Estimation from quantized Gaussian measurements: when and how to use dither

Subtractive dither is a powerful method for removing the signal dependence of quantization noise for coarsely quantized signals. However, estimation from dithered measurements often naively applies the sample mean or midrange, even when the total noise is not well described with a Gaussian or uniform distribution. We show that the generalized Gaussian distribution approximately describes subtractively dithered, quantized samples of a Gaussian signal. Furthermore, a generalized Gaussian fit leads to simple estimators based on order statistics that match the performance of more complicated maximum likelihood estimators requiring iterative solvers. The order statistics-based estimators outperform both the sample mean and midrange for nontrivial sums of Gaussian and uniform noise. Additional analysis of the generalized Gaussian approximation yields rules of thumb for determining when and how to apply dither to quantized measurements. Specifically, we find subtractive dither to be beneficial when the ratio between the Gaussian standard deviation and quantization interval length is roughly less than one-third. When that ratio is also greater than 0.822/K^0.930 for the number of measurements K > 20, estimators we present are more efficient than the midrange.https://arxiv.org/abs/1811.06856Accepted manuscrip

Interactive real time flow simulations

An interactive real time flow simulation technique is developed for an unsteady channel flow. A finite-volume algorithm in conjunction with a Runge-Kutta time stepping scheme was developed for two-dimensional Euler equations. A global time step was used to accelerate convergence of steady-state calculations. A raster image generation routine was developed for high speed image transmission which allows the user to have direct interaction with the solution development. In addition to theory and results, the hardware and software requirements are discussed

Hi-alpha forebody design. Part 1: Methodology base and initial parametrics

The use of Computational Fluid Dynamics (CFD) has been investigated for the analysis and design of aircraft forebodies at high angle of attack combined with sideslip. The results of the investigation show that CFD has reached a level of development where computational methods can be used for high angle of attack aerodynamic design. The classic wind tunnel experiment for the F-5A forebody directional stability has been reproduced computationally over an angle of attack range from 10 degrees to 45 degrees, and good agreement with experimental data was obtained. Computations have also been made at combined angle of attack and sideslip over a chine forebody, demonstrating the qualitative features of the flow, although not producing good agreement with measured experimental pressure distributions. The computations were performed using the code known as cfl3D for both the Euler equations and the Reynolds equations using a form of the Baldwin-Lomax turbulence model. To study the relation between forebody shape and directional stability characteristics, a generic parametric forebody model has been defined which provides a simple analytic math model with flexibility to capture the key shape characteristics of the entire range of forebodies of interest, including chines

Forskole/Startup Preschool. An examination of a program for migrant entrepreneurship in Norway

