March 30, 2015

DARPA’s Anti-Submarine Warfare developing submarine hunter killer drone

DARPA’s Anti-Submarine Warfare (ASW) Continuous Trail Unmanned Vessel (ACTUV) program seeks to develop a new type of unmanned surface vessel that could independently track adversaries’ ultra-quiet diesel-electric submarines over thousands of miles. One of the challenges that the ACTUV program is addressing is development of autonomous behaviors for complying with the International Regulations for Preventing Collisions at Sea, known as COLREGS. Substantial progress has been made in developing and implementing those behaviors. Currently, ACTUV’s system for sensing other vessels is based on radar, which provides a “90 percent solution” for detecting other ships. However, radar is less suitable for classification of the type of other vessels, for example determining whether the vessel is a powered vessel or a sailboat. Additionally, one of the requirements of COLREGS is to maintain “a proper lookout by sight and hearing.”

Nextbigfuture - This system will be the underwater equivalent of a Predator drone. There is no way you make a silent submarine drone for trailing enemy submarines criss-crossing the ocean without putting a torpedo or two into the system. The submarine hunter is in the DARPA project description, but the killer would come with the torpedo capability.

DARPA’s Anti-Submarine Warfare (ASW) Continuous Trail Unmanned Vessel (ACTUV) seeks to develop an independently deployed unmanned surface vessel that would operate under sparse remote supervisory control and safely follow the maritime “rules of the road” for collision avoidance known as COLREGS. The hull for the ACTUV prototype is under construction in preparation for planned water-borne testing of the full prototype later this year.

There was an earlier ACTUV concept



DARPA developing drop in retrofit to increase automation in existing airplanes

In the latest step in a decades-long process through which automation has taken on increasing responsibilities in the cockpit—allowing pilots to focus on flight tasks demanding their unique capabilities—DARPA has awarded three contracts for its Aircrew Labor In-Cockpit Automation System (ALIAS) program. ALIAS envisions a tailorable, drop‐in, removable kit that would enable high levels of automation in existing aircraft and facilitate reduced need for onboard crew. The program intends to leverage the considerable advances that have been made in aircraft automation systems over the past 50 years, as well as the advances that have been made in remotely piloted aircraft technologies, to help shift and refocus pilot workloads, augment mission performance and improve aircraft safety.

In Phase 1 of ALIAS, DARPA intends to focus on three critical technology areas:

* Development of minimally invasive interfaces between new automation systems and existing aircraft
* Knowledge acquisition on aircraft operations, to support rapid adaptation of the ALIAS toolkit across different aircraft
* Human-machine interfaces that would enable high-level human supervision instead of requiring pilots’ constant vigilance over lower-level flight maintenance tasks

“Because we want to develop a drop-in system for existing aircraft.

DARPA’s Aircrew Labor In-Cockpit Automation System (ALIAS) program envisions a tailorable, drop‐in, removable kit that would enable the addition of high levels of automation into existing aircraft to enable operation with reduced onboard crew. In an important step toward that goal, DARPA has awarded prime contracts for Phase 1 of ALIAS to the following companies: Aurora Flight Sciences Corporation (top), Lockheed Martin Corporation (middle) and Sikorsky Aircraft Corporation (bottom). The photos show the aircraft that each performer plans to use to test its respective technologies in Phase 1.

Entanglement-Based Machine Learning on a Photonic Quantum Computer in principle and if scaled would show exponential speed up

For the first time, physicists have performed machine learning on a photonic quantum computer, demonstrating that quantum computers may be able to exponentially speed up the rate at which certain machine learning tasks are performed—in some cases, reducing the time from hundreds of thousands of years to mere seconds. The new method takes advantage of quantum entanglement, in which two or more objects are so strongly related that paradoxical effects often arise since a measurement on one object instantaneously affects the other. Here, quantum entanglement provides a very fast way to classify vectors into one of two categories, a task that is at the core of machine learning.

In the future, the researchers hope to scale the method to larger numbers of qubits. They explain that higher-dimensional quantum states can be encoded using a photon's degree of freedom of orbital angular momentum, or by using other properties.

