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Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems

arXiv.org Artificial Intelligence

Millimeter wave vehicular channels exhibit structure that can be exploited for beam alignment with fewer channel measurements compared to exhaustive beam search. With fixed layouts of roadside buildings and regular vehicular moving trajectory, the dominant path directions of channels will likely be among a subset of beam directions instead of distributing randomly over the whole beamspace. In this paper, we propose a deep learning-based technique to design a structured compressed sensing (CS) matrix that is well suited to the underlying channel distribution for mmWave vehicular beam alignment. The proposed approach leverages both sparsity and the particular spatial structure that appears in vehicular channels. We model the compressive channel acquisition by a two-dimensional (2D) convolutional layer followed by dropout. We incorporate the low-resolution phase shifter constraint during neural network training by using projected gradient descent for weight updates. Furthermore, we exploit channel spectral structure to optimize the power allocated for different subcarriers. Simulations indicate that our deep learningbased approach achieves better beam alignment than standard CS techniques which use random phase shift-based design. Numerical experiments also show that one single subcarrier is sufficient to provide necessary information for beam alignment. Millimeter-wave (mmWave) vehicular communication enables massive sensor data sharing and various emerging applications related to safety, traffic efficiency and infotainment [2]-[4]. Yuyang Wang is with Apple Inc., One Apple park way, Cupertino, CA, 95014, USA, email: yuywang@utexas.edu. Nitin Jonathan Myers is with Samsung Semiconductor Inc., 5465 Morehouse Dr, San Diego, CA 92121 USA, email: nitinjmyers@utexas.edu. Nuria González-Prelcic, and Robert W. Heath Jr. are with the Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC 27606 USA, email: {ngprelcic, rwheathjr}@ncsu.edu. Part of this work has been presented at IEEE ICASSP 2020 [1]. This material is based upon work supported in part by the National Science Foundation under Grant No. ECCS-1711702, and by a Qualcomm Faculty Award.


AI Reportedly Matches Tumors to Best Drug Combinations

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University of California San Diego School of Medicine and Moores Cancer Center say they have created a new artificial intelligence (AI) system called DrugCell that reportedly matches tumors to the best drug combinations, but does so in way that clearly makes sense. "That's because right now we can't match the right combination of drugs to the right patients in a smart way," said Trey Ideker, PhD, professor at University of California San Diego School of Medicine and Moores Cancer Center. "And especially for cancer, where we can't always predict which drugs will work best given the unique, complex inner workings of a person's tumor cells." Currently, Only four percent of all cancer therapeutic drugs under development earn final approval by the FDA. In a paper "Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells" published in Cancer Cell, Ideker, Brent Kuenzi, PhD, and Jisoo Park, PhD, postdoctoral researchers in his lab, published a paper on their work.


New AI Tool Can Match Cancer Combination Therapies to Specific Tumor Types

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A new artificial intelligence (AI) system called DrugCell, developed by researchers at University of California San Diego School of Medicine and Moores Cancer Center can reportedly match tumors to the best drug combinations, in a way that has not bee possible previously. "That's because right now we can't match the right combination of drugs to the right patients in a smart way," said Trey Ideker, PhD, professor at University of California San Diego School of Medicine and Moores Cancer Center. "And especially for cancer, where we can't always predict which drugs will work best given the unique, complex inner workings of a person's tumor cells." Currently, Only four percent of all cancer therapeutic drugs under development earn final approval by the FDA. In a paper "Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells" published in Cancer Cell, Ideker, Brent Kuenzi, PhD, and Jisoo Park, PhD, postdoctoral researchers in his lab, published a paper on their work.


Emulating a PID Controller with Long Short-term Memory: Part 1

#artificialintelligence

Do you ever just get really excited about an idea? Maybe you're crazy like me and want to hike the Pacific Crest Trail (as I'm moving to Seattle soon, so I can't help but get excited about the idea of flying down to San Diego and walking home). Well, this project is one of those types of ideas for me, and I hope you enjoy the ride! Before I get started, though, I want to warn you that this is quite an extensive project, and so I'm breaking it up into parts. While working on a project for work one day, I came across a paper that introduced a novel idea.


Machine Learning Helps Plasma Physics Researchers Understand Turbulence Transport - Stories Display Page - XSEDE

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For more than four decades, University of California, San Diego, Professor of Physics Patrick H. Diamond and his research group have been advancing our understanding of fundamental concepts in plasma physics. Most recently, Diamond worked with graduate student Robin Heinonen on a model reduction study that used the Extreme Science and Engineering Discovery Environment (XSEDE)-allocated Comet supercomputer at the San Diego Supercomputer Center at UC San Diego to showcase how machine learning produced a new model for plasma turbulence. Plasmas have many applications, including fusion energy. When light nuclei fuse together, the mass of the products is less than that of the reactants, and the missing mass becomes energy – hence Albert Einstein's famous E mc2 equation. In order for this to occur, temperatures must literally reach astronomical levels, such as those found in the Sun's core.


