E. Jean Relationship Advice – Throwing a Big Wedding

Dear E. Jean: I’m engaged. He comes from old money—and all the dust that comes with it. We are planning on having a child, but first we must marry or the child won’t be eligible to receive the family inheritance governed by a trust drawn up more than a century ago.

My boyfriend does not like weddings. (He called off a previous engagement because he was traumatized by the over-the-top nuptial arrangements.) He’s the sweetest man in the world, but when I bring up the subject, he will not cooperate, and asks, “Can’t we please just go to the courthouse?”

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We both love big parties, balls, traveling to beautiful places, and dressing in fine clothes, so I don’t see why I can’t change his mind about a wedding. Should I just give in and go to the courthouse? —There Goes the Bride

Bride, You Attractive Young Fathead: Auntie Eeee is clutching her brow.

Here we have a chap—”the sweetest man in the world,” who has inherited sacks of old money and wishes to get hitched—and you want to “change his mind”? A big wedding, forsooth!

Hell, Reader, I will marry him, if you don’t.

Stop jabbering about bells and churches. Pay attention to what this excellent man is saying. Remember that this is his wedding too, and get thee to the courthouse. I believe that the two of you—although you will be absolutely stinking rich—have as great a chance at happiness and all the domestic blessings as anyone.

P.S. You may start planning your three-year-anniversary party with your 467-
person guest list, Jamie Oliver catering, vow-renewal officiated by Rihanna, etc. when you return from your honeymoon.

This letter is from the E. Jean archive.

Tiny molecule has big effect on brain's ability to learn

Prenatal brain development is a crucial period, and as new research has found, even small alterations to the way brain cells develop can have significant effects later in life. Scientists have shed light on the role that small molecules called microRNAs play in early brain development. The research found a close link between early brain developmental events and changes in cognitive function in adulthood.

Big fish in a small pond?

Feel like you’re a big fish in a small pond? If you’re an employee who perceives you’re overqualified for your position, chances are you’re unsatisfied with your job, uncommitted to your organization and experience psychological strain, according to a study.

Will brain-inspired chips make a dent in science’s big data problems? — ScienceDaily

The average human adult brain weighs about three pounds and is comprised mostly of fat and water, but it is extremely efficient at processing information. To simulate just one second of biological brain activity several years ago, researchers used 82,994 processors, one petabyte of system memory and 40 minutes on the Riken Research Institute’s K supercomputer. At the time, this system consumed enough electricity to power about 10,000 homes. In contrast, the brain uses the equivalent of about 20 watts of electricity — barely enough to power a dim light bulb.

Our brains are also much better than computers at tasks like recognizing images and navigating unfamiliar spaces. Although the precise mechanism by which our brain performs these tasks is still unknown, we do know that visual information is processed in a massively parallel and concerted fashion by millions of neurons connected by synapses. Each neuron responds to visual stimuli in a simple, on-demand fashion, but their collective responses can yield cognitive outcome that currently cannot by easily described by a simple mathematical model. These models are essentially the foundation of current image processing software executed on traditional computing systems. All computing systems since the 1940s — from smartphones to supercomputers — have been built from the same blueprint, called the von Neumann architecture, which relies on mathematical models to execute linear sequences of instructions.

The von Neumann design has also led computing to its current limits in efficiency and cooling. As engineers built increasingly complex chips to carry out sequential operations faster and faster, the speedier chips have also been producing more waste heat. Recognizing that modern computing cannot continue on this trajectory, a number of companies are looking to the brain for inspiration and developing “neuromorphic” chips that process data the way our minds do. One such technology is IBM’s TrueNorth Neurosynaptic System.

Although neuromorphic computing is still in its infancy, researchers in the Computational Research Division (CRD) at the U.S. Department of Energy’s (DOE’s) Lawrence Berkeley National Laboratory (Berkeley Lab) hope that these tiny, low-power, brain-inspired computing systems could one day help alleviate some of science’s big data challenges. With funding from the Laboratory Directed Research and Development (LDRD) program, two groups of researchers are exploring how science might benefit from this new technology.

