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    • 1. 发明授权
    • Barycentric coordinate technique for resampling quantized signals
    • 用于重新采样量化信号的重心坐标技术
    • US07573952B1
    • 2009-08-11
    • US11210570
    • 2005-08-23
    • Sajjit ThampyKenny C. GrossKeith Whisnant
    • Sajjit ThampyKenny C. GrossKeith Whisnant
    • H04L27/00
    • G06F17/18
    • One embodiment of the present invention provides a system that resamples a quantized signal. During operation, the system receives the quantized signal. Next, the system smoothes and resamples the quantized signal to produce a resampled signal. The system then quantizes the resampled signal to produce a quantized resampled signal. For a given time point, the system determines a probability distribution for the resampled signal across quantization levels at the given time point by using information about the values of the resampled signal at neighboring time points. Note that the probability distribution specifies the probability that the resampled signal would be sampled at specific quantization levels. The system then uses the probability distribution to probabilistically select a quantization level for the resampled signal for the given time point.
    • 本发明的一个实施例提供一种重新采样量化信号的系统。 在运行期间,系统接收量化信号。 接下来,系统平滑和重新采样量化信号以产生重采样信号。 然后,系统对重采样信号进行量化以产生量化的重采样信号。 对于给定的时间点,系统通过使用关于在相邻时间点的重采样信号的值的信息来确定在给定时间点处跨量化电平的重采样信号的概率分布。 注意,概率分布规定了在特定量化级别对采样信号进行采样的概率。 系统然后使用概率分布概率地选择给定时间点的重采样信号的量化电平。
    • 2. 发明授权
    • Method and apparatus for detecting memory leaks in computer systems
    • 用于检测计算机系统中的内存泄漏的方法和装置
    • US07716648B2
    • 2010-05-11
    • US11195015
    • 2005-08-02
    • Kalyanaraman VaidyanathanSajjit ThampyKenny C. Gross
    • Kalyanaraman VaidyanathanSajjit ThampyKenny C. Gross
    • G06F9/44
    • G06F11/3452G06F12/023
    • A system that identifies processes with a memory leak in a computer system. During operation, the system periodically samples memory usage for processes running on the computer system. The system then ranks the processes by memory usage and selects a specified number of processes with highest memory usage based on the ranking. For each selected process, the system computes a first-order difference of memory usage by taking a difference between the memory usage at a current sampling time and the memory usage at an immediately preceding sampling time. The system then generates a memory-leak index based on the first-order difference and a preceding memory-leak index computed at the immediately preceding sampling time.
    • 识别计算机系统中内存泄漏的进程的系统。 在运行期间,系统会周期性地对运行在计算机系统上的进程进行内存使用。 然后,系统通过内存使用对进程进行排序,并根据排名选择具有最高内存使用量的指定数量的进程。 对于每个所选择的处理,系统通过取得当前采样时间的存储器使用量与紧接在前的采样时间的存储器使用量之间的差异来计算存储器使用的一阶差。 然后,系统基于一阶差异和在紧接在前的采样时间计算的先前的存储器 - 泄漏索引来生成内存泄漏索引。
    • 4. 发明授权
    • Method and apparatus for optimizing support vector machine kernel parameters
    • 优化支持向量机内核参数的方法和装置
    • US07283984B1
    • 2007-10-16
    • US11049146
    • 2005-02-01
    • Sajjit ThampyAleksey M. UrmanovKenny C. Gross
    • Sajjit ThampyAleksey M. UrmanovKenny C. Gross
    • G06F17/00G06N5/00
    • G06K9/6269G06N99/005
    • One embodiment of the present invention provides a system that optimizes support vector machine (SVM) kernel parameters. During operation, the system assigns sets of kernel parameter values to each node in a multiprocessor system. Next, the system performs a cross-validation operation at each node in the multiprocessor system based on a data set. This cross-validation operation computes an error cost value reflecting the number of misclassifications that arise while classifying the data set using the assigned set of kernel parameter values. The system then communicates the computed error cost values between nodes in the multiprocessor system, and eliminates nodes with relatively high error cost values. Next, the system performs a cross-over operation in which kernel parameter values are exchanged between remaining nodes to produce new sets of kernel parameter values. This process is repeated until a global winning set of kernel parameter values emerges.
