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Home > Ecology & Zoology > Science 2.0
Google Lab-Bred Mosquitoes May Stop Diseases Without Pesticides
By Hank Campbell | May 4th 2020 09:31 AM | Print | E-mail
A new series of experiments by an Alphabet (the parent company Google created) group shows lab-bred mosquitoes that cannot successfully reproduce might be able to stop malaria and other mosquito-spread diseases in countries where those are still endemic - two billion people per year.
Malaria is not endemic to the U.S. any more and we can thank DDT for that but we still face risk of numerous diseases from mosquitoes. Other countries where diseases like dengue, chikungunya, Zika, and yellow fever are common barely blink at the coronavirus pandemic that has paralyzed the U.S. after only causing as many deaths as a bad flu year. But infectious diseases can always spread. How would we manage dengue and malaria today?
Politician William Ruckelshaus overruled scientists and banned DDT in the U.S. and though it is still in use in other countries (our Environmental Protection Agency literally writes the manual for how to spray it in homes in other countries) DDT is the poster child of the anti-science movement, so it is not coming back here. Instead, biologists have been looking for ways to eliminate the ecologically useless mosquitoes that spread these diseases using genetic engineering. OxiTec, for example, created a mosquito that can't successfully create offspring. When they first tried testing, U.S. opponents were out in full force(1) but that was before coronavirus caused all those people on the coasts who ordinarily distrust science to embrace it again.(2) While 'Science Is A Vast Corporate Conspiracy' types still buy Non-GMO Project cat litter, most Americans are once again fans of Clorox, Purell, and Lysol. This might be a good time for Google to push ahead with a GMO mosquito to stop diseases that kill a lot more each year than COVID-19 ever will.
Their sterile insect technique uses Wolbachia bacteria and MosquitoMate, the inventor of the Verily group mosquito, did a small test of 80,000 mosquitoes per day in three neighborhoods between April and October of 2018. During the testing period, raps in areas without Wolbachia-infected males had between 8-18X as many biting females as those with the genetically engineered kind. Overall, suppression was 93 percent.
a, Illustration of the entire sex-sorting pipeline, including the mechanical pupal sieve, real-time adult visual inspection, cloud-based machine learning classifier, and non-expert review. The probability of a female contaminant with 95% CIs for each step is shown along with the estimated overall female contamination rate for the entire pipeline in the final column. b, The fraction of mosquitoes imaged by the sex sorter after the pupal sieve that were male with s.d. intervals shaded for 179 production batches. c, Example images from the adult sex sorter (male on the left and female on the right) used by both the industrial vision system and machine learning classifier. d, The fraction of true males that were correctly labeled and accepted by the Industrial Vision system with s.d. interval shaded (n = mean of 96, range of 10–140 independent sex-sorter lane measurements per batch).
Activist ecologists stuck in that 'balance of nature' mythology scientifically debunked a century ago shriek when other scientists note we could just wipe those mosquitoes out. Aedes aegypti mosquitoes are ecologically useless, there is nothing positive they do that 3,500 other known species of mosquitoes could not do if one pest were eliminated as disease vectors. Biologists have shown they can cut the population by preventing reproduction without any of the worries about pesticides that get promoted.
It's worth pursuing. In the U.S., where family members are not dying from these diseases, it is easy to claim no mosquito control technique should be allowed because somewhere in the multiverse this might cause harm, but these cannot reproduce, there are no Frankenbugs. Even rich elites can understand that fact.
(1) In the Los Angeles Times I had to debunk a wealthy (naturally) real estate agent in Florida, who insisted a genetically engineered OxiTec mosquito that could not reproduce was nonetheless a slippery slope to Frankenbugs.
Such nomenclature proceeds from the same false premise that activists misunderstand about ecology. Frankenstein's monster was not a GMO, he was a hybrid grafted together. Not only would he be certified organic today, Non-GMO Project would sell Frankenstein a sticker for it, and it would be just as legitimate as the Non-GMO Rock Salt, Non-GMO Artificial Sweetener, and 67,000 other ridiculous products they make a fortune promoting for anyone who sends a check.
(2) The activists who oppose vaccines, GMOs, natural gas, etc. are now trying to claim coronavirus was caused by pesticides and 5G cell phone coverage (no, really) and of course Robert F. Kennedy, Jr. is their prophet but it is instead likely that COVID-19 will caused a surge in science acceptance. In 2008 President Obama said he was unsure vaccines were safe despite the science consensus and during the 2009 H1N1 pandemic his administation was criticized for not allowing multi-dose vials of the vaccine - because they contained the preservative thimerosal.
Vaccine confidence dropped 9% after that but even California, the American home of the anti-vac movement, passed a law banning capricious vaccine exemptions. Governor Gavin Newsom signed it under protest because he is from San Francisco, but he signed it.
Model That Claimed Only Lockdown Works To Prevent Coronavirus Spread Was Flawed, Shows New Look
I founded Science 2.0® in 2006 and since then it has become the world's largest independent science communications site, with over 300,000,000 direct...
Malaria Mosquito Anopheles Stephensi Found In Ethiopia For The First Time
What Could Climate Change Mean For Malaria?
Standards Of Identity, And Fighting Confusing Food Labels- Some Hope FDA Might Still Be On The Case
Scientists Identify Potential Vaccine Candidate For Pediatric Malaria
Genetic Engineering May Lead To A World Where Malaria Can Be Contained Without DDT


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Click the button below to add the 14ct Gold Pendant with Crystal Inlay Opal (1183) to your wish list.
This piece is a Crystal White Opal in a 14ct Gold Inlay setting. This Opal is bright, flashy, and shows all the colours of the rainbow in a pin fire pattern. Showcasing mostly blue and green colour this Opal flashes bright pink, yellow, orange, and red constantly. This particular Opal comes from an Opal mine in Southern Australia, near Mintabie.


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How a House Dem accused of drunken shenanigans revealed another secret 'hush fund'
Raul M. Grijalva
Rep. Raul Grijalva, Arizona Democrat, told The Washington Times that the pay to a former female staffer was a severance package and that the agreement was reached without a complaint lodged with the Office of Compliance, which handles workplace grievances ... more >
By S.A. Miller - The Washington Times - Monday, November 27, 2017
Rep. Raul M. Grijalva quietly arranged a "severance package" in 2015 for one of his top staffers who threatened a lawsuit claiming the Arizona Democrat was frequently drunk and created a hostile workplace environment, revealing yet another way that lawmakers can use taxpayer dollars to hide their misbehavior on Capitol Hill.
While the Office of Compliance has been the focus of outrage on Capitol Hill for hush-money payouts in sexual harassment cases, the Grijalva payout points to another office that lawmakers can use to sweep accusations under the rug with taxpayer-funded settlements negotiated by the House Employment Counsel, which acts as the attorney for all House offices.
The employment counsel negotiated a deal for taxpayers to give $48,395 — five additional months' salary — to the female aide, who left her job after three months. She didn't pursue the hostile workplace complaint further.
The arrangement appears to run contrary to House rules that constrain severance packages, and it caught the eye of watchdogs who were already demanding answers about payouts in the wake of harassment complaints.
"It seems like all of these House bodies are designed to help cover for members of Congress," said Melanie Sloan, an ethics lawyer in Washington. "A large part of the problem is that each member of Congress can treat their staff as their own fiefdom and also know that it will remain silent."
In the case of Mr. Grijalva, the senior employee left after three months on the job. Her position was filled immediately by another worker, and her email and cellphone were deactivated.
When the woman hired a lawyer and threatened a lawsuit, Mr. Grijalva halted her salary as part of the House Employment Counsel's strategy to force her to settle the matter, according to Capitol Hill sources.
The agreement provided for her pay to resume at her full salary for five months after she left the office.
Mr. Grijalva, Arizona Democrat, told The Washington Times that the pay was a severance package and that the agreement was reached without a complaint lodged with the Office of Compliance, which handles workplace grievances by congressional employees.
"On the advice of House Employment Counsel, I provided a severance package to a former employee who resigned. The severance did not involve the Office of Compliance and at no time was any allegation of sexual harassment made, and no sexual harassment occurred," Mr. Grijalva said in an email to The Washington Times.
"Under the terms of the agreement, had there been an allegation of sexual harassment, the employee would have been free to report it. Regrettably, for me to provide any further details on this matter would violate the agreement," he said.
Mr. Grijalva did not respond to repeated inquiries about why he agreed to a more than $48,000 severance package for an employee on the job for just three months.
The woman, whose name is being withheld by The Times, declined to comment for this article.
The payoff in the Grijalva case appeared to violate House rules that prohibit a Congress member from retaining "an employee who does not perform duties for the offices of the employing authority commensurate with the compensation such employee receives."
A legitimate severance package also should be paid in a lump sum and reported separately, according to House rules.
The role of the House Employment Counsel in squelching workplace complaints emerged amid flurry of sexual harassment accusations and shock that Congress has covered it up for years.
Most of the focus has been on Congress' Office of Compliance, which over the past 20 years paid out $17.2 million to settle 264 complaints of sexual harassment and other workplace violations on Capitol Hill, The Washington Post reported this month.
In the absence of a human resources department for Congress, the Office of Compliance handles sexual harassment and other workplace grievances. The settlements are secret, and the complaint process is arduous and can drag out for months with mandatory counseling for victims and other requirements.
Victims also are required to sign a confidentiality agreement before the matter can be resolved.
That appears to have happened in the Grijalva case with the Employment Counsel as well.
Peter Flaherty, president of the watchdog organization National Legal and Policy Center, said the secrecy of the congressional offices makes it difficult to figure out how widespread the problems are.
"Sexual harassment settlements are sometimes disguised as other transactions, like severance payments. These are very hard to unravel," he said. "These settlements were made with public funds. Since we taxpayers were parties to these agreements, we have the right to know who on whose behalf we settled."
Mr. Flaherty said House Speaker Paul D. Ryan, Wisconsin Republican, should make public all the names of all the lawmakers accused of sexual harassment and the amounts of settlements paid in each case, while preserving the anonymity of the victims.
Rep. Brian K. Fitzpatrick, Pennsylvania Republican, called for the House Ethics Committee to investigate every use of taxpayer dollars to settle harassment claims in Congress.
"It's unbelievable — and unacceptable — that elected officials have been using taxpayer dollars to cover up sexual harassment suits for years," Mr. Fitzpatrick said. "As if the American people needed another example of politicians playing by a different set of rules, this is an affront to the hardworking taxpayers forced to foot the bill for these heinous actions."
His office said the demand for investigation applied to the House Employment Counsel, the Office of Compliance and any other method of settling claims.
The complaints against members of Congress are piling up.
Ms. Sloan, the ethics lawyer, said she was victimized in 1998 while working for Rep. John Conyers Jr., Michigan Democrat. She said she was verbally abused and one occasion was called into the congressman's office, where she found him in his underwear.
Her complaints at the time were largely ignored, and Mr. Conyers' office said she was "mentally unstable."
Ms. Sloan is among three women who have come forward with complaints of inappropriate behavior by Mr. Conyers.
One female staffer was paid $27,000 in 2015 as part of a confidentiality agreement after she accused Mr. Conyers of firing her when she refused to have sex with him, BuzzFeed reported.
The House Ethics Committee launched an investigation into the sexual harassment accusation against Mr. Conyers, who denied wrongdoing but stepped down from his position as ranking Democrat on the House Judiciary Committee.
On the other side of the Capitol, Sen. Al Franken also faces a possible ethics investigation after radio newscaster Leeann Tweeden accused him of forcibly kissing her and groping her during a USO tour in 2006, before the Minnesota Democrat was elected in 2008.
Another woman came forward to accuse the senator of grabbing her buttocks while they were at the Minnesota State Fair in 2010.
Mr. Franken has made several apologies.
"I know that I've let a lot of people down," he said Monday when returning to work in the Senate.
It's part of a flood of sexual harassment accusations against powerful men that began with women speaking out about movie producer Harvey Weinstein and spread through Hollywood to journalism and Capitol Hill.
The Senate voted this month to institute mandatory training to combat sexual harassment, and Mr. Ryan ordered all House members and staff to receive training.
At the time, Mr. Grijalva applauded the move and called for more action.
• S.A. Miller can be reached at smiller@washingtontimes.com.


