Abstract-Transportation mode inference is an important research direction and has many applications. Existing methods are usually based on fine-grained sampling --collecting position data from mobile devices at high frequency. These methods can achieve high accuracy, but also incur cost and complexity in terms of the system implementation and computational resource requirements. Finally, fine-grained sampling is not always available, especially for large-scale deployment. This paper proposes a novel method to infer transportation mode based on coarse-grained call detail records. The method allows estimating the transportation mode share from a given origin to a given destination, looking also at how the share changes over time. The method can achieve acceptable accuracy with trivial cost and complexity and is suitable for the statistical analysis on transportation modes of a large population. The method can also be used as a complementary tool in situations where fine-grained sampling is unavailable or the balance between accuracy and complexity is critical. A case study using real call detail records data for the city of Boston shows the performance of the proposed method. I. INTRODUCTIONode of transportation specifies one of different kinds of transport facilities that are used to transport people, such as cars, buses, bicycles, and even walking. Transportation mode inference is a tool to determine the transportation mode of an individual traveler or a group of travelers, based on the speed, travel time or other information that can be collected from their trips. This tool has been used to provide traveling services, manage transportation and plan cities.The research on transportation mode inference has a history of more than a decade. At early stage, the technology was studied in the field of pervasive or ubiquitous computing, where the computation needs to understand the context. The context includes human activities, such as walking or driving, when the computation is being done. Body worn sensors (sensors placed in one or more positions on the body) are the major data source; see for instance [1]. However, since body worn sensors are not widely available, many research efforts tried to adopt mobile sensors, such as mobile phone or GPS, as data collection devices. Therefore, existing methods for transportation mode inference can be divided into two categories according to the data collection devices. A. Mobile phone based methodsMobile phone based methods infer the transportation mode by estimating the mobile phone's speed. The speed can be estimated by measuring the low level signals from the GSM network, such as the variance of GSM signal strength or the switch rate of cells. Generally, the variance of signal strength and the number of connected cells are greater when the mobile phone is moving faster. When the speed is within a range, it is believed that the mobile phone user is in a specific transportation mode. Since these methods are based on the speed, they can hardly distinguish transportation mode...

The objective of this study is to compare the effectiveness of olanzapine combined with ondansetron or ondansetron alone in preventing chemotherapy-induced nausea and vomiting (CINV) of non-small cell lung cancer (NSCLC). A total of 84 NSCLC patients were equally randomized into intervention group and control group. Both groups were intravenously administered with ondansetron 8 mg 30 min before chemotherapy. In the intervention group, olanzapin 10 mg was orally administered for 8 days, beginning from the first morning of chemotherapy. The antiemetic effectiveness was evaluated in the first chemotherapy cycle. The incidence of acute vomiting was 33.33 % (14/42) and 54.76 % (23/42) in the intervention group and control group (p < 0.05) whereas that of delayed vomiting was 16.57 % (7/42) and 47.62 % (20/42) (p < 0.05). Compared with ondansetron alone, the combination of olanzapine with ondansetron has better effectiveness in preventing CINV in NSCLC patients, particularly for the delayed type.

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Mambo [4] is IBM's full-system simulator which models PowerPC systems, and provides a complete set of simulation tools to help IBM and its partners in pre-hardware development and performance evaluation for future systems. Currently Mambo simulates target systems on a single host thread. When the number of cores increases in a target system, Mambo's simulation performance for each core goes down. As the so-called "multi-core era" approaches, both target and host systems will have more and more cores. It is very important for Mambo to efficiently simulate a multicore target system on a multi-core host system. Parallelization is a natural method to speed up Mambo under this situation.Parallel Mambo (P-Mambo) is a multi-threaded implementation of Mambo. Mambo's simulation engine is implemented as a user-level thread-scheduler. We propose a multischeduler method to adapt Mambo's simulation engine to multi-threaded execution. Based on this method a corebased module partition is proposed to achieve both high inter-scheduler parallelism and low inter-scheduler dependency. Protection of shared resources is crucial to both correctness and performance of P-Mambo. Since there are two tiers of threads in P-Mambo, protecting shared resources by only OS-level locks possibly introduces deadlocks due to user-level context switch. We propose a new lock mechanism to handle this problem. Since Mambo is an on-going project with many modules currently under development, co-existence with new modules is also important to P-Mambo. We propose a global-lock-based method to guarantee compatibility of P-Mambo with future Mambo modules.We have implemented the first version of P-Mambo in functional modes. The performance of P-Mambo has been evaluated on the OpenMP implementation of NAS Parallel Benchmark (NPB) 3.2 [12]. Preliminary experimental results show that P-Mambo achieves an average speedup of 3.4 on a 4-core host machine.

Abstract-Multi-core phones are now pervasive. Yet, existing applications rely predominantly on a client-server computing paradigm, using phones only as thin clients, sending sensed information via the cellular network to servers for processing. This makes the cellular network the bottleneck, limiting overall application performance. In this paper, we propose MobiStreams, a Distributed Stream Processing System (DSPS) that runs directly on smartphones. MobiStreams can offload computing from remote servers to local phones and thus alleviate the pressure on the cellular network. Implementing DSPS on smartphones faces significant challenges: 1) multiple phones can readily fail simultaneously, and 2) the phones' ad-hoc WiFi network has low bandwidth. MobiStreams tackles these challenges through two new techniques: 1) tokentriggered checkpointing, and 2) broadcast-based checkpointing. Our evaluations driven by two real world applications deployed in the US and Singapore show that migrating from a server platform to a smartphone platform eliminates the cellular network bottleneck, leading to 0.78∼42.6X throughput increase and 10%∼94.8% latency decrease. Also, MobiStreams' fault tolerance scheme increases throughput by 230% and reduces latency by 40% vs. prior state-of-the-art fault-tolerant DSPSs.