text:
Sagicor Life Final Expense Review
By Eric Stauffer on March 5, 2019 0
Sagicor Life is part of an international group of companies under the Sagicor banner that have been in operation since the mid-1800s. The company focuses its products on whole life insurance and term life insurance as well as annuities.
About Sagicor Life
Sagicor Life Final Expense
Ratings and Consumer Reviews
Sagicor Life Insurance Company is part of Sagicor Financial, a group with a history dating back to 1849 when it was founded as Barbados Mutual Life Assurance Society on its namesake Caribbean island. The company expanded over the years and now serves 22 countries including the US. The company became Sagicor in 2002 when it shifted from a mutual company to a stock company.
The company that would become Sagicor Life in the U.S. was previously known as American Founders Life and was founded in 1954. Sagicor acquired the company in 2005, marking its entrance to the American market.
Sagicor Life focuses on a few products; life insurance and annuities. They have U.S. offices in Florida, Arizona, and Oklahoma and operate in 45 states and the District of Columbia.
Sagicor does not sell a policy that is specifically described as being a final expense product, but they do have a basic whole life option that can be purchased for that purpose.
Sage Whole Life is a traditional whole life policy that can be used as part of funeral planning. Like all whole life plans, the coverage is permanent and includes a cash accumulation account that can be borrowed against.
There are several versions of this plan, one of which is aimed at the senior market, but two of which could be used as a final expense plan. The simplified issue Accelewriting Process plan is available with issue ages of 16 through 66 and has no requirements for any medical exams. It can be issued immediately and has face values between $25,000 and $250,000.
The fully underwritten plan is available to those between the ages of 65 and 85 and has the same death benefit amounts available. It does have several health classifications that will determine how much the premiums will be. This is the closest option to what other companies market as final expense life.
All of the plans included an accelerated death benefit for terminal illness, which pays out $250,000 or $5,000 less than the face value of the policy, whichever is less.
Other optional riders include an accidental death benefit, children's term rider, and waiver of premium.
Unlike many plans that are classified specifically as final expense insurance, Sagicor's whole life option does not use a graded death benefit in order to issue policies to those who are not in perfect health. This may be a good thing for those who are willing to pay more to get an immediate death benefit, but also means there is no simplified issue option for those with health problems like those seen at other companies.
Another major difference with a company that does not have a final expense-specific policy is that the death benefit amounts are much higher, starting at the level where many final expense policies cap coverage.
Sagicor does not offer online quoting, therefore, we were unable to obtain rates. While this is common among companies focused on life insurance, it is something we are hoping to see a change in the near future.
Insurance shoppers today expect to be able to gather information and quotes without having to make multiple phone calls or put themselves on the solicitation list of a lot of agents and companies. While the lack of online quoting is common, it is something that is becoming a deterrent for the modern insurance customer.
The Claims FAQ on the company website provides the contact information as well as offering advice on how to file a claim.
Claims can be filed by regular mail, email, fax, or over the phone. There is no option to start a claim online, although there is a customer login area and we cannot be certain what is located through that portal.
A claim packet will be sent out within three days of the initial notification of an insured's death, which can be sent back to the company through one of the methods above. After all the documentation is received, the company promises that non-contestable claims will be paid within seven business days. The death benefit or a portion of it can be assigned directly to a funeral home to pay final expenses.
Sagicor Life has an A+ rating with the Better Business Bureau (BBB), with only one complaint on file. They have been accredited since 2009. Three customers have left reviews on the file, including complaints regarding claims and billing. Sagicor responds to these reviews, which is always a positive thing.
The three reviews on Google provide an overall 4.7-star rating, but there are no comments to go with the positive star ratings given by the reviewers. The three positives seem to balance the three negatives on the BBB page.
Sagicor overall has a solid reputation; we found no red flags and nothing of concern that would prevent us from recommending this company.
Sagicor is not the right choice if you have some health problems and are seeking a simplified issue final expense plan with a low death benefit for the sole purpose of paying funeral expenses. They are a good pick, however, if you are looking to get a higher face value on your plan and are willing to undergo full underwriting – or are under 66 and want simplified issue with a high death benefit. We cannot compare them on rates, but found nothing to worry about in terms of reputation.
Eric Stauffer