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Passito wines
Ready sauces
Vegetables in oil and creams
Dried legumes in bag
Jams and dried figs
Extravirgin olive oil
Tradition and innovation
Native vines
Vini e Cucina 2022 food and wine events
Tanti bicchieri de mieru me biu,
tanti penseri dha capu me lleu. The more glasses of wine I drink,
The more I clear my head by thoughts. (Popular saying from Salento)
Native vines of Salento: Primitivo, Negroamaro, Malvasia Nera
Home/Opzionale/Native vines
Primitivo, a strong personality wine
It is one of the oldest vines in our area. Salento is his chosen land, but its origins lie on the other side of the Adriatic. It seems to have been brought to Italy about two thousand years ago, at the time of the Crusades, by pilgrims on their way to the Holy Land.
Primitivo has found here in Salento an almost perfect habitat to grow at its best and produce healthy grapes of excellent quality. Even the sea has certainly played an important role in its success: cultivated in land close to the coast, this vine welcomes breezes and scents that enrich its great variety of aromas.
Primitivo takes its name from the early ripening of its grapes, harvested between the end of August and the beginning of September. Its bunches are easily recognized by their compactness and their round and blue berries. Its ideal climate is dry heat since the bunch, due to its peculiar characteristics, fears prolonged humidity which would facilitate the development of harmful molds on the berries.
The result is wines of great body and structure, characterized by an alcohol content which can be very high if allowed to over-mature. The most refined oenology, on the other hand, seeks elegance in spite of the brute strength of the high gradations; in fact, the balance and the right acidity also allow it to age for a long time.
Until recently, Primitivo was mostly used as a blending wine for other northern wines and sold wholesale, hence the need for high alcohol content as the price was based on the alcohol content. In last decades, as a result of new production philosophies, it has found the place it deserves in the best production of Salento cellars.
We have done our part in promoting this vine, because we were among the first to vinify it in purity in 1975, with Vigne Vecchie, and to specify the wording "Primitivo" on the label.
Unlike Negroamaro, it is a low acidity vine and that is why we only use it to produce red wines. We offer four versions, all organic and vinified in purity. In addition to the aforementioned Vigne Vecchie we have Beomondo, 900 Primitivo and Burdi.
These four labels identify different nuances in the bouquet and body of the wine, obtained from selection, time of harvest and vinification.
Negroamaro, the main grape variety in Salento
Negroamaro is one of the best known and most appreciated Apulian wines in the world. Introduced in Puglia by the Greeks since the 7th century BC. by the Hellenic colonists, there are more historical interpretations regarding the origin of its name. The most supp orted and likely is that the name derives from the union of two dialectal terms niuru and maru, one coming from the Latin (niger) and another Greek (mavros) which are both simply translated with the word "black".
Therefore, "black-black".
What is certain is that its flavor is definitely unique, inimitable as well as its scent.
The Negroamaro vine has bunches of medium size, conical in shape, with tight densities, short in size and without wings. The berries are large, with thick black skins and violet veins.
Negroamaro prefers calcareous soils, but also likes a good presence of clay that isable to retain the water that flows abundantly in the Apulian subsoil. The harvest takes place later than the primitive, between September and October.Thanks mainly to its high concentration of sugars,as well as Primitivo in the past it was mainly used to cut winemix one or to be used to produce wines in purity.
Our cellar produces three labels obtained from the vinification of Negroamaro purity, all certified organic: Nativo, whose name underlines the typicality and the natural belonging to theterritory where its vine has be en cultivated for centuries; Pìutri, from the name of a plot called "delli Piutri" in the countryside of Torchiarolo, between Lecce and Brindisi, 200 meters from the Adriatic Sea, where we have owned the vineyards since 1100; 900 Negroamaro, a long-lived wine, elegant, rich and intense, which expresses with great finesse the best qualities that the famous grape can offer. From the rosé and white vinification of Negro amaro, the white Taersìa900 Negroamaro Bianco and the rosé Campo di Mare are born.
Malvasia Nera, an Apulian rarity
Malvasia is the name that denotes different vines, mostly white and not always related to each other.The reasons are to be found in history, in the port city of the Peloponnese called Malvasia in Italian and Monemvasia in Greek, which in ancient times was a crossroads for Mediterranean wines. From there thesewines arrived in Europe by ship, and of course the Venetians played a key role in this trade. In Venice the word "malvasia" ended up identifying shopswhere "navigated" wines, that is imported wines, were sold, and gradually becamesynonymous with sweet and aromatic wine.
The commercial success of the Malvasia family led many producers to create similar wines from different grape varieties, and this explains why there are so many varieties with the same name today. Among these two rarities stand out: Malvasia Nera di Brindisi and Malvasia Nera di Lecce, native Apulian cultivars from which superb wines are derived.
Our family has a special bond with Malvasia Nera. In fact, we have always used it to produce a rosé for our personal use, recently marketed under the name of Malìa Rosa.
Malìa, a pure black Malvasia, instead originates from an inspiration. In our ancient cellar, among other things, we keep some nineteenth-century bottles of Primitivo, Negroamaro and Malvasia Nera in purity.
We therefore decided in 2000 to resume this once elite production for in 1988 we had dedicated several hectares of our estate to the vine.
Malvasianera was and still is usually planted together with the Negroamaro in a percentage of 15-20% making the selection difficult during the harvest for which it was a rare wine.
Finally, we decided to celebrate the nine centuries of our family's business with 900 Malvasianera, a surprising long-aged and particularly fine wine.
AGRICULTURAL FARM
Offices: Largo Frisari 1 - 73020 Scorrano (LE)
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Knowing who your customer is can change your marketing.
Have you often wondered who you should sell to or even what they might want?
I am sure many of us have pondered the question more than once. We are told to target our audience and you think, "I don't care. I just want to sell to whoever will buy from me." Pointless effort to try selling to people who have no interest in your product.
If a potential customer gets lost during the selling process, they will assume the product is not for them. If you can't make it about the customer and what they can relate to, your marketing plan is lost. They may be your ideal client but unless they understand the connection to what they want or think they want, you lose the sale.
How you present your product or service is crucial if you are to make a sale. How you relate to your ideal customer in fulfilling their needs and desires will make the biggest impact if they buy from you.
Your ideal customer is not interested in the features of your product. Even if your features are so important to the function and performance of the product. Sorry to say but your customer is only interested in the benefits.
Benefits will persuade the art of buying. Will this product supply the customer with the right emotion? Will a desire or need be met? Only when a product fulfills the core emotions that drive the desire to own the product, will a person part with their money.
A product is desired not because someone can't live without it. But by having the product life might be easier or more rewarding. Whether it fulfills an image desire or an ease of doing a job or task more efficiently will be a factor in buying a product or service.
You may think the features of your product are important and they could be. Once you convince your customer the benefits are what they are looking for, you can solidify the deal by presenting the features, which will justify the purchase.
They may have tried a similar product and liked it. This is where the feature can be introduced. Maybe your product is made with metal fittings and not plastic this would be a convincing feature. Better manufactured parts in a product can justify a higher price tag. Features are important but benefits convince the customer to buy.


