Miracle-dz/UniFAR-FTUs huggingface dataset:
Advection of NH3 over a pasture field and its effect on gradient flux measurements

Abstract. Deposition of atmospheric ammonia (NH3) to semi-natural ecosystems leads to serious adverse effects, such as acidification and eutrophication. A step in quantifying such effects is the measurement of NH3 fluxes over semi-natural and agricultural land. However, measurement of NH3 fluxes over vegetation in the vicinity of strong NH3 sources is challenging, since NH3 emissions are highly heterogeneous. Indeed, under such conditions, local advection errors may alter the measured fluxes. In this study, local advection errors (ΔFz,adv) were estimated over a 14 ha grassland field, which was successively cut and fertilised, as part of the GRAMINAE integrated Braunschweig experiment. The magnitude of ΔFz,adv was determined up to 810 m downwind from farm buildings emitting between 6.2 and 9.9 kg NH3 day−1. The GRAMINAE experiment provided a unique opportunity to compare two methods of estimating ΔFz,adv: one inference method based on measurements of horizontal concentration gradients, and one based on inverse dispersion modelling with a two-dimensional model. Two sources of local advection were clearly identified: the farm NH3 emissions leading to positive ΔFz,adv ("bias towards emissions") and field NH3 emissions, which led to a negative ΔFz,adv ("bias towards deposition"). The local advection flux from the farm was in the range 0 to 27 ng NH3 m−2 s−1 at 610 m from the farm, whereas ΔFz,adv due to field emission was proportional to the local flux, and ranged between −209 and 13 ng NH3 m−2 s−1. The local advection flux ΔFz,adv was either positive or negative depending on the magnitude of these two contributions. The modelled and inferred advection errors agreed well. The inferred advection errors, relative to the vertical flux at 1 m height, were 52% on average, before the field was cut, and less than 2.1% when the field was fertilised. The variability of the advection errors in response to changes in micrometeorological conditions is also studied. The limits of the 2-D modelling approach are discussed.
Adaptive Token Allocation Algorithm For Enhancing The Fairness Among Long Live TCP Flows In Diffserv

In this paper, an attempt has been made to design an algorithm that provides optimal allocation of tokens to the individual long live TCP flows in Differentiated Services (DiffServ) networks. The optimality in token allocation is achieved using an optimization technique that employs Genetic Algorithm (GA). The trial performance of the proposed algorithm has been evaluated through C++ simulation. It has been observed from the simulation studies that the token allocation varies with respect to the incoming rate of the flows. The formulation of the objective function and the application of GA for the optimization process have been explained. The simulation results pertaining to the token allocation for two different cases are also furnished.
A Marked Point Process for Modeling Lidar Waveforms

Lidar waveforms are 1-D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based upon a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence ofparametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a reversible jump Markov chain Monte Carlo (RJMCMC) sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported.
Traffic Aided Opportunistic Scheduling with QoS Support for Multiservice CDMA Uplink

In this paper, we address the problem of resource allocation with efficiency and quality of service (QoS) support in uplink for a wireless CDMA network supporting real-time (RT) and nonreal-time (NRT) communication services. For RT and NRT users, there are different QoS requirements. We introduce and describe a new scheme, namely the traffic aided uplink opportunistic scheduling (TAUOS). While guaranteeing the different QoS requirements, TAUOS exploits the channel condition to improve the system throughput. In TAUOS, the cross-layer information, file size information, is used to improve the fairness of NRT users. Extensive simulation results show that our scheme can achieve high system throughput in uplink wireless CDMA system, while guaranteeing the QoS requirements.
Improving Main Memory Hash Joins on Intel Xeon Phi Processors: An Experimental Approach

