Data envelopment analysis (DEA) is widely used in various fields and for various models. Inverted data envelopment analysis (inverted DEA) is an extended model of DEA. Regression analysis (RA) is a statistical process for estimating the relationships among variables based on the model of averaged image. There are no essential relations among DEA and RA and inverted DEA. We creatively combine DEA, RA and inverted DEA to propose a new model: DEA-RA-Inverted DEA model. The model realises the efficiency estimation and evaluation through a discussion of the residual variables and the residual ratio coefficients. In addition, we will demonstrate the effectiveness of the model by applying it to efficiency estimation and evaluation of 16 Chinese logistics enterprises.

Nat PAVASANT,Takashi MORITA,Masayuki NUMAO,Ken-ichi FUKUI, Vol.E107-D, No.11, pp.1453-1457
We proposed a procedure to pre-process data used in a vector autoregressive (VAR) modeling of a temporal point process by using kernel density estimation. Vector autoregressive modeling of point-process data, for example, is being used for causality inference. The VAR model discretizes the timeline into small windows, and creates a time series by the presence of events in each window, and then models the presence of an event at the next time step by its history. The problem is that to get a longer history with high temporal resolution required a large number of windows, and thus, model parameters. We proposed the local density estimation procedure, which, instead of using the binary presence as the input to the model, performed kernel density estimation of the event history, and discretized the estimation to be used as the input. This allowed us to reduce the number of model parameters, especially in sparse data. Our experiment on a sparse Poisson process showed that this procedure vastly increases model prediction performance.
Publication Date: 2024/11/01

The post Estimation of the prevalence of anxiety during the COVID-19 pandemic: A meta-analysis of meta-analyses was curated by information for practice.

Producing more precise county-level estimates of crops and farmland cash rents will require integrating multiple data sources using model-based predictions that are more transparent and reproducible, says a new report from the National Academies of Sciences, Engineering, and Medicine.

Sven Johann talks with Steve McConnell about Software Estimation. Topics include when and why businesses need estimates and when they don’t need them; turning estimates into a plan and validating progress on the plan; why software estimates are always full of uncertainties, what these uncertainties are and how to deal with them. They continue with: estimation, planning and monitoring a Scrum project from the beginning to a possible end. They close with estimation techniques in the large (counting, empirical data) and in the small (e.g. poker planning).

Quality estimation methods and application guidelines for mitigation measures based on particle radiation tests

Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport - Video quality estimation module

Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport - Video quality estimation module

Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport - Audio quality estimation module

PSTR-PXNR - No-reference pixel-based video quality estimation algorithm

Location: Engineering Library- TA656.N53 2016

Jacek R. Wiśniewski
Dec 1, 2014; 13:3497-3506
Research

Yasushi Ishihama
Sep 1, 2005; 4:1265-1272
Research

The traditional methods of power analysis lag by various shortcomings and challenges:

• Getting an accurate measure of RTL power consumption during design exploration
• Getting consistent power through the design progress from RTL to P&R.
• System-level verification tools are disconnected from the implementation tools that translate RTL to gates and wires.

The Cadence Joules RTL Power Solution closes this gap by delivering time-based RTL power analysis with system-level runtimes, capacity, and high-quality estimates of gates and wires based on production implementation technology. The Cadence Joules RTL Power Solution is an RTL power analysis tool that provides a unified engine to compute gate netlist power and estimate RTL power. The Joules solution delivers 20X faster time-based RTL power analysis and can analyze multi-million instance designs overnight, with impressive accuracy within 15% of signoff power.

Moreover, it integrates seamlessly with numerous Cadence platforms, eliminating compatibility and correlation issues! In addition, the Joules RTL Power Solution GUI (Graphical User Interface) helps you analyze/debug the power estimation/results using several GUI capabilities.

Want to take a tour of this power estimation world? Gear up to attend the training class created just for you to dive deep into the entire flow and explore this exciting power estimation method/flow with hands-on labs in two days!