Stopping by the lounge this week is Sunshine and Kalani. After finishing up our month long Christmas party and NYE party circuit, its time to get back to normal. New Years resolutions. Not so much. But we are trying out some gluten free bar snacks at the request of a listener. Offering your guests a snack that is free of gluten is actually an easy task at the bar. Popcorn, pretzels, chips, cookies, cheese, nuts and so much more cal all be found sans gluten. And remember, all these things tend to be salty. So keep the cocktails flowing and your friends will be happy. The drink we pair with our snacks is the Shark. Not our recipe but a suggestion from a friend. Please Don't Tell is a trendy bar in NYC that boasts an extensive drink menu and has their own fully illustrated book of recipes. Although the drink menu does not give the measurements of ingredients, we do our best to recreate this creamy blue/green cocktail. Tasty.
With a gift bag this fabulous, who cares whats inside, right?
Subject matter on the show is fairly random. Sunshine discusses presents who's packages are better than the gift. Haven't you received a box or bag that was so well crafted that you didn't care what was inside? We certainly have. Inspired to use a wine bag in a piece of art, Sunshine will be looking for just the right application for this particular glittery creation. Kalani later goes on a rant about religious freedoms or lack of them. This is a conversation that we are both glad happened after stiff drinks.
We hope that you will tell others about the podcast and share the inebriated goodness. Come back often for cocktail recipes, party tips and conversation that can only happen in a dimly lit tiki bar.
A traditional pupu platter is more likely to have meats, seafood and at least one item involving bacon. But for a quick shelf stable option that is also gluten free, try rice crisps, gluten free pretzels, nuts, popcorn, dried fruits and more.


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\section{INTRODUCTION}

Facial expressions are an essential aspect of nonverbal communication. In our day-to-day lives, we rely on diverse facial expressions to convey our feelings and attitudes to others and interpret other people's emotions, desires and intentions \cite{plutchik1984emotions}. Facial mimicry \cite{reissland1988neonatal, meltzoff1989imitation, van2009love, piaget2013play} is also recognized as a vital stepping stone towards the early development of social skills for infants. Therefore, building robots that can automatically mimic diverse human facial expressions \cite{fong2003survey, blow2006art, breazeal2008social, saunderson2019robots} will facilitate more natural robotic social behaviors and further encourage stronger engagement in human-robot interactions. Mimicking human facial expressions is also the first step towards achieving adaptive facial reactions in robots. Despite the practical value of such systems, extant research in this domain mostly focuses on the hardware design and pre-programmed facial expressions, allowing robots to select one of the facial expressions from a predefined set. Generalizing across various human expressions has remained challenging.

\begin{figure}[t]
\begin{center}
    \includegraphics[width=.48\textwidth]{figures/teaser.jpg}
\end{center}
\vspace{-3mm}
\caption{\textbf{Eva 2.0} is a general animatronic robotic face for facial mimicry. The robot does so by learning the correspondence between facial landmarks and self-images as well as a learned inverse kinematic model. The entire learning process relies on the robot's motor babbling in a self-supervised manner. Our robot can mimic varieties of human expressions across many human subjects.}
\label{fig:teaser}
\vspace{-13pt}
\end{figure}

Current robotic face systems cannot mimic human facial expressions adaptively. The key limitation is the lack of a general learning framework that can learn from limited human supervision. Some traditional methods \cite{oh2006design, hashimoto2006development,hashimoto2008dynamic, ahn2012designing, loza2013application, lin2016expressional, asheber2016humanoid} define a set of pre-specified facial expressions. Others generalize this process to search for closest match from a database \cite{liu2019emotion} or by following an fitness function \cite{hyung2019optimizing, liu2019emotion}. However, as human expressions are highly diverse, these approaches have limited value in practical robot−human interactions.

In this work, we present Eva 2.0 (Fig. \ref{fig:teaser}) with significant upgrades to our previous Eva 1.0 \cite{faraj2021facially} platform with more flexible and stable control. We further propose a general learning-based framework to learn facial mimicry from visual observations that can generalize well to different human subjects and diverse expressions. Importantly, our approach does not rely on human supervisions to provide ground-truth robot commands. Our key idea is to decompose the problem into two stages: (1) given normalized human facial landmarks, we first use a generative model to synthesize a corresponding robot self-image with the same facial expression and then (2) leverage an inverse network to output the set of motor commands from the synthesized image.

Our experiments suggest that our approach outperforms previous nearest-neighbor search-based algorithms and direct mapping from human face to action methods. Moreover, quantitative visualizations of our robot imitations demonstrate that, when presented with diverse human subjects, our method generates appropriate and accurate facial expression imitations.

Our primary contributions are threefold. First, we present an animatronic robotic face with soft skin and flexible control mechanisms. Second, we propose a vision-based learning framework for robot facial mimicry that can be trained in a self-supervised manner. Third, we construct a human facial expression dataset from previous database, YouTube videos and real-world human subjects. Our approach enables strong generalization over $12$ human subjects and nearly $400$ salient natural expressions. Our robot has a response time within 0.18 s, indicating a strong capability of real-time reaction. Qualitative results and open-sourced platforms are in our supplementary video at \url{https://rb.gy/9koqpg}.

\section{RELATED WORKS}

\mypara{Animatronic Robotics Face} Designing physical animatronic robot face capable of imitating human facial expressions is an important topic in human-robot interaction \cite{breazeal2003emotion, goodrich2008human, yan2014survey, kalegina2018characterizing, dimitrievska2020behavior, song2009image, liu2017facial, gu2017local}. Early works \cite{oh2006design, hashimoto2006development, itoh2006mechanical, hashimoto2008dynamic, asheber2016humanoid, loza2013application, ahn2012designing, lin2016expressional} solely focus on hardware design of the robot face and pre-program the facial expressions. Kismet \cite{brooks1998cog, breazeal1998toward, scassellati1998imitation, breazeal2000infant, breazeal2002regulation, breazeal2003emotion, breazeal2004designing} generates diverse facial expressions by interpolating among predefined basis facial postures over a three-dimensional space. Albert HUBO \cite{oh2006design} imitates specific human body movements, including facial expression. WE-4R \cite{itoh2006mechanical} determines the expression with emotion vectors. Hashimoto et al. \cite{hashimoto2008dynamic} maps the human face configuration to a robot through life-mask worn by a human subject. Asheber et al. \cite{asheber2016humanoid} proposed a simplified design to enable easier programming and flexible movements. However, all these designs rely on a fixed set of pre-programmed facial expressions that cannot generalize to novel expressions and require extensive human efforts through trail-and-error.

Recent studies start to integrate more general motion control into the hardware designs. Affetto \cite{ishihara2011realistic, ishihara2015design, ishihara2018identification} adopts hysterical sigmoid functions to model motor displacement. However, the landmarks are tracked with OptiTrack sensors on the face. Our method does not require any hardware attachment on the robot face. XIN-REN \cite{ren2016automatic} uses an AAM model to track the facial features for a specific person, which consequently cannot generalize well to novel subjects. They also calculate the robot solution with the robot kinematic equation, while ours learns the model automatically.

Hyung et al. \cite{hyung2019optimizing} have proposed a genetic algorithms to search for the best facial expression. However, the algorithm is inefficient when the search space grows with the complexity of the robot. Therefore, the approach cannot perform online inference, while our robot can react to a novel facial expression within 0.18s in a fully online manner. Liu and Ren \cite{liu2019emotion} classified the input emotion and search in a discrete pre-defined expression set which constrains possible outcomes.

\mypara{Synthetic Video Generation and Animation}
Significant progresses have been made in synthetic video generation with a focus on motion re-targeting. The goal of video synthesis \cite{wang2018video, wang2019few, siarohin2019animating, shaham2019singan} is to learn the mapping from a source video to a target video domain to render photorealisitc videos. Our work does not aim to render a realistic video or image. Rather, we aim to map human facial expressions to a physical robot platform.

On the other hand, some works in character face animation \cite{cao2014displaced, bulthoff2016perceptual, seymour2017interactive, nagano2018pagan, wei2019vr} targets at driving a digital face with human motions. Cao et al. \cite{cao2014displaced} learns to recognize human facial landmarks and map them to a 3D avatar. Their method requires full 3D knowledge of the digital face, making it hard to generalize to real robots. Our approach learns a kinematic model of the physical robot through self-supervised motor babbling without any prior knowledge of the control mechanisms.

The most relevant works pertain to motion re-targeting \cite{thies2015real, zhou2019dance, gafni2019vid2game, siarohin2019first}. Chan et al. \cite{chan2019everybody} proposes to transfer dance motion to a different human subject in a rendered video. Similarly, X2Face \cite{wiles2018x2face} and Face2Face \cite{thies2016face2face} learns to synthesize novel face movements videos. However, the aim of these works is realistic video rendering, rather than a physical robot realization. Moreover, most prior works assume a strong correlation of kinematic configurations between the source and target domain. However, as most robotic platforms, our robot does not share same control mechanisms as humans. Therefore, existing approaches cannot be directly applied in real-life contexts.

\mypara{Imitation Learning} Our work also shares the same high-level goal as imitation learning. However, most imitation learning research focus on the manipulation \cite{asfour2008imitation, rozo2013robot, zhang2018deep, ratliff2007imitation, muhlig2012interactive}, locomotion \cite{nakanishi2004learning, ratliff2007imitation, RoboImitationPeng20} or navigation \cite{8460487, driving-imitation, agile-off}, as discussed in detail in comprehensive review articles \cite{hussein2017imitation, osa2018algorithmic, 10.1145/3054912}. Our work studies facial mimicry. With our learning framework, we can directly train our models with the robotic face on an offline dataset and generalize on different human face configurations.

