Green Carbon Footprint for Model Inference Serving via Exploiting Mixed-Quality Models and GPU Partitioning

This paper presents a solution to the challenge of mitigating carbon emissions from large-scale high performance computing (HPC) systems and datacenters that host machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to datacenter compute cycles and carbon emissions. We introduce Clover, a carbon-friendly ML inference service runtime system that balances performance, accuracy, and carbon emissions through mixed-quality models and GPU resource partitioning. Our experimental results demonstrate that Clover is effective in substantially reducing carbon emissions while maintaining high accuracy and meeting service level agreement (SLA) targets. Therefore, it is a promising solution toward achieving carbon neutrality in HPC systems and datacenters

Recommendation in Enterprise 2.0 Social Media Streams

A social media stream allows users to share user-generated content as well as aggregate different external sources into one single stream. In Enterprise 2.0 such social media streams empower co-workers to share their information and to work efficiently and effectively together while replacing email communication. As more users share information it becomes impossible to read the complete stream leading to an information overload. Therefore, it is crucial to provide the users a personalized stream that suggests important and unread messages. The main characteristic of an Enterprise 2.0 social media stream is that co-workers work together on projects represented by topics: the stream is topic-centered and not user-centered as in public streams such as Facebook or Twitter. A lot of work has been done dealing with recommendation in a stream or for news recommendation. However, none of the current research approaches deal with the characteristics of an Enterprise 2.0 social media stream to recommend messages. The existing systems described in the research mainly deal with news recommendation for public streams and lack the applicability for Enterprise 2.0 social media streams. In this thesis a recommender concept is developed that allows the recommendation of messages in an Enterprise 2.0 social media stream. The basic idea is to extract features from a new message and use those features to compute a relevance score for a user. Additionally, those features are used to learn a user model and then use the user model for scoring new messages. This idea works without using explicit user feedback and assures a high user acceptance because no intense rating of messages is necessary. With this idea a content-based and collaborative-based approach is developed. To reflect the topic-centered streams a topic-specific user model is introduced which learns a user model independently for each topic. There are constantly new terms that occur in the stream of messages. For improving the quality of the recommendation (by finding more relevant messages) the recommender should be able to handle the new terms. Therefore, an approach is developed which adapts a user model if unknown terms occur by using terms of similar users or topics. Also, a short- and long-term approach is developed which tries to detect short-term interests of users. Only if the interest of a user occurs repeatedly over a certain time span are terms transferred to the long-term user model. The approaches are evaluated against a dataset obtained through an Enterprise 2.0 social media stream application. The evaluation shows the overall applicability of the concept. Specifically the evaluation shows that a topic-specific user model outperforms a global user model and also that adapting the user model according to similar users leads to an increase in the quality of the recommendation. Interestingly, the collaborative-based approach cannot reach the quality of the content-based approach

Identification and validation of potential new biomarkers for prostate cancer diagnosis and prognosis using 2D-DIGE and MS

This study was designed to identify and validate potential new biomarkers for prostate cancer and to distinguish patients with and without biochemical relapse. Prostate tissue samples analyzed by 2D-DIGE (two-dimensional difference in gel electrophoresis) and mass spectrometry (MS) revealed downregulation of secernin-1 (P < 0.044) in prostate cancer, while vinculin showed significant upregulation (P < 0.001). Secernin-1 overexpression in prostate tissue was validated using Western blot and immunohistochemistry while vinculin expression was validated using immunohistochemistry. These findings indicate that secernin-1 and vinculin are potential new tissue biomarkers for prostate cancer diagnosis and prognosis, respectively. For validation, protein levels in urine were also examined by Western blot analysis. Urinary vinculin levels in prostate cancer patients were significantly higher than in urine from nontumor patients (P = 0.006). Using multiple reaction monitoring-MS (MRM-MS) analysis, prostatic acid phosphatase (PAP) showed significant higher levels in the urine of prostate cancer patients compared to controls (P = 0.012), while galectin-3 showed significant lower levels in the urine of prostate cancer patients with biochemical relapse, compared to those without relapse (P = 0.017). Three proteins were successfully differentiated between patients with and without prostate cancer and patients with and without relapse by using MRM. Thus, this technique shows promise for implementation as a noninvasive clinical diagnostic technique

