nils-herrmann/clean_scirepeval huggingface dataset:
Learning Sparse Neural Networks through L0 Regularization

We propose a practical method for L0 norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known model selection criteria, are special cases of L0 regularization. However, since the L0 norm of weights is non-differentiable, we cannot incorporate it directly as a regularization term in the objective function. We propose a solution through the inclusion of a collection of non-negative stochastic gates, which collectively determine which weights to set to zero. We show that, somewhat surprisingly, for certain distributions over the gates, the expected L0 regularized objective is differentiable with respect to the distribution parameters. We further propose the hard concrete distribution for the gates, which is obtained by “stretching” a binary concrete distribution and then transforming its samples with a hard-sigmoid. The parameters of the distribution over the gates can then be jointly optimized with the original network parameters. As a result our method allows for straightforward and efficient learning of model structures with stochastic gradient descent and allows for conditional computation in a principled way. We perform various experiments to demonstrate the effectiveness of the resulting approach and regularizer.
Interference Management in 5G Reverse TDD HetNets With Wireless Backhaul: A Large System Analysis

This work analyzes a heterogeneous network (HetNet), which comprises a macro base station (BS) equipped with a large number of antennas and an overlaid dense tier of small cell access points (SCAs) using a wireless backhaul for data traffic. The static and low mobility user equipment terminals (UEs) are associated with the SCAs while those with medium-to-high mobility are served by the macro BS. A reverse time division duplexing (TDD) protocol is used by the two tiers, which allows the BS to locally estimate both the intra-tier and inter-tier channels. This knowledge is then used at the BS either in the uplink (UL) or in the downlink (DL) to simultaneously serve the macro UEs (MUEs) and to provide the wireless backhaul to SCAs. A concatenated linear precoding technique employing either zero-forcing (ZF) or regularized ZF is used at the BS to simultaneously serve MUEs and SCAs in DL while nulling interference toward those SCAs in UL. We evaluate and characterize the performance of the system through the power consumption of UL and DL transmissions under the assumption that target rates must be satisfied and imperfect channel state information is available for MUEs. The analysis is conducted in the asymptotic regime where the number of BS antennas and the network size (MUEs and SCAs) grow large with fixed ratios. Results from large system analysis are used to provide concise formulae for the asymptotic UL and DL transmit powers and precoding vectors under the above assumptions. Numerical results are used to validate the analysis in different settings and to make comparisons with alternative network architectures.
A real-time extension to the Android platform

Android belongs to the leading operating systems for mobile devices, e.g. smartphones or tablets. The availability of Android's source code under general public license allows interesting developments and useful modifications of the platform for third parties, like the integration of real-time support. This paper presents an extension of Android improving its real-time capabilities, without loss of original Android functionality and compatibility to existing applications. In our approach we apply the RT_PREEMPT patch to the Linux kernel, modify essential Android components like the Dalvik virtual machine and introduce a new real-time interface for Android developers. The resulting Android system supports applications with real-time requirements, which can be implemented in the same way as non-real-time applications.
Improving the Dynamic Response of a Flying-Capacitor Three-Level Buck Converter

This study presents a modified flying capacitor three-level buck dc-dc converter with improved dynamic response. First, the limitations in the transient response improvement of the conventional and three-level buck converters are discussed. Then, the three-level buck converter is modified in a way that it would benefit from a faster dynamic during sudden changes in the load. Finally, a controller is proposed that detects load transients and responds appropriately. In order to verify the effectiveness of the modified topology and the proposed transient controller, a simulation model and a hardware prototype are developed. Analytical, simulation, and experimental results show a significant dynamic response improvement.
Use of turbine inertia for power smoothing of wind turbines with a DFIG

Fluctuating power is of serious concern in grid connected wind systems and energy storage systems are being developed to help alleviate this. This paper describes how additional energy storage can be provided within the existing wind turbine system by allowing the turbine speed to vary over a wider range. It also addresses the stability issue due to the modified control requirements. A control algorithm is proposed for a typical doubly fed induction generator (DFIG) arrangement and a simulation model is used to assess the ability of the method to smooth the output power. The disadvantage of the method is that there is a reduction in energy capture relative to a maximum power tracking algorithm. This aspect is evaluated using a typical turbine characteristic and wind profile and is shown to decrease by less than 1%. In contrast power fluctuations at intermediate frequency are reduced by typically 90%.
Brain tumor segmentation using Cuckoo Search optimization for Magnetic Resonance Images

