Additional Data Requirements Research Articles

Consequences of ecological aggregation in general equilibrium analysis of perturbed ecosystems

A computable general equilibrium (CGE) model is linked with a spatially explicit model of the Lake Michigan ecosystem to assess welfare impacts of the current threat of Bigheaded carp, a non-indigenous aquatic invasive species (AIS) projected to have spatially explicit and species-specific impacts on the ecosystem. Disaggregating to model spatial decisions and spatially explicit species compositions in general equilibrium frameworks comes at a cost of additional complexity and data requirements. Whether these investments are worth the effort over extant ecological aggregations is the focus of this analysis, as typical methods consider spaceless aggregations across locations and aggregate over species. Here, the implications of aggregating over space and/or species are quantified. Results signal welfare losses from the bigheaded carp invasion, and that aggregating over one/both dimensions may miss important bioeconomic tradeoffs especially with respect to species.

SCP: SCENE COMPLETION PRE-TRAINING FOR 3D OBJECT DETECTION

Abstract. 3D object detection using LiDAR point clouds is a fundamental task in the fields of computer vision, robotics, and autonomous driving. However, existing 3D detectors heavily rely on annotated datasets, which are both time-consuming and prone to errors during the process of labeling 3D bounding boxes. In this paper, we propose a Scene Completion Pre-training (SCP) method to enhance the performance of 3D object detectors with less labeled data. SCP offers three key advantages: (1) Improved initialization of the point cloud model. By completing the scene point clouds, SCP effectively captures the spatial and semantic relationships among objects within urban environments. (2) Elimination of the need for additional datasets. SCP serves as a valuable auxiliary network that does not impose any additional efforts or data requirements on the 3D detectors. (3) Reduction of the amount of labeled data for detection. With the help of SCP, the existing state-of-the-art 3D detectors can achieve comparable performance while only relying on 20% labeled data.

10.F. Workshop: Projecting future burden of disease for informed public health decision making

Abstract What are the most relevant diseases in a country? As the need for prioritising the use of available resources constantly increases, a timely, sound and comprehensive answer to this question is more than ever needed to inform public health decision making. The burden of disease metrics such as the Disability-adjusted Life Years (DALY) are important indicators for health information. The DALY provides a comprehensive indicator of the health gap in a country, addressing both premature mortality and health loss due to the occurrence of diseases. The European Burden of Disease Network (COST Action CA18218) acts as a technical platform for integrating and strengthening capacity in burden of disease assessment across Europe and beyond. The Action places a key focus on identifying and addressing data challenges, methodological choices and the need for translating research into policy. Where many Burden of disease (BoD) studies aim at producing the best estimates for the recent years, some countries are also making future projections of DALYs. By doing so, BoD studies become even more relevant for the policy making process since the do not cover only current best estimates, but also show what policy makers might be anticipating on. There are different methods to make future projections, varying from trend extrapolations and forecasts to developing scenarios as part of and advanced foresight approach. Future projections of the DALY estimates bring along additional data requirements. To be able to calculate future DALYs, projections of prevalence and causes of death, but also of population demographics, are needed. In addition, methodological choices have to be made to calculate future DALYs, for example regarding life tables. In this workshop, all these aspects will be addressed. Several countries (i.e. The Netherlands, Belgium, Scotland and Norway) will present their approaches to estimate future DALYs. The workshop will start with and interactive Mentimeter session to get a better view on how the audience is familiar with (future) Burden of Disease. Then, a brief presentation is given as introduction to the European Burden of Disease Network. This is followed by the four presentations of future DALYs. The workshop will be concluded by a discussion with the presenters on the lessons learned regarding their experience of doing future projections. Key messages • Burden of disease becomes even more relevant for policy makers when projections are included in the Burden of Disease study. • Data and methodological issues should be properly addressed to better inform policy makers.

Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification

Compared with text-independent speaker verification (TI-SV) systems, text-dependent speaker verification (TD-SV) counterparts often have better performance for their efficient utilization of speech content information. On this account, some TI-SV methods tried to boost performance by incorporating an extra automatic speech recognition (ASR) component to explore content information, such as c-vector. However, the introduced ASR component requires a large amount of annotated data and consumes high computation resources. In this paper, we propose a pseudo-phoneme label (PPL) loss for the TI-SR task by integrating content cluster loss at the frame level and speaker recognition loss at the segment level in a unified network by multitask learning, without additional data requirement and exhausting computation. By referring to HuBERT, we generate pseudo-phoneme labels to adjust a frame level feature distribution by deep cluster to ensure each cluster corresponds to an implicit pronunciation unit in the feature space. We compare the proposed loss with the softmax loss, center loss, triplet loss, log-likelihood-ratio cost loss, additive margin softmax loss and additive angular margin loss on the VoxCeleb database. Experimental results demonstrate the effectiveness of our proposed method.

