Fredrick Brown, 38, of Las Vegas, Nevada, a former civilian medical records administrator for the U.S. Army at the 65th Medical Brigade, Yongsan Garrison, South Korea, admitted yesterday to his role in an identity-theft and fraud scheme that victimized thousands of U.S. servicemembers and veterans.

A Las Vegas resident was sentenced to 65 months in prison today for his role in a stolen identity tax fraud, announced Principal Deputy Assistant Attorney General Richard E. Zuckerman of the Justice Department’s Tax Division and U.S. Attorney Nicholas A. Trutanich for the District of Nevada.

Derek take a closer look at a 70's classic.

From the museum of Sankey diagrams comes this beautiful visualization of migration flows between Canadian provinces in the years 1956 to 1961. Sorry but I didn’t note the source… still wanted to share it with you.
[See image gallery at www.sankey-diagrams.com]
Unfortunately, flows are not to scale: compare, for example, the exits from the Atlantic provinces in the period (86,078 people) which should have an arrow similar to the inflow of 85,476 to British Columbia. Or look at the exodus from Ontario provice, which seemingly is larger than the influx to the provice (but in fact is smaller).

Is Arch (by Kris S.) trying to construct his own male line, bridging time and space and fashion? The effort of Grade 1 Arkansas Derby winner Nadal (by the Arch stallion Blame) suggests that this is not out of the realm of possibility, although Arch, one of the supremely solid sires of the breed, left […]

The post Bloodlines: Nadal Poised To Extend His Branch Of Arch Sire Line appeared first on Horse Racing News | Paulick Report.

The U.S. Department of the Interior has released a proposed framework for adaptively managing Glen Canyon Dam over the next 20 years with the goal of creating certainty and predictability for power and water users while protecting environmental and cultural resources in Grand Canyon National Park and the Colorado River ecosystem. The draft environmental impact statement (EIS) is available to the public for review and comment for 90 days. https://www.nps.gov/grca/learn/news/interior-proposes-adaptive-management-framework-for-glen-canyon-dam.htm

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

Fire crews are actively working to suppress the lightning ignited Imperial Fire. Currently the fire is estimated to be three (3) acres in size and is located along the Cape Royal Road near Vista Encantada. https://www.nps.gov/grca/learn/news/imperial-fire-being-suppressed-on-north-rim-of-grand-canyon-national-park-20180718.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.

A slide show with permanent image changes for all modern PC browsers, Mac browsers and Safari Mobile browsers.

Responsible for access to digital resources and collections, including identifying and resolving access issues related to resources Perform original and complex copy cataloging and classification of materials in all formats and languages following natio......

The idea of the organization and companies as phenomena to... more

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.

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..

Canadian ratings firm NUMERIS has released data showing radio retaining 88% of its weekly reach during the pandemic in CANADA. The number, at 93% for adults 18+ in metered markets last FALL, … more

ENTERCOM AC WDZH (98.7 THE BREEZE)/DETROIT Middayer KIM ADAMS has teamed up with D.A. ALEXANDER AND COMPANY to surprise, honor and show appreciation to nurses in celebration of National … more

QUEEN’s BRIAN MAY and ROGER TAYLOR in the U.K. and singer ADAM LAMBERT in L.A. have connected virtually to record a new version of QUEEN’s classic anthem, “We Are The … more

Canada.com, featuring discussions about what Canadians are talking about, is owned and operated by Postmedia Network Inc., Canada’s largest publisher by circulation of paid English-language daily newspapers. canada.com complements Postmedia’s other properties including daily newspapers in cities across Canada like...

DALLAS, 2 de abril del 2020 – La American Heart Association, la organización voluntaria líder mundial dedicada a un mundo con vidas más duraderas y saludables, anunció hoy que se otorgarán más de $14 millones en becas de investigación científica a cuatro...

DALLAS, 8 de abril del 2020— El impacto mundial del COVID-19 continúa aumentando y, cada día, la comunidad científica aprende más sobre el efecto y la interacción de las enfermedades cardiovasculares con el COVID-19. Juntos, la American Heart Association...

CEDAR RAPIDS — Adam Todd, adjusting like many Iowans to a social-distanced lifestyle since the novel coronavirus hit, celebrated a milestone 21st birthday with a drive-by parade Friday...

CEDAR RAPIDS — Adam Todd, adjusting like many Iowans to a social-distanced lifestyle since the novel coronavirus hit, celebrated a milestone 21st birthday with a drive-by parade Friday evening.

Todd, best known for being ABC World News Tonight Person of the Week in 2016, stood outside the Cedar Rapids house where he lives with his parents, Dale Todd, a Cedar Rapids City Council member, and Sara Todd, watching the cars drive by wishing him a happy birthday.

“My son has a bigger following than I would ever hope to have,” Dale Todd said. “Everybody knows he loves a parade. He loves the trucks and cars and people that are a part of that.

“There’s no better example that ‘it takes a village.’ He has had a team for 21 years.”

Todd, who will be graduating from Washington High School in Cedar Rapids, has Lennox-Gastaut Syndrome, a severe form of epilepsy.

The parade was arranged by his mother, Sara, who invited friends and teachers to drive by between 5 and 6 p.m. to wave, shout happy birthday, hold up a sign, sing a song or do a dance, all from a safe distance — “whatever they’re able to,” she said.

Sara said Adam would have finished his last semester of education had the coronavirus not cut the school year short.

“That makes it extra special,” Sara said. “He hasn’t really gotten to say goodbye to his friends and teachers.

“We have appreciated his teachers so much. They have been a part of his team and really a part of our family.”

Adam, a member of the Washington High School junior varsity track team, captured national attention in 2016 when, during a cross-country race at Noelridge Park, he became distracted and veered from the course. Evan Hansen, a runner from Iowa City High, loped after him and guided him back to the track.

They ran the last mile together, the last two to cross the finish line.

That show of compassion and sportsmanship is what led to ABC News picking both of them as Persons of the Week.

In addition to Friday’s parade, Adam celebrated his birthday by taking his service dog, Turbo, on a run and trying to spot trains. He and his mom saw two.

“It was a good day,” Sara said.

Adam’s birthday cake, made by a family friend, was decorated with a train and a golden retriever that looks like Turbo.

“We were trying to think of a creative way we can help Adam have fun and celebrate him,” Sara said. “He deserves it.”

Comments: (319) 368-8664; grace.king@thegazette.com

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

Parkdale. Toronto, Canada 2019 Follow me @benhuang.photography

Yonge Street. Toronto, Canada 2020 Follow me @benhuang.photography

The yearly fishing trip, this year at the Bulkley river in British Columbia. Shot on the Panasonic GH-5, DJI Mavic 2 Zoom and some GoPro.

Adam thanks you — his blog viewers and supporters — and encourages you to continue the discussion and awareness raising about TBI and PTSD; the battle does not stop here.

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.

The web is awash with words. They’re everywhere. On websites, in emails, advertisements, tweets, pop-ups, you name it. More people are publishing more copy than at any point in history. That means a lot of information, and a lot of competition. In recent years a slew of ‘readability’ programs have appeared to help us tidy up the things we write. (Grammarly, Readable, and Yoast are just a handful that come to mind.

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.

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.