The U.S. Department of the Interior today released the Final Environmental Impact Statement (FEIS) for a Long-Term Experimental and Management Plan (LTEMP) for Glen Canyon Dam operations. https://www.nps.gov/grca/learn/news/final-eis-glen-canyon-adaptive-management.htm

Rupert Guinness is cycling across America — from his living room in Edgecliff.

In the Pacific Northwest, native salmon and trout are some of the toughest survivors on the block. Over time, these fish have evolved behavioral adaptations to natural disturbances, and they rely on these disturbances to deliver coarse sediment and wood that become complex stream habitat. Powerful disturbances such as wildfire, postfire landslides, and debris flows may be detrimental to fish populations in the short term, but over time, they enrich instream habitats, enhancing long-term fish survival and productivity.

This report examines public perspectives on disturbance-based management conducted in the central Cascade Range in Oregon as part of the Blue River Landscape Strategy.

The Northwest Forest Plan (the Plan) identifies adaptive management as a central strategy for effective implementation. Despite this, there has been a lack of any systematic evaluation of its performance.

Scientists, land managers and policy makers discussed whether riparian (stream side) forest management and policy for state, federal and private lands in western Washington are consistent with current science. Answers were mixed: some aspects of riparian policy and management have a strong basis in current science, while other aspects may not.

Climate change presents a major challenge to natural resource managers both because of the magnitude of potential effects of climate change on ecosystem structure, processes, and function, and because of the uncertainty associated with those potential ecological effects. Concrete ways to adapt to climate change are needed to help natural resource managers take the first steps to incorporate climate change into management and take advantage of opportunities to counteract the negative effects of climate change. We began a climate change adaptation case study at Olympic National Forest (ONF) in partnership with Olympic National Park (ONP) to determine how to adapt management of federal lands on the Olympic Peninsula, Washington, to climate change. The case study began in the summer of 2008 and continued for 1½ years. The case study process involved science-based sensitivity assessments, review of management activities and constraints, and adaptation workshops in each of four focus areas (hydrology and roads, fish, vegetation, and wildlife). The process produced adaptation options for ONF and ONP, and illustrated the utility of place-based vulnerability assessment and science-management workshops in adapting to climate change. The case study process provides an example for other national forests, national parks, and natural resource agencies of how federal land management units can collaborate in the initial stages of climate change adaptation. Many of the ideas generated through this process can potentially be applied in other locations and in other agencies.

National forest managers are charged with tackling the effects of climate change on the natural resources under their care. The Forest Service National Roadmap for Responding to Climate Change and the Climate Change Performance Scorecard require managers to make significant progress in addressing climate change by 2015. To help land managers meet this challenge, Forest Service scientists conducted three case studies on national forests and adjacent national parks and documented a wide range of scientific issues and solutions. They summarized the scientific foundation for climate change adaptation and made the information accessible to land managers by creating a climate change adaptation guidebook and web portal. Case study teams discovered that collaboration among scientists and land managers is crucial to adaptation planning, as are management plans targeted to the particular ecosystem conditions and management priorities of each region.

This guidebook contains science-based principles, processes, and tools necessary to assist with developing adaptation options for national forest lands. The adaptation process is based on partnerships between local resource managers and scientists who work collaboratively to understand potential climate change effects, identify important resource issues, and develop management options that can capitalize on new opportunities and reduce deleterious effects. Because management objectives and sensitivity of resources to climate change differ among national forests, appropriate processes and tools for developing adaptation options may also differ.

The North Cascadia Adaptation Partnership (NCAP) is a science-management partnership consisting of the U.S. Department of Agriculture Forest Service Mount Baker-Snoqualmie and Okanogan-Wenatchee National Forests and Pacific Northwest Research Station; North Cascades National Park Complex; Mount Rainier National Park; and University of Washington Climate Impacts Group.

On February 4, 2014, a seven-member expert panel provided objective technical information on the potential effectiveness and feasibility of activities to manage raptors (northern harriers and great horned owls) to aid the recovery of western snowy plovers.

A conservation and management strategy for yellow-cedar in Alaska is presented in the context of climate change.

The Blue Mountains Adaptation Partnership was developed to identify climate change issues relevant to resource management in the Blue Mountains region, to find solutions that can minimize negative effects of climate change, and to facilitate transition of diverse ecosystems to a warmer climate.