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America's Broken College Admissions Process
It's Fall of our junior year. Here come the PSAT, SAT, ACT, subject tests, college tours, GPAs, and so on. Exciting? Not really. As we've begun to scratch the surface of the college admissions process, we've realized how inequitable it is. Yes, this isn't a groundbreaking realization; according to a USA TODAY study, fewer than 20% of American students find the process to be "generally fair." This sentiment has become more prominent in the past decade, as the media has exposed various widespread admissions scandals.
The most notable scandal is Rick Singer's Operation Varsity Blues, which flooded the media in 2019. 53 high-profile individuals, such as actresses Lori Laughlin and Felicity Huffman, essentially bought their children's tickets to elite institutions by photoshopping sports pictures and hiring professionals to take standardized tests. In his 2014 self-published book, Singer confirmed that the college admissions process is "a science," leaving students with two options:
Use their four years of high school to explore their identities and pursue their true passions
Conform to the system to embody "the perfect applicant."
Unfortunately, too many students settle on option 2.
How does this highlight our unfair system?
Ultimately, students' ability to stand out in a sea of similar applicants boggles down to their economic standing. In fact, Harvard economist Raj Chetty writes that "students from the top 1% of households are 77 times more likely to be admitted to and attend an Ivy League school than students from families who make less than $30,000 a year." Overall, elite institutions enroll more students from the top 1% of the income bracket than the entire bottom 60%. Crazy, right?
Wealthy students' advantage in the college admissions process breaks down into 5 main parts:
1. Tuition
This one is simple: wealthy students can afford to attend elite colleges. In fact, an analysis of over 2,000 institutions by the NASFAA found that while half of US colleges are affordable for students with a family income of over $160,000, low income students can only afford 1-5% of colleges. These students rely on financial aid and scholarship funds. Unfortunately, however, wealthy students are also eating up aid. Rather than focusing on need-based scholarships, colleges are prioritizing merit-based ones; these scholarships are solely based on achievements, usually academic ones. Indeed, from 2014 to 2017, merit aid among colleges grew 16% faster than need-based aid. That is why in 2019, students in the highest income range received almost 4 thousand more dollars in financial aid than those in the lowest income range.
Unfortunately, this makes sense because colleges treat financial aid like a reward system. They want to increase their rankings by attracting higher-performing students who are more likely to graduate and donate to the college in the future; the majority of the time, these students come from wealthier backgrounds. Covid-19 will only worsen the problem because colleges are strapped for cash, meaning they must restructure their aid budgets. And since need-based and merit-based aid come from the same pool of money at most schools, America's most vulnerable students will be disproportionately harmed.
2. Higher-Caliber High Schools
Besides being able to afford college, wealthy students can attend top-tier high schools that give them a leg up in the college admissions process. For example, Bradshaw of the Chicago Tribune writes that in Harvard's Class of 2017, one out of every twenty freshman attended one of just seven high schools: Boston Latin, Phillips Academy's Andover and Phillips Exeter, Stuyvesant High School in New York, Noble and Greenough School in Massachusetts, Trinity School in New York and Lexington High School in Massachusetts. Six of these schools share one attribute: they are private schools. And even though Stuyvesant is a public school, it is highly selective; students are required to take a standardized entrance exam, and only 3% of applicants are admitted.
Attending a private school comes with a major advantage: more funding. While funding for district schools is based on property taxes in a given neighborhood, funding for private schools comes from a variety of sources, including student tuition, endowments, and donations from organizations or individuals. Thus, private schools have more financial resources to offer smaller class sizes, college counseling programs, and stronger curriculums — which better prepare students for college. For example, while the average student takes 3 AP exams over their high school career, students who attend selective high schools take anywhere between 7 and 12 AP courses.
3. College Counselors
Unfortunately, college advising is short in American public schools. In fact, Gewertz of Stanford writes that only 33% of public high schools have counselors devoted to college preparation, compared to 68% of private schools. Even schools with counselors struggle to compare to those in private schools. That's because the average public school's guidance counselor handles 482 students, with some states surpassing 600 students per counselor. This goes against the American School Counselor Association's recommendation of a 250:1 ratio. Public schools' large classes leave very limited time for individual attention. Moreover, public school counselors tend to have other non-college-related duties. As a result, Gewertz explains that counselors in public schools devote 21% of their time to college advising, compared to 47% in private schools. This gap is even larger in high-poverty schools, where counselors spend less than 20% of their time helping students with college admissions.
Even if a private school has a weak counselor program, students have easier access to independent college counselors that can provide expert advice on essays, interviews, etc. According to a national survey conducted by Lipman Hearne, 26% of students hire a private college counselor. This leaves independent college counselors working with approximately 160,000 applicants annually, a far cry from the 4.23 million students who graduate high school in the same period.
4. Test Prep
Like everything in the United States, standardized tests come with a major price tag. First, there are registration fees. Each SAT sitting costs $68, and each ACT sitting costs $70. That doesn't include late fees, which are an additional $30. While both the College Board and the ACT offer waivers, availability varies per state. For instance, Moody of US News reports that in the 2018 – 2019 school year, only 10 states covered the cost of the SAT and 20 states covered the cost of the ACT.
The larger problem, however, is tutoring. Simply stated, wealthy students can afford test prep materials, classes, and private tutors. Private college consultant Andy Lockwood confirms this, as 75% of kids in affluent areas get some sort of extra help for standardized test prep. Some of these services are absurdly expensive. For example, New York tutor Anthony Green charges a minimum of twenty-one thousand dollars for his SAT program. Not surprisingly, his students see at least a 430 point improvement. Green's service is not an isolated example. Overall, a study led by Ohio State's Claudia Buchmann found that taking a private test prep course significantly raises students' scores and their chance of college admission when compared to self-studying..
5. Costly Extracurriculars
Extracurriculars are arguably the most important part of college admissions because they measure aspects of students' personalities that test scores and GPAs cannot. But while some extracurriculars are free, — like reading to kids or volunteering at a homeless shelter — others require large investments. Let's take athletics, for example. The Aspen Institute's Sports and Society program found that household wealth is the primary driver of kids' athletic participation. According to a 2016 survey on youth sports statistics by TD Ameritrade, the typical parent spends between $100 and $500 per month, per child, on elite youth sports, and spending $1,000 per month is not unheard of. Wealthier families have enough money to spend on gear, private clubs, travel tournaments, and recruitment camps.
Another expensive activity is Speech and Debate. Teams attend various national tournaments in cities like Boston, New York, Chicago. Attending travel tournaments is critical for success on the circuit, but — not surprisingly— it is very expensive. In order to cover plane tickets and hotel rooms, each tournament costs between $400 and $550 per student. This can often be higher because some schools don't cover tournament registration, which costs up to $150 per person. These costs don't include the thousands of dollars families spend on personal laptops, suits, dress shoes, winter coats, and tournament meals. Simply stated, competitive extracurricular activities favor wealthy students, and since these activities are the main way that colleges assess their applicants, low income students tend to look less holistically valuable.
It goes without saying that Ivy League schools — or schools of a similar caliber — provide students with a top-notch education. This is in part because they have the financial resources to invest more in their students. In fact, elite schools spend 7.75 times more on each student than other colleges. That translates to $92,000 per student at Ivy League schools versus only $12,000 at second-tier institutions.
More importantly, elite institutions are a network; they offer vital connections to the highest-performing, most well-connected people in the world. These contacts place students at an inherent advantage when they enter the workforce. Additionally, attending a top-tier college helps employers distinguish between their applicants; having Harvard or Yale on your resume boosts your chances of getting your dream job. This is empirically true, as Goodnow of the Washington Post observes that the top earning graduates from Ivy League schools make about 35% more than top earners from other schools. At a more extreme level, "six of the eight Ivy Leagues are among the ten leading billionaire alma maters."
Establishing an equitable college admissions process is especially important for low-income students, since they disproportionately benefit from attending high-caliber colleges. Indeed, a 2017 study led by Harvard economist Raj Chetty found that lower-income students at an elite school have a "much higher chance of reaching the [top 1 percent] of the earnings distribution" than those at even an excellent public university." This is because wealthier students tend to already have connections in the "real world" that allow them to snatch internships and high-paying jobs. Reforming our extremely lopsided college admissions process is critical because for kids without connected parents, elite colleges can serve as a plug to profitable industries, reducing poverty rates and income inequality in the long run.
Note: this article was also written by Cecilia Granda-Scott.
Nicole Rodriguez is a junior at Ransom Everglades School in Miami, FL. Her curiosity drives her to study and pose questions about the rapid pace of our world — exactly what GenZ Identify Lab stands for! Nicole's passion for researching and writing comes across in her active participation on her Speech and Debate team. This year alone, she has debated a variety of topics, ranging from nuclear energy to charter schools. Through GenZ, Nicole hopes to motivate her readers to share their voices and explore new activities. Outside of school, you can find Nicole dancing, hanging out with friends and family, watching Netflix, or traveling!

redpajama_set_name: RedPajamaCommonCrawl


text:
A Breed Apart is the soundtrack album for the film A Breed Apart, composed and performed by Maurice Gibb. All of the instrumentals were recorded in 1984 but "Hold Her in Your Hand" and "On Time" were recorded in 1983. This soundtrack was the last recording session at the famous Gold Star Studios in Los Angeles. Even though he did not play on the soundtrack, Jimmie Haskell said years later that he was very impressed by Maurice's demos, especially his sensitive playing of the flute parts.  Jimmie mixed the synthesizer flute into the finished recordings because he liked the sound. "Hold Her in Your Hand" was released as a single in 1984.

Track listing
All the songs written and composed by Maurice Gibb except for "Hold Her in Your Hand", which is by Maurice and Barry Gibb.

Personnel
 Jimmie Haskell — orchestral arrangement
 Maurice Gibb — synthesizer, lead vocal on "Hold Her in Your Hand" and "On Time"
 Dennis Hetzendorfer — engineer

References

1984 soundtrack albums
Maurice Gibb albums
Albums produced by Maurice Gibb
Albums arranged by Jimmie Haskell
Instrumental albums
Unreleased albums
Drama film soundtracks

redpajama_set_name: RedPajamaWikipedia


text:
Q: How can I find out if a class that I use in my app is private or not? How can I find out if a class that I use in my app is private or not? Has this been specified in the class documentation?