\section{DESIGN}

Our robot design --- based on our previous Eva 1.0 \cite{faraj2021facially} with significant upgrades --- consists of two sub-assembly modules: facial movement module and neck movement module. We use micro servo motors (MG90S) to actuate all the components of our robotic face. All parts in our design are based on off-the-shelf hardware components that can be easily purchased online or 3D printed. To accelerate research, we open-source the design and step-by-step assembly process on our website. An overview of our hardware design is shown in Fig. \ref{fig:hardware}.

\mypara{Facial Movement Module} Our facial movement module can be further divided into skull frame, eye module, muscle module and jaw module. Compared to Eva 1.0, we redesign the 3D shape of the skull frame to enlarge the maneuvering space and thus allow for more flexible movements. The new skull frame also facilitates tighter connection to the skin with smoother and more natural looks on the face surface.

By adding a pair of ball joints and three pairs of parallelogram mechanisms, we now also ensure that the 6DoF eyeball module can move freely within a $\pm 20\degree$ range both horizontally and vertically, making the movements more stable. The rotation of each eyeball is controlled by two motors and three ball joint linkages. We also upgraded the eyelid design based on human face.

\begin{figure}[t]
\vspace*{5pt}
\begin{center}
    \includegraphics[width=.48\textwidth]{figures/hardware_pdf.pdf}
\end{center}
\caption{\textbf{Mechanical Design:} our robot is actuated by the motor servo module (A) controlled by a Raspberry Pi 4 located at the bottom. The soft skin is connected to $10$ motors via nylon cord. Our 6DoF eye module (B) is decoupled from the front skull. The RGB camera (C) is only used for random data collection of robot self-images but not for testing. The 6DoF neck module (D) follows Steward platform.}
\label{fig:hardware}
\vspace{-13pt}
\end{figure}

Our robot skin is attached to the front skull. The muscles driving facial expressions are attached to the inner side of the skin with a piece of fiber fabric, a strand of nylon cord, and a servo actuator inside the back of the skull. By pulling different nylon cords, a specific skin region can be deformed. We use the same region selection as Eva 1.0. To reduce the noise for muscle control, we run each nylon cord through a transparent vinyl tube linking the skull front and the motor. Our design ensures that the deformation in each muscle is proportional to the motor's rotation angle. Our design enables a large possible space of facial expressions by manipulating $10$ pairs of symmetrical muscles in different proportions.

The jaw module is responsible for deforming the two muscles around the mouth. This is similar to the aforementioned skin movement design, but benefits from two additional coupled motors that control the movement, allowing the jaw to open up to $20\degree$.

\mypara{Neck Movement Module} Our 6DoF neck module design is inspired by Stewart platform. Six motors are arranged in $3$ pairs evenly distributed on $3$ sides of the hexagon base. Each pair comprises two motors in a mirrored arrangement that are connected to a ball joint linkage.

\section{MODELS}

We propose a learning-based framework for controlling the animatronic robotic face to mimic varieties of human facial expressions. An overview of our learning algorithms is shown in Fig. \ref{fig:overview}. We consider the following problem setup: given an image of human face displaying a natural facial expression, the model needs to output the motor commands to actuate the robot to imitate the given facial expression.

Without human pre-programming for different expressions, the problem poses several key challenges and desired properties. First, we hope that the learning algorithm can generalize to diverse unseen human faces. We leveraged the recent advances in human facial landmark detection to obtain abstract representations from high-dimensional image frames (Section \ref{representation}) which can be shared among different human subjects.

The second challenge in attempting to achieve facial mimicry stems from the lack of ground-truth pairs of human expressions and robot motor commands. Without hard-coding and extensive trail-and-error, obtaining such a pair is not practical. In this paper, we overcome this issue by adopting a two stage learning-based method: (1) a generative model which first synthesizes a robot self-image from facial landmarks processed by a proposed normalization algorithm (Section \ref{generative-model} and Section \ref{normalization}), (2) and an inverse model that is trained to produce desired motor commands from the generated robot image (Section \ref{inverse-model}). As we will show, the ground-truth labels for both models can be acquired with one round of self-supervised data collection without any human input.

\subsection{Representation of Facial Expression} \label{representation}

We capture facial expressions via facial landmarks, as this has been shown as an effective means of representing the underlying emotions. More importantly, facial landmarks provide a unified abstraction from diverse high-dimensional human images under different lighting, background and poses.

Specifically, we extract facial landmarks with OpenPose \cite{8765346, simon2017hand} software. The output is a vector of $53 \times 3$ size representing the spatial position of $53$ landmarks on human faces, with the last dimension being confidence scores. We also extracted the head pose for direct neck movement control. As ground-truth pairs for generating motor commands directly from the extracted human landmarks are not available, we adopt a different strategy, as discussed below.

\subsection{Landmark Normalization} \label{normalization}

Before we send the landmarks from human faces for robot learning, we need to normalize the spatial locations of the landmark vector to the robot domain. This is necessary due to potential variations in the scale or landmark arrangements in the captured human faces. Formally we normalize each landmark coordinate from human space $L_H$ to robot space $L_R$ with:
\begin{equation*}
    L_{R} = \frac{(L_{H}-H_{min})(R_{max} - R_{min})}{H_{max} - H_{min}} + R_{min}
\end{equation*}
where $H_{min}, H_{max}, R_{min}, R_{max}$ represent the value ranges of the spatial location per corresponding landmark in the sampled human and robot image frames.

\begin{figure*}[!t]
    \vspace*{5pt}
    \centering
    \includegraphics[width=0.975\linewidth]{figures/model_overview.jpg}
    \caption{\textbf{Model Overview:} our two-stage framework consists of two major modules: a generative network and an inverse network. Given an image captured by a regular RGB camera, we first extract facial landmarks with OpenPose. We then normalize the human landmarks to the robot scale and embed it on an image. Together with a reference static robot self-image, these two images are concatenated to the generative network to synthesize a robot self-image as if the robot makes the same expression. The inverse model takes the synthetic robot self-image to output the final motor commands for execution.}
    \label{fig:overview}
    \vspace{-12pt}
\end{figure*}

\subsection{Generative Model} \label{generative-model}

The generative model takes in the normalized human landmarks and generates a synthetic RGB image allowing the robot to conceive the same facial expression, which we denote as robot's self-image. We parametrize the generative model $G$ with a deep neural network with parameter $\theta$.

A key challenge here is to map the coordinate vector to a high-dimensional image. This could be accomplished by encoding the input vectors with a fully-connected network, which is thus used by the robot to learn the entire spatial mapping. However, this simple approach requires a network of strong capacity and optimization algorithm and does not perform well in practice \cite{liu2018intriguing}.

To this end, we propose to encode the spatial coordinates of the landmarks to a two-channel image mask $\vect{M_{i}^{w \times h \times 2}}$ \cite{chan2019everybody}. The first channel has the value of $1$ if there is a landmark at the particular location and $0$ otherwise. The second channel is a greyscale image indicating the confidence score returned by the landmark detection algorithm. This encoding matches the size of the robot image, which helps with ensuring correct spatial correspondence.

Furthermore, instead of relying on the network to directly regress the absolute value in the output image, it only needs to output the "change" in the image introduced by landmark displacements. In practice, we achieve this by conditioning the network with a static robot self-image $\vect{I_{s}^{w \times h \times 3}}$ whereby the two images are concatenated along the depth channel. Our generative model can be expressed as: $\vect{I_i} \gets G(\vect{M_i}, \vect{I_s})$.

\mypara{Implementation Details}
We use a fully convolutional encoder-decoder architecture \cite{long2015fully, chen2021visual} where the resolution of the decoder network is enhanced by several hierarchical feature refinement convolutional layers \cite{dosovitskiy2015flownet}. Since the network is fully convolutional, we can preserve all the spatial information with high-quality outputs. Our network is optimized with a simple pixel-wise mean-squared error loss using Adam \cite{kingma2014adam} optimizer and a learning rate of 0.001. We train the network for 200 epochs with batch size $196$ until convergence on a validation dataset. We include more details in the supplementary. The formal objective function that we minimize is:
\begin{equation*}
    \mathcal{L}_{G} = \text{MSE}(G(\vect{M}, \vect{I_s}), \vect{I})
\end{equation*}

\subsection{Inverse Model} \label{inverse-model}

The inverse model $F$ maps the synthetic robot self-image to motor commands in order to learn an inverse mapping from the goal image to actions.

\mypara{Action representation}
Our robot utilizes $N$ motors to actuate the face muscles. Given the robot face image, it is straightforward to frame this problem as continuous value regression. That is, given an input image, the network needs to output $N$ values for $N$ motor encoders. However, this framing can quickly become impractical in complex real-world scenarios and may lead to over-engineering. In practice, we observe that the motors have certain angle threshold to produce salient and stable motion. As a result, we can achieve salient motions with a discrete parameterized angle encoder without loss of accuracy.

We normalized and discretized the motor values to the $[0, 1]$ range with $0.25$ step size, resulting in $5$ values per motor. This discretization converts the original problem to a multi-class classification problem. Given an input robot image, the inverse model will output $5 \times N$ numbers, where every five numbers represents the probability of choosing each motor angle for one actuator. Our inverse model can be expressed as: $\vect{A_i} \gets F(\vect{I_i})$.

