Miracle-dz/UniFAR-FTUs huggingface dataset:
Der Open-access-publikationsserver Der Zbw – Leibniz-informationszentrum Wirtschaft the Open Access Publication Server of the Zbw – Leibniz Information Centre for Economics Does Macroeconomics Need Microeconomic Foundations? Does Macroeconomics Need Microeconomic Foundations? Licensed under a Creati

Terms of use: The ZBW grants you, the user, the non-exclusive right to use the selected work free of charge, territorially unrestricted and within the time limit of the term of the property rights according to the terms specified at → http://www.econstor. eu/dspace/Nutzungsbedingungen By the first use of the selected work the user agrees and declares to comply with these terms of use. Abstract The author argues that it is microeconomics that needs foundations, not macroeconomics. Preferences need to be built on biology, and, in particular, on neuroscience. In contrast, macroeconomics could benefit from rationalizations of aggregate economic phenomena by non-equilibrium statistical physics. Paper submitted to the special issue " Reconstructing Macroeconomics "
Order Matters: Transmission Reordering in Wireless Networks

Modern wireless interfaces support a physical-layer capability called Message in Message (MIM). Briefly, MIM allows a receiver to disengage from an ongoing reception and engage onto a stronger incoming signal. Links that otherwise conflict with each other can be made concurrent with MIM. However, the concurrency is not immediate and can be achieved only if conflicting links begin transmission in a specific order. The importance of link order is new in wireless research, motivating MIM-aware revisions to link-scheduling protocols. This paper identifies the opportunity in MIM-aware reordering, characterizes the optimal improvement in throughput, and designs a link-layer protocol for enterprise wireless LANs to achieve it. Testbed and simulation results confirm the performance gains of the proposed system.
Online Labour Index : Measuring the Online Gig Economy for Policy and Research 1

Labour markets are thought to be in the midst of a dramatic transformation, where standard employment is increasingly supplemented or substituted by temporary work mediated by online platforms. Yet the scale and scope of these changes is hard to assess, because conventional labour market statistics and economic indicators are ill-suited to measuring this ’online gig work’. We present the Online Labour Index (OLI), a new economic indicator that provides the online gig economy equivalent on conventional labour market statistics. It measures the utilization of online labour across countries and occupations by tracking the number of projects and tasks posted on online gig platforms in near-real time. The purpose of this article is to introduce the OLI and describe the methodology behind it. We also demonstrate how it can be used to address previously unanswered questions about the online gig economy. To benefit policymakers, labour market researchers and the general public, our results are published in an interactive online visualisation which is updated daily.
A Lexically-Driven Algorithm for Disfluency Detection

This paper describes a transformationbased learning approach to disfluency detection in speech transcripts using primarily lexical features. Our method produces comparable results to two other systems that make heavy use of prosodic features, thus demonstrating that reasonable performance can be achieved without extensive prosodic cues. In addition, we show that it is possible to facilitate the identification of less frequently disfluent discourse markers by taking speaker style into account.
The controller placement problem for software-defined networks

With the popularization and spread of SDN, the size of SDN networks is increasing; the controller placement is a key problem in software defined networks (SDNs). In this paper, an optimized controller placement method is proposed, which takes into consideration the load of controllers, co-located deployment and propagation latency to determine the optimal number and location of controllers. Firstly, multi-objective genetic algorithm is used to get the connection relationship of controllers and switches, so the network can be partitioned into several domains. Secondly, minimum delay algorithm is adopted to find an optimal location which has a minimizing sum of the distance to the other points in each domain. Finally, in this article, the results obtained from the analytic and experimental performance can evaluate the benefits that could be achieved by deploying our scheme for the SDN controller placement problem.
Vertical structure of ocean surface currents under high winds from massive arrays of drifters

Abstract. Very-near-surface ocean currents are dominated by wind and wave forcing and have large impacts on the transport of buoyant materials in the ocean. Surface currents, however, are under-resolved in most operational ocean models due to the difficultly of measuring ocean currents close to, or directly at, the air–sea interface with many modern instrumentations. Here, observations of ocean currents at two depths within the first meter of the surface are made utilizing trajectory data from both drogued and undrogued Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) drifters, which have draft depths of 60 and 5 cm, respectively. Trajectory data of dense, colocated drogued and undrogued drifters were collected during the Lagrangian Submesoscale Experiment (LASER) that took place from January to March of 2016 in the northern Gulf of Mexico. Examination of the drifter data reveals that the drifter velocities become strongly wind- and wave-driven during periods of high wind, with the pre-existing regional circulation having a smaller, but non-negligible, influence on the total drifter velocities. During these high wind events, we deconstruct the total drifter velocities of each drifter type into their wind- and wave-driven components after subtracting an estimate for the regional circulation, which pre-exists each wind event. In order to capture the regional circulation in the absence of strong wind and wave forcing, a Lagrangian variational method is used to create hourly velocity field estimates for both drifter types separately, during the hours preceding each high wind event. Synoptic wind and wave output data from the Unified Wave INterface-Coupled Model (UWIN-CM), a fully coupled atmosphere, wave and ocean circulation model, are used for analysis. The wind-driven component of the drifter velocities exhibits a rotation to the right with depth between the velocities measured by undrogued and drogued drifters. We find that the average wind-driven velocity of undrogued drifters (drogued drifters) is ∼3.4 %–6.0 % (∼2.3 %–4.1 %) of the wind speed and is deflected ∼5–55∘ (∼30–85∘) to the right of the wind, reaching higher deflection angles at higher wind speeds. Results provide new insight on the vertical shear present in wind-driven surface currents under high winds, which have vital implications for any surface transport problem.
Lock-free Transactional Support for Distributed Data Stores