A: Create an archive of your app as you would if you were going to submit it to iTunes Connect. Open up Xcode's Organizer window, and run "Validate" on the archive. It will generate a warning if you have called a private API.

redpajama_set_name: RedPajamaStackExchange


I am a nineteen-year-old college student. As far back as I can remember there has been tension between my mother and I, and after going to college for a year I want to be able to resolve it more than anything. Whenever we talk, there is a lot of strain and awkwardness in the relationship. My mom has a personality that goes way up one minute and way down the next, but is never in the middle. This conflicts with my personality, which mostly just stays in the middle. When she is in one of her good moods, she tries to have a really close relationship with me, asking questions about my personal life and giving me lots of hugs and stuff. These hugs seem to be something that she uses to stay sure that everything between us is okay. But, for me, they are annoying because she will have just gotten done telling me that I am the biggest disappointment of her life, and then suddenly get in a good mood again, and wants a hug. I feel like these hugs are more of a punishment than a good thing, and so I am not enthusiastic, and that puts her in a bad mood again.
When my mom is upset, there is no way of calming her down. She uses what I told her in her good moods against me in when she is in a bad mood. For instance, if I tell her that I am in a fight with a friend, she seems to really care but next time she is upset she says "No wonder you have no friends". I have learned to not tell her anything too personal because I don't want to be too vulnerable. However, when she is in a good mood she wants to know everything about how I am and if I don't tell her that puts her in a bad mood again. I feel like it is a never-ending cycle and like I am in charge of whatever mood she is in. When she is in a bad mood, I feel it is my responsibility to bring her back up to a good mood. When she is in a good mood, I struggle to keep her there. I think I feel like this is sometimes because so many of her emotions are taken out on me, and so I feel like those emotions are all a result of who I am, something I have said, or something I have done.
Can I do anything to make the relationship work better while I am home this summer? How can I develop a better attitude about this?
Nicole, you have taken a big step towards making your relationship with your mom more conscious and mature. Unfortunately, the conscious one is YOU and not your mother. She is obviously operating from an unconscious state of mind, acting out her moods without any ability to self-regulate. This is most likely a very deeply rooted personality trait that she has had her whole life, and which you are just now starting to see more clearly. That may be painful for you to accept, but for the sake of your own liberation, you must see your mother's limitations. From your description of her, it is hard to tell the exact cause of her mood swings. On one end of the spectrum she could be suffering from some form of bipolar disorder - a biochemical disorder that causes fluctuations in mood from depression to "mania" or unusual highs. The severity and duration of these swings can vary greatly among different people. The other possibility (and more likely), is that she has a "personality disorder," but getting into labels is not necessarily what would be helpful for you here. The fact is this: She is emotionally "enmeshed" with you. She can't really see you as a separate person. She doesn't have a healthy identity of her own. That's why you're left feeling responsible for her moods. On one level, she has given you that task, and you have taken it on - probably since you were a baby. Even as infants we absorb our parents energy, feel responsible for their happiness and their pain and their anger.
Some parents, like your mother, who have a poor sense of self identity, invest more than an appropriate share of energy in their children, and in a way (unconsciously) try to make them into the parents they wish they had. She's trying to get something from you that she can never get from another person. She's feeling empty and is unable to feel content and whole by herself, so first she sees her beautiful daughter whom she loves and is proud of and wants to hear all about how great she is. She views you as an extension of herself. This sustains her for a while, but not long. Soon she's left feeling miserable again -- alone with herself. So then she gets bitter and angry, maybe even feels jealous of you for "having it all together," for being young and independent, etc. And she takes it out on you. What's worse, she violates your trust by using your words and your emotions against you. This is a terrible thing to do to your child. Of course I doubt she realizes what she's doing. But that is no excuse for her behavior. She will continue to do this as long as you continue to accept it and to feel guilty and responsible for her.
Often it is when we first leave home, at just around your age, that we begin to see our parents for who they really are, with all their faults and frailties. Some people don't take the time to learn from this, but I see that you have some good insight. It will take you far, but it's a hard road to walk. You will need to get angry at your mother. After 19 years of being your mother's support (a role that no child can fulfill) and the object of her emotional and verbal abuse -- you would have to have a great deal of buried rage. You may not know that yet, because you've learned to be so good at swallowing other people's crazy emotions at the expense of your own. But if you just allow the possibility of being angry, it will emerge. Inside that anger and rage, is a lost part of yourself that is essential to your wholeness and happiness. Don't turn that anger against yourself in the form of guilt and shame. Let it liberate you. You do not deserve to be verbally abused, put down, manipulated and emotionally used by your mother or anyone else. Make no mistake about it - what she does to you is abuse. Talking to a good counselor about this will be incredibly valuable in helping you to get more clear about all of this, and to start to set healthy boundaries with your mother and to empower yourself. Finding healthy ways to access and express your anger would be a very worthwhile goal in therapy.
As much as you want your mother to grow with you, there is a good possibility that she will not tolerate your changing. She may not be capable of changing. She will see your liberation and independence as an abandonment of her. But that's the choice you have to make. You can love her without playing her games. Be well.


text:
\section{Introduction}

Automatic generation of hyperlinks in video collections presents a problem of growing interest, where video segments are interlinked by their high level semantic similarity, based on one or more of their modalities. Video hyperlinking consists of establishing links between relevant video segments within a collection. Links are defined between source video segments, called anchors (video segments that the users are viewing and query for additional relevant video segments), and target video segments (video segments relevant for to each queried anchor). For every anchor, a multimodal retrieval task is performed and a set containing the most relevant related targets is returned.
Automatic generation of hyperlinks within large video collections has been a subject studied and evaluated yearly as part of the MediaEval and TRECVID initiatives, within their Search and Hyperlinking benchmark~\cite{awad2016trecvid}.
There are different approaches to handling multiple modalities in
multimodal retrieval and thus in video hyperlinking.  Straightforward
approaches using multiple modalities consist either in comparing each
modality separately, combining the scores or rankings, or in combining
descriptors from each modality. These methods however do not fully
exploit the multimodality of the datasets. Regardless of classical
methods such as canonical correlation analysis (CCA), the state of the
art methods yielding a joint multimodal representation space from the
initially disjoint representations heavily rely on multimodal
autoencoder
~\cite{feng2014cross,cha2015multimodal}.

Multimodal AEs operate with representations of each modality (in our case, speech and visual representations). It is possible to translate from one initial representation space to the other, but not completely to the original space. Being able to visualize the crossmodal mappings (e.g., what images are expected given a certain speech transcript) in video hyperlinking can offer important human-interpretable insight about the learned model and explain its performance. Recently, generative adversarial networks~\cite{goodfellow2014generative} have become popular as means of generating realistic artificial data in the original, initial space (as opposed to the original representation space). Even more recently, generative adversarial autoencoder models have been conditioned on the input with one modality (CGAN), while allowing them to generate realistic artificial data of the other modality~\cite{reed2016generative} thus allowing for crossmodal translation from one initial space (e.g., text) to the other initial space (e.g., image), as opposed to crossmodal translation between representation spaces. We explore the possibility of using CGANs in video hyperlinking and creating models that not only provide good multimodal embeddings but also provide human-interpretable visualizations of what the model has learned.

Multimodal AEs are trained in an unsupervised manner on a dataset of finite, often limited size. It is possible to enlarge the dataset by applying random transformations and noise to the existing samples but the amount of samples will sill remain limited. In this work, we explore the possibility of using CGANs to tackle this problem. A generator network can generate an unlimited number of synthetic samples to train with and can constantly be improved by competing with the discriminator network. The discriminator network can also benefit from the additional synthetic samples and provide even better multimodal embeddings.

\begin{table*}[!htb]
\centering
\caption{Visualization of generated synthetic images given automatic transcripts (in lowercase, with some stop words removed) as input. In the last column, a real image from the video segment corresponding to the input automatic transcript is shown.
CGANs provide good visualizations of the video hyperlinking model: in the last row, given speech transcripts related to war thematic, the model is expecting a news presenter, while the actual video segment contains footage from the war zone.}
\label{table:speech_to_images}
\begin{tabular}{|m{8cm}|Sc|Sc|}
\hline
\multicolumn{1}{|c|}{\textbf{Input - Automatic Speech Transcript}} & \textbf{Generated Images} & \textbf{Real Image} \\ \hline \hline