\mypara{Implementation Details}
Our architecture has $6$ convolutional layers followed by several fully-connected layers to adapt the output dimension to be $5 \times N$. We train our network with a multi-class Cross-Entropy loss with Adam optimizer and a learning rate of 0.00005 for $58$ epochs. We have $14,000$ pairs for training and $1,000$ pairs for validation and testing respectively. The formal objective function is:
\vspace{3pt}
\begin{equation*}
    \mathcal{L}_F = \Sigma_{n=0}^{N-1} \text{CE}(F_{n}(\vect{I}), \vect{A_{n}})
\end{equation*}

\begin{figure}[t]
\begin{center}
    \includegraphics[width=.48\textwidth]{figures/data_collection.png}
\end{center}
\vspace{-3mm}
\caption{\textbf{Training Data Collection:} the whole training process of our generative model and the inverse model rely on a single robot dataset without human supervision. We collect our training data with random motor babbling in a self-supervised manner whereby the camera facing the robot is used solely for gathering the training data, \textit{i.e.}, it is not used during evaluation}
\vspace{-10pt}
\label{fig:robot-data-collection}
\end{figure}

\subsection{Training Data Collection} \label{training-data}

Both models can be trained separately with the same data collected via self-supervised motor babbling (Fig. \ref{fig:robot-data-collection}). We randomized the angles of the $10$ motors ranging from $0$ to $1$ with an interval of $0.25$ for $16,000$ steps. We used Intel RealSense D435i to capture RGB images and cropped the image to $480 \times 320$ to center the robot head. For each step $i$, we recorded the motor command values $\vect{A_i}$, the corresponding robot images $\vect{I_i}$, as well as the extracted landmark $\vect{L_{R, i}}$ from the robot with OpenPose. We thus obtain the training pairs of the generative model as ($\vect{L_{R, i}}$, $\vect{I_i}$) and the training pairs of the inverse model as ($\vect{I_i}$, $\vect{A_i}$). Since the data collection is purely random, the process does not require any human labeling.

\subsection{Inference}

Once the generative model and the inverse model are trained, we can use them jointly to perform inference. Given the input human image captured by an RGB camera, we first extract the landmark coordinates. After our normalization procedure, we can send it to our generative model which then outputs a synthetic robot self-image. This synthetic robot image serves as input for the inverse model which outputs the motor commands. All the network training and testing can be accomplished on a single NVIDIA 1080Ti GPU, whereas the motor commands on the robot are executed via WiFi.

\section{EXPERIMENTS}

\subsection{Evaluation Dataset}
Even though the training of our models do not require any human face dataset, we constructed a human facial dataset with salient facial expressions to perform extensive evaluations. Our human expression dataset is a combination of MMI Facial Expression database \cite{pantic2005web} and a set of online videos featuring eight subjects. We uniformly down-sampled the original videos to obtain $380$ salient frames covering a variety of facial expressions and human subjects.

\subsection{Baselines}

We are interested in evaluating the effectiveness of our approach for the generative model (GM), the inverse model (IM), and finally the entire pipeline. To this end, we design our baseline and ablation studies for each of them. For the generative model, we compared our method with a randomly sampled (RS) image from a collection of real images. This comparison aims to ascertain whether the synthetic image can outperform the real image and whether our model can predict reasonable motion on the static robot template.

Even though our method successfully convert the inverse model to a classification problem, a purely random baseline only has a $20\%$ success rate. We also compared our model with another two less random simple baselines --- a randomly initialized network (RI) and a model trained for about $100$ iterations (RI-100).

For the entire pipeline, we evaluated different combinations of the above baselines. For example, we obtained one baseline by combining our generative model with an inverse model that has been trained for $100$ iterations. Additionally, we present another three baselines. The first one is to perform a nearest neighbor retrieval (NN) with the landmarks extracted from the output of our generative model. We can directly search within our robot dataset. This baseline replaces our inverse model with a NN model. Similarly, we can perform such NN operation with human landmarks as a direct input. Lastly, we assessed whether we can directly generate the motor commands without our two-stage algorithm by training a network to output motor commands from our embedded input landmarks.

We used the same architecture for all the above baselines while varying only the number of channels in the first layer for different input dimensions. We trained all the models with three random seeds and report the mean and standard errors.

\subsection{Evaluation Metrics}

Our evaluation metrics cover both the pixel-wise accuracy of synthetic image and the accuracy of the output commands. We also extracted landmarks from our synthetic image to measure if the model successfully learns the facial expression than simply copying the static image. Moreover, we provide qualitative visualizations for the final pipeline. We also extracted landmarks from our entire pipeline execution to compare against the input human landmarks. We use L2 metric for both the image and landmark distances.

\subsection{Results}

\mypara{Generative Model}
Tab. \ref{tab:generative-only} shows the quantitative evaluation for our generative model. Our generative model outperforms the random baseline by a large margin. Note that the image distance is normalized by the total number of pixel values ($480 \times 320 \times 3$) which range from $0$ and $1$, whereas the landmark distance is normalized by the total number of landmarks ($53$).

\begin{table}[h!]
\caption{Accuracy of the Generative Model}
\label{tab:generative-only}
\centering
\resizebox{0.9\columnwidth}{!}{
\begin{tabular}{@{}l|c|c@{}}
\toprule
\multicolumn{1}{c|}{\textbf{Method}} & \textbf{\begin{tabular}[c]{@{}c@{}}Image Distance $\downarrow$ ($\times 10^{-5}$)\end{tabular}} & \multicolumn{1}{l}{\textbf{Landmark Distance $\downarrow$}} \\ \midrule
\textbf{GM (ours)}              & \bm{$3.47 \pm 0.009$}                                                            & \bm{$0.46 \pm 0.002$}                      \\ \midrule
RS                 & $6.47 \pm 0.039$                                                                     & $0.84 \pm 0.007$                               \\ \bottomrule
\end{tabular}
}
\vspace{-3pt}
\end{table}

\begin{figure*}[t!]
    \vspace*{5pt}
    \centering
    \includegraphics[width=0.975\linewidth]{figures/visualizations.jpg}
    \caption{\textbf{Robot Visualizations:} we executed the output motor commands on our physical robot to demonstrate that our method supports accurate facial mimicry of a variety of human expressions across multiple human subjects.}
    \label{fig:visualizations}
    \vspace{-13pt}
\end{figure*}

\mypara{Inverse Model}
Tab. \ref{tab:inverse-only} shows the evaluation result for our inverse model. Compared with the random initialized model, the model trained for 100 iterations as well as the purely random baselines ($20\%$ accuracy), our inverse model produces much more accurate motor commands.

\begin{table}[h!]
\caption{Accuracy of the Inverse Model}
\label{tab:inverse-only}
\centering
\resizebox{0.9\columnwidth}{!}{
\begin{tabular}{@{}l|c|c@{}}
\toprule
\multicolumn{1}{c|}{\textbf{Method}} & \textbf{Command Distance $\downarrow$} & \multicolumn{1}{l}{\textbf{Command Accuracy $\uparrow$ (\%)}} \\ \midrule
\textbf{IM (ours)}                 & \bm{$0.53 \pm 0.009$} & \bm{$75.86 \pm 0.042$}                        \\ \midrule
RI                    & $1.39 \pm 0.009$          & $30.34 \pm 0.038$                                 \\ \midrule
RI-100         & $0.90 \pm 0.010$          & $56.40 \pm 0.041$                                 \\ \bottomrule
\end{tabular}
}
\vspace{-3pt}
\end{table}

\mypara{Two-Stage Pipeline}
By combining the generative and inverse model, we can use the synthetic output from the generative model as the input for the inverse model, allowing us to evaluate the entire two-stage pipeline. As shown in Tab. \ref{tab:two-stage}, our two-stage strategy outperforms all baselines, but is inferior to the performance of its component models. This is to be expected, as the inverse model takes the synthetic output from the generative model as its input when these are evaluated jointly, whereas individual evaluations are based on real images from our robot dataset. Nonetheless, our method still achieves the best predictions and is particularly advantageous compared to two single model baselines, indicating that decomposing the problem into two sub-steps is a prudent design choice.

\begin{table}[h!]
\caption{Accuracy of the Entire Two-Stage Pipeline}
\label{tab:two-stage}
\centering
\resizebox{\columnwidth}{!}{
\begin{tabular}{@{}cl|c|c@{}}
\toprule
\multicolumn{2}{c|}{\textbf{Method}}                                        & \textbf{\begin{tabular}[c]{@{}c@{}}Command Distance $\downarrow$ \end{tabular}}     & \textbf{\begin{tabular}[c]{@{}c@{}}Command Accuracy $\uparrow$ (\%)\end{tabular}} \\ \midrule
\multicolumn{1}{c|}{\textbf{GM}} & \multicolumn{1}{c|}{\textbf{IM}} &
\textbf{\begin{tabular}[c]{@{}c@{}}$\bm{0.83}$ $\bm{\pm 0.008}$\end{tabular}} & \textbf{\begin{tabular}[c]{@{}c@{}}$\bm{54.57}$ $\bm{\pm 0.048}$\end{tabular}} \\ \midrule
\multicolumn{1}{l|}{GM} & \multicolumn{1}{c|}{NN}                   & \begin{tabular}[c]{@{}c@{}}$1.06$ $\pm 0.009$\end{tabular}          & \begin{tabular}[c]{@{}c@{}}$45.74$ $\pm 0.045$\end{tabular}          \\ \midrule
\multicolumn{1}{l|}{GM} & \multicolumn{1}{c|}{RI}                  & \begin{tabular}[c]{@{}c@{}}$1.39$ $\pm 0.009$\end{tabular}          & \begin{tabular}[c]{@{}c@{}}$30.34$ $\pm 0.039$\end{tabular}          \\ \midrule
\multicolumn{1}{l|}{GM} & \multicolumn{1}{c|}{RI-100}               & \begin{tabular}[c]{@{}c@{}}$0.98$ $\pm 0.009$\end{tabular}          & \begin{tabular}[c]{@{}c@{}}$53.48$ $\pm 0.041$\end{tabular}          \\ \midrule
\multicolumn{2}{c|}{Landmark to Motor}                                      & \begin{tabular}[c]{@{}c@{}}$1.03$ $\pm 0.011$\end{tabular}         & \begin{tabular}[c]{@{}c@{}}$52.81$ $\pm 0.048$\end{tabular}          \\ \midrule
\multicolumn{2}{c|}{Landmark NN}                              &
\begin{tabular}[c]{@{}c@{}}$0.98$ $\pm 0.095$\end{tabular}         &
\begin{tabular}[c]{@{}c@{}}$49.3$ $\pm 0.045$\end{tabular}          \\ \bottomrule
\end{tabular}
\vspace{-17pt}
}
\end{table}

\mypara{Pipeline Execution}
We executed the output motor commands from the above two-stage pipeline on our physical robot and computed the landmark distance extracted from the resulted robot face with the ground-truth normalized landmarks. To provide qualitative evaluations, we visualized the final robot face together with the input human face in Fig. \ref{fig:visualizations}.