The data in large-scale data stores is distributed over hundreds or thousands of servers and is updated by hundreds of clients, where node crashes are often frequent. In such environments, supporting transactions is critical to enable the system to cope with partial changes of faulty clients. The behavior of the system when accessed by concurrent transactions is specified by an isolation level. Ideally, the data store provides serializability, in which the concurrent execution of transactions is equivalent to a serial execution of them. Serializability could be ensured by detecting non-serializable executions at run-time and aborting the corresponding transactions. This, however, leads to a high overhead on the data store and lowers the level of concurrency that the system can offer. Alternatively, a recent proposal [4] uses a centralized server to assign a serial order to transactions prior to their execution. The efficient implementation of this approach based on lowgranularity locks, requires a prior knowledge of the rows that will be touched by a transaction, obtaining which becomes complex as well as inefficient in the case of nontrivial transactions [4]. To allow for high concurrency between transactions, commercial data stores [3] often implement isolation levels that are weaker than serializability, such as snapshot isolation (SI) [1]. SI guarantees that all reads of a transaction are performed on a snapshot of the database that corresponds to a valid database state with no concurrent transaction. To implement SI, the database maintains multiple versions of the data and the transactions observe different versions of the data depending on their start time. Implementations of SI have the advantage that writes of a transaction do not block the reads of others. Two concurrent transactions still conflict if they write into the same data item. The conflict must be detected by SI implementation, and at least one of the transactions must abort. To be able to detect the conflicts, the SI implementation must have access to transactional data such as start and commit time of transactions, and the list of transactions that have written into a data item. Due to the large volume of data
University of Birmingham GloDyNE

—Learning low-dimensional topological representation of a network in dynamic environments is attracting much attention due to the time-evolving nature of many real-world networks. The main and common objective of Dynamic Network Embedding (DNE) is to efﬁciently update node embeddings while preserving network topology at each time step. The idea of most existing DNE methods is to capture the topological changes at or around the most affected nodes (instead of all nodes) and accordingly update node embeddings. Unfortunately, this kind of approximation, although can improve efﬁciency, cannot effectively preserve the global topology of a dynamic network at each time step, due to not considering the inactive sub-networks that receive accumulated topological changes propagated via the high-order proximity. To tackle this challenge, we propose a novel node selecting strategy to diversely select the representative nodes over a network, which is coordinated with a new incremental learning paradigm of Skip-Gram based embedding approach. The extensive experiments show GloDyNE, with a small fraction of nodes being selected, can already achieve the superior or comparable performance w.r. t. the state-of-the-art DNE methods in three typical downstream tasks. Particularly, GloDyNE signiﬁcantly outperforms other methods in the graph reconstruction task, which demonstrates its ability of global topology preservation.
Distributed Mobility Management solutions for next mobile network architectures

—The architecture of current operator infrastructures is being challenged by the non-stopping growing demand of data hungry services appearing every day. Deployed operator networks are focused on providing mobile services to users, relying on hierarchical architectures and protocols, such as Proxy Mobile IP, which force all the trafﬁc to and from the terminals to traverse speciﬁc gateways connecting the network to the Internet. Such approaches pose several scalability problems when dealing with high demanding users. This has triggered to study how to provide mobility services in a ﬂatter network architecture, enabling local access to the Internet, in order to reduce the amount of trafﬁc traversing the core. Due to the novelty of these ideas, several possible solutions are being discussed and evaluated. This work aims at comparing the three more prominent families of solutions currently discussed at the IETF, providing an analysis of their scalability characteristics, both from an analytic and experimental point of view.
Generating Deep Networks Explanations with Robust Attribution Alignment (Supplementary Material)