``\ldots
insects emerged to take advantage of the abundance . the warm weather sees the arrival of migrant birds stone chests have spent the winter in the south  \ldots''
& \cincludegraphics[scale=0.5]{"images/nature"}
& \cincludegraphics[scale=0.5]{"images/nature_real"}
\\ \hline

``\ldots
second navigation of the united kingdom . the north sea , it was at the north yorkshire moors between the 2 , starting point for the next leg of our journey along the coast \ldots''
& \cincludegraphics[scale=0.5]{"images/water"}
& \cincludegraphics[scale=0.5]{"images/water_real"}
\\ \hline

``\ldots  the role of my squadron afghanistan is to provide the the reconnaissance capability to use its or so forgave so using light armor of maneuvering around the area of
\ldots''
& \cincludegraphics[scale=0.5]{"images/news"}
& \cincludegraphics[scale=0.5]{"images/news_real"}
\\ \hline

\end{tabular}
\end{table*}

\section{Related Work}
Multimodal AEs are commonly deployed in tasks where multiple modalities are used.
Such tasks include but are not limited to classification~\cite{cha2015multimodal, ngiam2011multimodal} and retrieva
~\cite{feng2014cross, lu2015semantic}. More specifically, in video hyperlinking, the current state of the art is a variation on multimodal AEs, consisting of two interlocked crossmodal DNNs, translating in opposing directions~\cite{vukotic-icmr-16, vukotic-ivlmm-16} that achieved the best performance at the latest TRECVID evaluation~\cite{irisa2016trecvid}.

Generative adversarial networks have been first introduced by~\cite{goodfellow2014generative} as a two-network generative-discriminative model
for generating high-quality, single-modal, realistic samples that could be mistaken for real samples from the dataset the model is trained on. GANs quickly became popular and are now used in a multitude of tasks, such as generating super-resolution images,
inpainting,
de-occlusion
and many others. Conditional GANs~\cite{mirza2014conditional} have been shown to generate realistic samples of one modality, given a conditioning input of another modality. A typical example of a crossmodal CGAN model is text to image synthesis~\cite{reed2016generative, zhang2016stackgan} but the conditioned input is not necessarily bound to multiple modalities and can be conditioned also on the same modality

For the purpose of multimodal retrieval, we focus on crossmodal / multimodal CGANs and ways to obtain meaningful multimodal representations from them. While multimodal setups are currently less explored, there is a lot of evidence that GANs learn meaningful representations in single-modal setup
~\cite{makhzani2015adversarial, chen2016infogan, perarnau2016invertible},
most notably in the generator network. These representations are obtained in a completely unsupervised manner and can be used to model changes in style, pose,
color
or even style and structure in RGBD data.
Evidence suggests that both the generator network and the discriminator network can produce meaningful single-modal representations~\cite{radford2015unsupervised}. Our work falls into the category of text to image CGANs~\cite{reed2016generative, zhang2016stackgan}, where we explore the possibility of obtaining good multimodal representations from the discriminator, while using the generator to visualize crossmodal mappings for the purposes of multimodal retrieval in video hyperlinking and to improve the embeddings from the discriminator network.

\begin{figure}[tb]
    \centering
	\includegraphics[scale=0.3]{images/architecture}
	\caption{Used CGAN architecture, consisting of a generator and a discriminator network. Multimodal embeddings are obtained in the discriminator, after the last, 1D convolution operation, as denoted with a dashed rectangle.}
    \label{fig:arch}
    \vspace{-7mm}
\end{figure}

\section{Methodology}
We split the work in two separate parts. In the first part, we evaluate the current state of the art multimodal AEs. In the second part, we evaluate the feasibility of using CGANs to perform multimodal embedding and we compare the resulting representations against the current state of the art in the setup of multimodal retrieval within the video hyperlinking domain.

\subsection{Multimodal Autoencoders}
Multimodal AEs with two separate input and output bra\-nches~\cite{ngiam2011multimodal}, one for each modality have been shown to perform better than simple AEs where the two modalities are concatenated. Recently, BiDNN, a variation with two disjoint but tied networks, working in opposing directions has been shown to define the new state of the art in video hyperlinking~\cite{irisa2016trecvid}. We compare our proposed model against both classical multimodal AEs and the recent BiDNN variation. However, multimodal AEs need existing continuous representations as inputs. We opt for averaged Word2Vec for representing speech and VGG-19 features for representing video keyframes.

\subsection{Unsupervised Multimodal Representation Learning with CGANs}
\label{gan_theory}
The CGAN architecture we evaluate is based on the GAN-CLS text to image model~\cite{reed2016generative}. The model consists of a generator network $G$ and a discriminator network $D$. The generator network takes a noise vector $z$, sampled from a $\mathcal{U}(-1, 1)$ distribution and a text embedding $\varphi$, and generates a synthetic image $\hat{x} = G(z, \varphi)$. The generator consists of a separate fully-connected layer with a leaky-ReLU activation function (that allows for a small non-zero gradient on the negative side) for modeling the text embeddings. This is then concatenated with the noise input. A standard deconvolutional network follows. The discriminator network takes an image $x$ and a text embedding $\varphi$ and determines whether the pair is real or artificial $D(x, \varphi)$. Contrary to the basic CGAN model that only differentiates between real and synthetic images, CGAN-CLS is trained on three pairs. In our case \{real image, real text\}, \{incorrect image, real text\} and \{synthetic image, real text\}. As we are doing unsupervised learning, pairs with a non-matching image and text are chosen at random from different video segments and the dataset is not split. Pairs of real matching and non-matching text-image pairs are necessary to train the discriminator. The discriminator consists of a series of convolutional layers with batch normalization, followed by a leaky-ReLU for modelling the input image and a full-connected leaky-ReLU layer to model the text embeddings. The two branches are then concatenated and a $1 \times 1$ convolution is performed, followed by batch normalization and a leaky-ReLU activation before obtaining the discriminator score. For multimodal embedding, we use the vector obtained after the final $1\times 1$ convolution of the discriminator. To maintain the losses of both the generator and the discriminator at similar levels (having a discriminator that is performing too well would prevent the generator from converging), the generator is updated four times more often than the discriminator. A cosine distance between the obtained multimodal embeddings is used to measure the similarity of the desired video segments both in the case of multimodal AEs and CGANs.

\section{Experiments}
In this section we first define the dataset and the specific hyperparameters of each architecture and then analyze and discuss the results of each experiment.

\subsection{Dataset and Single-Modal Representations}
\label{dataset}
We test all the methods in a video hyperlinking setup on a BBC video hyperlinkig dataset with segments and the groundtruth obtained post-evaluation during MediaEval 2014. The task considered is that of ranking target segments given a source segment acting as a query. We use only the video segments that contain both speech transcripts and video keyframes (10,321 in total). Speech is represented with averaged embeddings of size 100 from a skip-gram \textit{Word2Vec} model with hierarchical sampling and a window size of 5~\cite{irisa2016trecvid}. Regarding the visual stream, a representative keyframe is chosen for each video segment by computing a histogram for each pixel over all the keyframes and choosing the keyframe closest to the media
as a representative of each short video segment. Such an image is then downsampled and cropped to a size of $64 \times 64$ pixels. To obtain a sensible comparison, the same data and image sizes are used for all the methods. The CGAN model works directly with the text embeddings and images. Multimodal AEs also need visual embeddings as inputs. We obtained them from the same images, with a VGG-19 network (pretrained on ImageNet) and they are of size 4096. Table~\ref{table:embeddings} show the initial single-modal results, which are 56.55\% for speech transcripts and 52.41\% for video keyframes, in terms of precision at 10~\cite{awad2016trecvid}.