This report is a study of a particular program under the auspices of Startup Migrants AS, called Forskole/Startup Preschool. The name difference reflects in which language the event is conducted. The study is limited to a data collection from the Forskole/Startup Preschool participants through Nettskjema, supplemented by direct observation from several Forskole/Startup Preschool-sessions. The report is to answer the following research questions: 1. What is the most important hindrance for migrant entrepreneurs in obtaining their first customer? 2. To what extent does the Norwegian environment hinder the formation of new business for those with a non-western background? 3. What is the effect of public support programs, such as Norsk Arbeids- og velferdsetaten (NAV) on the participant’s ability to start a business? The report is based on the researcher’s presence at six different Forskole/Startup Preschool between September 2021 and February 2022. Five of them in Oslo and the Oslofjord-region, one in Bergen, including survey responses from 32 participants in a survey via Nettskjema. The median response for this took four minutes and 45 seconds to complete. Based on the outcome of the research activity conducted, the apparent answers to the above research questions are as follows: 1. Understanding the rules and bureaucracy and getting through it, is noted as the number one reported difficulty for migrant entrepreneurs in starting a business in Norway. It is of merit to share that it appears to be statistical significance for those without co-founders or team members, in citing the main difficulty as finding good advisors (rather than sorting out the rules and bureaucracy). Most of the Forskole/Startup Preschool participants, had not established a company yet. 27 of 32 respondents (more than 84%) had not registered a company, and were therefore not yet eligible to have paying customers. 2. As a migrant’s length of time in Norway increases, so does the likelihood the individual will be satisfied with the Norwegian system with regards to establishing a business. There is no evidence from the results that those with a non-western background are facing an extra hindrance in this area. It is rather a more important factor in whether a migrant entrepreneur is satisfied with the support received from the Norwegian system, how long they have been living in in Norway. Those who have spent less time in Norway are more likely to be dissatisfied by the support they receive from Norwegian support systems. 3. There is no evidence of an effect from NAV on migrant entrepreneurs’ abilities to start a business. Of the 32 respondents, only one was receiving money from NAV to attend Forskole/Startup Preschool. The satisfaction levels with the Norwegian support system for starting a business are relatively high, with nearly 50% of the participants expressing satisfaction and fewer than 25% expressing dissatisfaction. Further details regarding the research and other insights gathered from the research appear in the text below. Regarding the research question of hindrance for migrant entrepreneurs in Norway, we have followed this up by this research question: What can Forskole/Startup Preschool do to improve so that participants can increase their chances at acquiring their first customers? The research shows that 29 of 32 respondents (more than 90%) for a long time have wanted to establish their own company. When combining this with the evidence that most of the participants still have not established any company, it would probably make sense to have a follow-up Forskole/Startup Preschool for those who complete the three-day weekend course to offer a customer-development workshop. While customer development is covered in the Forskole/Startup Preschool course to some extent, the timing seems not perfect for this item, for most participants. They may find themselves overwhelmed by the intensity of the three-day course and unable to follow up easily on the customer development issues after having established their business. As it happens, the researcher came across some of the previous Forskole/Startup Preschool participants in contexts of more extensive entrepreneurship training programs that last six to eight weeks, and we registered what is commented above as one of the things mentioned by this previous Forskole/Startup Preschool participants. Another possibility for Forskole/Startup Preschool could be to tie closer into the ecosystem and recommend alumni to participate or link up with more extensive customer development training from other ecosystem actors. We are uncertain whether this already may be the case. We find it also interesting to mention the participants’ motivations to attend Forskole/Startup Preschool. More than 33% (11 of 32) say it was to gain practical information about how to get started with establishing a business in Norway. For those who share deeper feelings about their motivations, 25% want to earn a living from their business; to support themselves and their families. More than 18% (6 of 32) want to use their creative skills and more than 15% (5 of 32) want to give back something to society. These motivations are not mutually exclusive, see quotes from the participants further down in the report. Since Forskole/Startup Preschool sessions already have a strong emphasis on motivation, through use of the five why technique (Serrat, 2017) future Forskole/Startup Preschool probably could gain in going deeper into tying these insights from the participants’ motivations for becoming entrepreneurs into the customer development processes.publishedVersio

Efficient LLM Inference on CPUs

Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks. However, deploying these models has been challenging due to the astronomical amount of model parameters, which requires a demand for large memory capacity and high memory bandwidth. In this paper, we propose an effective approach that can make the deployment of LLMs more efficiently. We support an automatic INT4 weight-only quantization flow and design a special LLM runtime with highly-optimized kernels to accelerate the LLM inference on CPUs. We demonstrate the general applicability of our approach on popular LLMs including Llama2, Llama, GPT-NeoX, and showcase the extreme inference efficiency on CPUs. The code is publicly available at: https://github.com/intel/intel-extension-for-transformers.Comment: NeurIPS'2023 on Efficient Natural Language and Speech Processin

Graph-guided joint prediction of class label and clinical scores for the Alzheimer’s disease

Accurate diagnosis of Alzheimer’s disease and its prodromal stage, i.e., mild cognitive impairment, is very important for early treatment. Over the last decade, various machine learning methods have been proposed to predict disease status and clinical scores from brain images. It is worth noting that many features extracted from brain images are correlated significantly. In this case, feature selection combined with the additional correlation information among features can effectively improve classification/regression performance. Typically, the correlation information among features can be modeled by the connectivity of an undirected graph, where each node represents one feature and each edge indicates that the two involved features are correlated significantly. In this paper, we propose a new graph-guided multi-task learning method incorporating this undirected graph information to predict multiple response variables (i.e., class label and clinical scores) jointly. Specifically, based on the sparse undirected feature graph, we utilize a new latent group Lasso penalty to encourage the correlated features to be selected together. Furthermore, this new penalty also encourages the intrinsic correlated tasks to share a common feature subset. To validate our method, we have performed many numerical studies using simulated datasets and the Alzheimer’s Disease Neuroimaging Initiative dataset. Compared with the other methods, our proposed method has very promising performance