One group of CRD researchers is looking at how neuromorphic chips might be able to provide low-power, real-time data processing for charged particle tracking in high energy physics experiments and prediction of movement from neural signals for brain machine interfaces. So they are working to implement Kalman filters on TrueNorth chips, effectively expanding the utilization of this neuromorphic technology to any computing problem benefiting from real-time, continuous tracking or control.

Meanwhile, another collaboration of researchers from CRD and the Molecular Biophysics and Integrated Bioimaging (MBIB) division looked at the viability of applying convolutional neural networks (CNNs) on IBM’s TrueNorth to classify images and extract features from experimental observations generated at DOE facilities. Based on their initial results, the team is currently working to identify problems in the areas of structural biology, materials science and cosmology that may benefit from this setup.

“The field of neuromorphic computing is very new, so it is hard to say conclusively whether science will benefit from it. But from a particle physics perspective, the idea of a tiny processing unit that is self-contained and infinitely replicable is very exciting,” says Paolo Calafiura, software & computing manager for the Large Hadron Collider’s ATLAS experiment and a CRD scientist.

He adds: “For one reason or another — be it I/O (input/output), CPU (computer processing unit) or memory — every computing platform that we’ve come across so far hasn’t been able to scale to meet our data processing needs. But if you can replicate the same tiny unit of processing 10 million times or more, as neuromorphic computing aims to do, and find the right balance between power consumption and processing speed, this sounds like it will meet our needs.”

Why Neuromorphic Computing?

In the traditional von Neumann design, computers are comprised primarily of two components: a CPU that handles data, and random access memory (RAM) that stores data and the instructions for what to do with it. The CPU fetches its first instruction from memory, and then data needed to execute it. Once the instruction is performed, the result is sent back to memory and the cycle repeats.

Rather than go back and forth between CPU and memory, the TrueNorth chip is a self-contained computing system in which processing units and memory are colocated. Each chip contains 4,096 neurosynaptic cores that contain 1 million programmable neurons and 256 million configurable synapses interconnected via an on-chip network. The neurons transmit, receive and accumulate signals known as spikes. A neuron produces a spike whenever accumulated inputs reach a programmed activation threshold. They are weighted and redirected by synapses that connect different layers of neurons to map input to output.

TrueNorth chips natively tile in two dimensions using the on-chip network, essentially allowing the system to seamlessly scale to any size. Because synapses serve a dual function of memory and CPU, neuromorphic chips pack a lot of computing power into a tiny footprint and use significantly less power. For instance, TrueNorth uses about 70 milliwatts of electricity while running and has a power density of 20 milliwatts per square centimeter — almost 1/10,000th the power of most modern microprocessors.

“Low-energy consumption and compact size are some of the reasons we’re interested in neuromorphic computing,” says Chao Yang, an applied mathematician in Berkeley Lab’s CRD. “With these miniature computing systems, we expect that soon we will enable scientific instruments to be more intelligent by doing real-time analysis as detectors collect information.”

According to CRD scientist Daniela Ushizima, incorporating these neuromorphic chips into detectors could mean huge computational savings for imaging facilities. Rather than send raw data directly to a storage facility and then figure out post-acquisition whether the information collected is relevant, good quality or includes the object of interest, researchers could just do this exploration in situ as the data is being collected.

The size of the chips also presents new possibilities for wearables and prosthetics. “In our time-series work, we’re exploring the potential of this technology for people who have prosthetics implanted in their brains to restore movement,” says Kristofer Bouchard, a Berkeley Lab computational neuroscientist. “While today’s supercomputers are powerful, it is not really feasible for someone to tote that around in everyday life. But if you have that same computing capability packed into something the size of a postage stamp, that opens a whole new range of opportunities.”

Translating Science Methods: From von Neumann to Neuromorphic

Because neuromorphic chips are vastly different than today’s microprocessors, the first step for both projects is to translate the scientific methods developed for modern computers into a framework for the TrueNorth architecture. Here is a more detailed look at these two projects.

Particle Physics and Brain Machine Interfaces

Co-leads: Kristofer Bouchard and Paolo Calafiura

In particle physics experiments, researchers smash beams of protons at the center of detectors and measure the energy and momentum of escaping particles. By tracking the trajectory of escaping material with algorithms called Kalman filters, physicists can infer the existence of massive particles that were created, or decayed, right after the collision.

