Joint Research. This Agreement is intended by the Parties to be considered a “Joint Research Agreement” for the purposes of 35 U.S.C. 102(c), or any successor to that statute in the United States, or other statutes having similar effect in jurisdictions outside the United States. Each Party consents to the other Party disclosing the names of the Parties to this agreement and identifying this Agreement as a “Joint Research Agreement” in any patent application filed pursuant to this Agreement, including, for the avoidance of doubt, patents claiming Indivior Improvements.
Joint Research a. The Parties shall support the joint research by their experts and scholars in the field of non- traditional security issues, and share the results of research between them; and
Joint Research. 7.1 SVI, UCSF and Researchers contemplate that performance of activities arising from this Agreement may include joint or collaborative research and activities between the Parties and/or affiliates or successors in interest thereof. Hence, this Agreement may be asserted as a joint research agreement for the performance of experimental, developmental or research work in the field of Interventional Magnetic Resonance (IMR) deemed to have been owned by the same person or subject to an obligation of assignment to the same person under 35 USC § 103(c) as provided for in the Cooperative Research and Technology Enhancement Act of 2004.
Joint Research. The parties will cooperate in performance of the Research pursuant to the following terms and conditions:
Joint Research. The parties shall, within six (6) months after the Effective Date, agree on a joint program to define and test the applicability of each other's intellectual property in the fields of high content drug screening and flow cytometry. Each party shall appoint a Program Manager for this effort. The Program managers shall be responsible for defining the scope of the joint program, the parties' activities under such program and the specific resources of each party that will be engaged.
Joint Research. Agreement under the Xxxxx-Xxxxx America Invents Act. Notwithstanding anything to the contrary in this ARTICLE 9, neither Party will have the right to make an election under 35 U.S.C. § 102(c) of the Xxxxx-Xxxxx America Invents Act when exercising its rights under this ARTICLE 9 without the prior written consent of the other Party, which consent will not be unreasonably withheld. With respect to any such permitted election, each Party will use reasonable efforts to cooperate and coordinate their activities with the other Party with respect to any submissions, filings or other activities in support thereof. The Parties acknowledge and agree that this Agreement is a “joint research agreement” as defined in 35 U.S.C. § 100(h).
Joint Research. 15 - Both Institutions will pay special attention to joint research projects. The details of specific research projects and the form of cooperation associated therewith will be determined by mutual agreement and concluded by a separate memorandum of understanding.
Joint Research. The joint research falls into the following three sub-activities A Compositional validation: The complexity of a given analysis method is not only determined by its accuracy (and issues addressed) but mainly by the sheer size of the model analysed measure in number of components, tasks, variables, etc. In order to achieve methods which scale to the need of industry compositionality is paramount. That is, it should be possible for composite models to be interrelated and properties to be inferred only by consideration of the components of the models and their interfaces. In the presence of composite models with heterogeneous components – in particular involving components where quantitative aspects are considered – this is a challenge that has not yet been dealt with satisfactory.
Joint Research. The main scientific challenges addressed in this activity are focused on how to map complex applications onto multi-core hardware platforms. This includes addressing allocation and scheduling issues like: scalability, flexibility, composability, predictability, design-time reduction and increased dynamism. The problem is complex and multi-faceted. On one hand, we have static (design/compile time) approaches, where applications are analyzed and optimal mapping decisions are taken before the platform is deployed in the field. On the other hand, we have dynamic, runt-time approaches where mapping decisions are taken online, and they are triggered by environmental and workload variations. While these approaches start from different premises, they should not be regarded as alternative, rather they are synergistic. Design time analysis and decisions can help in providing a good starting point for run-time adaptation, moreover off-line pre-computation can reduce the overhead of the online policies making them more reactive and less resource-hungry. One important requisite for any mapping strategy is to ensure predictability AND efficiency. Note that online adaptation is not adverse to predictability: if online adaptation is based on feedback control (e.g. finite horizon), it can be used to “stabilize” the system, and make it more robust (predictable) in response to environmental variations (e.g. temperature). Another scientific challenge addressed in this activity is the development innovative reliable multicore programming models and architecture platform able to address computation and control oriented applications. One key building block is the development of efficient synchronization & communication abstractions that are required for successfully deploying MPSoCs in embedded application domains. Efficiency is inherently related to both power and performance, hence it is an energy metric. In embedded systems, productivity-enhancing abstractions are acceptable only if they do not compromise efficiency, so the focus is on how to enable fast development (debugging, tuning) without losing efficiency. It is also extremely important to take into account variability of both hardware fabrics and application workloads, which is deemed to rapidly increase. Hence, the concurrency management layer should provide means for dynamically managing workload variations, as well as hardware unpredictability sources. In the first 18 month of the project, the partners will focus on...
Joint Research. The scientific challenges within the cluster include: (Adaptivity in system modelling – how is adaptivity modelled • Efficient adaptation – how can adaptation mechanisms be made resource efficient • Frameworks for adaptivity – unified frameworks for adaptivity (negotiation, contracts, QoS) • Predictable and dependable adaptivity – what types of formal guarantees concerning predictability and dependability can be stated for an adaptive system • Robustness and adaptivity – the relationships between robust design techniques and adaptive design techniques • Adaptivity from an application’s point of view – how should the adaptation mechanisms be exposed to the application developers (APIs etc) Both software and hardware related adaptivity issues will be considered within the cluster, although the majority of the teams are working on the software issues. The main focus will be run-time adaptivity, rather than off-line adaptivity.