In the submitted paper, we used PGD-`2 trained model to generate robust attribution maps, then leveraged the saliency maps of PGD adversarially trained models to align the generated attribution maps. The benefits of robustly trained models have been studies by several previous research. For example, (Tsipras et al., 2018) showed that the saliency maps of adversarially robust models align well with human perception. (Zhang and Zhu, 2019) demonstrated that the representation learned by robust models are more biased towards shape-based features and alleviates texture-based features. (Margeloiu et al., 2020) showed that adversarially trained CNNs are more interpretable for medical imaging diagnosis. Since previous works all investigated the influence of PGD adversarial training on the interpretable of CNNs, it raises the question whether the phenomenon can be generalized to other adversarial training approaches. In this section, we compare PGD training with other two adversarial defense approaches: Defensive distillation and Jacobian regularization, and see whether other adversarial defense approaches can also be used to generate faithful explanations.
ConvDip: A convolutional neural network for better M/EEG Source Imaging

1 EEG and MEG are well-established non-invasive methods in neuroscientific research and clinical diagnostics. 2 Both methods provide a high temporal but low spatial resolution of brain activity. In order to gain insight 3 about the spatial dynamics of the M/EEG one has to solve the inverse problem, which means that more than 4 one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. 5 Artificial neural networks have been previously used successfully to find either one or two dipoles sources. 6 These approaches, however, have never solved the inverse problem in a distributed dipole model with more 7 than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture that 8 solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that 9 (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms 10 state-of-the-art methods (eLORETA and LCMV beamforming) on all focused performance measures. (3) 11 It is more flexible when dealing with varying number of sources, produces less ghost sources and misses 12 less real sources than the comparison methods. (4) It produces plausible inverse solutions for real-world 13 EEG recordings and needs less than 40 ms for a single forward pass. Our results qualify ConvDip as an 14 efficient and easy-to-apply novel method for source localization in EEG and MEG data, with high relevance 15 for clinical applications, e.g. in epileptology and real time applications. 16
ConvDip: A convolutional neural network for better M/EEG Source Imaging

2 Introduction 2 The M/EEG and the inverse problem . 2 Methods 4 Training Data – simulations of sources and EEG . 4 EEG simulation . 4 Convolutional neural network . 5 Implementation of eLORETA & LCMV . 7 Performance metrics . 8 Results 8 Variance explained . 8 Mean localization error . 9 Normalized mean squared error . 9 Numbers of missed sources and ghost sources . 9 Covariates of source reconstruction success . 9 Discussion 12 Overview . 12 General discussion of results . 12 Limitations . 14 Outlook . 15 Appendix 15 A: Simulated sample . 15 B: Real sample . 15
Usability and Expressiveness in Database Keyword Search: Bridging the Gap

Business processes in modern enterprises require interlinking critical business information distributed across structured and unstructured data sources. This interlinking process is often initiated by a users' informational need represented as a keyword query, either created manually by the user or automatically extracted from an unstructured document. Although keyword queries possess high usability, they lack expressiveness to precisely describe the user's informational need and therefore may return irrelevant or biased results. In this paper we present challenges in database keyword search which have not been sufficiently addressed by the existing approaches including aspects of query expressiveness, relevance and incremental refinement. We discuss our plans to address these problems bridging the gap between usability and expressiveness in database keyword search. Overcoming these challenges will facilitate effective disambiguation of a broader range of queries and thus increase usability of database systems for end users.
Supervised Learning Method for the Prediction of Subcellular Localization of Proteins Using Amino Acid and Amino Acid Pair Composition

Background: Occurrence of protein in the cell is an important step in understanding its function. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. Most studied methods for prediction of subcellular localization of proteins are signal peptides, the location by sequence homology, and the correlation between the total amino acid compositions of proteins. Taking amino-acid composition and amino acid pair composition into consideration helps improving the prediction accuracy. Results: We constructed a dataset of protein sequences from SWISS-PROT database and segmented them into 12 classes based on their subcellular locations. SVM modules were trained to predict the subcellular location based on amino acid composition and amino acid pair composition. Results were calculated after 10-fold cross validation. Radial Basis Function (RBF) outperformed polynomial and linear kernel functions. Total prediction accuracy reached to 71.8% for amino acid composition and 77.0% for amino acid pair composition. In order to observe the impact of number of subcellular locations we constructed two more datasets of nine and five subcellular locations. Total accuracy was further improved to 79.9% and 85.66%. Conclusions: A new SVM based approach is presented based on amino acid and amino acid pair composition. Result shows that data simulation and taking more protein features into consideration improves the accuracy to a great extent. It was also noticed that the data set needs to be crafted to take account of the distribution of data in all the classes. from The 2007 International Conference on Bioinformatics & Computational Biology (BIOCOMP'07) Las Vegas, NV, USA. 25-28 June 2007 Published: 20 March 2008 BMC Genomics 2008, 9(Suppl 1):S16 doi:10.1186/1471-2164-9-S1-S16 <supplement> <title> <p>The 2007 International Conference on Bioinformatics &amp; Computational Biology (BIOCOMP'07)</p> </title> <editor>Jack Y Jang, Mary Qu Yang, Mengxia (Michelle) Zhu, Youping Deng and Hamid R Arabnia</editor> <note>Research</note> </supplement> This article is available from: http://www.biomedcentral. com/1471-2164/9/S1/S16 © 2008 Habib et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. BMC Genomics 2008, 9(Suppl 1):S16 http://www.biomedcentral. com/1471-2164/9/S1/S16
Tracing mangrove forest dynamics of Bangladesh using historical Landsat data

