Using machine learning for assessing customer suitability in business to business environment

The industry has gone through multiple revolutions as a result of advancements in technology. Now, it might be on the verge of another one because of machine learning. In business to business setting, sales processes include many manual tasks. They consume a lot of time, that could otherwise be used for deepening relationships with customers or for acquiring new ones. Machine learning could be used to support and automate these tasks. Also, it could be used to forecast how a sales process ends. This thesis proposes a new approach for using machine learning in the said environment. Instead of using sales data to just predict the outcomes of active sales processes, it is used to estimate how good a potential customer would be for a company. With these kinds of estimates, a company could more effectively screen the customers making their sales efforts potentially more profitable. The thesis aims to research questions that arise from this approach. These include the question of how to provide suitability estimates without saved data, when to start using machine learning for providing the estimates and how to maintain the performance of selected machine learning method. In the thesis, an open business to business dataset is used. It is introduced and analyzed. Also, a selection of machine learning models is introduced and one of them selected for the thesis. To answer the open questions of the thesis code is developed and tested. The code forms a two-mode system for providing suitability estimates. The purpose of the code was to verify the ideas useful for such a system. As results of the thesis it can be stated that without saved data it is possible to offer suitability estimates by defining an optimal customer and comparing all the new customers to it. The system can start using machine learning once a classifier fitted to the data gives good enough cross-validation results. Once a machine learning model is in use, its performance can be maintained by retraining the model and finding the optimal parameters each time the dataset has changed. Finally, it is however stressed that the suitability estimates are not absolute truths. They should not be used blindly, but as decision support when selecting new customers for a company

Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking

In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on a vehicle, and the target state should be represented in a global (absolute) coordinate frame. Then it is important to consider the uncertain location of the vehicle on which the sensor is mounted for MTT. In this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states. Measurements collected by different sensors mounted on multiple vehicles with varying location uncertainty are incorporated sequentially based on the arrival of new sensor measurements. In doing so, targets observed from a sensor mounted on a well-localized vehicle reduce the state uncertainty of other poorly localized vehicles, provided that a common non-empty subset of targets is observed. A low complexity filter is obtained by approximations of the joint sensor-feature state density minimizing the Kullback-Leibler divergence (KLD). Results from synthetic as well as experimental measurement data, collected in a vehicle driving scenario, demonstrate the performance benefits of joint vehicle-target state tracking.Comment: 13 pages, 7 figure

New mining ventures in Venezuela and links to foreign capital

This thesis studies the discourse of governmental actors in resource-rich countries that base much of their economic structures on the extraction of natural resources. The goal of the study is to explore the links between foreign capital and government-led resource extraction ventures and understand what kind of a discourse is built around natural resource ventures and how governments represent these ventures as a viable model for ‘development’. The focus of this study is the case of the new Orinoco Mining Arch – project in Venezuela, established in 2016, that represents a new extractivist turn in the traditionally oil-based economy of the country. In this thesis the link between foreign capital and resource extraction is understood as fundamentally interconnected through the theoretical framework that positions extractivism as part of a developmentalist and neoliberal ontology. The methodological approach of this thesis is that of Critical Discourse Analysis which is presented based on the poststructuralist views of Ernesto Laclau and Chantal Mouffe on discourse. Furthermore, Fairclough’s approach to Critical Discourse Analysis is used as a way to study and analysis of the research material through textual analysis, discursive practice and social practice. The data consists of three types of material that the Venezuelan governmental actors have published regarding the mining activities of the Orinoco Mining Arch: the opening speech by president Nicolás Maduro at the event to officiate the AMO project, the communications and news articles related to this new project published by the country’s Ministry of Mining and country’s the National Development Plans’ sections that relate to mining. This study shows that attempts to legitimize governmental mining ventures are carried through by building a public image of an ecologically sustainable, dynamic and sovereign mining industry that is deeply linked to the Chavist-nationalist imaginary, and intertwined with more subtle elements, including foreign capital, in the discourse. The analysis of the data found that this resource nationalist discourse, its origin and its features are currently reproducing a developmentalist based neo-extractivist narrative which praises ’development’, considers resource extraction as necessary, and follows a neoliberal logic of accumulation of capital. Thus, despite of its apparent potential for conflict, foreign capital it is part of the developmentalist narrative that the governmental discourse creates. Its manifestation as neo-extractivism has an immense potential for destruction in the socio-ecological context

Quasi-relativistic behavior of cold atoms in light fields

We study the influence of three laser beams on the center of mass motion of cold atoms with internal energy levels in a tripod configuration. We show that similar to electrons in graphene the atomic motion can be equivalent to the dynamics of ultra-relativistic two-component Dirac fermions. We propose and analyze an experimental setup for observing such a quasi-relativistic motion of ultracold atoms. We demonstrate that the atoms can experience negative refraction and focussing by Veselago-type lenses. We also show how the chiral nature of the atomic motion manifests itself as an oscillation of the atomic internal state population which depends strongly on the direction of the center of mass motion. For certain directions an atom remains in its initial state, whereas for other directions the populations undergo oscillations between a pair of internal states.Comment: 4 pages, updated version, Phys. Rev. A 77, (R)011802 (2008

Individual tree properties from ALS data as input to habitat analysis in boreal forest

This study shows examples of detailed analysis of forest canopy from ALS data with potential use as input to habitat analysis in forests. This includes delineation of individual tree crowns and analysis of the distribution of tree heights and the tree species composition