A comparison of the World Health Organisation's HEAT model results using a non-linear physical activity dose response function with results from the existing tool

Introduction: The WHO-Europe’s Health Economic Assessment Tool is a tool used to estimate the costs and benefits of changes in walking and cycling. Due to data limitations the tool’s physical activity module assumes a linear dose response relationship be-tween physical activity and mortality. Methods: This study estimates baseline population physical activity distributions for 44 countries included in the HEAT. It then compares, for three different scenarios, the results generated by the current method, using a linear dose-response relationship, with results generated using a non-linear dose-response relationship. Results: The study finds that estimated deaths averted are relatively higher (lower) using the non-linear effect in countries with less (more) active populations. This difference is largest for interventions which affect the activity levels of the least active the most. Since more active populations, e.g. in Eastern Europe, also tend to have lower Value of a Statistical Life estimates the net monetary benefit estimated by the scenarios are much higher in western-Europe than eastern-Europe. Conclusions: Using a non-linear dose response function results in materially different estimates where populations are particularly inactive or particularly active. Estimating base-line distributions is possible with limited additional data requirements, although the method has yet to be validated. Given the significant role of the physical activity module within the HEAT tool it is likely that in the evaluation of many interventions the monetary benefit estimates will be sensitive to the choice of the physical activity dose response function.

A comparison of the World Health Organisation's HEAT model results using a non-linear physical activity dose response function with results from the existing tool

IntroductionThe WHO-Europe’s Health Economic Assessment Tool is a tool used to estimatethe costs and benefits of changes in walking and cycling. Due to data limitationsthe tool’s physical activity module assumes a linear dose response relationship be-tween physical activity and mortality.MethodsThis study estimates baseline population physical activity distributions for 44 coun-tries included in the HEAT. It then compares, for three different scenarios, the re-sults generated by the current method, using a linear dose-response relationship,with results generated using a non-linear dose-response relationship.ResultsThe study finds that estimated deaths averted are relatively higher (lower) using thenon-linear effect in countries with less (more) active populations. This difference islargest for interventions which affect the activity levels of the least active the most.Since more active populations, e.g. in Eastern Europe, also tend to have lowerValue of a Statistical Life estimates the net monetary benefit estimated by the sce-narios are much higher in western-Europe than eastern-Europe.ConclusionsUsing a non-linear dose response function results in materially different estimateswhere populations are particularly inactive or particularly active. Estimating base-line distributions is possible with limited additional data requirements, although themethod has yet to be validated. Given the significant role of the physical activitymodule within the HEAT tool it is likely that in the evaluation of many interventionsthe monetary benefit estimates will be sensitive to the choice of the physical activitydose response function.

Comprehensive framework for human health risk assessment of nanopesticides.

Nanopesticides are not only in an advanced state of research and development but have started to appear on the market. Industry and regulatory agencies need a consolidated and comprehensive framework and guidance for human health risk assessments. In this perspective we develop such a comprehensive framework by exploring two case studies from relevant product types: an active ingredient delivered with a nanocarrier system, and a nanoparticle as an active ingredient. For a nanocarrier system, three entities are tracked during the assessment: the nanocarrier-active ingredient complex, the empty nanocarrier remaining after the complete release of the active ingredient, and the released active ingredient. For the nanoparticle of pure active ingredient, only two entities are relevant: the nanoparticle and the released ions. We suggest important adaptations of the existing pesticide framework to determine the relevant nanopesticide entities and their concentrations for toxicity testing. Depending on the nature of the nanopesticides, additional data requirements, such as those pertaining to durability in biological media and potential for crossing biological barriers, have also been identified. Overall, our framework suggests a tiered approach for human health risk assessment, which is applicable for a range of nanopesticide products to support regulators and industry in making informed decisions on nanopesticide submissions. Brief summaries of suitable methods including references to existing standards (if available) have been included together with an analysis of current knowledge gaps. Our study is an important step towards a harmonized approach accepted by regulatory agencies for assessing nanopesticides.

Ultrasound differential phase contrast using backscattering and the memory effect.