Modern processor technologies have driven new designs and implementations in main-memory hash joins. Recently, Intel Many Integrated Core (MIC) co-processors (commonly known as Xeon Phi) embrace emerging x86 single-chip many-core techniques. Compared with contemporary multi-core CPUs, Xeon Phi has quite different architectural features: wider SIMD instructions, many cores and hardware contexts, as well as lower-frequency in-order cores. In this paper, we experimentally revisit the state-of-the-art hash join algorithms on Xeon Phi co-processors. In particular, we study two camps of hash join algorithms: hardware-conscious ones that advocate careful tailoring of the join algorithms to underlying hardware architectures and hardware-oblivious ones that omit such careful tailoring. For each camp, we study the impact of architectural features and software optimizations on Xeon Phi in comparison with results on multi-core CPUs. Our experiments show two major findings on Xeon Phi, which are quantitatively different from those on multi-core CPUs. First, the impact of architectural features and software optimizations has quite different behavior on Xeon Phi in comparison with those on the CPU, which calls for new optimization and tuning on Xeon Phi. Second, hardware oblivious algorithms can outperform hardware conscious algorithms on a wide parameter window. These two findings further shed light on the design and implementation of query processing on new-generation single-chip many-core technologies.
The relationship between credit default swaps and credit ratings for sovereigns

Diese Arbeit stellt eine Event-Studie Analyse der Beziehung zwischen Credit Default Swap (CDS) Spreads und Ratings fur Staatsanleihen im Rahmen der europaischen Schuldenkrise dar. Die empirische Analyse untersucht diese Beziehung auch mit Rucksicht auf PIIGS-Staaten und auf die Lander der Eurozone. Die wichtigsten Ergebnisse zeigen: PIIGS-Staaten sind betroffen und litten mehr an dem Ergebnis der Finanzkrise unter den anderen europaischen Landern. Die Periode der europaischen Schuldenkrise erklart deutlich die negative Korrelation zwischen CDS-Spreads und Ratings, insbesondere die starkere Auswirkungen negativer Ereignisse auf CDSSpread-Veranderungen.
Comparison of voice activity detection algorithms for wireless personal communications systems

Voice activity detection (VAD) algorithms have become an integral part of many of the standardized wireless cellular and personal communications systems (PCS). We present a comparative study of the performance of three proposed VAD algorithms under various acoustical background noise conditions. We also propose new ideas to enhance the performance of a VAD algorithm in wireless PCS speech applications.
Learning from bug-introducing changes to prevent fault prone code

A version control system, such as CVS/SVN, can provide the history of software changes performed during the evolution of a software project. Among all the changes performed there are some which cause the introduction of bugs, often resolved later with other changes. In this paper we use a technique to identify bug-introducing changes to train a model that can be used to predict if a new change may introduces or not a bug. We represent software changes as elements of a n-dimensional vector space of terms coordinates extracted from source code snapshots. The evaluation of various learning algorithms on a set of open source projects looks very promising, in particular for KNN (K-Nearest Neighbor algorithm) where a significant tradeoff between precision and recall has been obtained.
Classifying Socially Sensitive Data Without Discrimination: An Analysis of a Crime Suspect Dataset

Social discrimination against certain sensitive groups within society (e.g., females, blacks, minorities) is prohibited by law in many countries. To prevent discrimination arising from the use of discriminatory data, recent data mining research has focused on methods for making classifiers learned over discriminatory data discrimination-aware. Most of these methods have been tested on standard classification datasets that have been tweaked for discrimination analysis rather than over actual discriminatory data. In this paper, we study discrimination-aware classification when applied to a real world dataset of Statistics Netherlands, which is a census body in the Netherlands. Specifically, we consider the use of classifiers for predicting whether an individual is a crime suspect, or not, to support law enforcement and security agencies' decision making. Our results show that discrimination does exist in real world datasets and blind use of classifiers learned over such datasets can exacerbate the discrimination problem. We demonstrate that discrimination-aware classification methods can mitigate the discriminatory effects and that they lead to rational and legally acceptable decisions.
Combinatorial Algorithms for Protein Folding in Lattice Models: A Survey of Mathematical Results