Kalman filters are essentially optimal estimators. They can infer structures of interest, relatively accurately, from a series of measurements taken over time in difficult environments that produce data with statistical noise and other inaccuracies. Because these algorithms are recursive, new measurements can be processed in real time, making them convenient for online processing. In addition to particle physics, Kalman filters are also widely used for navigation, signal processing and even modeling the central nervous system’s control of movement.

Currently, Bouchard and Calafiura are working to set up their scientific framework on the TrueNorth architecture. They implemented Kalman filters using IBM TrueNorth Corelet Programming Language and they explored strengths and weaknesses of the various TrueNorth’s transcoding schemes that convert incoming data into spikes. Once fully tested, this TrueNorth Kalman filter will be broadly applicable to any research group interested in sequential data processing with the TrueNorth architecture.

“As these transcoding schemes have different strengths and weakness, it will be important to explore how the transcoding scheme affects performance in different domain areas. The ability to translate any input stream into spikes will be broadly applicable to any research group interested in experimenting with the TrueNorth architecture,” says Calafiura.

“Brain-machine interfaces (BMIs) for restoring lost behavioral functions entail recording brain signals and transforming them for a particular task. The computations required for a BMI need to occur in real time, as delays can cause instabilities in the system,” says Bouchard. “Today, the majority of state-of-the-art BMIs utilizes some variation of the Kalman filter for transforming observed brain signals into a prediction of intended behavior.”

Once the team has successfully set up their workflow on TrueNorth, they will train their spiking neural network Kalman filters on real neural recordings taken directly from the cortical surface of neurosurgical patients collected by Dr. Edward Chang at the University of California, San Francisco. This consists of neural recordings from 100-256 electrodes with signal rates of ~400 Hz, well within the constraints of a single TrueNorth system. The team will also train their implementations with high energy physics data collected at the Large Hadron Collider in Geneva, Switzerland and Liquid Argon Time Processing Chambers at FermiLab.

Image Analysis and Pattern Recognition

Co-leads: Chao Yang, Nick Sauter and Dani Ushizima

Convolutional neural networks are extremely useful for image recognition and classification. In fact, companies like Google and Facebook are using CNNs to identify and categorize faces, locations, animals, etc., using billions of images uploaded to the Internet every day. Users essentially help “train” these CNNs every time they tag a location or friend in a picture. CNNs learn from these tags, so the next time someone tries to tag a face in an uploaded image the system can offer suggestions based on what it’s learned.

Because CNN designs evolved from early research of the brain’s visual cortex and how neurons propagate information through complex cell organizations, Yang and his colleagues thought that this algorithm might be a good fit for neuromorphic computing. So they explored a number of CNN architectures, targeting image-based data that requires time-consuming feature extraction and classification. Given the broad interest of Berkeley Lab in the areas of structural biology, materials science and cosmology, different scientists came together to select adequate problems that can be efficiently processed on the TrueNorth architecture.

X-ray Crystallography

In biology and materials science, X-ray crystallography is a popular technique for determining the three-dimensional atomic structure of salts, minerals, organic compounds, and proteins. When researchers tap the crystalline atoms or molecules with an X-ray beam, light is scattered in many directions. By measuring the angles and intensities of these diffracted beams, scientists can create a 3D picture of the density of electrons inside the crystals.

One of the key steps in X-ray crystallography is to identify images with clear Bragg peaks, which are essentially bright spots created when light waves constructively interfere. Scientists typically keep images with Bragg peaks for further processing and discard those that don’t have these features. Although an experienced scientist can easily spot these features, current software requires a lot of manual tuning to identify these features. Yang’s team proposed to use a set of previously collected and labeled diffraction images to train a CNN to become a machine classifier. In addition to separating good images from bad ones, CNNs can also be used to segment the Bragg spots for subsequent analysis and indexing.

“Our detectors produce images at about 133 frames per second, but currently our software takes two seconds of CPU time to compute the answer. So one of our challenges is analyzing our data quickly,” says Nicholas Sauter, a structural biologist in Berkeley Lab’s Molecular Biophysics and Integrated Bioimaging Division. “We can buy expensive parallel computing systems to keep up with the processing demands, but our hope is that IBM TrueNorth may potentially provide us a way to save money and electrical power by putting a special chip on the back of the detector, which will have a CNN that can quickly do the job that those eight expensive computers sitting in a rack would otherwise do.”