Forest managers are seeking practical guidance on how to adapt their current practices and, if necessary, their management goals, in response to climate change. Science-management collaboration was initiated on national forests in eastern Washington where resource managers showed a keen interest in science-based options for adapting to climate change at a 2-day workshop. Scientists and managers reviewed current climate change science and identified resources vulnerable to expected climate change. Vulnerabilities related to vegetation and habitat management included potential reductions in forest biodiversity and low forest resilience to changing disturbance regimes. The vulnerabilities related to aquatic and infrastructure resources included changing water quality and quantity, the risk to roads and other facilities from changes to hydrologic regimes, and the potential loss of at-risk aquatic species and habitats. Managers then worked in facilitated groups to identify adaptations that could be implemented through management and planning to reduce the vulnerability of key resources to climate change. The identified adaptations were grouped under two major headings: Increasing Ecological Resiliency to Climate Change, and Increasing Social and Economic Resiliency to Climate Change. The information generated from the science-management collaborative represents an initial and important step in identifying and prioritizing tangible steps to address climate change in forest management. Next would be the development of detailed implementation strategies that address the identified management adaptations..

Film star Daniel Radcliffe launches new Harry Potter at Home series with the first celebrity video reading

Here's a video of our laptop kit, what it contains, and why we have what we have in it. Full transcript after the jump.

En 2016, la route du photographe finlandais Konsta Punkka croisait celle de deux pumas. Il se situait alors au cœur de la Patagonie, au Chili, dans le vaste parc national Torres del Paine. Spécialiste des clichés d’aventure et d’animaux dans leur habitat naturel, le photographe a passé une dizaine de jours à suivre les félins pour tirer de […]

In late August, we launched the private beta program of our advanced audio algorithm parameters. After feedback by our users and many new experiments, we are proud to release a complete rework of the Adaptive Leveler parameters:

In the previous version, we based our Adaptive Leveler parameters on the Loudness Range descriptor (LRA), which is included in the EBU R128 specification.
Although it worked, it turned out that it is very difficult to set a loudness range target for diverse audio content, which does include speech, background sounds, music parts, etc. The results were not predictable and it was hard to find good target values.
Therefore we developed our own algorithm to measure the dynamic range of audio signals, which works similarly for speech, music and other audio content.

The following advanced parameters for our Adaptive Leveler allow you to customize which parts of the audio should be leveled (foreground, all, speech, music, etc.), how much they should be leveled (dynamic range), and how much micro-dynamics compression should be applied.

To try out the new algorithms, please join our private beta program and let us know your feedback!

Leveler Preset

The Leveler Preset defines which parts of the audio should be adjusted by our Adaptive Leveler:

• Default Leveler:
  Our classic, default leveling algorithm as demonstrated in the Leveler Audio Examples. Use it if you are unsure.
• Foreground Only Leveler:
  This preset reacts slower and levels foreground parts only. Use it if you have background speech or background music, which should not be amplified.
• Fast Leveler:
  A preset which reacts much faster. It is built for recordings with fast and extreme loudness differences, for example, to amplify very quiet questions from the audience in a lecture recording, to balance fast-changing soft and loud voices within one audio track, etc.
• Amplify Everything:
  Amplify as much as possible. Similar to the Fast Leveler, but also amplifies non-speech background sounds like noise.

Leveler Dynamic Range

Our default Leveler tries to normalize all speakers to a similar loudness so that a consumer in a car or subway doesn't feel the need to reach for the volume control.
However, in other environments (living room, cinema, etc.) or in dynamic recordings, you might want more level differences (Dynamic Range, Loudness Range / LRA) between speakers and within music segments.

The parameter Dynamic Range controls how much leveling is applied: Higher values result in more dynamic output audio files (less leveling). If you want to increase the dynamic range by 3dB (or LU), just increase the Dynamic Range parameter by 3dB.
We also like to call this Loudness Comfort Zone: above a maximum and below a minimum possible level (the comfort zone), no leveling is applied. So if your input file already has a small dynamic range (is within the comfort zone), our leveler will be just bypassed.

Example Use Cases:
Higher dynamic range values should be used if you want to keep more loudness differences in dynamic narration or dynamic music recordings (live concert/classical).
It is also possible to utilize this parameter to generate automatic mixdowns with different loudness range (LRA) values for different target environments (very compressed ones like mobile devices or Alexa, very dynamic ones like home cinema, etc.).

