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Thomas Smith
Thomas Smith

Deep Inspire

Multiplication (e.g., convolution) is arguably a cornerstone of modern deep neural networks (DNNs). However, intensive multiplications cause expensive resource costs that challenge DNNs' deployment on resource-constrained edge devices, driving several attempts for multiplication-less deep networks. This paper presented ShiftAddNet, whose main inspiration is drawn from a common practice in energy-efficient hardware implementation, that is, multiplication can be instead performed with additions and logical bit-shifts. We leverage this idea to explicitly parameterize deep networks in this way, yielding a new type of deep network that involves only bit-shift and additive weight layers. This hardware-inspired ShiftAddNet immediately leads to both energy-efficient inference and training, without compromising the expressive capacity compared to standard DNNs. The two complementary operation types (bit-shift and add) additionally enable finer-grained control of the model's learning capacity, leading to more flexible trade-off between accuracy and (training) efficiency, as well as improved robustness to quantization and pruning. We conduct extensive experiments and ablation studies, all backed up by our FPGA-based ShiftAddNet implementation and energy measurements. Compared to existing DNNs or other multiplication-less models, ShiftAddNet aggressively reduces over 80% hardware-quantified energy cost of DNNs training and inference, while offering comparable or better accuracies. Codes and pre-trained models are available at -EIC/ShiftAddNet.

Deep Inspire

I love this thermal cap that I use when deep conditioning my hair. The fact that the flaxseed is in separate "compartments" ensures that the cap stays warm all over the head, rather than having all of them fall to one place. The cap is made of high-quality material, is comfortable to wear, and stays warm for quite a while after heating according to the package directions. I'm very happy with this purchase. Jennifer on Apr 18th 2022

This cap is reversible but I choose to have the print on the outside. If you heat using directions, it stays pretty warm for at least 30 mins or longer. Its not heavy, very easy to clean, and quite stylish to wear as you continue with your day while deep conditioning. This is my fourth purchase and I will never use a different product. It works for me! Unknown on Mar 2nd 2022

I just received My Hot Head from Thermal Hair Care this week in gift package format. The product came well packaged, with clear instructions for use. The Hot Head is well made and so comfy to wear when deep conditioning my hair. It really holds the heat and my curls love it! I have three daughters who will be getting this for Christmas.Warm regards from a satisfied Canadian Customer. Mary Anne on Aug 16th 2021

I am so happy that I finally ordered my Hot Head! I am very happy about the results and told my family about it as well because we all wear natural hair styles and love to deep condition! It felt really good on my scalp and made my hair oh so soft. Thank you for this amazing product!! Michele on Jun 3rd 2021

This notion of being deeply inspired runs through Gübelin at all levels. As gemmologists, Gübelin is inspired by the magnificent gems nature has created. It is passionate to learn all it can about them, to unravel their mysteries, and to share what it knows.

When you take a deep breath and hold it, your diaphragm (a large, dome-shaped muscle located at the base of the lungs) pulls your heart away from your chest. This is known as a deep inspiration breath hold (DIBH).

One way to protect your heart while you are receiving radiation therapy is to hold your breath via DIBH. The radiation is then delivered to your breast while you are holding your breath deeply for 20 seconds. This will provide protection for your heart.

There are commercial devices that can help you hold your breath during radiation treatment for breast cancer. These devices allow you to practice deep breathing before the radiation session, and will physically help you hold your breath.

You can also help prepare for DIBH during radiation treatment by practicing taking deep breaths and holding them at home, before your treatments. Studies have shown that practicing at home every day can help you improve your skills in DIBH.

In the DIBH technique, the patient is initially maintained at quiet tidal breathing (i.e. normal, relaxed breathing),[3] followed by a deep inspiration, a deep expiration, a second deep inspiration, and breath-hold. At this point the patient is at approximately 100% vital capacity, and simulation, verification, and treatment take place during this phase of breath-holding.[4] DIBH is performed with several tangential fields for left-sided breast cancer. A patient is instructed to hold the breath while viewing the breathing pattern and the breath-hold position through a head-mounted mirror, thereby ensuring reproducibility of the breath-hold position in each delivery.[5] A pair of video goggles may also be used for monitoring the breathing cycle. Patients who cannot maintain DIBH can still benefit from lung tracking techniques, for example 4DCT.[6]

Finally, I ended my deep work session at one my favorite secret locations. Nestled in a quiet corner beyond the elevators on the third floor of the Native American History museum is a pair of comfortable leather chairs arranged by a wall of floor to ceiling windows.