Machine Learning Helps Plasma Physics Researchers Understand Turbulence Transport

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This snapshot of turbulence density and vorticity from a simulation using SDSC's'Comet' supercomputer illustrates a notable physics concept: the formation of zonal (i.e. For more than four decades, UC San Diego Professor of Physics Patrick H. Diamond and his research group have been advancing fundamental concepts in plasma physics, which is an important aspect of furthering advancements in fusion energy. Most recently, Diamond worked with graduate student Robin Heinonen on a model reduction study that used the Comet supercomputer at the San Diego Supercomputer Center at the University of California San Diego to show how machine learning produced a novel model for plasma turbulence. Diamond and Heinonen say that advances in machine learning, such as new deep learning techniques, have provided them with new tools to better understand the self-organization process that emerges from what the researchers term as a seemingly chaotic process. "Turbulence and its transport is chaotic in a sense, but this chaos is ordered and constrained," said Heinonen, who co-authored Turbulence Model Reduction by Deep Learning with Diamond in the academic journal entitled Physical Review E. "Moreover, in certain turbulent systems, the chaos conspires to spontaneously form large, long-lived coherent structures and in many cases, we only have a tenuous understanding of why and now. There are definitely aspects of structure formation and self-organization which we do understand, but it's still an active area of research."


Machine Learning Helps Plasma Physics Researchers Understand Turbulence Transport

#artificialintelligence

For more than four decades, UC San Diego Professor of Physics Patrick H. Diamond and his research group have been advancing fundamental concepts in plasma physics, which is an important aspect of furthering advancements in fusion energy. Most recently, Diamond worked with graduate student Robin Heinonen on a model reduction study that used the Comet supercomputer at the San Diego Supercomputer Center at the University of California San Diego to showcase how machine learning produced a novel model for plasma turbulence. Diamond and Heinonen say that advances in machine learning, such as new deep learning techniques, have provided them with new tools to better understand the self-organization process that emerges from what the researchers term as a seemingly chaotic process. "Turbulence and its transport is chaotic in a sense, but this chaos is ordered and constrained," said Heinonen, who co-authored Turbulence Model Reduction by Deep Learning with Diamond in the academic journal entitled Physical Review E. "Moreover, in certain turbulent systems, the chaos conspires to spontaneously form large, long-lived coherent structures and in many cases, we only have a tenuous understanding of why and now. There are definitely aspects of structure formation and self-organization which we do understand, but it's still an active area of research."


Knowledge Graphs And AI: Interview With Chaitan Baru, University Of California San Diego (UCSD)

AITopics Custom Links

One of the challenges with modern machine learning systems is that they are very heavily dependent on large quantities of data to make them work well. This is especially the case with deep neural nets, where lots of layers means lots of neural connections which requires large amounts of data and training to get to the point where the system can provide results at acceptable levels of accuracy and precision. Indeed, the ultimate implementation of this massive data, massive network vision is the currently much-vaunted Open AI GPT-3, which is so large that it can predict and generate almost any text with surprising magical wizardry. However, in many ways, GPT-3 is still a big data magic trick. Indeed, Professor Luis Perez-Breva makes this exact point when he says that what we call machine learning isn't really learning at all.


Using AI to identify the aggressiveness of prostate cancer

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These promising results indicate that the deep learning system has the potential to support expert-level diagnoses and expand access to high-quality cancer care. To evaluate if it could improve the accuracy and consistency of prostate cancer diagnoses, this technology needs to be validated as an assistive tool in further clinical studies and on larger and more diverse patient groups. However, we believe that AI-based tools could help pathologists in their work, particularly in situations where specialist expertise is limited. Our research advancements in both prostate and breast cancer were the result of collaborations with the Naval Medical Center San Diego and support from Verily. Our appreciation also goes to several institutions that provided access to de-identified data, and many pathologists who provided advice or reviewed prostate cancer samples.


The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence

arXiv.org Artificial Intelligence

Division of Biological Sciences, University of California San Diego, La Jolla, California 92093 USA Abstract: Deep learning networks have been trained to recognize speech, caption photographs and translate text between languages at high levels of performance. Although applications of deep learning networks to real world problems have become ubiquitous, our understanding of why they are so effective is lacking. These empirical results should not be possible according to sample complexity in statistics and non-convex optimization theory. However, paradoxes in the training and effectiveness of deep learning networks are being investigated and insights are being found in the geometry of high-dimensional spaces. A mathematical theory of deep learning would illuminate how they function, allow us to assess the strengths and weaknesses of different network architectures and lead to major improvements. Deep learning has provided natural ways for humans to communicate with digital devices and is foundational for building artificial general intelligence. Deep learning was inspired by the architecture of the cerebral cortex and insights into autonomy and general intelligence may be found in other brain regions that are essential for planning and survival, but major breakthroughs will be needed to achieve these goals. This book was written as a satire on Victorian society, but it has endured because of its exploration of how dimensionality can change our intuitions about space. Flatland was a two-dimensional world inhabited by geometrical creatures. The mathematics of two dimensions was fully understood by these creatures, with circles being more perfect than triangles. In it a gentleman square has a dream about a sphere and wakes up to the possibility that his universe might be much larger than he or anyone in flatland could imagine.