\begin{table}[!htb]
\centering
\caption{Comparison of initial modal single-modal representations and multimodal embeddings obtained with different methods. For each method, precision at 10 and its respective standard deviation are reported.}
\label{table:embeddings}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Representation} & \multicolumn{1}{l|}{\textbf{$P_{10}$ (\%)}} & \multicolumn{1}{l|}{$\sigma$ (\%)} \\ \hline \hline
Speech Transcripts Only &              56.55                 & -                                    \\ \hline
Visual Only (VGG-19)    &              52.41                 & -                                    \\ \hline \hline
Multimodal AE           &              57.94                 & 0.82                               \\ \hline
BiDNN                   &              59.66                 & 0.84                                \\ \hline
\textbf{CGAN}           &              \textbf{62.84 }       & \textbf{1.36 }                      \\ \hline
\end{tabular}
\vspace{-5mm}
\end{table}

\subsection{Multimodal Autoencoders}
We evaluate both classical multimodal AEs and BiDNNs, their state-of-the-art variation in video hyperlinking. We chose an AE architecture with separate input and output branches of size 1000 for each modality  (first and last layers of consisting of two separate hidden layers of size 1000 each) and a common hidden layer or size 1000. For BiDNNs, we use the implementation provided by~\cite{irisa2016trecvid}. We use only the video segments that contain multimodal information (both speech transcripts and visual information) and we use averaged \textit{Word2Vec} embeddings for speech and VGG-19 embeddings for the keyframes, as defined in section~\ref{dataset}. BiDNNs expectedly outperform classical multimodal autoencoders and obtain a precision at 10 of 59.66\%.

\subsection{Multimodal Embedding with CGANs}
To evaluate the feasibility of obtaining multimodal embeddings with CGANs, we used the architecture described in Sec.~\ref{gan_theory} with a speech embedding of dimension 100, a noise input of size 10, a fully connected layer for modeling text of size 256 and 4 \textit{``deconvolutional''} layers of increasing size, each with 256, 128, 64 and 32 feature maps respectively in the generator network. The output of the generator is an image of size $64 \times 64$. The discriminator network consists of an image input of size $64 \times 64$ followed by 4 convolutional layers with batch normalization, each with 32, 64, 128 and 256 feature maps respectively and decreasing sizes and a speech embedding input of size 100, followed by a fully connected layer of size 256. The two branches are then concatenated and followed by another, final convolution.  We trained the network for 1000 epochs by using the Adam optimizer with a learning rate $r$ of $0.0001$, a momentum $\beta$ of $0.5$ and a batch size of $64$. As specified in Section~\ref{gan_theory}, we use the one-dimensional convolutional layer in the discriminator that follows the merging of the two branches, before batch normalization and activation as a multimodal embedding layer, as illustrated in Figure~\ref{fig:arch}. Other layers did not perform well and provided a lower or equal quality than the initial single-modal inputs.
The generator network does not only provide a mean to visualize crossmodal mappings but is also crucial for obtaining high-quality embeddings in the discriminator by competing with it and providing additional synthetic samples.

Compared to multimodal AEs, CGANs are computationally expensive to train (20h on a GPU, compared to a few hours on a CPU for BiDNN), even for small images, and require both modalities to be present. However, the discriminator of a CGAN provides multimodal embeddings that are greatly improved over the initial representation spaces or each modality. The results are shown in Table~\ref{table:embeddings}. Representations learned with a CGAN not only outperformed multimodal AEs but they also significantly ($p=99.9\,\%$ with a single-sided t-test) outperformed the state of the art BiDNN model and obtained 62.84\%.

\subsection{Crossmodal Visualizations}
The existence of a generator network is crucial to train a discriminator that provides well-performing multimodal embeddings. Interestingly, the generator network can also be used to visualize crossmodal mappings in video hyperlinking. The generator can straightforwardly create synthetic images given an embedding of the speech transcripts as input. Examples of images generated for real transcripts on a few video segments, as well as their real visual counterparts are displayed in Table~\ref{table:speech_to_images}.  The generator can also be reversed by simply applying the transposed learned kernels in an inversed  architecture and slicing the obtained vector. In this case, given an image, embeddings in the textual domain can be obtained. A few examples of that translation are shown in Table~\ref{table:images_to_text}.

\section{Conclusions and Future Work}
In this work we experimentally demonstrated the interest of CGANs as an alternative method to design multimodal embeddings. While CGANs are more computationally intensive to train than multimodal autoencoders, and are thus limited to smaller image sizes, they can provide better multimodal embeddings in the last layer of their discriminator network that outperform embeddings of multimodal autoencoders.
The generator network, that provides unlimited synthetic samples and is crucial for training the discriminator, can additionally be used to visualize one modality from another and provide human-interpretable insights of the crossmodal translations learned with this type of video hyperlinking models. To use CGANs for multimodal embedding more efficiently, it is necessary to investigate different architectures that are able to converge with bigger image sizes within a feasible training timeframe and evaluate possible methods of decreasing the training time for uses where online learning is required.

\section{Acknowledgments}
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.

\begin{table}[!htb]
\centering
\caption{Visualization of the top words in the representation space of automatic transcripts, given an input image.}
\label{table:images_to_text}
\vspace{-3mm}
\begin{tabular}{|c|m{6cm}|}
\hline
\textbf{Input Image} & \textbf{Top Words in the Speech Modality} \\ \hline \hline
\cincludegraphics[scale=0.5]{"images/image_to_text_1"} &
britain, protecting, shipyard, carriers, jobs, vessels, current, royal, aircraft, securing, critics, flagships, foreclosures, economic, national
\\ \hline

\cincludegraphics[scale=0.5]{"images/image_to_text_2"} &
north, central, rain, northern, eastern, across, scotland, southwest, west, north-east, northeast, south, affecting, england, midlands
\\ \hline

\cincludegraphics[scale=0.5]{"images/image_to_text_3"} &
pepper, garlic, sauce, cumin, chopped, ginger, tomatoes, peppers,  onion,  crispy, parsley, grated, coconuts, salt, crust
\\ \hline

\end{tabular}
\vspace{-5mm}
\end{table}

\section{Introduction}

Automatic generation of hyperlinks in video collections presents a problem of growing interest, where video segments are interlinked by their high level semantic similarity, based on one or more of their modalities. Video hyperlinking consists of establishing links between relevant video segments within a collection. Links are defined between source video segments, called anchors (video segments that the users are viewing and query for additional relevant video segments), and target video segments (video segments relevant for to each queried anchor). For every anchor, a multimodal retrieval task is performed and a set containing the most relevant related targets is returned.
Automatic generation of hyperlinks within large video collections has been a subject studied and evaluated yearly as part of the MediaEval and TRECVID initiatives, within their Search and Hyperlinking benchmark~\cite{awad2016trecvid}.
There are different approaches to handling multiple modalities in
multimodal retrieval and thus in video hyperlinking.  Straightforward
approaches using multiple modalities consist either in comparing each
modality separately, combining the scores or rankings, or in combining
descriptors from each modality. These methods however do not fully
exploit the multimodality of the datasets. Regardless of classical
methods such as canonical correlation analysis (CCA), the state of the
art methods yielding a joint multimodal representation space from the
initially disjoint representations heavily rely on multimodal
autoencoder
~\cite{feng2014cross,cha2015multimodal}.

Multimodal AEs operate with representations of each modality (in our case, speech and visual representations). It is possible to translate from one initial representation space to the other, but not completely to the original space. Being able to visualize the crossmodal mappings (e.g., what images are expected given a certain speech transcript) in video hyperlinking can offer important human-interpretable insight about the learned model and explain its performance. Recently, generative adversarial networks~\cite{goodfellow2014generative} have become popular as means of generating realistic artificial data in the original, initial space (as opposed to the original representation space). Even more recently, generative adversarial autoencoder models have been conditioned on the input with one modality (CGAN), while allowing them to generate realistic artificial data of the other modality~\cite{reed2016generative} thus allowing for crossmodal translation from one initial space (e.g., text) to the other initial space (e.g., image), as opposed to crossmodal translation between representation spaces. We explore the possibility of using CGANs in video hyperlinking and creating models that not only provide good multimodal embeddings but also provide human-interpretable visualizations of what the model has learned.