Nature enthused algorithms are the most potent for optimization. Cuckoo Search (CS) algorithm is one such algorithm which is efficient in solving optimization problems in varied fields. This paper appraises the basic concepts of cuckoo search algorithm and its application towards the segmentation of brain tumor from the Magnetic Resonance Images (MRI). The human brain is the most complex structure where identifying the tumor like diseases are extremely challenging because differentiating the components of the brain is complex. The tumor may sometimes occur with the same intensity of normal tissues. The tumor, edema, blood clot and some part of brain tissues appear as same and make the work of the radiologist more complex. In general the brain tumor is detected by radiologist through a comprehensive analysis of MR images, which takes substantially a longer time. The key inventiveness is to develop a diagnostic system using the best optimization technique called the cuckoo search, that would assist the radiologist to have a second opinion regarding the presence or absence of tumor. This paper explores the CS algorithm, performing a profound study of its search mechanisms to discover how it is efficient in detecting tumors and compare the results with the other commonly used optimization algorithms.
Tracking by Sampling Trackers

We propose a novel tracking framework called visual tracker sampler that tracks a target robustly by searching for the appropriate trackers in each frame. Since the real-world tracking environment varies severely over time, the trackers should be adapted or newly constructed depending on the current situation. To do this, our method obtains several samples of not only the states of the target but also the trackers themselves during the sampling process. The trackers are efficiently sampled using the Markov Chain Monte Carlo method from the predefined tracker space by proposing new appearance models, motion models, state representation types, and observation types, which are the basic important components of visual trackers. Then, the sampled trackers run in parallel and interact with each other while covering various target variations efficiently. The experiment demonstrates that our method tracks targets accurately and robustly in the real-world tracking environments and outperforms the state-of-the-art tracking methods.
Semantic Structure Analysis of Noun Phrases using Abstract Meaning Representation

We propose a method for semantic structure analysis of noun phrases using  Meaning Representation (AMR). AMR is a graph representation for the meaning of a sentence, in which noun phrases (NPs) are manually annotated with internal structure and semantic relations. We extract NPs from the AMR corpus and construct a data set of NP semantic structures. We also propose a transition-based algorithm which jointly identifies both the nodes in a semantic structure tree and semantic relations between them. Compared to the baseline, our method improves the performance of NP semantic structure analysis by 2.7 points, while further incorporating external dictionary boosts the performance by 7.1 points.
Usable gestures for mobile interfaces: evaluating social acceptability

Gesture-based mobile interfaces require users to change the way they use technology in public settings. Since mobile phones are part of our public appearance, designers must integrate gestures that users perceive as acceptable for pub-lic use. This topic has received little attention in the litera-ture so far. The studies described in this paper begin to look at the social acceptability of a set of gestures with re-spect to location and audience in order to investigate possi-ble ways of measuring social acceptability. The results of the initial survey showed that location and audience had a significant impact on a user's willingness to perform ges-tures. These results were further examined through a user study where participants were asked to perform gestures in different settings (including a busy street) over repeated trials. The results of this work provide gesture design rec-ommendations as well as social acceptability evaluation guidelines.
Comparative analysis of buck-boost converters used to obtain I–V characteristic curves of photovoltaic modules

In this paper, the usefulness of several topologies of DC-DC converters for measuring the characteristic curves of photovoltaic (PV) modules is theoretically analyzed. Eight topologies of DC-DC converters with step-down/step-up conversion relation (buck-boost single inductor, CSC (canonical switching cell), Cuk, SEPIC (single-ended primary inductance converter), zeta, flyback, boost-buck-cascaded, and buck-boost-cascaded converters) are compared and evaluated. This application is based on the property of these converters for emulating a resistor when operating in continuous conduction mode. Therefore, they are suitable to implement a system capable of measuring the I-V curve of PV modules. Other properties have been taken into account: input ripple, devices stress, size of magnetic components and input-output isolation. The study determines that SEPIC and Cuk converters are the most suitable for this application mainly due to the low input current ripple, allow input-output insulation and can be connected in parallel in order to measure PV modules o arrays with greater power. CSC topology is also suitable because it uses fewer components but of a larger size. Experimental results validate the comparative analysis.
State-of-Charge Determination From EMF Voltage Estimation: Using Impedance, Terminal Voltage, and Current for Lead-Acid and Lithium-Ion Batteries