    • 本发明的一个实施例提供一种优化支持向量机(SVM)内核参数的系统。 在运行期间,系统会为多处理器系统中的每个节点分配一组内核参数值。 接下来,系统基于数据集在多处理器系统中的每个节点处执行交叉验证操作。 该交叉验证操作计算反映在使用分配的一组内核参数值对数据集进行分类时出现的错误分类数量的错误成本值。 然后,系统在多处理器系统中的节点之间传送计算出的错误成本值,并消除具有相对较高错误成本值的节点。 接下来,系统执行交叉操作,其中在剩余节点之间交换内核参数值以产生新的内核参数值集合。 重复此过程,直到出现全局获胜的内核参数值集。
    • 5. 发明申请
    • GENERATING A SCORE FOR A COUPON CAMPAIGN
    • 生成优惠券
    • US20130179239A1
    • 2013-07-11
    • US13345551
    • 2012-01-06
    • Kavel PatelSajjit Thampy
    • Kavel PatelSajjit Thampy
    • G06Q30/02
    • G06Q30/0211G06Q30/0207
    • Techniques are provided for generating, by a coupon distributor, a score that represents a quality of a coupon campaign that a coupon provider offers to the coupon distributor. The score may be generated based on the brand of the product, the product category to which the product belongs, the coupon value, and the percentage discount reflected by the coupon. One or more of these factors may be based on historical data that indicates the success (or failure) of previous coupon campaigns (e.g., of the same product, brand, and/or product category, and/or similar coupon value). The coupon distributor uses the score to, e.g., determine whether to accept a proposed coupon campaign, project the number of prints/redemptions of the coupon campaign, determine how to present the coupon, determine an amount to charge for running the coupon campaign, and/or determine products or product categories of coupons to which the coupon distributor should seek.
    • 技术被提供用于由优惠券分销商生成表示优惠券提供商向优惠券分销商提供的优惠券活动的质量的分数。 该分数可以根据产品的品牌,产品所属的产品类别,优惠券价值以及优惠券所反映的百分比折扣来生成。 这些因素中的一个或多个可以基于指示先前优惠券活动(例如,相同产品,品牌和/或产品类别和/或类似优惠券价值)的成功(或失败)的历史数据。 优惠券经销商使用分数,例如确定是否接受提议的优惠券活动,投影优惠券活动的打印/兑换数量,确定如何呈现优惠券,确定运行优惠券活动的费用金额,以及 /或确定优惠券分销商应寻求的优惠券的产品或产品类别。
    • 9. 发明申请
    • GROCERY RECOMMENDATION ENGINE
    • GROCERY推荐发动机
    • US20140074649A1
    • 2014-03-13
    • US13612848
    • 2012-09-13
    • Kavel PatelSajjit Thampy
    • Kavel PatelSajjit Thampy
    • G06Q30/00
    • G06Q30/0631
    • State-based approaches, techniques, and mechanisms are disclosed for recommending items to a user. A method comprises detecting a user state, from a plurality of different enumerated user states, based on items that the user recently selected, and/or location data. Based upon the detected user state, a particular algorithm, from a plurality of algorithms, is selected for recommending items. Information about the recommended items is presented to the user. Responsive to presenting the information about the recommended items, input is received selecting one or more of the recommended items for at least one of: adding to a shopping list, or requesting a coupon. Examples of possible detected user states include a recipe state, a grocery shopping state, and a quick shopping-run state. In an embodiment, state detection occurs at a client device, such as a smartphone featuring a shopping list management application or coupon application. A server-side recommendation engine provides recommendations.
    • 公开了基于状态的方法,技术和机制来向用户推荐项目。 一种方法包括基于用户最近选择的项目和/或位置数据从多个不同的枚举用户状态检测用户状态。 基于检测到的用户状态,选择来自多个算法的特定算法来推荐项目。 向用户显示有关推荐项目的信息。 响应于呈现关于推荐项目的信息,接收到输入选择以下至少一个的推荐项目中的一个或多个:添加到购物清单或请求优惠券。 可能检测到的用户状态的示例包括配方状态,杂货购物状态和快速购物状态。 在一个实施例中,状态检测发生在客户端设备,例如具有购物清单管理应用或优惠券应用的智能手机。 服务器端推荐引擎提供建议。