Multimodal AEs are trained in an unsupervised manner on a dataset of finite, often limited size. It is possible to enlarge the dataset by applying random transformations and noise to the existing samples but the amount of samples will sill remain limited. In this work, we explore the possibility of using CGANs to tackle this problem. A generator network can generate an unlimited number of synthetic samples to train with and can constantly be improved by competing with the discriminator network. The discriminator network can also benefit from the additional synthetic samples and provide even better multimodal embeddings.

\begin{table*}[!htb]
\centering
\caption{Visualization of generated synthetic images given automatic transcripts (in lowercase, with some stop words removed) as input. In the last column, a real image from the video segment corresponding to the input automatic transcript is shown.
CGANs provide good visualizations of the video hyperlinking model: in the last row, given speech transcripts related to war thematic, the model is expecting a news presenter, while the actual video segment contains footage from the war zone.}
\label{table:speech_to_images}
\begin{tabular}{|m{8cm}|Sc|Sc|}
\hline
\multicolumn{1}{|c|}{\textbf{Input - Automatic Speech Transcript}} & \textbf{Generated Images} & \textbf{Real Image} \\ \hline \hline

``\ldots
insects emerged to take advantage of the abundance . the warm weather sees the arrival of migrant birds stone chests have spent the winter in the south  \ldots''
& \cincludegraphics[scale=0.5]{"images/nature"}
& \cincludegraphics[scale=0.5]{"images/nature_real"}
\\ \hline

``\ldots
second navigation of the united kingdom . the north sea , it was at the north yorkshire moors between the 2 , starting point for the next leg of our journey along the coast \ldots''
& \cincludegraphics[scale=0.5]{"images/water"}
& \cincludegraphics[scale=0.5]{"images/water_real"}
\\ \hline

``\ldots  the role of my squadron afghanistan is to provide the the reconnaissance capability to use its or so forgave so using light armor of maneuvering around the area of
\ldots''
& \cincludegraphics[scale=0.5]{"images/news"}
& \cincludegraphics[scale=0.5]{"images/news_real"}
\\ \hline

\end{tabular}
\end{table*}

\section{Related Work}
Multimodal AEs are commonly deployed in tasks where multiple modalities are used.
Such tasks include but are not limited to classification~\cite{cha2015multimodal, ngiam2011multimodal} and retrieva
~\cite{feng2014cross, lu2015semantic}. More specifically, in video hyperlinking, the current state of the art is a variation on multimodal AEs, consisting of two interlocked crossmodal DNNs, translating in opposing directions~\cite{vukotic-icmr-16, vukotic-ivlmm-16} that achieved the best performance at the latest TRECVID evaluation~\cite{irisa2016trecvid}.

Generative adversarial networks have been first introduced by~\cite{goodfellow2014generative} as a two-network generative-discriminative model
for generating high-quality, single-modal, realistic samples that could be mistaken for real samples from the dataset the model is trained on. GANs quickly became popular and are now used in a multitude of tasks, such as generating super-resolution images,
inpainting,
de-occlusion
and many others. Conditional GANs~\cite{mirza2014conditional} have been shown to generate realistic samples of one modality, given a conditioning input of another modality. A typical example of a crossmodal CGAN model is text to image synthesis~\cite{reed2016generative, zhang2016stackgan} but the conditioned input is not necessarily bound to multiple modalities and can be conditioned also on the same modality

For the purpose of multimodal retrieval, we focus on crossmodal / multimodal CGANs and ways to obtain meaningful multimodal representations from them. While multimodal setups are currently less explored, there is a lot of evidence that GANs learn meaningful representations in single-modal setup
~\cite{makhzani2015adversarial, chen2016infogan, perarnau2016invertible},
most notably in the generator network. These representations are obtained in a completely unsupervised manner and can be used to model changes in style, pose,
color
or even style and structure in RGBD data.
Evidence suggests that both the generator network and the discriminator network can produce meaningful single-modal representations~\cite{radford2015unsupervised}. Our work falls into the category of text to image CGANs~\cite{reed2016generative, zhang2016stackgan}, where we explore the possibility of obtaining good multimodal representations from the discriminator, while using the generator to visualize crossmodal mappings for the purposes of multimodal retrieval in video hyperlinking and to improve the embeddings from the discriminator network.

\begin{figure}[tb]
    \centering
	\includegraphics[scale=0.3]{images/architecture}
	\caption{Used CGAN architecture, consisting of a generator and a discriminator network. Multimodal embeddings are obtained in the discriminator, after the last, 1D convolution operation, as denoted with a dashed rectangle.}
    \label{fig:arch}
    \vspace{-7mm}
\end{figure}

\section{Methodology}
We split the work in two separate parts. In the first part, we evaluate the current state of the art multimodal AEs. In the second part, we evaluate the feasibility of using CGANs to perform multimodal embedding and we compare the resulting representations against the current state of the art in the setup of multimodal retrieval within the video hyperlinking domain.

\subsection{Multimodal Autoencoders}
Multimodal AEs with two separate input and output bra\-nches~\cite{ngiam2011multimodal}, one for each modality have been shown to perform better than simple AEs where the two modalities are concatenated. Recently, BiDNN, a variation with two disjoint but tied networks, working in opposing directions has been shown to define the new state of the art in video hyperlinking~\cite{irisa2016trecvid}. We compare our proposed model against both classical multimodal AEs and the recent BiDNN variation. However, multimodal AEs need existing continuous representations as inputs. We opt for averaged Word2Vec for representing speech and VGG-19 features for representing video keyframes.

\subsection{Unsupervised Multimodal Representation Learning with CGANs}
\label{gan_theory}
The CGAN architecture we evaluate is based on the GAN-CLS text to image model~\cite{reed2016generative}. The model consists of a generator network $G$ and a discriminator network $D$. The generator network takes a noise vector $z$, sampled from a $\mathcal{U}(-1, 1)$ distribution and a text embedding $\varphi$, and generates a synthetic image $\hat{x} = G(z, \varphi)$. The generator consists of a separate fully-connected layer with a leaky-ReLU activation function (that allows for a small non-zero gradient on the negative side) for modeling the text embeddings. This is then concatenated with the noise input. A standard deconvolutional network follows. The discriminator network takes an image $x$ and a text embedding $\varphi$ and determines whether the pair is real or artificial $D(x, \varphi)$. Contrary to the basic CGAN model that only differentiates between real and synthetic images, CGAN-CLS is trained on three pairs. In our case \{real image, real text\}, \{incorrect image, real text\} and \{synthetic image, real text\}. As we are doing unsupervised learning, pairs with a non-matching image and text are chosen at random from different video segments and the dataset is not split. Pairs of real matching and non-matching text-image pairs are necessary to train the discriminator. The discriminator consists of a series of convolutional layers with batch normalization, followed by a leaky-ReLU for modelling the input image and a full-connected leaky-ReLU layer to model the text embeddings. The two branches are then concatenated and a $1 \times 1$ convolution is performed, followed by batch normalization and a leaky-ReLU activation before obtaining the discriminator score. For multimodal embedding, we use the vector obtained after the final $1\times 1$ convolution of the discriminator. To maintain the losses of both the generator and the discriminator at similar levels (having a discriminator that is performing too well would prevent the generator from converging), the generator is updated four times more often than the discriminator. A cosine distance between the obtained multimodal embeddings is used to measure the similarity of the desired video segments both in the case of multimodal AEs and CGANs.