"We are working on controlling an increasingly large number of quantum bits for more powerful quantum machine learning," Lu said. "By controlling multiple degrees of freedom of a single photon, we aim to generate 6-photon, 18-qubit entanglement in the near future. Using semiconductor quantum dots, we are trying to build a solid-state platform for approximately 20-photon entanglement in about five years. With the enhanced ability in quantum control, we will perform more complicated quantum artificial intelligence tasks."

Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing “big data” could become intractable for classical computers. Recently, quantum machine learning algorithms were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.


Experimental setup for quantum machine learning with photonic qubits. Ultraviolet laser pulses with a central wavelength of 394 nm, pulse duration of 120 fs, and a repetition rate of 76 MHz pass through two type-II β-barium borate (BBO) crystals with a thickness of 2 mm to produce two entangled photon pairs

Entanglement-Based Machine Learning on a Quantum Computer, X.-D. Cai, D. Wu, Z.-E. Su, M.-C. Chen, X.-L. Wang, Li Li, N.-L. Liu, C.-Y. Lu, and J.-W. Pan,

Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411, Quantum algorithms for supervised and unsupervised machine learning

Scott Aaronson on the Harrow, Hassidim, Lloyd Machine Learning Quantum Algorithm

Scott Aaronson has a 4 pager on the machine learning mini-revolution in quantum computing.

The algorithm at the center of the “quantum machine learning” mini-revolution is called HHL , after my colleagues Aram Harrow, Avinatan Hassidim, and Seth Lloyd, who invented it in 2008. Many of the subsequent quantum learning algorithms extend HHL or use it as a subroutine, so it’s important to understand HHL first.

The HHL algorithm “solves Ax = b in logarithmic time,” but it does so only with the following four caveats, each of which can be crucial in practice.

1. The vector b = (b1, . . . , bn) somehow needs to be loaded quickly into the quantum computer’s memory

2. The quantum computer also needs to be able to apply unitary transformations of the form e^−iAt, for various values of t.

3. The matrix A needs to be not merely invertible, but robustly invertible, or “well-conditioned.”

4. The limitation noted earlier—that even writing down the solution vector x = (x1, . . . , xn) already requires n steps—also applies in the quantum world. When HHL is finished, its output is not x itself, but rather a quantum state |xi of log2 n qubits, which (approximately) encodes the entries of x in its amplitudes.

HHL is not exactly an algorithm for solving a system of linear equations in logarithmic time. Rather, it’s an algorithm for approximately preparing a quantum superposition.

HHL algorithm still be useful for something? Absolutely—as long as one can address all the caveats, and explain why they’re not fatal for one’s desired application. To put it differently, perhaps the best way to see HHL is as a template for other quantum algorithms.

How excited should we be about the new quantum machine learning algorithms? To whatever extent we care about quantum computing at all, I’d say we should be excited indeed: HHL and its offshoots represent real advances in the theory of quantum algorithms, and in a world with quantum computers, they’d probably find practical uses.

The new algorithms provide a general template, showing how quantum computers might be used to provide exponential speedups for central problems like clustering, pattern-matching, and principal component analysis.

Woolly Mammoth DNA Inserted into Elephant Cells but passenger pigeon will become de-extincted first

Harvard geneticist George Church and his colleagues used a gene-editing technique known as CRISPR to insert mammoth genes for small ears, subcutaneous fat, and hair length and color into the DNA of elephant skin cells. The work has not yet been published in a scientific journal, and has yet to be reviewed by peers in the field.

Woolly mammoths (Mammuthus primigenius) have been extinct for millennia, with the last of the species dying out about 3,600 years ago. But scientists say it may be possible to bring these and other species back from the grave, through a process known as de-extinction.

But we won't be seeing woolly mammoths prancing around anytime soon, "because there is more work to do," Church told U.K.'s The Times, "But we plan to do so," Church added.