Training

In the Joules Power Calculator Training course, you will identify solutions and features for RTL power using Cadence Joules RTL Power Solution. You will set up and run the RTL power flow with Joules RTL Power Solution and identify Joules's Graphical User Interface (GUI) capabilities. The training also explores how you can estimate power using vectorless power, stimulus flow, RTL Stim to Gate flow, and replay flow, and also interfaces Joules with Cadence's Palladium Emulation Platform. You will estimate power at the chip level and understand how to navigate the design and data mining using Joules.

The training also covers power exploration features and how to analyze ideal power and ODC-driven sequential clock gating. You will identify low-activity registers at the clock gate. You will also identify techniques to analyze power, generate various reports, and analyze results through Joules GUI. The training covers multiple strategies to debug low stimulus annotation and how you can better correlate RTL power with signoff. You also identify Genus-Joules Integration. In addition, we ensure that your learning journey is smooth with hands-on labs covering various design scenarios.

Lab Videos

To start you on your exciting journey as an RTL power analysis expert, we have created a series of short channel lab videos on our Customer Support site: Lab Demo: Setting Up and Running Basic RTL Power Flow in Joules RTL Power Solution (Video). You can refer to each lab module's instructions in demo format. This will help accelerate your tool ramp-up and help you perform the lab steps more quickly if you are stuck. You might be a beginner in the RTL power analysis world, but we can help you sail through it smoothly.

What's Next?

Grab your badge after finishing the training and flaunt your expertise!

Related Training

• Genus Low-Power Synthesis Flow with IEEE 1801
• Low-Power Synthesis Flow with Genus Stylus Common UI

Related Blogs

• Do You Want to Flaunt Your Expertise? Grab the Digital Badge Today!
• Become Cadence Certified
• Let's Replay the Process of Power Estimation with the Power of 'x'
• Let's Discover the Secret to Enhance Design's PPAC in a Single Cockpit
• What's Inside Joules Graphical User Interface
• Joules – Power Exploration Capabilities

National Bureau of Economic Research

National Bureau of Economic Research

This document outlines basic background information on release estimation techniques (RETs) for point sources and provides resources on available RETs of different national/regional Pollutant Release and Transfer Register systems. It was originally published in 2002, and has been updated to reflect new and additional information.

Methods are presented that detect three types of aberrations in single-particle cryo-EM data sets: symmetrical and antisymmetrical optical aberrations and magnification anisotropy. Because these methods only depend on the availability of a preliminary 3D reconstruction from the data, they can be used to correct for these aberrations for any given cryo-EM data set, a posteriori. Using five publicly available data sets, it is shown that considering these aberrations improves the resolution of the 3D reconstruction when these effects are present. The methods are implemented in version 3.1 of the open-source software package RELION.

Producing more precise county-level estimates of crops and farmland cash rents will require integrating multiple data sources using model-based predictions that are more transparent and reproducible, says a new report from the National Academies of Sciences, Engineering, and Medicine.

It is difficult to compare estimates of noise exposure across EU Member States because the methods used to produce the data vary between countries. A new study has investigated five methods of estimating noise exposure and identified some of the reasons for variation in the data they produce.

Estimates of national forest recreation use are available at the national, regional, and forest levels via the USDA Forest Service National Visitor Use Monitoring (NVUM) program. In some resource planning and management applications, analysts desire recreation use estimates for subforest areas within an individual national forest or for subforest areas that combine portions of several national forests. In this research note we have detailed two approaches whereby the NVUM sampling data may be used to estimate recreation use for a subforest area within a single national forest or for a subforest area combining portions of more than one national forest. The approaches differ in their data requirements, complexity, and assumptions. In the "new forest" approach, recreation use is estimated by using NVUM data obtained only from NVUM interview sites within the area of interest. In the "all-forest information" approach, recreation use is estimated by using sample data gathered on all portions of the national forest(s) that contain the area of interest.

Estimates of forest carbon stores and flux for California circa 1990 were modeled from forest inventory data in support of California's legislatively mandated greenhouse gas inventory. Reliable estimates of live-tree carbon stores and flux on timberlands outside of national forest could be calculated from periodic inventory data collected in the 1980s and 1990s; however, estimation of circa 1990 flux on national forests and forests other than timberland was problematic owing to a combination of changing inventory protocols and definitions and the lack of remeasurement data on those land categories. We estimate annual carbon flux on the 7.97 million acres of timberlands outside of national forests (which account for 24 percent of California's forest area and 28 percent of its live tree aboveground biomass) at 2.9 terragrams per year.