\section{Experiments}
In this section we first define the dataset and the specific hyperparameters of each architecture and then analyze and discuss the results of each experiment.

\subsection{Dataset and Single-Modal Representations}
\label{dataset}
We test all the methods in a video hyperlinking setup on a BBC video hyperlinkig dataset with segments and the groundtruth obtained post-evaluation during MediaEval 2014. The task considered is that of ranking target segments given a source segment acting as a query. We use only the video segments that contain both speech transcripts and video keyframes (10,321 in total). Speech is represented with averaged embeddings of size 100 from a skip-gram \textit{Word2Vec} model with hierarchical sampling and a window size of 5~\cite{irisa2016trecvid}. Regarding the visual stream, a representative keyframe is chosen for each video segment by computing a histogram for each pixel over all the keyframes and choosing the keyframe closest to the media
as a representative of each short video segment. Such an image is then downsampled and cropped to a size of $64 \times 64$ pixels. To obtain a sensible comparison, the same data and image sizes are used for all the methods. The CGAN model works directly with the text embeddings and images. Multimodal AEs also need visual embeddings as inputs. We obtained them from the same images, with a VGG-19 network (pretrained on ImageNet) and they are of size 4096. Table~\ref{table:embeddings} show the initial single-modal results, which are 56.55\% for speech transcripts and 52.41\% for video keyframes, in terms of precision at 10~\cite{awad2016trecvid}.

\begin{table}[!htb]
\centering
\caption{Comparison of initial modal single-modal representations and multimodal embeddings obtained with different methods. For each method, precision at 10 and its respective standard deviation are reported.}
\label{table:embeddings}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Representation} & \multicolumn{1}{l|}{\textbf{$P_{10}$ (\%)}} & \multicolumn{1}{l|}{$\sigma$ (\%)} \\ \hline \hline
Speech Transcripts Only &              56.55                 & -                                    \\ \hline
Visual Only (VGG-19)    &              52.41                 & -                                    \\ \hline \hline
Multimodal AE           &              57.94                 & 0.82                               \\ \hline
BiDNN                   &              59.66                 & 0.84                                \\ \hline
\textbf{CGAN}           &              \textbf{62.84 }       & \textbf{1.36 }                      \\ \hline
\end{tabular}
\vspace{-5mm}
\end{table}

\subsection{Multimodal Autoencoders}
We evaluate both classical multimodal AEs and BiDNNs, their state-of-the-art variation in video hyperlinking. We chose an AE architecture with separate input and output branches of size 1000 for each modality  (first and last layers of consisting of two separate hidden layers of size 1000 each) and a common hidden layer or size 1000. For BiDNNs, we use the implementation provided by~\cite{irisa2016trecvid}. We use only the video segments that contain multimodal information (both speech transcripts and visual information) and we use averaged \textit{Word2Vec} embeddings for speech and VGG-19 embeddings for the keyframes, as defined in section~\ref{dataset}. BiDNNs expectedly outperform classical multimodal autoencoders and obtain a precision at 10 of 59.66\%.

\subsection{Multimodal Embedding with CGANs}
To evaluate the feasibility of obtaining multimodal embeddings with CGANs, we used the architecture described in Sec.~\ref{gan_theory} with a speech embedding of dimension 100, a noise input of size 10, a fully connected layer for modeling text of size 256 and 4 \textit{``deconvolutional''} layers of increasing size, each with 256, 128, 64 and 32 feature maps respectively in the generator network. The output of the generator is an image of size $64 \times 64$. The discriminator network consists of an image input of size $64 \times 64$ followed by 4 convolutional layers with batch normalization, each with 32, 64, 128 and 256 feature maps respectively and decreasing sizes and a speech embedding input of size 100, followed by a fully connected layer of size 256. The two branches are then concatenated and followed by another, final convolution.  We trained the network for 1000 epochs by using the Adam optimizer with a learning rate $r$ of $0.0001$, a momentum $\beta$ of $0.5$ and a batch size of $64$. As specified in Section~\ref{gan_theory}, we use the one-dimensional convolutional layer in the discriminator that follows the merging of the two branches, before batch normalization and activation as a multimodal embedding layer, as illustrated in Figure~\ref{fig:arch}. Other layers did not perform well and provided a lower or equal quality than the initial single-modal inputs.
The generator network does not only provide a mean to visualize crossmodal mappings but is also crucial for obtaining high-quality embeddings in the discriminator by competing with it and providing additional synthetic samples.

Compared to multimodal AEs, CGANs are computationally expensive to train (20h on a GPU, compared to a few hours on a CPU for BiDNN), even for small images, and require both modalities to be present. However, the discriminator of a CGAN provides multimodal embeddings that are greatly improved over the initial representation spaces or each modality. The results are shown in Table~\ref{table:embeddings}. Representations learned with a CGAN not only outperformed multimodal AEs but they also significantly ($p=99.9\,\%$ with a single-sided t-test) outperformed the state of the art BiDNN model and obtained 62.84\%.

\subsection{Crossmodal Visualizations}
The existence of a generator network is crucial to train a discriminator that provides well-performing multimodal embeddings. Interestingly, the generator network can also be used to visualize crossmodal mappings in video hyperlinking. The generator can straightforwardly create synthetic images given an embedding of the speech transcripts as input. Examples of images generated for real transcripts on a few video segments, as well as their real visual counterparts are displayed in Table~\ref{table:speech_to_images}.  The generator can also be reversed by simply applying the transposed learned kernels in an inversed  architecture and slicing the obtained vector. In this case, given an image, embeddings in the textual domain can be obtained. A few examples of that translation are shown in Table~\ref{table:images_to_text}.

\section{Conclusions and Future Work}
In this work we experimentally demonstrated the interest of CGANs as an alternative method to design multimodal embeddings. While CGANs are more computationally intensive to train than multimodal autoencoders, and are thus limited to smaller image sizes, they can provide better multimodal embeddings in the last layer of their discriminator network that outperform embeddings of multimodal autoencoders.
The generator network, that provides unlimited synthetic samples and is crucial for training the discriminator, can additionally be used to visualize one modality from another and provide human-interpretable insights of the crossmodal translations learned with this type of video hyperlinking models. To use CGANs for multimodal embedding more efficiently, it is necessary to investigate different architectures that are able to converge with bigger image sizes within a feasible training timeframe and evaluate possible methods of decreasing the training time for uses where online learning is required.