Compressor

Controls Micro-Dynamics Compression:
The compressor reduces the volume of short and loud spikes like "p", "t" or laughter ( short-term dynamics) and also shapes the sound of your voice (it will sound more or less "processed").
The Leveler, on the other hand, adjusts mid-term level differences, as done by a sound engineer, using the faders of an audio mixer, so that a listener doesn't have to adjust the playback volume all the time.
For more details please see Loudness Normalization and Compression of Podcasts and Speech Audio.

• Auto:
  The compressor setting depends on the selected Leveler Preset. Medium compression is used in Foreground Only and Default Leveler presets, Hard compression in our Fast Leveler and Amplify Everything presets.
• Soft:
  Uses less compression.
• Medium:
  Our default setting.
• Hard:
  More compression, especially tries to compress short and extreme level overshoots. Use this preset if you want your voice to sound very processed, our if you have extreme and fast-changing level differences.
• Off:
  No short-term dynamics compression is used at all, only mid-term leveling. Switch off the compressor if you just want to adjust the loudness range without any additional micro-dynamics compression.

Separate Music/Speech Parameters

Use the switch Separate MusicSpeech Parameters (top right), to see separate Adaptive Leveler parameters for music and speech segments, to control all leveling details separately for speech and music parts:

For dialog intelligibility improvements in films and TV, it is important that the speech/dialog level and loudness range is not too soft compared to the overall programme level and loudness range. This parameter allows you to use more leveling in speech parts while keeping music and FX elements less processed.
Note: Speech, music and overall loudness and loudness range of your production are also displayed in our Audio Processing Statistics!

Example Use Case:
Music live recordings or dynamic music mixes, where you want to amplify all speakers (speech dynamic range should be small) but keep the dynamic range within and between music segments (music dynamic range should be high).
Dialog intelligibility improvements for films and TV, without effecting music and FX elements.

Other Advanced Audio Algorithm Parameters

We also offer advanced audio parameters for our Noise, Hum Reduction and Global Loudness Normalization algorithms:

For more details, please see the Advanced Audio Algorithms Documentation.

Want to know more?

If you want to know more details about our advanced algorithm parameters (especially the leveler parameters), please listen to the following podcast interview with Chris Curran (Podcast Engineering School):
Auphonic’s New Advanced Features, with Georg Holzmann – PES 108

Advanced Parameters Private Beta and Feedback

At the moment the advanced algorithm parameters are for beta users only. This is to allow us to get user feedback, so we can change the parameters to suit user needs.
Please let us know your case studies, if you need any other algorithm parameters or if you have any questions!

Here are some private beta invitation codes:

jbwCVpLYrl 6zmLqq8o3z RXYIUbC6al QDmIZLuPKa JIrnGRZBgl SWQOWeZOBD ISeBCA9gTy w5FdsyhZVI qWAvANQ5mC twOjdHrit3
KwnL2Le6jB 63SE2V54KK G32AULFyaM 3H0CLYAwLU mp1GFNVZHr swzvEBRCVa rLcNJHUNZT CGGbL0O4q1 5o5dUjruJ9 hAggWBpGvj
ykJ57cFQSe 0OHAD2u1Dx RG4wSYTLbf UcsSYI78Md Xedr3NPCgK mI8gd7eDvO 0Au4gpUDJB mYLkvKYz1C ukrKoW5hoy S34sraR0BU
J2tlV0yNwX QwNdnStYD3 Zho9oZR2e9 jHdjgUq420 51zLbV09p4 c0cth0abCf 3iVBKHVKXU BK4kTbDQzt uTBEkMnSPv tg6cJtsMrZ
BdB8gFyhRg wBsLHg90GG EYwxVUZJGp HLQ72b65uH NNd415ktFS JIm2eTkxMX EV2C5RAUXI a3iwbxWjKj X1AT7DCD7V y0AFIrWo5l

If you have an invitation code, you can enter it here to activate the advanced audio algorithm parameters:
Auphonic Algorithm Parameters Private Beta Activation

You enjoy photographing in the field, whether its landscapes or events.  When it comes to viewing your captured images, polishing them up and showing to people, you need a laptop to make this possible, especially when you are far from home or office.  But you are not a techie and Cores make your head spin! How do you decide on Continue Reading

The post 9 Best Laptops for Photography appeared first on Photodoto.

