Beyond a Paycheck - Employment as an Act of Consumption for Gen Y Talents

Purpose: This master thesis aims at bridging the distinct notions of production and consumption by investigating how well-established consumption frameworks gain significance in the employment world. Therefore, we ask how Gen Y talents experience their jobs as commodities and act as consumers in the workplace as a reaction to this comodification. The purpose of this paper is to gain a general understanding of how students and recent graduates consume their work. Conclusion: We conclude that even those areas of our daily life that have previously been distinctly separated, such a employment, can now be viewed from a consumption standpoint. Employees experience and construct employment as a commodity, where jobs are packaged, priced, placed and promoted by capitalist means to sell employers’ offerings to current and potential hires. Employees further see themselves as active consumers in the workplace that choose what they want to do and who they want to do it for. We found that they assume the three primary consumer roles of communicator, hedonist and identity-seeker in the workplace. Theoretical contribution: This paper offers new insights into how consumer frameworks can be applied to areas that have previously not been investigated as areas of consumption, namely the workplace. We therewith extend the relevance of consumption practices and suggest that consumer culture is even encroaching on the productive side of our life. Managerial implications: We see this research topic as especially important for employers, who can gain a new understanding of how employees and job-hunters see themselves as consumers in the work environment. It can either provide insights into how offerings can be better marketed or how the offerings have to be changed to appeal to this new type of empowered and demanding consumer. It might also assist students and recent graduates in understanding their position in the employer-employee relationship better and therewith help them to find a job that matches their demands best. Suggestion for future research: Since the research in this field is in an early phase, there are a lot of additional questions that can be asked. We are particularly interested in how this consumption of work differs between various target groups, such as Gen Y and Gen X or highly educated and less educated employees. We further suggest a longitudinal study to explore how the consumer roles of employees change throughout their life time. Moreover, a critical stance could be taken to explore the negative effects of consumption of the workplace for employees

Effects of Different Seeds Pretreatments on the Germination of Five Local Trees: Four From The Fabaceae Family and One From the Bombacacea

The problem of the effectiveness of the established pre-treatments of seeds of local tree species is posed more and more often, with acuity. It appears necessary that studies are led to explore new methods of pre-treatment, or to update the old instructions applied, for a better success and at lower cost, of the production of seedlings. The present study concerned five species: Acacia nilotica, Adansonia digitata, Parkia biglobosa, Piliostigma reticulatum and Tamarindus indica. The methodology involved subjecting the seeds to three different pretreatments: (i) seeds treated with sulfuric acid (T0), (ii) seeds soaked in hot water for 24 hours (T1), (iii) seeds soaked in hot water for 48 hours (T2). For Acacia nilotica seeds, the different pretreatments did not result in statistically different germination rates. The germination rates are 77%, 65% and 62% (respectively for soaking in hot water for 48 hours, sulfuric acid and soaking in hot water for 24 hours). The different pretreatments also do not result in different germination rates for Tamarindus indica. Indeed, the germination rates after 30 days for this species are good but statistically identical (85% for the pretreatment with sulfuric acid and hot water for 24 hours). For Adansonia digitata, Parkia biglobosa and Piliostigma reticulatum, the sulfuric acid pretreatment gave the best germination rate (49%, 54% and 41% respectively). The results of this study may have practical consequences in terms of management of the different species studied. They show that immersing in boiling water and left for 24 hours and 48 hours yields fairly satisfactory germination rates for Acacia nilotica and Tamarindus indica seeds. These inexpensive techniques, accessible to all, can be considered as means to easily produce seedlings of these species

Chemokine-enhanced DNA vaccination in cancer immunotherapy.

We have demonstrated that priming of intratumoral and intradermal vaccination sites with chemokines enhances cytotoxic immune response against established neoplasms. Additional insights into the molecular mechanisms that underlie these findings and the optimization of such an approach may lead to the development of cost-effective and generic immunotherapeutic regimens against cancer

Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds

Rotational invariance is a popular inductive bias used by many fields in machine learning, such as computer vision and machine learning for quantum chemistry. Rotation-invariant machine learning methods set the state of the art for many tasks, including molecular property prediction and 3D shape classification. These methods generally either rely on task-specific rotation-invariant features, or they use general-purpose deep neural networks which are complicated to design and train. However, it is unclear whether the success of these methods is primarily due to the rotation invariance or the deep neural networks. To address this question, we suggest a simple and general-purpose method for learning rotation-invariant functions of three-dimensional point cloud data using a random features approach. Specifically, we extend the random features method of Rahimi & Recht 2007 by deriving a version that is invariant to three-dimensional rotations and showing that it is fast to evaluate on point cloud data. We show through experiments that our method matches or outperforms the performance of general-purpose rotation-invariant neural networks on standard molecular property prediction benchmark datasets QM7 and QM9. We also show that our method is general-purpose and provides a rotation-invariant baseline on the ModelNet40 shape classification task. Finally, we show that our method has an order of magnitude smaller prediction latency than competing kernel methods

