare the buses under BRT system with the regular buses under PMPML. ‘Smart City-Villages track’
Extracting Adverse Drug Event Information with Minimal Engineering

In this paper we describe an evaluation of the potential of classical information extraction methods to extract drug-related attributes, including adverse drug events, and compare to more recently developed neural methods. We use the 2018 N2C2 shared task data as our gold standard data set for training. We train support vector machine classifiers to detect drug and drug attribute spans, and pair these detected entities as training instances for an SVM relation classifier, with both systems using standard features. We compare to baseline neural methods that use standard contextualized embedding representations for entity and relation extraction. The SVM-based system and a neural system obtain comparable results, with the SVM system doing better on concepts and the neural system performing better on relation extraction tasks. The neural system obtains surprisingly strong results compared to the system based on years of research in developing features for information extraction.
Parameter Estimation For Blind lq Hyperspectral Unmixing Using Bayesian Optimization

Blind hyperspectral unmixing is the task of estimating both the pure material spectra (endmembers), and the abundances in hyperspectral images. The performance of hyperspectral unmixing methods is very often dependent on tuning parameters. Accurately estimating these parameters is computationally intensive and this can severely limit the complexity of the underlying model. In this paper, we propose using Bayesian optimization to estimate tuning parameters for blind hyperspectral unmixing. Using real data, we show that the proposed method can be successfully applied to estimate tuning parameters for hyperspectral unmixing. Also, we show that increasing the number of tuning parameters can improve the unmixing results.
A Review on Lightweight Node Authentication Algorithms in Wireless Sensor Networks

Wireless sensor nodes are prone to several critical attacks due to the distributed placement of sensor nodes in unattended environments. Sensor nodes are the core part of the WSN therefore, securing these sensor nodes are the utmost important task. Without security, sensor nodes are easily compromised by the attacker and then the attacker get the access to the sensor networks, which result in the total degradation of the WSN. Therefore, an authentication mechanism is needed to check the genuineness of sensor node in the network, before communication actually takes place. The main challenge in WSN security is to design an authentication algorithm that is lightweight because sensor nodes have low energy and low memory. Many works have so far been discussed towards securing WSN in terms of lightweight node authentication algorithms. This paper, presented the various lightweight node authentication algorithms in WSN. Also, their performance is compared in terms of communication overhead, computational overhead, memory overhead & security level.
Service Restoration Using Different Types of DGs Considering Dynamic Characteristics

After major outages of distribution system, critical loads on distribution feeders can be locally restored by multiple types of distributed generators (DGs) before the utility power is available. However, due to the low inertia and small capacity of DGs, it may cause large disturbances when conducting switching operations, which may cause failure of restoration. In addition, dynamic characteristics of different types of DGs are different and should be considered in implementation of restoration strategies. This paper proposes a two-step restoration method to determine the sequence of switching operations, including startup of DGs, line switching operations and load pick-up, to mitigate transients caused by the switching operations. First, the operating point of the post-restoration system is identified by formulating the problem as a relaxed mixed-integer second-order conic program. Then, a restoration rule is proposed to determine the sequence of switching operations to reach the operating point. The entropy weight method is used to find the sequence of the startup of DGs considering the dynamic features of each DG. A 32-bus test feeder with several types of DGs is utilized to demonstrate the effectiveness of the proposed method.
A Semantic Feature Extraction Method For Hyperspectral Image Classification Based On Hashing Learning

Aiming at extraction the semantic feature of hyperspectral image, a semantic feature extraction method based on supervised hashing learning is proposed in the paper. Firstly, a set of hash functions are defined based on hyperspectral target subspace constraint which take into account the locality and discriminability between classes. Secondly, a semantic subspace is obtained through discriminative learning algorithm by the label information of hyperspectral image. Finally, the sparse binary hash codes are obtained by eigenvector mapping which represents the semantic features of targets. In the method, hashing learning uses the similarity binary codes to express the similarity of the original hyperspectral spatial data, and it uses both spectral features and spatial neighborhood features, which leads to strong distinguishing ability on a certain class of hyperspectral image. The experimental on real hyperspectral images classification results show that the fusion of the extracted semantic features with the original hyperspectral features can effectively improve the classification accuracy.
A General Control Variate Method

The control variate method is a popular variance reduction technique used in Monte Carlo methods, which are frequently used to price complex derivatives. This technique is used in much financial literature to exploit information about the errors in estimates of known quantities – which are usually values of the derivatives that can be analytically priced – to reduce the error for estimating an unknown quantity, which is usually the price of a complex derivative of interest. This paper generalizes the core idea of the control variate method so that it can be applied to reduce the pricing errors incurred in many numerical pricing methods, such as the tree method, the characteristic-function-based pricing method, and the convolution-based pricing method. For example, a numerical method for pricing a complex derivative, say an Asian option, may need to calculate the density function of the average price of the underlying asset in the convolution-based pricing method. However, this function can not be analytically solved and must be numerically approximated. Thus we find another analytical function that is close to the function of interest and exploit information about the errors in estimates of the analytical function to reduce the error for estimating the function of interest. Numerical results shows that our approach can effectively increase the pricing efficiency of many numerical pricing methods. JEL classification: C02; G13
Machine translation of conversation on the digitized battlefield