State-of-charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process, which can be employed to increase battery life. This reduces the risk of overvoltage and gassing, which degrade the chemical composition of the electrolyte and plates. The proposed model in this paper determines the SOC by incorporating the changes occurring due to terminal voltage, current load, and internal resistance, which mitigate the disadvantages of using impedance only. Electromotive force (EMF) voltage is predicted while the battery is under load conditions; from the estimated EMF voltage, the SOC is then determined. The method divides the battery voltage curve into two regions: 1) the linear region for full to partial SOC and 2) the hyperbolic region from partial to low SOC. Algorithms are developed to correspond to the different characteristic changes occurring within each region. In the hyperbolic region, the rate of change in impedance and terminal voltage is greater than that in the linear region. The magnitude of current discharge causes varying rates of change to the terminal voltage and impedance. Experimental tests and results are presented to validate the new models.
Working memory capacity and strategy use.

In this study, we examine the role of strategy use in working memory (WM) tasks by providing short-term memory (STM) task strategy training to participants. In Experiment 1, the participants received four sessions of training to use a story-formation (i.e., chaining) strategy. There were substantial improvements from pretest to posttest (after training) in terms of both STM and WM task performance. Experiment 2 demonstrated that WM task improvement did not occur for control participants, who were given the same amount of practice but were not provided with strategy instructions. An assessment of participants' strategy use on the STM task before training indicated that more strategic participants displayed better WM task performance and better verbal skills. These results support our hypothesis that strategy use influences performance on WM tasks.
Guided inpainting and filtering for Kinect depth maps

Depth maps captured by Kinect-like cameras are lack of depth data in some areas and suffer from heavy noise. These defects have negative impacts on practical applications. In order to enhance the depth maps, this paper proposes a new inpainting algorithm that extends the original fast marching method (FMM) to reconstruct unknown regions. The extended FMM incorporates an aligned color image as the guidance for inpainting. An edge-preserving guided filter is further applied for noise reduction. To validate our algorithm and compare it with other existing methods, we perform experiments on both the Kinect data and the Middlebury dataset which, respectively, provide qualitative and quantitative results. The results show that our method is efficient and superior to others.
CRAHNs: Cognitive radio ad hoc networks

Cognitive radio (CR) technology is envisaged to solve the problems in wireless networks resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. CR networks, equipped with the intrinsic capabilities of the cognitive radio, will provide an ultimate spectrumaware communication paradigm in wireless communications. CR networks, however, impose unique challenges due to the high fluctuation in the available spectrum as well as diverse quality-of-service (QoS) requirements. Specifically, in cognitive radio ad hoc networks (CRAHNs), the distributed multi-hop architecture, the dynamic network topology, and the time and location varying spectrum availability are some of the key distinguishing factors. In this paper, intrinsic properties and current research challenges of the CRAHNs are presented. First, novel spectrummanagement functionalities such as spectrum sensing, spectrum sharing, and spectrum decision, and spectrum mobility are introduced from the viewpoint of a network requiring distributed coordination. A particular emphasis is given to distributed coordination between CR users through the establishment of a common control channel. Moreover, the influence of these functions on the performance of the upper layer protocols, such as the network layer, and transport layer protocols are investigated and open research issues in these areas are also outlined. Finally, a new direction called the commons model is explained, where CRAHN users may independently regulate their own operation based on pre-decided spectrum etiquette. ! 2009 Elsevier B.V. All rights reserved.
HCMM: Modelling spatial and temporal properties of human mobility driven by users' social relationships

Please cite this article in press as: C. Boldrini, A relationships,  (2010), doi:10 In Mobile Ad Hoc Networks (MANETs), the mobility of the network users can heavily affect the performance of networking protocols because it causes sudden connectivity changes and topological variations. This is even more important in recent promising paradigms proposed in this field, such as opportunistic and delay tolerant networks. For this reason, it is important to understand the characteristics of the user movements in order to properly handle mobility when designing networking protocols for mobile ad hoc networks. In addition, it is highly desirable to have a mobility model that accurately reproduces the user mobility, thus enabling researchers to evaluate, either analytically or by means of simulations, their protocols under realistic mobility conditions. Recently, there have been many studies aimed to uncover the nature of human movements. In this paper, based on recent literature, we identify three main properties that are fundamental to characterize human mobility. Then, we propose a mobility model (HCMM) that integrates all these three features. To the best of our knowledge, the model proposed is the first one that combines notions about the sociality of users with spatial properties observed in real users movement patterns, i.e., their preference to spend time in a limited number of popular locations and to preferentially select short distances over longer ones. We study the HCMM both through simulation and analysis. Based on this study, we highlight some of its important temporal and spatial features, and we show that they are correctly reproduced in terms of key indicators such as jump size and inter-contact time distribution.
Annotation order matters: Recurrent Image Annotator for arbitrary length image tagging