Our evaluation was conducted on $380$ salient frames which covers $10$ video clips from $8$ subjects. We show the quantitative results compared with a random baseline in Fig. \ref{fig:10-bar-plot}. Our approach demonstrates a flexible learning-based framework to mimic human facial expression. Our method also generalizes across various human subjects without any human supervision.

\begin{figure}[h!]
\begin{center}
    \includegraphics[width=.48\textwidth]{figures/results_data.png}
\end{center}
\caption{\textbf{Pipeline Execution:} we executed the motor commands output by our entire two-stage pipeline and extracted the landmarks from the resulting physical robot face to compare against the ground-truth human landmarks as well as a random baseline to benchmark the task difficulty. The results show that our robot can imitate different human expressions accurately.}
\label{fig:10-bar-plot}
\vspace{-15pt}
\end{figure}

\section{CONCLUSIONS AND FUTURE WORK}

We present a new animatronic robot face design with soft skin and visual perception system. We also introduce a two-stage self-supervised learning framework for general face mimicry. Our experiments demonstrate that the two-stage algorithm improves the accuracy and diversity of imitating human facial expressions under various conditions. Our method enables real-time planning and opens new opportunities for practical applications.

Although we show that robots based on our design are capable of mimicking diverse human facial expressions from visual observations, it is necessary to study this problem with other modalities. For example, when several humans are involved in a conversation, the context will also influence their facial expressions. Thus, for future investigations, it would be beneficial to incorporate speech signal into the decision-making process while also allowing robots to reciprocate verbally.

Furthermore, even though imitation is an important step towards imbuing robots with more complex skills, being able to generate appropriate reaction expressions will be essential for interactive social robots. More broadly, building interactive robot face will require higher-level understanding of other's emotion, desires and intentions. Hence, an interesting direction is to explore higher-order thinking for robots.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


The only difference between summer in Los Angeles and the rest of the year is about 20 degrees. But now that it's summer for real for real, that means pool parties and NEW music! With a ton of artists putting out singles vying for that ultimate song-of-summer, it's easy to flip on the radio and hear the latest hit, but we like to keep it #perfectlybalanced and thought we'd share some songs we're bobbing our heads to that you won't hear on the radio this summer (yet). Checkout these jams and keep it cool for the summer!
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Wake up early in the morning for breakfast and leave Kampala drive across the Equator through Uganda's rolling green hills to lake Mburo National park (the drive will take approx. 5 hours).we shall have lunch, we will do a boat ride on lake Mburo and a late afternoon game drive in search of zebra ,cape buffalos, impala, topi, eland, oribi, and with luck, Roan antelopes, leopards, hyenas and jackals.
Take breakfast at the lodge, we drive you through the county sides of Kanungu and continue on to Bwindi, impenetrable Forest National Park, home of the mountain gorillas. In the evening we shall walk to the near by community for a cultural performance.
Dinner and over night at Gorilla resort Camp / Buhoma homestead / Buhoma community Banda's.
After breakfast, we will assemble at the tracking point for briefing from ranger guides. Then proceed to the jungle to search for these gentle giants. The gorillas are shy, peaceful animals and an encounter of a family of gorillas with their dominants the silver backs is a worth while! Return to the lodge for dinner and overnight.
Depart for Queen Elizabeth National Park. We'll pass through the southern sector of the park known as Ishasha, eminent for its tree- climbing lions. This National Park is a UNESCO World Biosphere and has one of the highest biodiversity ratings of any game reserve in the world! Almost 100 mammal species and a remarkable 606 bird species can be found. Elephants, Lots of hippos, the elusive giant forest hog and the handsome Uganda Kob are regularly encountered. In the afternoon, we will take a visit to the crater regions. Dinner at the Mweya safari lodge/ Jacana safari Lodge / Institute of Ecology.
Take a cup of coffee and go for a Morning game drive to search for the early riser and nocturnal. Expect encounters for Lions, Hyenas, leopards, antelopes among others. In the afternoon go for the launch cruise along the Kazinga channel. This will expose you to diversity of schools of hippos, crocodiles, and many bird species, back to the hostel for dinner and over night.
After breakfast, we depart to Kibale Forest National Park. Take lunch in the afternoon Then after we will assemble Kanyankyu tourist centre for briefing and then the rangers will lead you to the forest for chimpanzee provide detailed explanation of the primates we are likely to see as we;; as the forest's fauna and flora. On our half-day hike we will look for chimpanzees, phoestes, redcolobuses and red-tailed and grey cheeked mangabeys. Dinner and over night at Ndali lodge / Primates lodge / Rwenzori View guest house/ Kanyankyu tourist Centre.
Drive to Murchison Falls national park have lunch en route , visit the top of the falls on arrival Dinner and over night at Paraa Safari lodge/ Nile Safari Camp/ Sambiya River Lodge / Red Chillis Camp.
Wake up very early in the morning. If you slept in the lodges on the southern bank of the River , you will cross the ferry and go for an early morning game drive in search for Giraffes, Lions, leopards and many other animals. Return to the lodge for mid morning breakfast and then relax as you wait for lunch. In the afternoon, you will go for a launch cruise to the bottom of the falls where the Nile is evidenced to plunge from a 50 metres wide gorge into a narrow gorge about 7 metres wide. This is exhilarating! If you are interested, you will hike to the top of the falls to see the wide gorge plummeting .The hike takes around 1 hour but it also depends on how fit you are. Return to the lodge for dinner and overnight.
Transfer to Kampala via Masindi Visit the Zziwa Rhino sanctuary and then after tracking Rhinos. Have lunch and return to Kampala.


I wish I could relay to you the dozens and dozens of testimonies where the answers to prayer helped heal broken marriages, where drug abusers were set free and where lost people were saved. Prayer changes the world and the more we pray the better off the world will be. If we do nothing, then nothing much will happen. The Body of Christ, the church, can only move forward on her knees but moves not at all without prayer. Prayer is asking God's supernatural power to change the natural world and to do it for His glory. If it's not for His glory, nothing about the world is better.
Jesus once said "Ask, and it will be given to you; seek, and you will find; knock, and it will be opened to you" (Matt 7:7) which indicates that it is up to the one who needs help to initiate the action of asking, seeking, and knocking, otherwise nothing is given, nothing is found, and nothing is opened. It's like a little boy looking for his mother in the house. He starts by asking for her…no answer. Then he begins to seek her but she isn't found. Finally he starts knocking on the doors of each room in the house and only then does the door open and out comes momma. If the boy never asked, if he never sought, and if he never knocked he still would be wondering where his momma was. For him, the world was better only because he was asking, seeking, and knocking.
James once wrote that "The prayer of a righteous person has great power as it is working" (James 5:16c) which seems to indicate that we must not have any unconfessed sin if we are to ask for something in prayer. The Psalmist declared "If I had cherished iniquity in my heart, the Lord would not have listened" (Psalm 66:18). Come to God with a clean conscience or as once again James writes "Draw near to God, and he will draw near to you. Cleanse your hands, you sinners, and purify your hearts, you double-minded" (James 4:8).
God hears the prayers of someone with clean hands, someone who has confessed their sin and are in a right relationship with Him, who is asking, seeking, and knocking, and needs God's supernatural power in their lives. Only when this happens will the world be a better place because "The more praying there is in the world, the better the world will be." It is as simple as that.