Timber availability, aboveground tree biomass, and changes in aboveground carbon pools are important consequences of landscape management.

The economic linkages between national forests and surrounding communities have become increasingly important in recent years. One way national forests contribute to the economies of surrounding communities is by attracting recreation visitors who, as part of their trip, spend money in communities on the periphery of the national forest. We use survey data collected from visitors to all units in the National Forest System to estimate the average spending per trip of national forest recreation visitors engaged in various types of recreation trips and activities. Average spending of national forest visitors ranges from about $33 per party per trip for local residents on day trips to more than $983 per party per trip for visitors downhill skiing on national forest land and staying overnight in the local national forest area. We report key parameters to complete economic contribution analysis for individual national forests and for the entire National Forest System.

In this paper, we are concerned with the numerical solution of one type integro-differential equation by a probability method based on the fundamental martingale of mixed Gaussian processes. As an application, we will try to simulate the estimation of ruin probability with an unknown parameter driven not by the classical L'evy process but by the mixed fractional Brownian motion.

Recent studies of optimization methods and GNC of spacecraft near small bodies focusing on descent, landing, rendezvous, etc., with key safety constraints such as line-of-sight conic zones and soft landings have shown promising results; this paper considers descent missions to an asteroid surface with a constraint that consists of an onboard camera and asteroid surface markers while using a stochastic convex MPC law. An undermodeled asteroid gravity and spacecraft technology inspired measurement model is established to develop the constraint. Then a computationally light stochastic Linear Quadratic MPC strategy is presented to keep the spacecraft in satisfactory field of view of the surface markers while trajectory tracking, employing chance based constraints and up-to-date estimation uncertainty from navigation. The estimation uncertainty giving rise to the tightened constraints is particularly addressed. Results suggest robust tracking performance across a variety of trajectories.

Autonomous robot manipulation involves estimating the translation and orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose. Methods using RGB-D data have shown great success in solving this problem. However, there are situations where cost constraints or the working environment may limit the use of RGB-D sensors. When limited to monocular camera data only, the problem of object pose estimation is very challenging. In this work, we introduce a novel method called SilhoNet that predicts 6D object pose from monocular images. We use a Convolutional Neural Network (CNN) pipeline that takes in Region of Interest (ROI) proposals to simultaneously predict an intermediate silhouette representation for objects with an associated occlusion mask and a 3D translation vector. The 3D orientation is then regressed from the predicted silhouettes. We show that our method achieves better overall performance on the YCB-Video dataset than two state-of-the art networks for 6D pose estimation from monocular image input.

Sentence level quality estimation (QE) for machine translation (MT) attempts to predict the translation edit rate (TER) cost of post-editing work required to correct MT output. We describe our view on sentence-level QE as dictated by several practical setups encountered in the industry. We find consumers of MT output---whether human or algorithmic ones---to be primarily interested in a binary quality metric: is the translated sentence adequate as-is or does it need post-editing? Motivated by this we propose a quality classification (QC) view on sentence-level QE whereby we focus on maximizing recall at precision above a given threshold. We demonstrate that, while classical QE regression models fare poorly on this task, they can be re-purposed by replacing the output regression layer with a binary classification one, achieving 50-60\% recall at 90\% precision. For a high-quality MT system producing 75-80\% correct translations, this promises a significant reduction in post-editing work indeed.

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the time that patients might convert to the severe stage, for designing effective treatment plan and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time, and if yes, predict the possible conversion time that the patient would spend to convert to the severe stage. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of high-dimensional data and learn the shared information across the classification task and the regression task. To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 422 chest computed tomography (CT) scans, where 52 cases were converted to severe on average 5.64 days and 34 cases were severe at admission. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.

Thin structures, such as wire-frame sculptures, fences, cables, power lines, and tree branches, are common in the real world.

It is extremely challenging to acquire their 3D digital models using traditional image-based or depth-based reconstruction methods because thin structures often lack distinct point features and have severe self-occlusion.