Babies capture everyone’s heart, and this is the time to cherish in the form of a series of photographs that will remind you of how idyllic your child looked at the newborn stage. Photographing children, especially babies, poses a problem for the image taker, as you cannot simply ask for a pose, rather you have to engineer it. Here is some information to help make the session go smoothly, and get the perfect shots. The Right Age The ideal age to have photos taken of your baby is between 5 and 12 days old, as they tend to sleep most of the time and it is much easier to find the ideal pose and position. Of course, you can wait a little longer, but to be honest, you have lost that newborn look, which is what everyone wants to capture. If you happen to be in Western Australia, and are ... Read more

The post Capture that Precious Moment: How to Create a Calm Mood for the Infant appeared first on Digital Photography Tutorials.

Chris Guillebeau has traveled to 193 countries. And just to be clear, that is all of the countries recognized on the planet. He is the first person to do it before the age of 35. More importantly, he has built online businesses, side hustles, and mastered the art of a non-conforming lifestyle since he was 19 years old. There’s almost nobody better person equipped to talk about starting lots of businesses. He has a daily podcast called Side Hustle School where he’s featured more than 850 different side hustle businesses + featured many in his new book called 100 Side Hustles. Chris also started half a dozen or more himself, started one of my favorite conferences, The World Domination Summit, and is a New York Times bestselling author of books like the $100 Start Up and The Art of Non-Conformity and many others. This guy is a beast and he’s so savvy, so humble. In this episode: We go into details about some of Chris’ favorite side hustle businesses he’s come across in his podcast & writing this book – what are some of the most common traits and failures. Chris shares his experience with depression, how he’s faced it, […]

The post Adaptation, Self-Awareness and Art of the Side Hustle with Chris Guillebeau appeared first on Chase Jarvis Photography.

How to generate testing scenario libraries for connected and automated vehicles (CAVs) is a major challenge faced by the industry. In previous studies, to evaluate maneuver challenge of a scenario, surrogate models (SMs) are often used without explicit knowledge of the CAV under test. However, performance dissimilarities between the SM and the CAV under test usually exist, and it can lead to the generation of suboptimal scenario libraries. In this paper, an adaptive testing scenario library generation (ATSLG) method is proposed to solve this problem. A customized testing scenario library for a specific CAV model is generated through an adaptive process. To compensate the performance dissimilarities and leverage each test of the CAV, Bayesian optimization techniques are applied with classification-based Gaussian Process Regression and a new-designed acquisition function. Comparing with a pre-determined library, a CAV can be tested and evaluated in a more efficient manner with the customized library. To validate the proposed method, a cut-in case study was performed and the results demonstrate that the proposed method can further accelerate the evaluation process by a few orders of magnitude.

Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear. To alleviate this issue, we are going to investigate unsupervised domain adaptation on RC, wherein a model is trained on labeled source domain and to be applied to the target domain with only unlabeled samples. We first show that even with the powerful BERT contextual representation, the performance is still unsatisfactory when the model trained on one dataset is directly applied to another target dataset. To solve this, we provide a novel conditional adversarial self-training method (CASe). Specifically, our approach leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self-training. On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains. Extensive experiments show our approach achieves comparable accuracy to supervised models on multiple large-scale benchmark datasets.

Correlation alignment (CORAL), a representative domain adaptation (DA) algorithm, decorrelates and aligns a labelled source domain dataset to an unlabelled target domain dataset to minimize the domain shift such that a classifier can be applied to predict the target domain labels. In this paper, we implement the CORAL on quantum devices by two different methods. One method utilizes quantum basic linear algebra subroutines (QBLAS) to implement the CORAL with exponential speedup in the number and dimension of the given data samples. The other method is achieved through a variational hybrid quantum-classical procedure. In addition, the numerical experiments of the CORAL with three different types of data sets, namely the synthetic data, the synthetic-Iris data, the handwritten digit data, are presented to evaluate the performance of our work. The simulation results prove that the variational quantum correlation alignment algorithm (VQCORAL) can achieve competitive performance compared with the classical CORAL.

Reinforcement learning methods have been used to compute dialog policies from language-based interaction experiences. Efficiency is of particular importance in dialog policy learning, because of the considerable cost of interacting with people, and the very poor user experience from low-quality conversations. Aiming at improving the efficiency of dialog policy learning, we develop algorithm LHUA (Learning with Hindsight, User modeling, and Adaptation) that, for the first time, enables dialog agents to adaptively learn with hindsight from both simulated and real users. Simulation and hindsight provide the dialog agent with more experience and more (positive) reinforcements respectively. Experimental results suggest that, in success rate and policy quality, LHUA outperforms competitive baselines from the literature, including its no-simulation, no-adaptation, and no-hindsight counterparts.