We describe a simple and fast technique to perform ultrasound differential phase contrast (DPC) imaging in arbitrarily thick scattering media. Although configured in a reflection geometry, DPC is based on transmission imaging and is a direct analog of optical differential interference contrast. DPC exploits the memory effect and works in combination with standard pulse-echo imaging, with no additional hardware or data requirements, enabling complementary phase contrast (in the transverse direction) without any need for intensive numerical computation. We experimentally demonstrate the principle of DPC using tissue phantoms with calibrated speed-of-sound inclusions.

Tourism Demand Forecasting: A Decomposed Deep Learning Approach

Tourism planners rely on accurate demand forecasting. However, despite numerous advancements, crucial methodological issues remain unaddressed. This study aims to further improve the modeling accuracy and advance the artificial intelligence (AI)-based tourism demand forecasting methods. Deep learning models that predict tourism demand are often highly complex and encounter overfitting, which is mainly caused by two underlying problems: (1) access to limited data volumes and (2) additional explanatory variable requirement. To address these issues, we use a decomposition method that achieves high accuracy in short- and long-term AI-based forecasting models. The proposed method effectively decomposes the data and increases accuracy without additional data requirement. In conclusion, this study alleviates the overfitting issue and provides a methodological contribution by proposing a highly accurate deep learning method for AI-based tourism demand modeling.

Calibrating Structured Output Predictors for Natural Language Processing.

We address the problem of calibrating prediction confidence for output entities of interest in natural language processing (NLP) applications. It is important that NLP applications such as named entity recognition and question answering produce calibrated confidence scores for their predictions, especially if the applications are to be deployed in a safety-critical domain such as healthcare. However, the output space of such structured prediction models is often too large to adapt binary or multi-class calibration methods directly. In this study, we propose a general calibration scheme for output entities of interest in neural network based structured prediction models. Our proposed method can be used with any binary class calibration scheme and a neural network model. Additionally, we show that our calibration method can also be used as an uncertainty-aware, entity-specific decoding step to improve the performance of the underlying model at no additional training cost or data requirements. We show that our method outperforms current calibration techniques for named-entity-recognition, part-of-speech and question answering. We also improve our model's performance from our decoding step across several tasks and benchmark datasets. Our method improves the calibration and model performance on out-ofdomain test scenarios as well.

Evaluation of the Generalized and Rescaled Complementary Evaporation Relationships

AbstractA rigorous approach to the complementary relationship (CR) in land surface evaporation was introduced by Brutsaert (2015, https://doi.org/10.1002/2015WR017720) in which the problem was cast in nondimensional form for generality, boundary conditions (BCs) were established from physical constraints, and a suitable mathematical solution was formulated. Building on Brutsaert's insightful foundation, Crago et al. (2016, https://doi.org/10.1002/2016WR019753) showed the need, for rational reasons, to modify Brutsaert's BCs by introducing Emax, an upper limit to the apparent potential evaporation corresponding to an actual evaporation of 0. Following the rigorous approach of Brutsaert, this paper presents the derivation and resulting CR. The BC associated with Emax requires a solution that is rescaled with regard to Brutsaert's dimensionless framework and represents a fundamentally different solution from that of Brutsaert. In essence, this formulation acknowledges a pattern of organized variability, which exists within the data when scaled in Brutsaert's dimensionless framework, and our rescaled CR reorganizes the data, collapsing it toward a more universal representation. Our rescaled CR, implemented with two versions of Emax, one based on mass transfer and another on the Penman equation, is evaluated alongside Brutsaert's original formulation. Multiyear data sets from seven Fluxnet sites in Australia, ranging from a sparsely vegetated ephemeral tropical wetland to a temperate forest with a 75‐m‐tall canopy were used to test the formulations on a weekly basis. All three formulations performed adequately. Overall, the rescaled model with Emax based on the Penman equation performed best; it extracts more information with no additional observational data requirements.

Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval

The water cloud model (WCM) is a widely used radar backscatter model applied to SAR images to retrieve soil moisture over vegetated areas. The WCM needs vegetation descriptors to account for the impact of vegetation on SAR backscatter. The commonly used vegetation descriptors in WCM, such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), are sometimes difficult to obtain due to the constraints in data availability in in-situ measurements or weather dependency in optical remote sensing. To improve soil moisture retrieval, this study investigates the feasibility of using all-weather SAR derived vegetation descriptors in WCM. The in-situ data observed at an agricultural crop region south of Winnipeg in Canada, RapidEye optical images and dual-polarized Radarsat-2 SAR images acquired in growing season were used for WCM model calibration and test. Vegetation descriptors studied include HV polarization backscattering coefficient ( σ H V ° ) and Radar Vegetation Index (RVI) derived from SAR imagery, and NDVI derived from optical imagery. The results show that σ H V ° achieved similar results as NDVI but slightly better than RVI, with a root mean square error of 0.069 m3/m3 and a correlation coefficient of 0.59 between the retrieved and observed soil moisture. The use of σ H V ° can overcome the constraints of the commonly used vegetation descriptors and reduce additional data requirements (e.g., NDVI from optical sensors) in WCM, thus improving soil moisture retrieval and making WCM feasible for operational use.

Roof model recommendation for complex buildings based on combination rules and symmetry features in footprints

ABSTRACTCurrently, very few roof shape information for complex buildings is available on OSM. Moreover, additional data requirements (e.g. 3D point clouds) limit the applicability of many roof reconstruction approaches. To mitigate this issue, we propose an approach to roof shape recommendations for complex buildings by exploring the inherited characteristics of building footprints: the disclosure of rectangles combinations in a partition of footprints and the symmetrical features of footprints. First, it decomposes a complex footprint into rectangles by using an advanced minimal non-overlapping cover algorithm. Second, a graph-based symmetry detection algorithm is proposed to identify all the symmetrical sub-clusters in partitions. Then, a set of selection rules are defined to rank partitions, and the best ones are chosen for roof shape recommendation. Finally, a set of combination rules and a symmetry rule are defined. It enables to evaluate the probability of a footprint being a certain combination of roof shapes. Experimental results show the growth of the probability of correctly recommending roof shapes for single rectangles and buildings from a prior probability of 17–45% and from a prior probability of 0.29–14.3%, removing 60% and 93% of the incorrect roof shape options, respectively.

Sparse Bayesian multiway canonical correlation analysis for EEG pattern recognition

L1-regularized multiway canonical correlation analysis (L1-MCCA) has been introduced to reference signal optimization in steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI). The effectiveness of L1-regularization on significant trial selection highly depends on the regularization parameter setting, which can be typically determined by cross-validation (CV). However, CV will substantially reduce the practicability of BCI system due to additional data requirement for the parameter validation and relatively high computational cost. To solve the problem, this study proposes a Bayesian version of L1-MCCA (called SBMCCA) by exploiting sparse Bayesian learning. The SBMCCA method avoids CV and can efficiently estimate the model parameters under the Bayesian evidence framework. Experimental results show that the SBMCCA method achieved comparable recognition accuracy but much higher computational efficiency in contrast to the L1-MCCA method.

Comparison of post-authorisation measures from regulatory authorities with additional evidence requirements from the HTA body in Germany - are additional data requirements by the Federal Joint Committee justified?

ObjectivesThe aim of this study was to compare post-authorisation measures (PAMs) from the European Medicines Agency (EMA) with data requests in fixed-termed conditional appraisals of early benefit assessments from the German Federal Joint Committee (G-BA).MethodsMedicinal products with completed benefit assessments during an assessment period of 3.5 years were considered. PAMs extracted from European Public Assessment Reports (EPARs) were compared with data requests issued by the G-BA in the context of conditional appraisals.ResultsTwenty conditional appraisals (19 products) and 34 EPARs containing PAMs (33 products) were identified. Data categories (efficacy, safety, etc.), data types (type of study required to address the request) and clarity of requests were determined. Conditional appraisals disproportionately focused on oncology products (13/19 products with conditional appraisals vs. 14/33 products with PAMs). No clear rationale for the G-BA issuing conditional appraisals could be identified in public sources. Both EMA and G-BA requested mainly efficacy and safety data (44/54 and 23/35 categories requested, respectively); however, 28/35 G-BA data requirements went beyond requests made by the EMA. Almost half of the G-BA requests (9/20), but no PAMs, were unclear, and no methodological guidance for fulfilling the data requirements was provided by the G-BA.ConclusionsBetter alignment between data requests from regulatory authorities and health technology assessment bodies is strongly recommended.