We present a comprehensive survey of combinatorial algorithms and theorems about lattice protein folding models obtained in the almost 15 years since the publication in 1995 of the first protein folding approximation algorithm with mathematically guaranteed error bounds [60]. The results presented here are mainly about the HP-protein folding model introduced by Ken Dill in 1985 [37]. The main topics of this survey include: approximation algorithms for linear-chain and side-chain lattice models, as well as off-lattice models, NP-completeness theorems about a variety of protein folding models, contact map structure of self-avoiding walks and HP-folds, combinatorics and algorithmics of side-chain models, bi-sphere packing and the Kepler conjecture, and the protein side-chain self-assembly conjecture. As an appealing bridge between the hybrid of continuous mathematics and discrete mathematics, a cornerstone of the mathematical difficulty of the protein folding problem, we show how work on 2D self-avoiding walks contact-map decomposition [56] can build upon the exact RNA contacts counting formula by Mike Waterman and collaborators [96] which lead to renewed hope for analytical closed-form approximations for statistical mechanics of protein folding in lattice models. We also include in this paper a few new results, research directions within reach of rigorous results, and a set of open problems that merit future exploration. ∗corresponding author sorin@cs.brown. edu †Present address: University of California Davis, Department of Computer Science, Davis, lam@cs.brown. edu
Corticothalamic Feedback Controls Sleep Spindle Duration In Vivo

Spindle oscillations are commonly observed during stage 2 of non-rapid eye movement sleep. During sleep spindles, the cerebral cortex and thalamus interact through feedback connections. Both initiation and termination of spindle oscillations are thought to originate in the thalamus based on thalamic recordings and computational models, although some in vivo results suggest otherwise. Here, we have used computer modeling and in vivo multisite recordings from the cortex and the thalamus in cats to examine the involvement of the cortex in spindle oscillations. We found that although the propagation of spindles depended on synaptic interaction within the thalamus, the initiation and termination of spindle sequences critically involved corticothalamic influences.
Control of a multiple sections flexible endoscopic system

The use of flexible endoscopes in new surgical procedures such as NOTES, i.e. Natural Orifice Transluminal Endoscopic Surgery, raises many problems. Indeed, the movements of conventional flexible endoscopes are limited and surgeons can only perform basic tasks with these systems. In order to enhance endoscope possibilities and workspace, we are currently developing a robotized system. The prototype we propose is based on the combination of several flexible endoscopes. The kinematic model of the system is detailed in order to develop a method of control. The paper presents the implementation of a control strategy using an external sensor which allows to deal with non linearities induced by the cable mechanism of the endoscopes.
Bidirectional dual active bridge converter using a tap changer for extended voltage ranges

A bidirectional DC-DC converter with high voltage gain and wide input and output voltage range is presented. The input voltage range is 30 V to 60 V and the output voltage range is 100 V to 400 V. The nominal power is 10 kW. The converter is based on a Dual Active Bridge in which the voltage range has been extended with a tap changer between the transformer and the high voltage H-bridge.
Using Augmented Reality to Help Children with Autism Stay Focused

Children with autism have difficulty sustaining their selective attention during therapy sessions. Attention management techniques involve the use of verbal and visual prompting, annotated on top of the physical objects used during therapies. Here, the authors explore how augmented reality helps integrate the physical and digital worlds, mimicking current strategies for attention management in autism. They describe their design decisions when developing the Mobile Object Identification System (Mobis), a mobile augmented reality application that lets teachers superimpose digital content on top of physical objects. The results of a five-week deployment study demonstrate that Mobis is useful and easy to use, increases the sustained and selective attention of children with autism, and elicits positive emotions during therapies. This article is part of a special issue on managing attention.
From Multi-Band Full Combination to Multi-Stream Full Combination Processing in Robust ASR

The multi-band processing paradigm for noise robust ASR was originally motivated by the observation that human recognition appears to be based on independent processing of separate frequency sub-bands, and also by ``missing data'' results which have shown that ASR can be made significantly more robust to band-limited noise if noisy sub-bands can be detected and then ignored. Of the different multi-band models which have been proposed, only the ``Full Combination'' or ``all-wise'' multi-band HMM/ANN hybrid approach allows us to consistently overcome the difficult problem of deciding which sub-bands are noisy, by integrating over all possible positions of noisy sub-bands. While this system has performed better than any other multi-band system which we have tested, %[in the framework of both HMM/MLP hybrid systems and standard HMMs], we have also found that it only shows significantly improved robustness to noise when the noise is strongly band-limited. In real noise environments this is rarely the case. An alternative paradigm for noise robust ASR is multi-stream, as opposed to multi-band, ASR. In multi-stream processing the aim is to combine evidence from a number of different representations of the full speech signal, rather than from a number of frequency sub-bands. Several models for multi-stream ASR have recently reported significant performance improvements for speech with real noise. In this article we first present evidence to show how multi-band ASR has a strong advantage over the baseline system with band-limited noise, but no clear advantage with wide-band noise. We then show how the principled theoretical basis for Full Combination multi-band ASR can be directly transfered to multi-stream combination, and we show how this model can be used to combine data streams comprising three commonly used types of acoustic features. Preliminary results show significantly improved recognition with clean speech.
Multi-modality learning from visual and remotely sensed data