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The Real Husbands Of Beverly Hills Are More Dramatic Than Their Wives
What's going on with The Real Husbands of Beverly Hills?
The Real Housewives of Beverly Hills is supposed to be a show about the lives of glamorous, wealthy women who gossip about each other and inevitably get into altercations. However, it seems that the men behind these Housewives have taken a more active role in the conflicts that involve their wives.
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Whether it be dishing out hilarious digs, acting as their wife's bodyguard or defending themselves against false allegations; it's safe to say that the Beverly Hills boys have been giving the women a run for their money this year.
Ken Todd is Lisa Vanderpump's husband of over thirty years, and is the most vocal - and controversial - of all the current husbands. He has frequently come to the defence of both Lisa and Brandi - who does not have a significant other - when he feels that they are being attacked. Earlier this season, Kim confronted Lisa about not turning up to her daughter Kimberley's graduation party. Ken immediately went to his wife's defence by bringing up the fact that there have been many events that Kim has not turned up to; alluding to the former child star's alcoholism. Kim did not take this well, and their feud escalated during the trip to Puerto Rico - Kim referred to Todd as a 'stubborn old man' when Ken accused the ladies of orchestrating an attack on Lisa.
While he isn't Kim's favourite person, Ken has been good friends with Kyle's husband - Mauricio - for a number of years. Kyle and Lisa actually met each other through their husbands. Much like that of their wives though, Ken and Mauricio's involvement on the show has taken a toll on their once-close friendship.
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This year was difficult for Mauricio. There were a number of tabloid stories about his supposed infidelity, and these were inevitably brought up on the show. Initially, Ken was supportive of his friend; telling Lisa that he didn't believe a word of the rumours. However, once Kyle and Mauricio started accusing Lisa of deliberately bringing up the cheating allegations, Ken lost his cool. He chastised Mauricio for believing Brandi's version of events, even though Lisa had repeatedly refuted the accusations. It appears that Beverly Hills' biggest bromance may be headed for a split …
More wood has been flung on the Househusband fire courtesy of Michael, the husband of newbie Housewife Jacquel - I mean Joyce. In the midst of the mammoth Brandi/Joyce feud, Michael put his foot down and defended his wife during a dinner confrontation with Brandi. Highlights from the shouting match included accusing Brandi of being a bad mother, and an alcoholic who needed rehab. In typical Brandi fashion, Glanville retaliated by accusing Joyce of 'settling' with an ugly overweight man who has a lot of money.
Disappointingly, not every husband gets involved in reality TV feuds; some are content to stay below the radar. Both Yolanda's husband - supreme songwriter David Foster - and Carlton's man, David, who runs a successful financial services firm, are men of few words. However, with David Foster's track record - Yolanda is his fourth wife - I think it's inevitable that there will be some divorce drama in the future. Here's hoping!
Even though Ken can deliver a fantastic insult, and Mauricio is a fine piece of eye candy; I'm getting a little bit sick of the husbands worming their way into every little fight. They don't really bring anything exciting, and usually come off as either annoying or intimidating. Unless they want to channel New Jersey and beat the crap out of each other, I'm not interested. So as a word of advice to the Beverly Hills hubbies - leave it to your wives; they're the professionals.