Joseph Bharat Cornell is a world-renowned naturalist, educator, and storyteller. He founded the nature awareness program Sharing Nature Worldwide and, among other works, authored Sharing Nature with Children, which ushered in a transformation in nature education. His latest book, Deep Nature Play: A Guide to Wholeness, Aliveness, Creativity, and Inspired Learning, shows through vivid storytelling and tried-and-true nature activities how children and adults alike can achieve greater engagement, retention, and inspiration through absorption in deep play.

Flow Learning is a teaching system I developed that creates a step-by-step accelerating flow of inspiration. By using a sequence of playful activities, you can elevate play to deep play and, in doing so, remove human barriers that separate children and adults from the natural world. The four stages of Flow Learning are: 1) Awaken Enthusiasm, 2) Focus Attention, 3) Offer Direct Experiences, and 4) Share Inspiration.

Flow Learning is an excellent process for achieving a deep resonance with any subject matter. Although the activities I have created to use with Flow Learning are nature oriented, becoming familiar with these activities can give you many ideas for making play more meaningful in your programs.

Climbing with others is one way to help hone in on technique practice while also observing different ideas and skills from those around you. Even though bouldering is a solo sport, it can be collaborative! When struggling to figure out how anyone could make a seemingly impossible move, coming together with other climbers can help deflate frustrations, inspire new tactics, and build your comfortability in the gym.

The deep-learning software driving the modern artificial intelligence revolution has mostly run on fairly standard computer hardware. Some tech giants such as Google and Intel have focused some of their considerable resources on creating more specialized computer chips designed for deep learning. But IBM has taken a more unusual approach: It is testing its brain-inspired TrueNorth computer chip as a hardware platform for deep learning.

Instead of firing every cycle, the neurons in spiking neural networks must gradually build up their potential before they fire. To achieve precision on deep-learning tasks, spiking neural networks typically have to go through multiple cycles to see how the results average out. That effectively slows down the overall computation on tasks such as image recognition or language processing.

The IBM TrueNorth design may better support the goals of neuromorphic computing that focus on closely mimicking and understanding biological brains, says Zachary Chase Lipton, a deep-learning researcher in the Artificial Intelligence Group at the University of California, San Diego. By comparison, deep-learning researchers are more interested in getting practical results for AI-powered services and products. He explains the difference as follows:

To evoke the cliche metaphor about birds and airplanes, you might say the computational neuroscience/neuromorphic community is more concerned with studying birds, and the machine learning community more interested in understanding aerodynamics, with or without the help of biology. The deep learning community is generally bullish on the benefits of specialized hardware. [Therefore,] the neuromorphic chips don't inspire as much excitement because the spiking neural networks they focus on are not so popular in deep learning.

Such biologically inspired chips would probably become popular only if they show that they can outperform other hardware approaches for deep learning, Lipton says. But he suggested that IBM could leverage its hardware expertise to join Google and Intel in creating new specialized chips designed specifically for deep learning.

The aim of this study was to compare dose-volume histogram (DVH) with dose-mass histogram (DMH) parameters for treatment of left-sided breast cancer in deep inspiration breath-hold (DIBH) and free breathing (FB). Additionally, lung expansion and anatomical factors were analyzed and correlated to dose differences.

My ongoing discussions with Safety Case experts got me wondering: Could insights garnered from these experts' day-to-day dealings with distant future worlds aid us in re-thinking humanity's place within the deeper history of our environment? Could they inspire positive changes in our ways of living on a damaged planet during what is increasingly called the Anthropocene?

But reasoning by analogy is really nothing new for scientists. And indeed analogue studies of a very similar kind are also ventured elsewhere. Space analogue studies, for instance, have examined regions of Earth thought to harbor environmental conditions resembling those of celestial bodies (like Mars) to aid future space exploration missions. And, as MIT anthropologist Stefan Helmreich discussed in his 2012 article on "extraterrestrial relativism," scientists "looking for life on Mars scout for microbes analogous to those archaebacteria in Earth that live in such sites as deep-sea hydrothermal vents." 041b061a72

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