Autonomous vehicles are often tasked to explore unseen environments, aiming to acquire and understand large amounts of visual image data and other sensory information. In such scenarios, remote sensing data may be available a priori, and can help to plan and execute an autonomous mission. In this paper, we propose a multi-modality learning algorithm to model the relationship between visual images taken by an Autonomous Underwater Vehicle (AUV) during a survey, and remotely sensed acoustic bathymetry (ocean depth) data that are available prior to the survey. The algorithm is based on a mixture of Restricted Boltzmann Machines, and models the joint distribution between the bathymetry and visual modalities. The model is able to cluster the input data, generate useful features for classification, and predict visual image features in unseen dive sites from just the ocean depth information, facilitating image-based queries. These capabilities are useful in planning future AUV dives in unseen environments.
Self-management of aphasia: a scoping review

ABSTRACT Background: Self-management approaches are routinely used in chronic conditions to enable patients to take responsibility for their own care. A self-management approach may be appropriate for individuals with aphasia, but this has not been systematically investigated. The purpose of this review was to explore self-management in relation to aphasia. Aims: The study aimed to explore existing research and intervention approaches for aphasia that incorporate self-management principles. A secondary aim was to examine the presence of the term self-management in the research literature and online resources relating to aphasia. Methods & Procedures: A scoping review methodology was selected to explore literature relating to self-management of aphasia. Five databases were systematically searched in May 2017: PubMed, Embase, CINAHL, PsycINFO, and Linguistics & Language Behaviour Abstracts. Search terms used were aphasia OR dysphasia AND self-management OR self-directed OR self-care OR self-efficacy OR independence OR independent AND intervention OR treatment OR rehabilitation. A structured website search of aphasia, speech pathology and stroke associations from four English-speaking countries was performed in September 2017. Outcomes & Results: A total of 43 studies met inclusion criteria for the literature review. Analysis of eligible studies showed three areas of aphasia intervention which incorporated principles self-management: 1) technology-based interventions, 2) group/community-based interventions, and 3) communication partner training. The structured website search revealed no information relating specifically to aphasia self-management. Conclusions: Principles of self-management are being used in some aphasia interventions, but there is little evidence of a self-management approach being applied in aphasia. The term self-management is not widely present in aphasia literature and when it has been used tends to refer to self-administered treatment rather than a structured self-management approach.
Backcasting for Industrial Transformations and System Innovations towards Sustainability : is it useful for Governance ?

Radical changes to present production and consumption systems, especially in the developed world, are required to achieve sustainable development. Those changes on a system level are often described as industrial transformations, while terms like system innovations or transitions towards sustainability are also used. These require combinations of technological, cultural, social, institutional and organisational changes, while affecting many stakeholders when diffusing into society, and involving complex processes of social change on the long term. Backcasting has been applied in the Netherlands as a participatory approach to identify and explore system innovations towards sustainability, while also aiming at follow-up and implementation in public research, at companies & public interest groups, and at the government. Backcasting can be defined as first creating a desirable (sustainable) future vision or normative scenario, followed by looking back on how this desirable future could have been achieved, before defining and planning follow-up activities and developing strategies leading towards that desirable future. Though quite some results of backcasting have been reported already, rather little has been done on comparing different backcasting experiments, on conceptual issues, on how useful they are for governance, and how they relate to other recently emerging approaches like transition management and strategic niche management. The focus of this paper is on these issues and it includes the following: A brief history of backcasting since the 1970s, paying attention to energy backcasting and further development in Sweden and the Netherlands. A ‘generic’ description for a participatory backcasting approach existing of five stages. It also includes core features, an outline of a toolkit and four main activities: Orientation, Design, Analysis, and Participation. A brief description of two cases in the Netherlands on food production and consumption in which backcasting involving a wide range of stakeholders was applied. Conclusions and discussion with respect to backcasting, its broader relevance, related approaches, government and governance.