Cryo-Electron Microscopy (CryoEM)

To determine the 3D structures of molecules without crystalizing them first, researchers use a method called cryo-electron microscopy (cryoEM), which involves freezing a large number of randomly oriented and purified samples and photographing them with electrons instead of light. The 2D projected views of randomly oriented but identical particles are then assembled to generate a near-atomic resolution 3D structure of the molecule.

Because cryoEM images tend to have very low signal-to-noise ratio — meaning it is relatively hard to spot the desired feature from the background — one of the key steps in the analysis process is to group images with the similar views into the same class. Averaging images within the same class boosts the signal-to-noise ratio.

Yang and his teammates used simulated projection images to train a CNN to classify images into different orientation classes. For noise-free images, their CNN classifier successfully grouped images into as many as 84 distinct classes with over 90 percent success rate. The team also investigated the possibility of lowering the precision of the CNN by constraining both the input and CNN weights and found that reliable prediction can be made when the input and weights are constrained down to 3 or 4 bits. They are currently examining the reliability of this approach to noisy images.

Grazing Incidence Small Angle X-ray Scattering (GISAXS)

Grazing incidence small angle X-ray scattering (GISAXS) is an imaging technique used for studying thin films that play a vital role as building blocks for the next generation of renewable energy technology. One of the challenges in GISAXS imaging is to accurately infer the crystal structure of a sample from its two-dimensional diffraction pattern.

In collaboration with Advanced Light Source (ALS) Scientist Alex Hexemer, Ushizima used categorization algorithms to label large collections of computer simulated-images, each containing a variety of crystal structures. They used this dataset to train a deep CNN to classify these images by their structures. When they tested the performance of their classifier on multiple datasets, they achieved about 83 to 92 percent accuracy depending on the number of crystal lattices of each test case. Preliminary classification results using real images point out that models trained on massive simulations, including realistic background noise levels, have the potential to enable categorization of experimentally obtained data.

“We believe that these initial results are really encouraging, and an indication that we should continue to study the use of CNNs for GISAXS and other synchrotron based scientific experiments,” says Ushizima.


To find Type Ia supernovas and other transient events in the night sky, astronomers rely on sky surveys that image the same patches of sky every night for months and years. Astronomers warp and average these some of images together to create a template of a particular patch of sky. When a new observation comes in, they will compare it to the template and subtract the known objects to uncover new events like supernova. Because images of the night sky have to be warped to correct for optical effects or artifacts — caused by defect sensors, cosmic ray hits and foreground objects — the subtractions are not always perfect. In fact, 93 percent of potential candidates identified by the subtraction pipeline are artifacts.

To sift out the false from real candidates post-subtraction, Throsten Kurth, an HPC Consultant at the National Energy Research Scientific Computing Center (NERSC) created a two-layer CNN and applied a method that involved 80 percent training, 10 percent validation and 10 percent testing to evaluate the performance of their algorithm on TrueNorth. To test the robustness of his algorithm, he also included images of the night sky in varying orientations in their training dataset. Ultimately, they achieved about 95 percent classification accuracy.

“Increasing the network with more layers does not mean to improve performance,” says Ushizima. “The next step involves trying our approach on a different dataset, which contains images with low signal-to-noise ratio, images with defects, as well as noise and defect pixel maps. With this dataset, the neural network can learn correlations between all those characteristics and thus hopefully deliver a better performance.”

Micro tomography (MicroCT)

Micro tomography (MicroCT) is an imaging method that is very similar to what hospitals use when they do CT or CAT scans on a patient, but it images on a much smaller scale. It actually allows researchers to image the internal structure of objects at very fine scales and in a non-destructive way. This means that no sample preparation needs to occur — no staining, no thin slicing — and a single scan can capture the sample’s complete internal structure in 3D and at high resolution.