Multi-view data capture permits free-viewpoint video (FVV) content creation. To this end, several users must capture video streams, calibrated in both time and pose, framing the same object/scene, from different viewpoints. New-generation network architectures (e.g. 5G) promise lower latency and larger bandwidth connections supported by powerful edge computing, properties that seem ideal for reliable FVV capture. We have explored this possibility, aiming to remove the need for bespoke synchronisation hardware when capturing a scene from multiple viewpoints, making it possible through off-the-shelf mobiles. We propose a novel and scalable data capture architecture that exploits edge resources to synchronise and harvest frame captures. We have designed an edge computing unit that supervises the relaying of timing triggers to and from multiple mobiles, in addition to synchronising frame harvesting. We empirically show the benefits of our edge computing unit by analysing latencies and show the quality of 3D reconstruction outputs against an alternative and popular centralised solution based on Unity3D.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE) and AUC achieved by our method are 91.79%, 93.05%, 89.95% and 96.35%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Software development has been changing rapidly. This development process can be influenced through changing developer friendly approaches. We can save time consumption and accelerate the development process if we can automatically guide programmer during software development. There are some approaches that recommended relevant code snippets and APIitems to the developer. Some approaches apply general code, searching techniques and some approaches use an online based repository mining strategies. But it gets quite difficult to help programmers when they need particular type conversion problems. More specifically when they want to adapt existing interfaces according to their expectation. One of the familiar triumph to guide developers in such situation is adapting collections and arrays through automated adaptation of object ensembles. But how does it help to a novice developer in real time software development that is not explicitly specified? In this paper, we have developed a system that works as a plugin-tool integrated with a particular Data Mining Integrated environment (DMIE) to recommend relevant interface while they seek for a type conversion situation. We have a mined repository of respective adapter classes and related APIs from where developer, search their query and get their result using the relevant transformer classes. The system that recommends developers titled automated objective ensembles (AOE plugin).From the investigation as we have ever made, we can see that our approach much better than some of the existing approaches.

Person re-identification (re-ID) remains challenging in a real-world scenario, as it requires a trained network to generalise to totally unseen target data in the presence of variations across domains. Recently, generative adversarial models have been widely adopted to enhance the diversity of training data. These approaches, however, often fail to generalise to other domains, as existing generative person re-identification models have a disconnect between the generative component and the discriminative feature learning stage. To address the on-going challenges regarding model generalisation, we propose an end-to-end domain adaptive attention network to jointly translate images between domains and learn discriminative re-id features in a single framework. To address the domain gap challenge, we introduce an attention module for image translation from source to target domains without affecting the identity of a person. More specifically, attention is directed to the background instead of the entire image of the person, ensuring identifying characteristics of the subject are preserved. The proposed joint learning network results in a significant performance improvement over state-of-the-art methods on several benchmark datasets.

In this work, we consider a group of robots working together to manipulate a rigid object to track a desired trajectory in $SE(3)$. The robots have no explicit communication network among them, and they do no know the mass or friction properties of the object, or where they are attached to the object. However, we assume they share data from a common IMU placed arbitrarily on the object. To solve this problem, we propose a decentralized adaptive control scheme wherein each agent maintains and adapts its own estimate of the object parameters in order to track a reference trajectory. We present an analysis of the controller's behavior, and show that all closed-loop signals remain bounded, and that the system trajectory will almost always (except for initial conditions on a set of measure zero) converge to the desired trajectory. We study the proposed controller's performance using numerical simulations of a manipulation task in 3D, and with hardware experiments which demonstrate our algorithm on a planar manipulation task. These studies, taken together, demonstrate the effectiveness of the proposed controller even in the presence of numerous unmodelled effects, such as discretization errors and complex frictional interactions.

Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on using emerging RRAM technologies for improving energy efficiency, thanks to their no leakage property and high integration density. As the complexity and dynamism of applications supported by such devices escalate, it has become difficult to maintain ideal performance by static RRAM controllers. Machine Learning provides a promising solution for this, and hence, this work focuses on extending such controllers to allow dynamic parameter updates. In this work we propose an Adaptive RRAM Variability-Aware Controller, AVAC, which periodically updates Wait Buffer and batch sizes using on-the-fly learning models and gradient ascent. AVAC allows Edge devices to adapt to different applications and their stages, to improve computation performance and reduce energy consumption. Simulations demonstrate that the proposed model can provide up to 29% increase in performance and 19% decrease in energy, compared to static controllers, using traces of real-life healthcare applications on a Raspberry-Pi based Edge deployment.

With all due respect to Greta Thunberg and Billie Eilish, nobody had a better 2019 than Baby Yoda. The real star of the Disney+ flagship Star Wars series The Mandalorian, the little green puppeteering/CGI marvel (aka "the Child") might be the most adorable creature ever created.…

Drink Local For the majority of regional craft breweries, most revenue comes from two avenues: direct-to-consumer sales out of a tasting room and selling beer to local bars and restaurants.…

Methods of capturing and immobilizing radioactive nuclei with metal fluorite-based inorganic materials are described. For example, a method of capturing and immobilizing radioactive nuclei includes flowing a gas stream through an exhaust apparatus. The exhaust apparatus includes a metal fluorite-based inorganic material. The gas stream includes a radioactive species. The radioactive species is removed from the gas stream by adsorbing the radioactive species to the metal fluorite-based inorganic material of the exhaust apparatus.

Method and system for an adapter is provided. The adapter includes a plurality of function hierarchies, with each function hierarchy including a plurality of functions and each function being associated with an event. The adapter also includes a plurality of processors for processing one or more events generated by the plurality of functions. The adapter further includes a first set of arbitration modules, where each arbitration module is associated with a function hierarchy and receives interrupt signals from the functions within the associated function hierarchy and selects one of the interrupt signals. The adapter also includes a second set of arbitration modules, where each arbitration module receives processor specific interrupt signals and selects one of the interrupt signals for processing an event associated with the selected interrupt signal.

Techniques for adaptive thread scheduling on a plurality of cores for reducing system energy are described. In one embodiment, a thread scheduler receives leakage current information associated with the plurality of cores. The leakage current information is employed to schedule a thread on one of the plurality of cores to reduce system energy usage. On chip calibration of the sensors is also described.

An adaptable integrated circuit is disclosed having a plurality of heterogeneous computational elements coupled to an interconnection network. The interconnection network changes interconnections between the plurality of heterogeneous computational elements in response to configuration information. A first group of computational elements is allocated to form a first version of a functional unit to perform a first function by changing interconnections in the interconnection network between the first group of heterogeneous computational elements. A second group of computational elements is allocated to form a second version of a functional unit to perform the first function by changing interconnections in the interconnection network between the second group of heterogeneous computational elements. One or more of the first or second group of heterogeneous computational elements are reallocated to perform a second function by changing the interconnections between the one or more of the first or second group of heterogeneous computational elements.

An apparatus and an article of manufacture for controlling a voice site using a haptic input modality include validating a haptic input from an instrument capable of accessing a voice site, processing the haptic input on a server to determine a voice site command corresponding to the haptic input, and processing the voice site command at the server to control an interaction with the voice site.

The present invention is based on the finding that parameters including: a first set of parameters of a representation of a first portion of an original signal and a second set of parameters of a representation of a second portion of the original signal can be efficiently encoded when the parameters are arranged in a first sequence of tuples and a second sequence of tuples. The first sequence of tuples includes tuples of parameters having two parameters from a single portion of the original signal and the second sequence of tuples includes tuples of parameters having one parameter from the first portion and one parameter from the second portion of the original signal. A bit estimator estimates the number of necessary bits to encode the first and the second sequence of tuples. Only the sequence of tuples, which results in the lower number of bits, is encoded.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adapting user interfaces for devices that include widescreen displays. In one aspect, a method includes determining a size characteristic of a display of a mobile device, determining a size characteristic of content that is to be displayed on the display, and comparing the size characteristic of the content to the size characteristic of the display. The method also includes selecting one or more controls to display in a portion of the display that is not to be used to display the content based on comparing the size characteristic of the content to the size characteristic of the display, displaying the content, and displaying the selected controls in a portion of the display that is not used to display the content.

