The increasing demand for online advertising leads to a strong competition in Real-Time Biddinng (RTB) industry. It requires Demand-Side Platforms (DSPs) to perform a proper market price modeling that predicts the landscape of competitors’ bids, in order to maximize their profits. Under this circumstance, RTB industry has recently been changing from second-price auctions (SPA) to first-price auctions (FPA), and thus DSPs now face two different auction types simultaneously. Most previous studies on market price modeling, however, have been suggested mainly for SPA, and the censorship problem of FPA has still been largely unexplored. Moreover, since those studies focused on only one auction type (either SPA or FPA), it takes additional computational and operational resources to apply these approaches to an environment where two types of auction are mixed. To this end, we introduce a novel unified approach named Conditional Distribution Modeling (CDM) to estimate market price probability distribution for SPA and FPA altogether. We utilize survival analysis and neural network to handle both right-censored problem in SPA and doubly-censored problem in FPA. Our model outperformed the previous models specifically developed either for SPA or FPA on two large-scale real-world datasets. Furthermore, our approach showed robust performance even when applied to mixed datasets with two auction types. These results indicate that our proposed model has an advantage in terms of both performance metrics and operational efficiency in a complex RTB environment.

Handling of storage IO in modern operating systems assumes that such devices are slow and CPU cycles are valuable. Consequently, to effectively exploit the underlying hardware resources, for example, CPU cycles, storage bandwidth and the like, whenever an IO request is issued to such device, the requesting thread is switched out in favor of another thread that may be ready to execute. Recent advances in nonvolatile storage technologies and multicore CPUs make both of these assumptions increasingly questionable, and an unconditional context switch is no longer desirable. In this article, we propose a novel mechanism called SmartCon, which intelligently decides whether to service a given IO request in interrupt-driven manner or busy-wait--based manner based on not only the device characteristics but also dynamic parameters such as IO latency, CPU utilization, and IO size. We develop an analytic performance model to project the performance of SmartCon for forthcoming devices. We implement SmartCon mechanism on Linux 2.6 and perform detailed evaluation using three different IO devices: Ramdisk, low-end SSD, and high-end SSD. We find that SmartCon yields up to a 39% performance gain over the mainstream block device approach for Ramdisk, and up to a 45% gain for PCIe-based SSD and SATA-based SSDs. We examine the detailed behavior of TLB, L1, L2 cache and show that SmartCon achieves significant improvement in all cache misbehaviors.

In this work, we developed a Heap-Based Persistent Object Store (HEAPO) to manage persistent objects in byte-addressable Nonvolatile RAM (NVRAM). HEAPO defines its own persistent heap layout, the persistent object format, name space organization, object sharing and protection mechanism, and undo-only log-based crash recovery, all of which are effectively tailored for NVRAM. We put our effort into developing a lightweight and flexible layer to exploit the DRAM-like access latency of NVRAM. To address this objective, we developed (i) a native management layer for NVRAM to eliminate redundancy between in-core and on-disk copies of the metadata, (ii) an expandable object format, (iii) a burst trie-based global name space with local name space caching, (iv) static address binding, and (v) minimal logging for undo-only crash recovery. We implemented HEAPO at commodity OS (Linux 2.6.32) and measured the performance. By eliminating metadata redundancy, HEAPO improved the speed of creating, attaching, and expanding an object by 1.3×, 4.5×, and 3.8×, respectively, compared to memory-mapped file-based persistent object store. Burst trie-based name space organization of HEAPO yielded 7.6× better lookup performance compared to hashed B-tree-based name space of EXT4. We modified memcachedb to use HEAPO in maintaining its search structure. For hash table update, HEAPO-based memcachedb yielded 3.4× performance improvement against original memcachedb implementation which uses mmap() over ramdisk approach to maintain the key-value store in memory.

Characterizing the transcriptomic change between human and chimpanzee brains can provide clues for identifying the cellular functions underlying human's enhanced cognition and the increased vulnerability to the neurodegenerative and psychiatric disorders. Despite some successes, previous studies might have limited sensitivity to detect specific cellular functions since the focus has been only on the strong signals appeared by individual genes or pairs. To overcome these limitations, we carried out two functional module-centric transcriptome analyses. We evaluated the coordinated differential expression and the altered coexpression within the various types of functional modules. In our comparative assessment, the functional module-centric coexpression analysis identified the most extensive functional implications, in comparison to the conventional gene-centric and functional module-centric differential expression analyses. Through the functional module-centric coexpression analysis, we detected several human-specific modules associated with human-specific phenotypes related on neurobiological processes. In summary, our results suggest the rearrangement of transcriptional regulation over individual and multiple functional modules as a plausible basis of human brain specializations.

BACKGROUND: The selection of genes that discriminate disease classes from microarray data is widely used for the identification of diagnostic biomarkers. Although various gene selection methods are currently available and some of them have shown excellent performance, no single method can retain the best performance for all types of microarray datasets. It is desirable to use a comparative approach to find the best gene selection result after rigorous test of different methodological strategies for a given microarray dataset. RESULTS: FiGS is a web-based workbench that automatically compares various gene selection procedures and provides the optimal gene selection result for an input microarray dataset. FiGS builds up diverse gene selection procedures by aligning different feature selection techniques and classifiers. In addition to the highly reputed techniques, FiGS diversifies the gene selection procedures by incorporating gene clustering options in the feature selection step and different data pre-processing options in classifier training step. All candidate gene selection procedures are evaluated by the .632+ bootstrap errors and listed with their classification accuracies and selected gene sets. FiGS runs on parallelized computing nodes that capacitate heavy computations. FiGS is freely accessible at http://gexp.kaist.ac.kr/figs. CONCLUSION: FiGS is an web-based application that automates an extensive search for the optimized gene selection analysis for a microarray dataset in a parallel computing environment. FiGS will provide both an efficient and comprehensive means of acquiring optimal gene sets that discriminate disease states from microarray datasets.