Not all of the mammoth's genetic code was spliced into the elephant genome. In fact, only 14 genes were inserted -- ones most representative of the hairy, cold-enduring traits of the modern elephant's ancient relative. The genes were spliced into elephant skins cells using a technique called CRISPR (clustered regularly interspaced short palindromic repeat).

Church and his assistants specifically selected the 14 spliced genes -- sourced from the skin cells of a frozen woolly mammoth carcass -- for their uniqueness to the woolly mammoth's hardy appearance.

"We prioritized genes associated with cold resistance including hairiness, ear size, subcutaneous fat and, especially, hemoglobin," Church told The Sunday Times.


March 29, 2015

DARPA progress to small ships each with UAV air forces

DARPA has chosen two performers to work on new systems that would cost-effectively provide capabilities on par with land-based systems.

DARPA has awarded prime contracts for Phase 2 of Tern, a joint program between DARPA and the U.S. Navy’s Office of Naval Research (ONR). The goal of Tern is to give forward-deployed small ships the ability to serve as mobile launch and recovery sites for medium-altitude, long-endurance unmanned aerial systems (UAS). These systems could provide long-range intelligence, surveillance and reconnaissance (ISR) and other capabilities over greater distances and time periods than is possible with current assets, including manned and unmanned helicopters. Further, a capacity to launch and retrieve aircraft on small ships would reduce the need for ground-based airstrips, which require significant dedicated infrastructure and resources. The two prime contractors selected by DARPA are AeroVironment, Inc., and Northrop Grumman Corp.

Tern, a joint program between DARPA and the U.S. Navy’s Office of Naval Research (ONR), seeks to enable forward-deployed small ships to serve as mobile launch and recovery sites for medium-altitude, long-endurance unmanned aerial systems (UAS). In an important step toward that goal, DARPA has awarded prime contracts for Phase 2 of Tern to two companies: AeroVironment, Inc. and Northrop Grumman Corp.

DARPA in search of breakthrough Analog systems for Petaflops on Desktops

DARPA is interested in pursuing the somewhat counterintuitive premise that “old fashioned” analog approaches may be part of the solution. Analog computers, which solve equations by manipulating continuously changing values instead of discrete measurements, have been around for more than a century. In the 1930s, for example, Vannevar Bush—who a decade later would help initiate and administer the Manhattan Project—created an analog “differential analyzer” that computed complex integrations through the use of a novel wheel-and-disc mechanism. And in the 1940s, the Norden bombsight made its way into U.S. warplanes, where it used analog methods to calculate bomb trajectories. But in the 1950s and 1960s, as transistor-based digital computers proved more efficient for most kinds of problems, analog methods fell into disuse.

DARPA invites input on how to speed up computation of the complex mathematics that characterize scientific computing.

The DARPA program is called Analog and Continuous-variable Co-processors for Efficient Scientific Simulation (ACCESS).



DARPA wants to solve the following needs, either singly or in combination:

* Scalable, controllable, and measurable processes that can be physically instantiated in co-processors for acceleration of computational tasks frequently encountered in scientific simulation

* Algorithms that use analog, non-linear, non-serial, or continuous-variable computational primitives to reduce the time, space, and communicative complexity relative to von Neumann/CPU/GPU processing architectures

* System architectures, schedulers, hybrid and specialized integrated circuits, compute languages, programming models, controller designs, and other elements for efficient problem decomposition, memory access, and task allocation across multi-hybrid co-processors

* Methods for modeling and simulation via direct physical analogy

DARPA is particularly interested in engaging nontraditional contributors to help develop leap-ahead technologies in the focus areas above, as well as other technologies that could potentially improve the computational tractability of complex nonlinear systems.

Fighter jet satellite launching, robot sub hunters and more from DARPA

DARPA released biennial report of Breakthrough Technologies for National Security. Here are some featured DARPA projects.


The Airborne Launch Assist Space Access (ALASA) program seeks to propel 100-pound satellites into orbit for less than $1 million per flight by using low-cost, expendable upper stages launched from unmodified conventional aircraft. ALASA aims to provide more affordable, flexible and reliable access to space.