Cooperative Localization of Vehicles without Inter-vehicle Measurements

While cooperation among vehicles can improve localization, standard communication technologies (e.g., 802.11p) cannot provide reliable range or angle measurements. To allow cooperation without explicit inter-vehicle measurements, we propose a cooperative localization method whereby vehicles track mobile features in the environment and use associations of features among vehicles to improve the vehicles\u27 localization accuracy. The proposed algorithm, which scales linearly in the number of vehicles and quadratically in the number of tracked features, shows superior localization performance compared to a non-cooperative approach

Influence of footprint size and geolocation error on the precision of forest biomass estimates from space-borne waveform LiDAR

Space-borne LiDAR systems can potentially assist large-area assessments of forest resources, in particular when a subset of the acquired LiDAR footprints is combined with field surveys of forest stand characteristics at footprint location. When combined, space-borne LiDAR geolocation error and the footprint size may however have considerable effects on the estimation accuracy of forest stand variables, such as aboveground biomass (AGB). The aim of this study was to draw recommendations for future space-borne LiDAR systems, which should deliver data for unbiased AGB assessments. The recommendations were drawn from AGB estimations based on space borne LiDAR waveforms simulated over a 1300 ha large study site in southern Sweden. Large-footprint, nadir looking satellite waveforms were simulated by stacking individual small-footprint, airborne LiDAR waveforms observed near a predefined sampling pattern. The stacked waveforms, represented by their metrics, were used as input for a two-phase systematic sampling in combination with model-assisted estimation or hybrid inference for estimating AGB and its variance. The second-phase sample included 264 inventory plots, whereas the first-phase sample included 1010 sample locations, where satellite waveforms were simulated. After simulating satellite waveforms with different footprint sizes and analyzing the AGB variance, the recommendation is to have a footprint size that is similar to the size of the field plots used for collecting reference data, i.e. 20 m diameter in our case. For the optimal footprint size, AGB was estimated with a precision of 2.9 Mg per hectare (2.9% of the average). The results also showed that variance estimates increased constantly with increasing geolocation error. For a geolocation error of 14 m, variance estimates increased by 17%, which justifies investing additional efforts in minimizing it

Antimyelin antibodies in clinically isolated syndromes correlate with inflammation in MRI and CSF

Objective: We investigated the correlation of anti-myelin oligodendrocyte glycoprotein- (anti-MOG) and anti-myelin basic protein antibodies (anti-MBP) in serum of CIS patients with inflammatory signs in MRI and in CSF and, as previously suggested, the incidence of more frequent and rapid progression to clinically definite MS (CDMS). Methods: 133 CIS patients were analysed for anti-MOG and anti-MBP (Western blot). Routine CSF and cranial MRI (quantitatively and qualitatively) measures were analyzed. 55 patients had a follow-up of at least 12 months or until conversion to CDMS. Results: Patients with anti-MOG and anti-MBP had an increased intrathecal IgG production and CSF white blood cell count (p = 0.048 and p = 0.036). When anti-MBP alone, or both antibodies were present the cranial MRI showed significantly more T2 lesions (p = 0.007 and p = 0.01, respectively). There was a trend for more lesion dissemination in anti-MBP positive patients (p = 0.076). Conversely, anti-MOG- and/or anti-MBP failed to predict conversion to CDMS in our follow-up group (n = 55). Only in female patients with at least one MRI lesion (n = 34) did the presence of anti-MOG correlate with more frequent (p = 0.028) and more rapid (p = 0.0209) transition to CDMS. Conclusions: Presence of anti-MOG or anti-MBP or both was not significantly associated with conversion to CDMS in our CIS cohort. However, patients with anti-MOG and anti-MBP had higher lesion load and more disseminated lesions in cranial MRI as well as higher values for CSF leucocytes and intrathecal IgG production. Our data support a correlation of anti-MOG and anti-MBP to inflammatory signs in MRI and CSF. The prognostic value of these antibodies for CDMS, however, seems to be less pronounced than previously reporte

Interventions following a high violence risk assessment score : a naturalistic study on a Finnish psychiatric admission ward

Background: Patient aggression and violence against staff members and other patients are common concerns in psychiatric units. Many structured clinical risk assessment tools have recently been developed. Despite their superiority to unaided clinical judgments, staff has shown ambivalent views towards them. A constant worry of staff is that the results of risk assessments would not be used. The aims of the present study were to investigate what were the interventions applied by the staff of a psychiatric admission ward after a high risk patient had been identified, how frequently these interventions were used and how effective they were. Methods: The data were collected in a naturalistic setting during a 6-month period in a Finnish psychiatric admission ward with a total of 331 patients with a mean age of 42.9 years (SD 17.39) suffering mostly from mood, schizophrenia-related and substance use disorders. The total number of treatment days was 2399. The staff assessed the patients daily with the Dynamic Appraisal of Situational Aggression (DASA), which is a structured violence risk assessment considering the upcoming 24 h. The interventions in order to reduce the risk of violence following a high DASA total score (>= 4) were collected from the patients' medical files. Inductive content analysis was used. Results: There were a total of 64 patients with 217 observations of high DASA total score. In 91.2% of cases, at least one intervention aiming to reduce the violence risk was used. Pro re nata (PRN)-medication, seclusion and focused discussions with a nurse were the most frequently used interventions. Non-coercive and non-pharmacological interventions like daily activities associated significantly with the decrease of perceived risk of violence. Conclusion: In most cases, a high score in violence risk assessment led to interventions aiming to reduce the risk. Unfortunately, the most frequently used methods were psychopharmacological or coercive. It is hoped that the findings will encourage the staff to use their imagination when choosing violence risk reducing intervention techniques.Peer reviewe