Usage of Social Network Analysis (Sna) in Crisis Management

Abstract Social systems are complex structures that consist of different sub-systems. Therefore, understanding social systems is more difficult than comprehending electronic or mechanical systems. What makes social systems more complex than other systems is that society is not simply the sum of each individual in the society. In the current global system, the countries, which have become small villages, try to meet national security needs by converting the unknown to known and identifying the correlation among political, military, social and economic events. The current crisis management concepts are conducted through systematic approaches. Besides, the management of social, economic and political crises need to be conducted in a holistic approach covering all sub-systems. At this point, the function of Social Network Analysis (SNA) emerges. SNA, which forms the main subject of this paper, is a tool for examining the structure of a crisis through correlating the sub-elements. The aim of this study is to show how SNA can be used in crisis management. First, SNA is performed on a generic crisis situation and the results are presented. Then, the additional critical data requirements are put forward to manage the crisis effectively.

The Resource-Based View on Project Risk Management

AbstractThe benefits of conducting risk analyses in the planning of construction projects are widely acknowledged. However, the actual implementation of procedures notably differs significantly across industry segments. Even within an organization, different units represent risks differently, depending on their primary function. As a result, a holistic picture of the risk exposure is often hard to describe and even harder to manage. The objective of this paper is to provide a link between strategic planning theory, namely the resource-based view (RBV), and project risk management. The proposed resource-based approach is based on the development of a project causal network which then allows for both data-intensive and conceptual evaluation of the project risks, primarily focusing on cost impacts. The key contribution of this paper is the capacity of the developed framework to integrate with a number of methods already used in project management practices with little to no additional data requirement. These...

Visualizing Water Quality Sampling-Events in Florida

Abstract. Water quality sampling in Florida is acknowledged to be spatially and temporally variable. The rotational monitoring program that was created to capture data within the state’s thousands of miles of coastline and streams, and millions of acres of lakes, reservoirs, and ponds may be partly responsible for inducing the variability as an artifact. Florida’s new dissolved-oxygen-standard methodology will require more data to calculate a percent saturation. This additional data requirement’s impact can be seen when the new methodology is applied retrospectively to the historical collection. To understand how, where, and when the methodological change could alter the environmental quality narrative of state waters requires addressing induced bias from prior sampling events and behaviors. Here stream and coastal water quality data is explored through several modalities to maximize understanding and communication of the spatiotemporal relationships. Previous methodology and expected-retrospective calculations outside the regulatory framework are found to be significantly different, but dependent on the spatiotemporal perspective. Data visualization is leveraged to demonstrate these differences, their potential impacts on environmental narratives, and to direct further review and analysis.

Development of Visual Model for Exploring Relationship between Nighttime Driving Behavior and Roadway Visibility Features

Driving is often considered a visually oriented task. This visual task is constrained when drivers drive at night. Visibility is reduced because visual cues available during the day are not present at night. This study attempted to develop a link between driver safety and the nighttime visual environment. This research required creating and integrating new technology, observing and collecting data, and developing a model framework called the Dynamic Driver Visual Model (DDVM). Conceptually, a DDVM is a system of rules, statistics, and expectations that can be used to define how a driver collects visual information from the environment. Several information sources were investigated, and several dependent variables were identified. Data were collected on how information from signage, objects, lighting, pavement markings, and other vehicles moderates a driver's visual search of the roadway environment. Several logistic regression analyses were performed on the collected data to identify common characteristics to be implemented in the DDVM. These variables included age, lighting, vehicle headlamps, several different objects, glance time, target luminance, and contrast information. The results suggest that a number of target and visibility elements have nonlinear effects on a driver's detection performance at a variety of detection distances. This paper discusses the implications of these findings and the initial framework of the DDVM. Future research and additional data requirements are also discussed.

A knowledge-based search engine to navigate the information thicket of nanotoxicology

The risk assessment of nano-sized materials (NM) currently suffers from great uncertainties regarding their putative toxicity for humans and the environment. An extensive amount of the respective original research literature has to be evaluated before a targeted and hypothesis-driven Environmental and Health Safety research can be stipulated. Furthermore, to comply with the European animal protection legislation in vitro testing has to be preferred whenever possible. Against this background, there is the need for tools that enable producers of NM and risk assessors for a fast and comprehensive data retrieval, thereby linking the 3Rs principle to the hazard identification of NM.Here we report on the development of a knowledge-based search engine that is tailored to the particular needs of risk assessors in the area of NM. Comprehensive retrieval of data from studies utilising in vitro as well as in vivo methods relying on the PubMed database is presented exemplarily with a titanium dioxide case study. A fast, relevant and reliable information retrieval is of paramount importance for the scientific community dedicated to develop safe NM in various product areas, and for risk assessors obliged to identify data gaps, to define additional data requirements for approval of NM and to create strategies for integrated testing using alternative methods.