Using a multimodal immersive environment to investigate perceptions in augmented virtual reality systems

The Collaborative-Research Augmented Immersive Virtual Environment Laboratory at Rensselaer is a state-of-the-art space that offers users the capabilities of multimodality and immersion. Realistic and abstract sets of data can be explored in a variety of ways, even in large group settings. This paper discusses the motivations of the immersive experience and the advantages over smaller scale and single-modality expressions of data. One experiment focuss on the influence of immersion on perceptions of architectural renderings. Its findings suggest disparities between participants’ judgment when viewing either two-dimensional printouts or the immersive CRAIVE-Lab screen. The advantages of multimodality are discussed in an experiment concerning abstract data exploration. Various auditory cues for aiding in visual data extraction were tested for their affects on participants’ speed and accuracy of information extraction. Finally, artificially generated auralizations are paired with recreations of realistic spaces to analyze the influences of immersive visuals on the perceptions of sound fields. One utilized method for creating these sound fields is a geometric ray-tracing model, which calculates the auditory streams of each individual loudspeaker in the lab to create a cohesive sound field representation of the visual space

Interstate Voter Registration Database Matching: The Oregon-Washington 2008 Pilot Project

Voter registration databases maintain lists of registered voters that are used to determine who is and is not eligible to vote in an election. As such, accurate voter registration databases form a cornerstone of the electoral process. In the United States, each state maintains its own voter registration database. It is not uncommon for a voter to become registered in two states, for example as a result of moving from one state to the other or of living in one state and working in one another. In this paper, we report on a pilot interstate voter registration database matching project between the two states of Oregon andWashington whose goal was to explore the feasibility of using database matching to identify voters registered in the two states, and to do so with as much openness and transparency as possible. We describe the matching algorithms used, the procedures taken with found matches, and the resulting actions taken on actual voter registrations. We also discuss several directions for improving matching algorithms and procedures

Ladarixin, a dual CXCR1/2 inhibitor, attenuates experimental melanomas harboring different molecular defects by affecting malignant cells and tumor microenvironment.

CXCR1 and CXCR2 chemokine receptors and their ligands (CXCL1/2/3/7/8) play an important role in tumor progression. Tested to date CXCR1/2 antagonists and chemokine-targeted antibodies were reported to affect malignant cells in vitro and in animal models. Yet, redundancy of chemotactic signals and toxicity hinder further clinical development of these approaches. In this pre-clinical study we investigated the capacity of a novel small molecule dual CXCR1/2 inhibitor, Ladarixin (LDX), to attenuate progression of experimental human melanomas. Our data showed that LDX-mediated inhibition of CXCR1/2 abrogated motility and induced apoptosis in cultured cutaneous and uveal melanoma cells and xenografts independently of the molecular defects associated with the malignant phenotype. These effects were mediated by the inhibition of AKT and NF-kB signaling pathways. Moreover, systemic treatment of melanoma-bearing mice with LDX also polarized intratumoral macrophages to M1 phenotype, abrogated intratumoral de novo angiogenesis and inhibited melanoma self-renewal. Collectively, these studies outlined the pre-requisites of the successful CXCR1/2 inhibition on malignant cells and demonstrated multifactorial effects of Ladarixin on cutaneous and uveal melanomas, suggesting therapeutic utility of LDX in treatment of various melanoma types

Mitigating Molecular Aggregation in Drug Discovery with Predictive Insights from Explainable AI

As the importance of high-throughput screening (HTS) continues to grow due to its value in early stage drug discovery and data generation for training machine learning models, there is a growing need for robust methods for pre-screening compounds to identify and prevent false-positive hits. Small, colloidally aggregating molecules are one of the primary sources of false-positive hits in high-throughput screens, making them an ideal candidate to target for removal from libraries using predictive pre-screening tools. However, a lack of understanding of the causes of molecular aggregation introduces difficulty in the development of predictive tools for detecting aggregating molecules. Herein, we present an examination of the molecular features differentiating datasets of aggregating and non-aggregating molecules, as well as a machine learning approach to predicting molecular aggregation. Our method uses explainable graph neural networks and counterfactuals to reliably predict and explain aggregation, giving additional insights and design rules for future screening. The integration of this method in HTS approaches will help combat false positives, providing better lead molecules more rapidly and thus accelerating drug discovery cycles.Comment: 17 pages, plus S