We propose the first approach that simultaneously estimates camera motion and reconstructs the geometry of complex 3D thin structures in high quality from a color video captured by a handheld camera.

Specifically, we present a new curve-based approach to estimate accurate camera poses by establishing correspondences between featureless thin objects in the foreground in consecutive video frames, without requiring visual texture in the background scene to lock on.

Enabled by this effective curve-based camera pose estimation strategy, we develop an iterative optimization method with tailored measures on geometry, topology as well as self-occlusion handling for reconstructing 3D thin structures.

Extensive validations on a variety of thin structures show that our method achieves accurate camera pose estimation and faithful reconstruction of 3D thin structures with complex shape and topology at a level that has not been attained by other existing reconstruction methods.

Previous methods on estimating detailed human depth often require supervised training with `ground truth' depth data. This paper presents a self-supervised method that can be trained on YouTube videos without known depth, which makes training data collection simple and improves the generalization of the learned network. The self-supervised learning is achieved by minimizing a photo-consistency loss, which is evaluated between a video frame and its neighboring frames warped according to the estimated depth and the 3D non-rigid motion of the human body. To solve this non-rigid motion, we first estimate a rough SMPL model at each video frame and compute the non-rigid body motion accordingly, which enables self-supervised learning on estimating the shape details. Experiments demonstrate that our method enjoys better generalization and performs much better on data in the wild.

To provide a latent variable model estimation apparatus capable of implementing the model selection at high speed even if the number of model candidates increases exponentially as the latent state number and the kind of the observation probability increase. A variational probability calculating unit 71 calculates a variational probability by maximizing a reference value that is defined as a lower bound of an approximation amount, in which Laplace approximation of a marginalized log likelihood function is performed with respect to an estimator for a complete variable. A model estimation unit 72 estimates an optimum latent variable model by estimating the kind and a parameter of the observation probability with respect to each latent state. A convergence determination unit 73 determines whether a reference value, which is used by the variational probability calculating unit 71 to calculate the variational probability, converges.

In accordance with one or more aspects, an initial output string is generated by a random number generator. The initial output string is sent to a random number service, and an indication of failure is received from the random number service if the initial output string is the same as a previous initial output string received by the random number service. Operation of the device is ceased in response to the indication of failure. Additionally, entropy estimates for hash values of an entropy source can be generated by an entropy estimation service based on hash values of various entropy source values received by the entropy estimation service. The hash values can be incorporated into an entropy pool of the device, and the entropy estimate of the pool being updated based on the estimated entropy of the entropy source.

An estimation device is configured to estimate a feature value of a specific component contained in a sample and includes: a spectral estimation parameter storage module; a calibration parameter storage module; a multiband image acquirer; an optical spectrum operator configured to compute an optical spectrum from a multiband image using a spectral estimation parameter; and a calibration processor configured to compute the feature value from the optical spectrum using a calibration parameter.

Techniques for determining time of arrivals (TOAs) of signals in a wireless communication network are described. Each cell may transmit (i) synchronization signals on a set of contiguous subcarriers in the center portion of the system bandwidth and (ii) reference signals on different sets of non-contiguous subcarriers distributed across the system bandwidth. A UE may determine TOA for a cell based on multiple signals transmitted on different sets of subcarriers. The UE may perform correlation for a first signal (e.g., a synchronization signal) from the cell to obtain first correlation results for different time offsets. The UE may perform correlation for a second signal (e.g., a reference signal) from the cell to obtain second correlation results for different time offsets. The UE may combine the first and second correlation results and may determine the TOA for the cell based on the combined correlation results.

A system to monitor the temperature of power electronic devices in a motor drive includes a base plate defining a planar surface on which the electronic devices and/or circuit boards within the motor drive may be mounted. The power electronic devices are mounted to the base plate through the direct bond copper (DBC). A circuit board is mounted to the base plate which includes a temperature sensor mounted on the circuit board proximate to the power electronic devices. The temperature sensor generates a digital signal corresponding to the temperature measured by the sensor. A copper pad is included between each layer of the circuit board and between the first layer of the circuit board and the sensor. The circuit board also includes vias extending through each layer of the board. The copper pads and vias establish a thermally conductive path between the temperature sensor and the base plate.