The Airborne Launch Assist Space Access will work even better when the US has hypersonic jet fighters in the mach 5 to 10+ ranges.

Genetics breakthrough is a game changer for type 1 diabetes research

The genes that increase the risk of Type 1 diabetes have lost their hiding place.

A research group that includes a University of Florida genetics expert has located and narrowed down the number of genes that play a role in the disease, according to a study published Monday in the journal Nature Genetics. Knowing the identities and location of causative genes is a crucial development: Other researchers can use this information to better predict who might develop Type 1 diabetes and how to prevent it.

“It’s a game-changer for Type 1 diabetes,” said Patrick Concannon, director of the University of Florida Genetics Institute.

Diabetes mellitus type 1 (also known as type 1 diabetes, or T1DM; formerly insulin-dependent diabetes or juvenile diabetes) is a form of diabetes mellitus that results from the autoimmune destruction of the insulin-producing beta cells in the pancreas. The subsequent lack of insulin leads to increased blood and urine glucose. The classical symptoms are polyuria (frequent urination), polydipsia (increased thirst), polyphagia (increased hunger) and weight loss.

Diabetes mellitus type 1 accounts for between 5% and 10% of cases of diabetes. Globally, the number of people with DM type 1 is unknown, although it is estimated that about 80,000 children develop the disease each year. Within the United States the number of affected persons is estimated at one to three million. The development of new cases vary by country and region; the lowest rates appears to be in Japan and China with approximately 1 person per 100,000 per year; the highest rates are found in Scandinavia where it is closer to 35 new cases per 100,000 per year. The United States and northern Europe fall somewhere in between with 8-17 new cases per 100,000 per year.

Type 1 diabetes is estimated to cause $10.5 billion in annual medical costs ($875 per month per diabetic) and an additional $4.4 billion in indirect costs ($366 per month per person with diabetes) in the U.S



DARPA is expanding the technological frontier by harnessing quantum physics and new chemistry

DARPA today released Breakthrough Technologies for National Security DARPA described how they are expanding the technological frontier.

DARPA’s core work has involved overcoming seemingly insurmountable physics and engineering barriers and, once showing those daunting problems to be tractable after all, applying new capabilities made possible by these breakthroughs directly to national security needs. That tradition holds true today. Maintaining momentum in this core component of the agency, DARPA is working to achieve new capabilities relating to the following opportunities:

Applying Deep Mathematics

From cyber defense to big data analysis to predictive modeling of complex phenomena, many practical technological challenges are short of solutions because the relevant mathematics remain incomplete. Among other initiatives aimed to address such shortcomings, DARPA is constructing and applying new mathematical approaches for representing, designing, and testing complex systems and, separately, is developing new mathematical tools for modeling extremely complex systems quickly without sacrificing resolution.

• Inventing New Chemistries, Processes and Materials

Military systems are fundamentally limited by the materials from which they are made. Only rarely, however, do any of the many new materials developed in laboratories make the transition into operational systems. To facilitate the assessment and adoption of novel materials in practical settings, DARPA is pursuing new modeling and measurement tools for evaluating and predicting functional reliability and is developing low-cost fabrication methods to allow customized and small-volume production. DARPA is also creating the technologies needed to assemble systems directly from atomic-scale feedstock.


DARPA’s Intense and Compact Neutron Sources (ICONS) program seeks to develop portable, next-generation imaging tools that combine the complementary benefits of X-ray and neutron radiography to enable highly detailed scanning in field settings. Neutron scanning provides the capability to see through many otherwise visually impenetrable objects; Asiatic lilies in a lead cask (left) are invisible to x-ray imaging but clearly visible in high resolution via neutron imaging (right).

DARPA plans to harness biology and master neurotechnologies

DARPA today released Breakthrough Technologies for National Security, a biennial report summarizing the Agency’s historical mission, current and evolving focus areas and recent transitions of DARPA-developed technologies to the military Services and other sectors.

One of DARPA's four main areas of focus is to harness biology.