Task Recommendation in Crowdsourcing Platforms

Task distribution platforms, such as micro-task markets, project assignment portals, and job search engines, support the assignment of tasks to workers. Public crowdsourcing platforms support the assignment of tasks in micro-task markets to help task requesters to complete their tasks and allow workers to earn money. Enterprise crowdsourcing platforms provide a marketplace within enterprises for the internal placement of tasks from employers to employees. Most of both types of task distribution platforms rely on the workers' selection capabilities or provide simple filtering steps to reduce the number of tasks a worker can choose from. This self-selection mechanism unfortunately allows for tasks to be performed by under- or over-qualified workers. Supporting the workers by introducing a task recommender system helps to solve such deficits of existing task distributions. In this thesis, the requirements towards task recommendation in task distribution platforms are gathered with a focus on the worker's perspective, the design of appropriate assignment strategies is described, and innovative methods to recommend tasks based on their textual descriptions are provided. Different viewpoints are taken into account by analyzing the domains of micro-tasks, project assignments, and job postings. The requirements of enterprise crowdsourcing platforms are compiled based on the literature and a qualitative study, providing a conceptual design of task assignment strategies. The demands of workers and their perception of task similarity on public crowdsourcing platforms are identified, leading to the design and implementation of additional methods to determine the similarity of micro-tasks. The textual descriptions of micro-tasks, projects, and job postings are analyzed in order to provide innovative methods for task recommendation in these domains

Characterization of population heterogeneity in a model biotechnological process using Pseudomonas putida

Biotechnological processes are distinguished from classical chemistry by employing bio-molecules or whole cells as the catalytic element, providing unique reaction mechanisms with unsurpassed specificity. Whole cells are the most versatile \''factories\'' for natural or non-natural products, however, the conversion of e.g. hydrophobic substrates can quickly become cytotoxic. One host organism with the potential to handle such conditions is the gram-negative bacterium Pseudomonas putida, which distinguishes itself by solvent tolerance, metabolic flexibility, and genetic amenability. However, whole cell bioconversions are highly complex processes. A typical bottleneck compared to classical chemistry is lower yield and reproducibility owing to cell-to-cell variability. The intention of this work was therefore to characterize a model producer strain of P. putida KT2440 on the single cell level to identify non-productive or impaired subpopulations. Flow cytometry was used in this work to discriminate subpopulations regarding DNA content or productivity, and further mass spectrometry or digital PCR was employed to reveal differences in protein composition or plasmid copy number. Remarkably, productivity of the population was generally bimodally distributed comprising low and highly producing cells. When these two subpopulations were analyzed by mass spectrometry, only few metabolic changes but fundamental differences in stress related proteins were found. As the source for heterogeneity remained elusive, it was hypothesized that cell cycle state may be related to production capacity of the cells. However, subpopulations of one, two, or higher fold DNA content were virtually identical providing no clear hints for regulatory differences. On the quest for heterogeneity the loss of genetic information came into focus. A new work flow using digital PCR was created to determine the absolute number of DNA copies per cell and, finally, lack of expression could be attributed to loss of plasmid in non-producing cells. The average plasmid copy number was shown to be much lower than expected (1 instead of 10-20). In conclusion, this work established techniques for the quantification of proteins and DNA in sorted subpopulations, and by these means provided a highly detailed picture of heterogeneity in a microbial population

Addressing surface wave inversion non-uniqueness and the implications for seismic site response analyses

Surface ground motions predicted from a seismic site response analysis are strongly dependent on the shear wave velocity (Vs) profile used to represent the small-strain shearing stiffness of subsurface materials as a function of depth. Moreover, uncertainties are present in the input Vs profile and this uncertainty leads to uncertainty in the predicted site response. When Vs profiles are obtained from surface wave inversion, the final derived Vs profiles are non-unique, with many different interpretations of the subsurface shearing stiffness that are consistent with the measured surface wave field data. This non-uniqueness is exacerbated by the ambiguous interpretation of surface wave modes and the subjectivity of defining the inversion parameterization (trial number of layers and ranges in their respective Vs, compression wave velocities, and mass densities). Thus, it is necessary to develop strategies to systematically address these issues in order to develop Vs profiles with realistic estimates of uncertainty for use in site response analyses. First, a-priori information should be sought to aid in interpreting modes and to develop a realistic parameterization to guide the surface wave inversion. In complex geologic settings, it can be extremely challenging or even impossible to obtain geologically-realistic Vs profiles without this a-priori information. However, there are many situations when a-priori information is scarce or nonexistent. In such cases, alternative mode interpretations and/or parameterizations must be considered. Even with abundant a-prior information, the non-uniqueness issue generally cannot be eliminated and the variation in seismic site response associated with non-unique Vs profiles derived from the same surface wave dataset is of interest. At the two sites considered in this study, very different Vs profiles derived from surface wave inversion of the same dataset produced very similar site response estimates, provided that the experimental surface wave dispersion data was well-fit. Furthermore, the site response estimates associated with these Vs profiles were more accurate and less variable than those associated with Vs profiles that were developed using common strategies of accounting for Vs uncertainty. Thus, despite the non-uniqueness issue, Vs profiles derived from a rigorous surface wave inversion can yield robust site response estimates.Civil, Architectural, and Environmental Engineerin