Present, accurate, and reliable estimation of mangrove forests in Bangladesh is limited. Former estimation of mangroves extent and density has been more or less restricted to Sundarbans and do not represent the whole country. In this study, a time series analysis was performed using Landsat images from four epochs, namely: 1976 (Landsat MSS), 1989 (Landsat TM), 2000 (Landsat ETM+) and 2015 (Landsat L8 OLI) to accurately quantify mangroves extent and density change/variation in the study area. An atmospheric correction using the Dark Object Subtraction method and a radiometric normalization using pseudo-invariant features was performed to reduce haze and sedimentation effects of the images. A standard approach to study changes in forest characteristics is to perform a time series analysis using images that have undergone a supervised classification. Results are indicating a gradual increase of forest area in most parts of the study area. Overall, the areal coverage increased by 3.10% (58140 ha) from 1976 to 2015, where 1.79% (58140 ha) of this increase took place from 2000 to 2015. The Sundarbans area turned out to be an exception. There, the mangrove forest area remained almost unchanged, although a little change (decrease by 1.03%) was found between 2000 and 2015. The study also claimed that one of the oldest mangrove forests in Bangladesh (Chakaria Sundarbans) had lost 4135 ha (32%) of forest area between 1976 and 1989. Similarly, a vegetation index (NDVI) analysis suggested that not only the area but also the density of the mangroves has changed over the years. The forests seem to have been denser in 1976 than in 1989. In 2000 the density appeared to have increased again, while decreased again in 2015. The study also found a substantial increase between 1989 and 2000 while a considerable density decreases in the Sundarbans region between 2000 and 2015. However, Mangroves area change was not significant in the context of classification uncertainty. A little error source was found due to the similar spectral reflectance between mangroves and non-mangrove vegetation, for example, in Patuakhali-Bhola. An accuracy assessment was performed using confusion matrices, showed maximum likelihood algorithm produce a better result for mangrove classes than other Land use/ Land cover classes. The overall accuracy of Landsat 8 OLI, ETM+, TM, and the MSS classified images (five classes) were found to be 97%, 87%, 80%, and 80% respectively, with Kappa values of 0.96, 0.82, 0.73, 0.74. Several possible factors such as cyclones, sedimentation and erosion, deforestation, shrimp and salt farming, and mangrove plantation were identified, which might be responsible for mangrove variations/changes in Bangladesh. (Less)
An Implementation of the Look-Ahead Lanczos Algorithm for Non-Hermitian Matrices

The nonsymmetric Lanczos method can be used to compute eigenvalues of large sparse non-Hermitian matrices or to solve large sparse non-Hermitian linear systems. However, the original Lanczos algorithm is susceptible to possible breakdowns and potential instabilities. We present an implementation of a look-ahead version of the Lanczos algorithm which overcomes these problems by skipping over those steps in which a breakdown or near-breakdown would occur in the standard process. The proposed algorithm can handle look-ahead steps of any length and is not restricted to steps of length 2, as earlier implementations are. Also, our implementation has the feature that it requires roughly the same number of inner products as the standard Lanczos process without look-ahead. Categories and Subject Descriptors: G.1. 3 [Numerical Analysis]: Numerical Linear Algebra{eigenvalues; linear systems (direct and iterative methods); sparse and very large systems; G.4 [
A Solution for SLAM through Augmenting Vision and Range Information

This paper proposes a method for augmenting the information of a monocular camera and a range finder. This method is a valuable step towards solving the SLAM problem in unstructured environments free from problems of using encoders’ data. Proposed algorithm causes the robot to benefit from a feature-based map for filtering purposes, while it exploits an accurate motion model, based on point-wise raw range scan matching rather than unreliable feature-based range scan matching, in unstructured environments. Moreover, robust loop closure detection procedure is the other consequence of this method. Experiments with a low-cost IEEE 1394 webcam and a range finder illustrate the effectiveness of the proposed method in drift-free SLAM at loop closing motions in unstructured environments.