Harness Biology as Technology: To leverage recent breakthroughs in neuroscience, immunology, genetics and related fields, DARPA in 2014 created its Biological Technologies Office, which has enabled a new level of momentum for the Agency’s portfolio of innovative, bio-based programs. DARPA’s work in this area includes programs to accelerate progress in synthetic biology, outpace the spread of infectious diseases and master new neurotechnologies.

Harnessing Biology as a Technology

• Accelerating Progress in Synthetic Biology

Biological systems have evolved tremendously sophisticated and highly efficient approaches to synthesizing compounds, including some with the potential to address current challenges in fields ranging from medicine to materials science. DARPA is developing technologies to harness biology’s synthetic and functional capabilities, with the goal of creating revolutionary bio-based manufacturing platforms that can enable new production paradigms and create materials with novel properties

• Outpacing Infectious Diseases

As the 2014 Ebola outbreak demonstrated, emerging infectious diseases can be a significant threat not just to health but also to national stability. DARPA is developing unconventional biological approaches to reduce the threats posed by infectious disease. Among the Agency’s goals are the development of genetic and immunological technologies to detect, diagnose and treat infectious diseases with unprecedented precision and rapidity, and platforms for exploring the evolution of viruses, predicting mutational pathways and developing drugs and vaccines in advance of need.

Mastering New Neurotechnologies

Recent advances in microelectronics, information science and neuroscience are enabling the development of novel therapies to accelerate recovery after a range of injuries and, in the longer run, new approaches to optimizing human performance. Among the Agency’s goals in this domain are implantable neural interfaces for human clinical use to bridge gaps in the injured brain, help overcome memory deficits and precisely deliver therapeutic stimuli in patients with neuropsychiatric and neurological disease; and systems to provide sensor-enabled feedback from prosthetic hands to the nervous system to provide enhanced dexterity and even the sense of touch for amputees.


DARPA’s Electrical Prescriptions (ElectRx, pronounced “electrics”) program aims to develop ultraminiaturized feedback-controlled neuromodulation technologies that would monitor health status and intervene as needed to deliver patient-specific therapeutic patterns of stimulation designed to restore a healthy physiological state. Peripheral neuromodulation therapies based on ElectRx research could help maximize the immunological, physical and mental health of military Service members and veterans.

DARPA vision of Breakthroughs like new X-planes

DARPA today released Breakthrough Technologies for National Security, a biennial report summarizing the Agency’s historical mission, current and evolving focus areas and recent transitions of DARPA-developed technologies to the military Services and other sectors.

DARPA is focusing its strategic investments in four main areas:

1. Rethink Complex Military Systems: To help enable faster development and integration of breakthrough military capabilities in today’s rapidly shifting landscape, DARPA is working to make weapons systems more modular and easily upgraded and improved; assure superiority in the air, maritime, ground, space and cyber domains; improve position, navigation and timing (PNT) without depending on the satellite-based Global Positioning System; and augment defenses against terrorism.

2. Master the Information Explosion: DARPA is developing novel approaches to deriving insights from massive datasets, with powerful big-data tools. The Agency is also developing technologies to ensure that the data and systems with which critical decisions are made are trustworthy, such as automated cyber defense capabilities and methods to create fundamentally more secure systems. And DARPA is addressing the growing need to ensure privacy at various levels of need without losing the national security value that comes from appropriate access to networked data.



March 28, 2015

Innovations from Three Russian Prototype Tanks and T95 were merged into the Armata Tank Design

Object 477, Object 775, and Object-640 (Black Eagle) were three experimental Russian-built tanks that never went into mass production. Nevertheless, the unique innovations used in them form the basis for development of the Russian army’s modern combat vehicle – the Armata Universal Combat Platform.

* Object-477 Molot (Hammer). Mid-80s design. Had an unmanned turret and a 152-mm cannon.
* Object-775. Did not fire conventional tank shells but had a 125-mm missile launcher. Had a hydro-pneumatic suspension that kept it low to the ground. 1960s design
* Object 640 – Chyorniy Orel (Black Eagle). Mid-90s design that had modular armour and the “Cactus” dynamic protection system

Object 640

Object 775

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