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Home Health & Beauty Health Benefits of Tomato | Tomato Home Remedies, Medicinal Uses & Unknown...
Health Benefits of Tomato | Tomato Home Remedies, Medicinal Uses & Unknown Facts that you never knew!
Even though tomato is a berry, we call it a vegetable. Let's learn some good health benefits of tomato…
The scientific name of tomato is Solanum Lycopersicon. Tomatoes are very beneficial for our body's skin care and heart health. Also, it can help in weight loss.
There is no difference in the nutrients of tomatoes whether they are cooked or eaten raw. Its special feature is that no matter how we take it, we will get the nutrients of tomato without diminishing.
Health Benefits of Tomato
A nutrient Lycopene found in tomatoes can prevent cancer. Iron, calcium, vitamin A and vitamin B1, B2, B6, and vitamin C are abundant in tomatoes.
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The lycopene in tomatoes is an excellent anti-cancer agent. Potassium, fiber and vitamin C are good nutrients for the heart and prevent heart diseases.
Calcium and vitamin K in tomatoes are very useful for bone growth. Nutrients such as lycopene, lutein and beta-carotene prevent eye-related diseases.
Collagen is an important nutrient for our skin, nails, hair and connective tissue. This collagen is abundant in tomatoes. Also, the folic acid found in it greatly helps in the development of the nervous system of the baby in the fetus during pregnancy.
Niacin (Vitamin B3) in tomatoes helps to prevent fat from accumulating in the blood vessels and arteries.
Usually, we use tomatoes widely only in side dishes and curries like sambar and rasam. Instead, tomatoes can be added to soups and also we can eat it raw often.
There are many varieties of tomatoes, among which the country tomato is the best for us. Avoid genetically modified tomato varieties as much as possible. Likewise, it is better to avoid ready-made tomato sauce, tomato jam etc. as lot of chemicals are mixed in it for taste and to prevent it from spoiling.
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The Bridge: Extension of Slavery
Posted in African Americans, Barack Obama, Black Interests, Black Men, Guest Columnists, The Bridge - Darryl James with tags Black Men, The Bridge - Darryl James on October 4, 2011 by Gary Johnson
By Darryl James
No other people in the world have the ugly history with a nation as African descendants with America.
We were once property and instead of becoming truly free, we allowed ourselves to be duped into a game of make believe, pretending to be free, but having none of the intrinsic mobility as a group that would come with freedom.
Instead of making our ancestors proud by achieving freedom as a group, far too many of us seek freedom as individuals, even if that freedom is merely freedom from the burden of being Black. A burden that may be masked, but that can not be eradicated for any of us until most of us have truly become free.
But how can we become free when many of us deny that we were ever enslaved?
Just as there are morons who claim that the Holocaust never happened for the Jews, there are ignominious idiots who claim that slavery was merely a business arrangement, instead of the evil peculiar institution that it was.
We hear about how slavery wasn't as bad as people say it was, how it wasn't really evil because it was pure commerce and how Blacks only came here to "help" the slave masters.
Here's the simple truth: whites enslaved us for hundreds of years and oppressed us in Jim Crow for scores more. Now even many of us say that we should stop talking about white people and racism, even though the ugly institution and its aftereffects linger on like a ghost clinging to the corporeal world, long after the body is dead.
I want to shoot fools right in the face when they claim that racism is over and that we should stop talking about it. They claim that slavery was so long ago that it no longer matters.
Want me to stop talking about racism? Then stop talking about God and the sun, because they came into existence so long ago.
But there is clearly a dichotomy of thought surrounding slavery and the Black existence in America in the Black community.
In the Black community, those African descendants who claim that there is no racism or that our experience in this nation was beautiful are trying too hard to move away from Black reality and trying too hard to be white.
Yes, I said it. In 2011, there are African Americans who want nothing more than to be white and lying about the Black existence in this nation is the first step.
It is also a clear sign of the insanity resulting from unresolved issues that are often ignored, or pretended away.
In America, people of African descent were never given the opportunity to fully process and resolve issues stemming from slavery and the lingering Jim Crow era, nor institutionalized racism and racial preference.
The nation tells us we are healed, currently touting Barack Obama's ascension to the presidency as evidence. And while this is clearly a great start, there has been no intrinsic healing. Neither the election of a Black president, the end of slavery nor the Sixties have caused healing to occur.
Unarguably, the pervasiveness of the dominant white American culture has delivered issues of self-esteem and cultural confusion to the descendants of Africans in America.
Conversely, people of German Jewish descent were given multiple opportunities to fully process and resolve issues stemming from the Holocaust and the lingering negative psychological effects.
Mighty nations across the globe, including the United States came to the aid of German Jewish people, helping in the restoration of the culture and standing of those people.
Without the same opportunities and assistance, African descendants of slavery in The US have taken schizophrenic avenues following the end of slavery.
Unlike the descendants of German Jews, descendants of Africans in America have little usable cultural wealth, continuously diminishing cultural cohesion and as a result, cultural confusion.
Just as the effects of slavery are indelibly stamped on the souls of Black folk in America, so is the influence of white existence in America.
In many ways, some Blacks in America are living lives designed to imitate whites.
This wouldn't be a huge problem if we could simply imitate whites and accordingly, garner the privileges of white preference. We don't, but many of us continue to imitate whites as though we don't understand that we can never be white.
We take the icing and pretend that it is the cake. But it always turns out the same way.
It's like kids mimicking their parents. Junior can walk big in my shoes and jacket, but he can't go to work and get a check.
We're so quick to abandon what we are in order to become something else, that we forget to be ourselves, and forget that we have value.
Even when we are acting Black, we are acting the way that the dominant culture sees us, doing what's acceptable to them. Our literature, comedy, music and even our public behavior plays this out. Our very existence is pimped out for white entertainment and then sold back to us as a guide for what is "authentically" Black.
Rap music and comedy are full of stupid, laughing Negroes, pandering to whites by playing Niggers, all the while claiming to be revolutionary because they are making money. In their make-believe rebellion, they are playing to the favor of white folks. Instead of sustaining Public Enemy, we have sustained P-Diddy.
What was revolutionary was when Blacks were talking about something real and didn't care. Now Negroes talk about whatever is supposed to be cool according to the mainstream, because they care too much what whites think and so imitate whites or parody what whites think we should be.
We are caricatures of caricatures.
Far too many Black men are caught up in a dichotomy demonstrated by either playing dress up with stupid hip hop gear at age 40 or business suits and "Malcolm X" glasses while doing nothing productive. And some of them who shouldn't even be having sex, have babies they don't take care of, giving the world the impression that they are the majority of us.
On the other side, far too many Black women are having children alone on purpose, after having too much sex and then turning around and calling themselves strong Black women for such behavior. No one seemed to understand how ignorant and counterproductive the song "Baby Mama" was…
All of the above is behavior indicative of attempts to be white, who have no image problem, so bad behavior is clearly assigned to the groups of whites who, well, behave badly.
Blacks have never had that luxury, because our worst behavior is assigned to all of us, even if the behavior is only exhibited by a small number of us.
Yet, we stick our chests out and pretend that we have arrived, and most of us aren't even aware of the destination. Barack Obama is president and too many of us are still acting like the Niggers that racists think we are, while pretending that we are dignified.
The problem is that we don't even realize that we are not guests at the party. We're like the waiter who went into the kitchen, took his white coat off and pretended that he was a guest. Eventually, he is discovered and expelled from the party, after losing his job.
The challenge that lies before us as we turn from celebrating the first African American President to chastising him harder than any white racist is to identify our own cultural path and then follow it for our own benefit, no matter who says what about it.
We will finally overcome when we begin to be ourselves.
That will be our final journey to freedom.
Darryl James is an award-winning author of the powerful new anthology "Notes From The Edge." James' stage play, "Love In A Day," opened in Los Angeles this Spring and will be running throughout 2011. View previous installments of this column at http://www.bridgecolumn.proboards36.com. Reach James at djames@theblackgendergap.com.
MY WAY = ED MURPHY'S WAY!
Posted in Black America, Black Interests, Black Men, Guest Columnists with tags Black Business Leaders, Ed Murphy, Harold Bell on October 4, 2011 by Gary Johnson
By Harold Bell
Legendary vocalist Frank Sinatra's classic rendition of "My Way" best describes the life and times of D. C. businessman Ed Murphy.
Murph was born in Raleigh, North Carolina but grew up in Washington. D.C. He attended Shaw Middle School and graduated from Cardozo High School.
After graduation he enlisted in the United States Army where he received an honorable discharge in 1950. Murph returned to his DC hometown to embark on his life-long dream of establishing a business in the heart of the inner city.
He was known on the streets as "Eight Ball" because of his love for the game of Billiards (pool).
Murph was the black community's Donald Trump without the pedigree, bankroll, education and business background. Trump's father was a New York real-estate developer and Trump was educated at the famed Wharton School of Business.
Murph had a Ph.D. earned the hard way, on the mean streets of DC's Business Schools of Hard Knocks!
His success as a businessman can be attributed to his hustler's mentality and time spent in the U. S. Army. They were the attributes that helped prepare and equip him with the "Street Sense and Common Sense" for the real Game Called Life.
During his stay in the Army, Murph used his "Street Smarts" as a gambler to support himself and send money home to his mother. Whatever goal he set in his mind he was willing to prepare himself to work hard to perfect his craft and gambling was no exception.
I don't think Murph was aware that this talent would be the key for his success in his ventures as a developer and businessman. There was one characteristic that I loved about Murph–"Playing Safe" was not a part of his vocabulary! He took risks and was never afraid to roll the dice!
Ed's first business venture in 1955 was a Variety Store, followed by a restaurant and Murphy's Market which led to his ownership and opening of his pride and joy, Ed Murphy's Supper Club in 1963.
The Ed Murphy Supper Club became one of the most popular attractions for "The In-Crowd" in Washington, D.C. It was frequented by a Who's Who in politics, entertainment, pro sports, media personalities and everyday people. They all could be found hanging out at the club on any given day or night.
Legendary radio and television personality Petey Green and I made it our place to meet,eat and hang out.
The restaurant became a network oasis for information, social gatherings and political meetings. It was here information and opinions where shared. It was quite unique for one business to attract such a wide range of patrons.
Many business contacts, lasting friendships, and even marriages began at the club. The good times were happening over cocktails. There was also live entertainment and the best soul food you could find anywhere in the city.
Murph had the gift of gab, he was a dapper dresser and his outgoing personality made him the perfect host. He had a genuine love for his people and I could not detect a phony bone in his body. If you didn't want to know the truth about yourself it was not a good idea to be around him.
In 1972, Murp relocated the club across Georgia Avenue and called it the New Ed Murphy Supper Club. The old club site was where he launched the development plans for his ultimate dream, the Harambee House Hotel.
H. Bell, Boston Celtics legends Red Auerbach and K. C. Jones having lunch at Ed Murphy's Supper Club
It took more than 15 years from when he opened his first store, and five presidential administrations — Johnson, Nixon, Ford, Carter and Reagan—before he finally saw the Harambee House Hotel open in 1978.
The hotel was the first black owned and operated, full-service, first-class hotel in the United States. The 10 million dollar, 150-room luxury hotel, featured many full service amenities such as: indoor, climate control swimming pool and health spa, coffee shop, two bar/lounges, 150-seat supper club, 200 seat ballroom, banquet and meeting rooms, deluxe suites with complete kitchens and saunas, lobby level beauty and barber shop, a boutique and indoor parking.
In 1977 Muph hired Sharon Pearl as his administrated assistant. In 1980 Howard University took over the Harambee House Hotel. The hotel had become a financial burden for Murph.
As President of Harambee International in the 80's he began plans for the new Ed Murphy's Restaurant and Catering, located in the Frank D. Reeves Municipal Center at 14th U Streets, N. W.
With this new facility, he presented another first in the food service industry—a full service fine dining restaurant, offering a combination of quick, cafeteria-style service for breakfast and lunch, full table service with bar/lounge, live entertainment and catering facilities all in one operation. This business was also the first of its kind in a DC Government building.
Murph despite the adversity he never quit. He marched to his own drum beat.
He enjoyed the spotlight and during my career as a sports talk show host I would often coordinate celebrity tennis and golf tournaments, but my Inside Sports Celebrity fashion show was usually the talk of the town.
Athletes came from all around the country to participate in the fashion show. They included, (NBA) Earl "The Pearl" Monroe, Sonny Hill, Spencer Haywood, K.C. Jones, Sam Jones, (NFL) Roy Jefferson, Freddy Scott, George Nock, Johnny Sample, (Boxing) Sugar Ray Leonard, Johnny Gant, Thomas Hearns, Hilmer Kenty, Derrick Holmes, (Media) Jim Vance, James Brown, Dave Dupree, Fred Thomas, Paul Berry, Martin Wyatt, (Entertainment) Melvin Lindsey, Robert Hooks, Stacy Lattisaw and a host of others that I have forgotten!
During a lunch conversation with Murph's good friend actor/producer Robert Hooks I discovered that Murph was upset with me because I had never asked him to participate.
I was surprised because he never gave me any indication he wanted to participate. But I think his ego got the best of him and I got the message loud and clear. My next fashion show was being held at the popular Foxtrappe Club. Murph was front and center and I was honored to have him participate.
Robert and Ed were very close friends and colleagues throughout the decade of the 70s.
He said, "Murph and I shared a love for DC and its people. We wanted to see the city make a strong comeback from the upheaval after the 1968 riots.
When I returned home to start the DC Black Repertory Company in 1970 and had no place to locate our workshops-it was Murph who offered his warehouse building (free of charge). The warehouse was located next to his restaurant. It was only then could we begin to recruit the local talent. Without this gracious and heartfelt gesture by Murph-it would have been much more difficult to get the company started."
Time always brings about change and in the 90s Murph constantly re-invented himself to rise up with even more creative and unique business ventures.
Murp's attitude was if one thing failed he had no problem in trying and trying again. He could go from Superman, to Batman to Spiderman in the blink of an eye. He was always ready for the next challenge and he never needed a phone booth to change.
In 1992, he founded the African-American Business Association (AABA), which addressed the needs of small businesses in the heart of the black community.
Long before President Obama told the Congressional Black Caucus to stop whining, groaning and complaining, Murp was telling the black community to stop complaining and practice "self-help" but his shout-out was all about love for his people. Murph led by example—he was black and proud!
Murph had no problem calling out politicians, ministers, and business leaders to support one another. He was convinced that if we spent just a portion of our 4 billion dollar annual income within our own community, we would be upholding the true meaning of Harambee (Swahili for pulling together).
Unlike today's black businessmen, politicians, clergy, and community advocates, Murp was a man for all seasons and for all people. He made an effort to be Inclusive instead of being Exclusive!
Murph also started the Annual Business Awards Dinner, which each year honored small business owners, who otherwise received little or no recognition. He was a constant small business advocate. He created the motto and creed of the AABA "Spending our dollars where we create jobs and opportunity in our community."
The husband and wife team of Ed and Pearl Murphy founded the African-American Media Incubator (AAMI) which was the first African-American radio broadcasting school in the country.
There were other black restaurants in town during that era, Sylvia's and Billy Simpson's but none could match the locker room camaraderie of Ed Murphy's Supper Club—when you walked in you could feel the energy and everyone felt connected!
Face's Restaurant was another Georgia Avenue landmark that came close to matching Murph's supper club but in the final analysis it was still a distant second. Ed Murphy's Supper Club was one of a kind much like the man himself.
On Saturday October 15, 2011 the legacy of Ed Murphy will live on as City Leaders will meet on the front lawn of Howard University Hospital at 10:00 am. They will re-name Byrant Street, N. W, "Ed Murphy Way." Congratulations to Murph, a man who did it his way!
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PAWS Humane Society of Rockford, Illinois, a volunteer organization supported by donations, was founded in 1988 as a spay/neuter assistance operation. We have grown into a full-service humane society, even reaching into our public school systems to educate future animal owners.
Our mission is to promote, support and provide for the humane treatment and welfare of animals in Winnebago County and the surrounding areas. We work towards a day when there is a home for every pet and domestic animals are no longer euthanized simply because they have no home.
In 2016, we opened the first enclosure-based shelter in the Rockford area called the Sonal Puri Family Animal Rescue, located at 7282 N. Cherryvale Mall Drive. This facility was donated by First Midwest Group Founder and President Sunil Puri as a tribute to his family member Sonal, an animal lover.
This location houses available felines for adoption as they lounge in large enclosures or run around one of the two large community rooms. Our adoptable dogs are in foster homes, joining us on select weekends for adoption events.
PSA for PAWS Humane Society of Rockford, Illinois. from Stephen Guerrero on Vimeo.
PAWS was chosen as one of the top three non-profits in 2016, 2017 and 2019 in the Rock River Valley "What Rocks" contest sponsored by the Rockford Register Star. This is an honor because there are approximately 50 non-profits in the area, and PAWS was selected based on community voting.
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