Using microCT, scientists can test the robustness of materials that may one day be used in batteries, automobiles, airplanes, etc. by searching for microscopic deformations in its internal structure. But sometimes finding these fissures can be a lot like searching for a needle in a haystack. So Ushizima and Yang teamed up with the ALS’s Dula Parkinson to develop algorithms to extract these features from raw microCT images.

“Computer vision algorithms have allowed us to construct labeled data banks to support supervised learning algorithms, like CNNs. One particular tool that we created allows the researcher to segment and label image samples with high accuracy by providing an intuitive user interface and mechanisms to curate data,” says Ushizima.

Although these tools were developed specifically to extract features from microCT images, she notes that it is applicable to other science areas as well.

“As the volume and complexity of science data increases, it will become important to optimize CNNs and explore cutting-edge architectures like TrueNorth,” says Yang. “Currently, we are determining the CNN parameters — number of layers, size of the filters and down sampling rate — with ad hoc estimates. In our future work, we would like to examine systematic approaches to optimizing these parameters.”

For these LDRD projects, the researchers primarily used IBM’s TrueNorth because it was the first neuromorphic chip they had access to. In the future they hope to explore the viability of other neuromorphic computing architectures.

3 Secrets for Turning a Little Idea into a Big Time Business

Intent on making 2017 your Best Year Ever? We can help with that, thanks to our 2017 Coach of the Month series. For June, Heather Cabot and Samantha Walravens, authors of the just released book, Geek Girl Rising: Inside the Sisterhood Shaking Up Tech, offer a four-week course in professional acumen, designed to serve you whether you’re a tech founder, an artist, or anything in between. For the second installment, Cabot offers a protagonist’s approach to figuring out what business ventures are actually worth pursuing.

How many times have you been jogging on the treadmill, driving to work, or drifting off to sleep when a brilliant idea hits you? The inspiration pops into your head, you scramble to scribble it down or type it into your phone only to let it fade away. Sometimes, those flashes of genius keep popping into your mind and you realize there might really be something there. So how does that flicker turn into a real business? In our research for our book, Geek Girl Rising: Inside the Sisterhood Shaking Up Tech, we spoke with hundreds of innovators, entrepreneurs, and investors and discovered three universal steps that took initial ideas jotted on a scrap of paper to the big time.

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#1: Know What You Don’t Know

The most successful entrepreneurs we met hustled to find experts who could fill their own knowledge gaps on everything from supply chains to hiring to marketing. Persistence and consistency in approaching potential advisers worked well for Caren Maio, CEO and co-founder of the online real estate platform Nestio. Every time she was introduced to someone who she thought could help her learn the ropes, she would start to act as if they already worked together.

“I would fake it until you make it. I would follow up with Joanne [Wilson, the prolific angel investor] constantly with updates: ‘Here are the things that I need help [with]. Here are some key updates with the business.’ I would just continuously update her.” Eventually, Wilson would go on to invest in Nestio and now sits on the company’s board of directors.

Every time she was introduced to someone who she thought could help her learn the ropes, she would start to act as if they already worked together.

Before she started Backstage Capital, the fund that invests in startups led by women, LGBTQ founders, and entrepreneurs of color, Arlan Hamilton spent a year devouring every blog and book she could to learn about the insular world of venture capital. Then she started cold emailing some of the boldface names of Silicon Valley for guidance.

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“I said, ‘Hey, this is what I’m doing. I don’t know anything about that world. I’m learning. What’s your advice here?” Many of them wrote back and offered to mentor her and a few, including famed venture capitalist Marc Andreesen, eventually invested in her fund.

#2: Test the Market and Keep Testing

It’s one thing to have a bright idea in your head and quite another to prove that customers will pay for it. Adda Birnir, founder and CEO of Skillcrush, the online coding boot camp aimed at women had a gut feeling in 2010 that she could entice entry level and mid-career professionals to learn computer programming if she hit them with the right messaging. But she didn’t know for sure how the unapologetic feminist tone of Skillcrush would fly until she started asking potential customers and listening to feedback from thousands of students who eventually enrolled in Skillcrush classes. It’s a process she continues today. “I think everything smart that I have done in my business has come from talking to users obsessively,” says Birnir, who advises aspiring entrepreneurs to “build into the DNA of your company a process for validating assumptions.”