Ray: A Distributed Execution Engine for the Machine Learning Ecosystem

In recent years, growing data volumes and more sophisticated computational procedures have greatly increased the demand for computational power. Machine learning and artificial intelligence applications, for example, are notorious for their computational requirements. At the same time, Moores law is ending and processor speeds are stalling. As a result, distributed computing has become ubiquitous. While the cloud makes distributed hardware infrastructure widely accessible and therefore offers the potential of horizontal scale, developing these distributed algorithms and applications remains surprisingly hard. This is due to the inherent complexity of concurrent algorithms, the engineering challenges that arise when communicating between many machines, the requirements like fault tolerance and straggler mitigation that arise at large scale and the lack of a general-purpose distributed execution engine that can support a wide variety of applications.In this thesis, we study the requirements for a general-purpose distributed computation model and present a solution that is easy to use yet expressive and resilient to faults. At its core our model takes familiar concepts from serial programming, namely functions and classes, and generalizes them to the distributed world, therefore unifying stateless and stateful distributed computation. This model not only supports many machine learning workloads like training or serving, but is also a good t for cross-cutting machine learning applications like reinforcement learning and data processing applications like streaming or graph processing. We implement this computational model as an open-source system called Ray, which matches or exceeds the performance of specialized systems in many application domains, while also offering horizontally scalability and strong fault tolerance properties

What drives Consumers\u27 Trust in Proactive Services: A Best-Worst scaling approach

Increasing advancements in digital technologies, especially in artificial intelligence, are changing the nature of services. Services no longer rely on the consumers making the first move, but instead, service providers can anticipate consumers’ needs and address them proactively by so-called proactive services (PAS). Within this new service type, consumers may enable the service provider to decide upon the consideration, decision, and enactment of the service. In PAS, consumers assign these previously “owned” phases to the service provider and thereby, also devolve power to the provider. Thus, trust is an indisputable prerequisite for consumer acceptance. However, it is unclear how individual characteristics of PAS impact consumers’ trust. Addressing this research gap, this research-in-progress paper proposes a Best-Worst scaling survey in which potential consumers of two exemplary PAS state their trust with respect to different PAS characteristics. Thereby, this paper will extend the knowledge in understanding PAS

Load-bearing capacity of screw-retained CAD/CAM-produced titanium implant frameworks (I-Bridge®2) before and after cyclic mechanical loading

Implant-supported screw-retained fixed dental prostheses (FDPs) produced by CAD/ CAM have been introduced in recent years for the rehabilitation of partial or total endentulous jaws. However, there is a lack of data about the long-term mechanical characteristics. OBJECTIVE: The aim of this study was to investigate the failure mode and the influence of extended cyclic mechanical loading on the load-bearing capacity of these frameworks. MATERIAL AND METHODS: Ten five-unit FDP frameworks simulating a free-end situation in the mandibular jaw were manufactured according to the I-Bridge®2-concept (I-Bridge®2, Biomain AB, Helsingborg, Sweden) and each was screw-retained on three differently angulated Astra Tech implants (30º buccal angulation/0º angulation/30º lingual angulation). One half of the specimens was tested for static load-bearing capacity without any further treatment (control), whereas the other half underwent five million cycles of mechanical loading with 100 N as the upper load limit (test). All specimens were loaded until failure in a universal testing machine with an occlusal force applied at the pontics. Load-displacement curves were recorded and the failure mode was macro- and microscopically analyzed. The statistical analysis was performed using a t-test (p=0.05). RESULTS: All the specimens survived cyclic mechanical loading and no obvious failure could be observed. Due to the cyclic mechanical loading, the load-bearing capacity decreased from 8,496 N±196 N (control) to 7,592 N±901 N (test). The cyclic mechanical loading did not significantly influence the load-bearing capacity (p=0.060). The failure mode was almost identical in all specimens: large deformations of the framework at the implant connection area were obvious. CONCLUSION: The load-bearing capacity of the I-Bridge®2 frameworks is much higher than the clinically relevant occlusal forces, even with considerably angulated implants. However, the performance under functional loading in vivo depends on additional aspects. Further studies are needed to address these aspects