https://cis.temple.edu/~latecki/Papers/mldm07.pdf
Abstract. Outlier detection has recently become an important prob-lem in many industrial and ̄nancial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foun-dation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Out-liers are then detected by comparing the ...
https://faculty.cst.temple.edu/~tuf43817/PHYS5701/S18/HamiltonianEM.pdf
We can derive the Hamiltonian from the Lagrangian, but it is not obvious how to build the Lagrangian when there is no obvious “potential energy” function for a charged particle in a magnetic field.
https://cphapps.temple.edu/wiki/it/student/sas_installation
For Mac users: SAS can only be installed on Windows operating systems. Do not attempt to follow these instructions on an Apple device unless you are running Bootcamp with Windows 10 or have another way of running Windows 10. If you have a Mac, you may be able to configure Bootcamp to run Winows and SAS, depending on how much memory is installed in your Mac, but Bootcamp will not run on the new ...
https://community.mis.temple.edu/mis3537beaver2016/files/2016/01/Bullwhip-Effect-on-SC.pdf
Temple University
https://community.mis.temple.edu/mis3534001fall2014/files/2014/09/NPV-IRR-and-Payback-Period-Rex-Wu.pdf
Net Present Value (NPV) Net present value (NPV) is a sophisticated capital budgeting technique; found by subtracting a project’s initial investment from the present value of its cash inflows discounted at a rate equal to the firm’s cost of capital.
https://sites.temple.edu/trail/files/2021/11/XieXinDamesIROS2021.pdf
Zhanteng Xie, Pujie Xin, and Philip Dames Abstract—This paper proposes a novel neural network-based control policy to enable a mobile robot to navigate safety through environments filled with both static obstacles, such as tables and chairs, and dense crowds of pedestrians. The network architecture uses early fusion to combine a short history of lidar data with kinematic data about nearby ...
https://cis.temple.edu/~yu/research/CrispBP-Mobicom21.pdf
We find that 2 layer, 14 neurons per layer, embedding dropout 0.2, and batch size 32 provide the optimum balance between complexity and accuracy. Therefore, we chose the above parameters as our default network through empirical studies.
https://sites.temple.edu/ltspm/research/charge-density-waves/
Chiral Charge Density Waves in TiSe 2 and Cu x TiSe 2 STM topography of CDW in TiSe2 and line profiles along the unit vectors corresponding to the two regions with opposite chirality, showing that the intensity grows clockwise in one region and counterclockwise in the other.
https://cis.temple.edu/~latecki/Courses/AI-Fall11/Lectures/ch7EL.ppt
2.2 Generate hypothesis hC . 2.3 Increase the weight of the misclassified examples in hypothesis hC 3 Weighted majority combination of all M hypotheses (weights according to how well it performed on the training set). Many variants depending on how to set the weights and how to combine the hypotheses. ADABOOST quite popular!!!!
https://cis.temple.edu/~yu/research/mobicomPOS06-bo.pdf
ABSTRACT In this work, we present SilentSense, a framework to authenticate users silently and transparently by exploiting the user touch behav-ior biometrics and leveraging the integrated sensors to capture the micro-movement of the device caused by user’s screen-touch ac-tions. By tracking the fine-detailed touch actions of the user, we build a “touch-based biometrics” model of the ...