The technology of the information age and the digitized battlefield apply not only to machine-to-machine communications but support an unprecedented advancement in communication across human language barriers. Modern military operations, including "peace-keeping, " coordination with joint and multinational forces, and operations other than war, often involve the interaction of troops with indigenous peoples where language issues can have a serious impact on mission success. The Audio Voice Translation Guide System (Tongues) is a mobile, networked, speech-to-speech machine translation system that both benefits from and contributes to the digitized battlefield. The Tongues prototype runs on a commercial off-the-shelf (COTS) mobile computing platform, capable of fitting in a cargo pocket, and running a Microsoft Windows operating system. It supports real time interactive translation of bilingual conversation, within and without the selected target domain, with the highest quality appearing within the target domain. Tongues has a modular architecture, allowing for easy upgrades to the speech and language engines and multiple conversational domains and languages. The prototype system supports English/Croatian conversation for deployed US Army chaplains, a domain that includes religious support, humanitarian aid, and operations other than war. The system supports text-entry as well as speech input and provides feedback for verification of translation accuracy. The Tongues system can automatically capture and store textual and audio conversation transcripts in formats that comply with the Joint Technical Architecture-Army (JTA-A). It supports a wide variety of JTA-A compliant means for transferring that information to other information systems. Tongues can run in a stand-alone mode, or it can make a wireless network connection to remote machine translation servers, allowing for increased performance and flexibility.
Emotion modeling in speech production using emotion space

This paper describes the modeling scheme of emotions appearing in a speech production by using a neural network and the synthesizing technique of emotional condition from neutral speech. To model emotion conditions in speech production, emotion space is introduced. Emotion space can represent the emotion condition appearing in speech production in a two dimensional space and realize both mapping and inverse mapping between the emotion condition and the speech production. We developed the emotional speech synthesizer to synthesize emotional speech. The emotional speech synthesizer has an ability to synthesize emotional speech by modifying neutral speech in its timing, pitch and intensity. This paper also describes the subjective evaluation result of synthesized speech from the emotion space.
Prediction of radio wave propagation in four blocks of New York City using 3D ray tracing

A three dimensional ray tracing model for microcellular radio wave propagation scenarios is presented based on ray optics and the uniform geometrical theory of diffraction. Propagation mechanisms for specular reflection, specular transmission, diffraction and diffuse scattering are included in the model. A semideterministic model is also used to provide complementary predictions in specific situations expanding the model's capabilities to include interference analysis in multiple base station environments. The accuracy of predictions is examined with measurement results in an urban local loop environment in a residential area in New York City. These results include propagation loss and polarization effects for different transmit/receive configurations in the immediate surroundings of a trial site for wireless loop applications.
Data-driven, data-intensive computing for modelling and analysis of biological networks: application to bioethanol production

Modelling biological networks is inherently data-driven and data-intensive. The combinatorial nature of this type of modelling, however, requires new methods capable of dealing with the enormous size and irregularity of the search. Searching via ‘backtracking’ is one possible solution that avoids exhaustive searches by constraining the search space to the subspace of feasible solutions. Despite its wide use in many combinatorial optimization problems, there are currently few parallel implementations of backtracking capable of effectively dealing with the memory-intensive nature of the process and the extremely unbalanced loads present. In this paper, a parallel, scalable, and memory-efficient backtracking algorithm within the context of maximal clique enumeration is presented, and its applicability to large-scale biological networks aimed at studying the mechanisms for efficient bioethanol production is discussed.
Gatekeeping Under Time Pressure : An Empirical Study of Hospital Admission Decisions in the Emergency

We study admission and discharge errors made by physicians in a congested emergency department (ED) using a data set comprising more than 600,000 visits over a seven-year period. We find that when the ED becomes busier, physicians make increasingly more admission errors but also fewer discharge errors. This leads to a bullwhip-type e↵ect: demand surges in the ED leads to relatively greater demand pressures in the hospital, as more patients are admitted unnecessarily. While this behavior can be rationalized at the level of the individual ED physician, who deploys a “safety first” principle and admits patients in case of doubt, the overall system e↵ect is detrimental. In particular, unnecessary use of specialist hospital services leads to higher system costs, while a higher level of hospital occupancy is also known to have detrimental e↵ects on patient outcomes e.g. longer lengths of stay and higher mortality rates. Having established the bullwhip phenomenon, we consider whether replacing the direct one-stage (ED physician to hospital) referral system with a two-stage process that also allows ED physicians to stream patients to an intermediate “semispecialist” referral unit – in our context operationalized by a Clinical Decisions Unit – when diagnostic uncertainty is high can improve care coordination in gatekeeping systems. We find that such a unit can significantly reduce the negative demand-propagation e↵ect, and the two-stage process to be significantly more e↵ective than simply adding ED capacity.
Experimental and Numerical Modeling of Storm Surge and Tsunami Effects on Structures by

The joint research work at Oregon State University and the University of Hawaii at Manoa on experimental and numerical modeling of storm surge and tsunami interaction with structures for both model basin test and prototype scenarios are described in this paper. The numerical modeling capabilities for storm surge, and storm waves as well as tsunami propagation and runup using a nested-grid approach are presented first. Modeling capabilities at Oregon State University for coupled fluid-structure interaction using the computational fluid dynamics and computational structural dynamics approaches are then delineated. A roadmap to combine the computational and experimental capabilities for the analysis and design of prototype structures in future research is finally presented.