Automatic image annotation has been an important research topic in facilitating large scale image management and retrieval. Existing methods focus on learning image-tag correlation or correlation between tags to improve annotation accuracy. However, most of these methods evaluate their performance using top-k retrieval performance, where k is fixed. Although such setting gives convenience for comparing different methods, it is not the natural way that humans annotate images. The number of annotated tags should depend on image contents. Inspired by the recent progress in machine translation and image captioning, we propose a novel Recurrent Image Annotator (RIA) model that forms image annotation task as a sequence generation problem so that RIA can natively predict the proper length of tags according to image contents. We evaluate the proposed model on various image annotation datasets. In addition to comparing our model with existing methods using the conventional top-k evaluation measures, we also provide our model as a high quality baseline for the arbitrary length image tagging task. Moreover, the results of our experiments show that the order of tags in training phase has a great impact on the final annotation performance.
Organizational Ambidexterity : Past , Present and Future

Organizational ambidexterity refers to the ability of an organization to both explore and exploit—to compete in mature technologies and markets where efficiency, control, and incremental improvement are prized and to also compete in new technologies and markets where flexibility, autonomy, and experimentation are needed. In the past 15 years there has been an explosion of interest and research on this topic. We briefly review the current state of the research, highlighting what we know and don’t know about the topic. We close with a point of view on promising areas for ongoing research.
A Quantitative Comparison Analysis of Radial-Flux, Transverse-Flux, and Axial-Flux Magnetic Gears

The performances of three types of magnetic gears (MGs), which are radial-flux MGs, transverse-flux MGs, and axial-flux MGs, are quantitatively analyzed and compared using 3-D finite-element method of magnetic field and mechanical motion coupled computation. To fairly compare the torque capability of different topologies of MGs, all the MGs under study have the same gear ratio, the same outer diameter, and the same axial stack length. To maximize the torque density, several important structure parameters are optimized. Scenarios using different iron core materials and different magnetization directions of permanent magnets are also studied. Based on the comparative analysis, appropriate topologies of MGs that can achieve a torque density as high as 198 kNm/m3 are suggested. The results in this paper give a good review of the torque density levels of different MGs, and hence they can be used as application guidelines.
City-Scale Location Recognition

We look at the problem of location recognition in a large image dataset using a vocabulary tree. This entails finding the location of a query image in a large dataset containing 3times104 streetside images of a city. We investigate how the traditional invariant feature matching approach falls down as the size of the database grows. In particular we show that by carefully selecting the vocabulary using the most informative features, retrieval performance is significantly improved, allowing us to increase the number of database images by a factor of 10. We also introduce a generalization of the traditional vocabulary tree search algorithm which improves performance by effectively increasing the branching factor of a fixed vocabulary tree.
Narrative Visualization: Telling Stories with Data

Data visualization is regularly promoted for its ability to reveal stories within data, yet these “data stories” differ in important ways from traditional forms of storytelling. Storytellers, especially online journalists, have increasingly been integrating visualizations into their narratives, in some cases allowing the visualization to function in place of a written story. In this paper, we systematically review the design space of this emerging class of visualizations. Drawing on case studies from news media to visualization research, we identify distinct genres of narrative visualization. We characterize these design differences, together with interactivity and messaging, in terms of the balance between the narrative flow intended by the author (imposed by graphical elements and the interface) and story discovery on the part of the reader (often through interactive exploration). Our framework suggests design strategies for narrative visualization, including promising under-explored approaches to journalistic storytelling and educational media.
Groups and Political Discourse : Do Online Discussion Spaces Facilitate Exposure to Political Disagreement ?

To what extent do online discussion spaces expose participants to political talk and to cross-cutting political views in particular? Drawing on a representative national sample of over 1000 Americans reporting participation in chat rooms or message boards, we examine the types of online discussion spaces that create opportunities for cross-cutting political exchanges. Our findings suggest that the potential for deliberation occurs primarily in online groups where politics comes up only incidentally, but is not the central purpose of the discussion space. We discuss the implications of our findings for the contributions of the Internet to cross-cutting political discourse.