The case (1) of the watch carries, along its periphery (5), hooking members in the form of rings (6a, 6a', 6b, 6b', 6c, 6c'), these rings being disposed radially relative to the center (7) of the dial (2) and hence of the watch. A chain (8) is fixed by loops (9, 10) at its ends to two (6c, 6c') of these rings and passes through the other rings (6a, 6a', 6b, 6b'), passing several of times around the wearer's wrist. After the loops (9, 10) have been detached from their respective rings, the chain (8) can be passed in a different manner around the case, so that the watch can be hung around the neck. In order to make this change, the chain is equipped with a flat link (13) which can pass through radial slots provided in the periphery of the rings.

The present invention relates to a method for increasing L-methionine productivity and organic acid productivity. More particularly, the present invention relates to a method which involves adding a mixture containing methyl mercaptan and dimethyl sulfide at a appropriate ratio to O-acetyl homoserine or O-succinyl homoserine and to an enzyme having an activity of converting methionine precursor into L-methionine, so as to perform an enzyme reaction, to thereby improve the conversion rate of L-methionine and organic acid from the L-methionine precursor, and thus increasing L-methionine yield as compared to conventional method.

A reactive rectification column suitable for the production of 2-hydroxy-4-methylmercaptobutyric acid and/or methionine contains a weir having a height of 100 mm or more.

A method of making amino-mercapto functional organopolysiloxanes is disclosed by reacting (A) a dialkoxydialkylsilane, (B) an amino functional alkoxy silane, and (C) a mercapto functional alkoxy silane, via a condensation reaction. The amino-mercapto functional organopolysiloxanes products are useful in textile and fabric treatments.

A calibration system may be provided for calibrating displays in electronic devices during manufacturing. The calibration system may include calibration computing equipment and a test chamber having a light sensor. The calibration computing equipment may be configured to operate the light sensor and the display to gather display performance data. The calibration computing equipment may extract display performance statistics from the display performance data and adaptively select and perform display calibration sequences using the display performance statistics. The calibration computing equipment may be configured to determine whether or not to perform display calibration and whether or not to gather additional display performance data based on display performance statistics extracted during a preceding display calibration sequence. The calibration computing equipment may be configured to iteratively and adaptively perform display calibration sequences until a display is successfully calibrated.

Disclosed herein is a method for determining a trajectory for a transfer of a spacecraft from a starting space body to a target space body with respect to a given central space body, wherein the determined trajectory is optimal with respect to a given space mission requirement to be met by the transfer of the spacecraft. The method comprises providing, according to the Pontryagin maximum principle, a physical-mathematical model relating model quantities and physical quantities representing the transfer of the spacecraft with respect to the given central space body.

An adaptive gaseous fuel ignition control method for use in consumer and commercial appliances that reduces stress on and increases the life of a hot surface igniter without resulting in a failure to ignite condition is provided. The method provides a preheating period, a full temperature period, and a trial for ignition period. The preheating period gradually increases the power applied to reduce the stress resulting therefrom. Once a gas valve has been commanded open, the controller monitors the time for ignition of the gaseous fuel. If the time is longer than a threshold, either the applied power or the period of time during which the power is applied is increased to shorten the time. If, however, the ignition period is shorter than the threshold, either the power applied or the period of time during which the power is applied before commanding the gas valve open is lowered or shortened.

Methods and apparatus are disclosed for interference mitigation of an open-access node. The method includes determining, at the open-access node, whether uplink interference from a mobile entity is above a threshold. The method includes adjusting a transmission power to trigger a hand-in of the mobile entity in response to determining the uplink interference is above the threshold. The method includes handing-in the mobile entity from a first cell in response to adjusting the transmission power. The method includes redirecting the mobile entity to a second cell different from the first cell.

Disclosed is a grinding tool, which includes a first disk having an installation hole in a center portion thereof such that the grinding tool is installed on a grinder, a second disk forming a discharge passage with partitions on a bottom surface of the first disk, wherein air intaken from a central lower portion of the grinding tool is discharged to an outside thereof through the discharge passage, and a ring-shaped shank disposed under the second disk and including grinding tips to grind a target.

Techniques are provided describing a first connector unit that receives first data from a first transceiver and a second connector unit that receives second data from a second transceiver. A switch unit is configured to receive first switch data from the first connector unit at a first data rate and second switch data from the second connector unit at the first data rate. A third connector unit receives the first switch or the second switch data from the switch unit and a second portion of the second data from the second connector unit. The third connector unit also sends the first switch data to a host port when the first connector unit receives the first data and to send the second switch data and the second portion of the second data to the host port when the second connector unit receives the second data.