\section{Acknowledgments}
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.

\begin{table}[!htb]
\centering
\caption{Visualization of the top words in the representation space of automatic transcripts, given an input image.}
\label{table:images_to_text}
\vspace{-3mm}
\begin{tabular}{|c|m{6cm}|}
\hline
\textbf{Input Image} & \textbf{Top Words in the Speech Modality} \\ \hline \hline
\cincludegraphics[scale=0.5]{"images/image_to_text_1"} &
britain, protecting, shipyard, carriers, jobs, vessels, current, royal, aircraft, securing, critics, flagships, foreclosures, economic, national
\\ \hline

\cincludegraphics[scale=0.5]{"images/image_to_text_2"} &
north, central, rain, northern, eastern, across, scotland, southwest, west, north-east, northeast, south, affecting, england, midlands
\\ \hline

\cincludegraphics[scale=0.5]{"images/image_to_text_3"} &
pepper, garlic, sauce, cumin, chopped, ginger, tomatoes, peppers,  onion,  crispy, parsley, grated, coconuts, salt, crust
\\ \hline

\end{tabular}
\vspace{-5mm}
\end{table}

redpajama_set_name: RedPajamaArXiv


text:
Nevada Farmers, Ranchers
Gird for Political Action
copyright © 1997, Electric Nevada
Nevada farmers and ranchers have concluded that insuring an effective voice in state government today is going to require them to go the route of other industries and form a PAC.
"We haven't officially taken the action of forming it" yet, said Nevada Farm Bureau Executive Vice President Doug Busselman, referring to the Farm Bureau's prospective new political action and education arm. That, he said, would follow close of the 1997 Nevada Legislature.
But Busselman noted that voting delegates to the Farm Bureau's annual meeting last December in Elko had approved the idea and that now "we have our county leaders going through the process of putting together their ideas on who they would like to represent their county farm bureaus" on the new organization.
"As soon as the [legislative] session is over," he said, "the plan is we would then call an organizational meeting for the development of the details of how we go about doing this."
Busselman said member interest in forming the PAC -- as yet still unnamed -- did not stem from reaction to any particular recent issues.
Rather, he said, it was more a continuation of the priority the Nevada Farm Bureau has already placed on the state legislature.
"As much as anything," he said, creation of the political action and
education committee, would reflect "a desire on the part of our leaders and members to become a full player in the public process."
That would entail, he said, "doing what we can to elect the type of people who would represent us in the legislature.
"There are legislators who have been very supportive of legislative initiatives or things we needed to get done in the legislature. And working to re-elect your friends, or to attempt to elect individuals who support the concepts that you support -- that's how the process works," he said.
The Nevada Farm Bureau Federation is composed of 13 county farm bureaus, made up of individual farmers and ranchers. The statewide federation, in turn, is a member of the American Farm Bureau Federation.
"There has been discussions of the creation of a PAC at the national level as well as here in Nevada for several years," said Busselman.
-- Steve Miller
§ § §
Want to share your opinion? Electric Nevada's comment page is open!
Back to Electric Nevada's Front Page


California League of Food Processors 23 Students who complete the Farms Leadership Program can participate in programs that provides them with a deeper understanding of the agricultural industry and paid internships and job shadowing. Kimball said this multi-year program involving California locations also helps encour- age students interested in careers in agriculture and food to pursue long-term positions locally, something many California agricultural employers want. "We can help you bring in people from your own commu- nity," Kimball said. The Center is always looking for new partners for the Farms Leadership Program, including food processors, and Kimball encourages CLFP members (some of whom have participated in Center programs) to consider involvement. The Center for Land-Based Learning also has a program for adults called the California Farm Academy, the largest of its kind in California, which includes a Beginning Farmer Training Program, an Apprenticeship Program and a Farm Business Incubator Program. The Farm Academy Program has been in existence for eight years and has 130 graduates to date with approximately 20 individuals participating in it each year, women making up about 50 percent of the participants. The Center is currently celebrating its 25th anniversary and depends on those in the agricultural and food companies investing in the program. For more information about the Center for Land-Based Learning visit www.landbasedlearning.org. Mary Kimball can be reached at mary@landbasedlearning.org. Fostering the Next Generation of Farmers By LISA JAGER - CLFP Marketing & Communications Director CLFP member the Mariani Packing Company hosted a group of Center for Land-Based Learning program students. Students participating in Center for Land-Based Learning programs have visited several CLFP member companies, including the California Olive Ranch.


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Dynamic Safety & Security Las Vegas, NV - 89118
Dynamic Safety & Security is categorized under Self-Defense Sprays in Las Vegas, NV and active since 2012.
Dynamic Safety & Security was established in 2012, and today employs 1 to 4, earning Unknown per year. This is a Self-Defense Sprays business, which does work in the B2B market, and is classified as a Self-Defense Sprays, under code number by the NAICS.
If you are seeking more information, feel free to contact Bob Morin Full Name Report at the company's single location by writing to 5940 S. Rainbow Blvd
Suite 1000, Las Vegas, Nevada NV 89118 or by phoning (888) 978-7886 Full Phone Report. You can also visit this business on Facebook by going to their profile page at Facebook or on find them on Twitter.
Business Name: Dynamic Safety & Security
Contact Person: Bob Morin Full Name Report
Address: 5940 S. Rainbow Blvd
Suite 1000, Las Vegas, Nevada 89118
Website Address: dynamicsafetyandsecurity.com
Annual Revenue (USD): Unknown
Business Category: Self-Defense Sprays
Dynamic Safety & Security was started in 2012 to provide professional Self-Defense Sprays under the SIC code and NAICS code . Since its inception, the company has gone on to take a total of 1 to 4 personnel under its employment and has achieved earnings of Unknown per annum.
Feel free to contact Bob Morin Full Name Report for inquiries that concern Dynamic Safety & Security by calling the company number (888) 978-7886 Full Phone Report, as your correspondence is most welcome. Additionally, the physical location of the single location of Dynamic Safety & Security can be found at the coordinates 36.07967,-115.24266 as well as the street address 5940 S. Rainbow Blvd
Suite 1000 in Las Vegas, Nevada 89118.
For its online presence, you may visit Dynamic Safety & Security's website at dynamicsafetyandsecurity.com and engage with its social media outlets through on Twitter and on Facebook.
Self-Defense Sprays in United StatesSelf-Defense Sprays in Nevada Self-Defense Sprays in Las Vegas, NVSelf-Defense Sprays in 89118 Las Vegas, NV All businesses in Nevada StateAll businesses in Las Vegas, NVAll businesses in 89118 Las Vegas, NV


As announced previously, Google has refreshed the new Panda update this Friday. The #25 Google Panda update might bring a lot of changes to your organic traffic. Many bloggers and webmasters have already complied about the huge traffic loss yesterday, and are getting ready to recover from this update. Few authority blogs have already experienced a huge traffic drop yesterday, and are unable to understand this Panda refresh motive. Many SEO forums and blogs have already mentioned about this update, and were expecting to be a big one this time.
Google has started the Panda update yesterday (Friday) afternoon, and seems to be continuing the propagation till today morning. Some blogs have experienced good traffic change in their websites, and are unable to confirm whether its a Panda refresh or not. Whereas few blogs lost around 40% traffic already, and might lose little more in future. So better to get rid of low quality articles and duplicate content, from your websites, and concentrate on high quality content.
Matt Cutts from Google has mentioned that the recent Panda Update will be the last manual update from now. Which means Google Panda update will be more of a rolling update, rather than a manual update. Matt has also mentioned that these updates might be effective from the next update onwards, and the impact on the over search results might be less compared to the previous updates.
Hopefully Google will deploy the Panda update gradually as they rebuild the index. So the chances of seeing these small panda updates will slow down and the large scale changes might take the charge in future.
I seriously don't know whether we should be happy about this decision, or sad. Lets hope the future panda updates be more generic, smart and friendly to SEO experts, bloggers and webmasters. Looks the transparency about Panda updates will be lot less to SEO experts and bloggers in near future. Be careful about the quality content you post in your site, and make sure to follow the important factors of SEO.
Nice info bro. Well in my case I didn't see any traffic drop yesterday, anyway let's hope for the best.
Thank you for info about the panda update and hope you will update about the DA and PR updates also !Bro if you 1 week or 10 days before of update it will be very useful for Bloggers Hope You will Update 🙂 🙂 !
On 15th or 16th March I experienced a huge boost in traffic and searched over many SEO forums and blogs to find whether any SEO update is rolled by google or not but I couldn't find any.
This update proved to be highly benefit for me,One of my Tech blog got oover 200% traffic boost and other WordPress blog over 100% boost.I am really enjoying this update…Kudos Google.
Thanks for the update. It has been a few weeks since they came out with this update and for the most part my traffic has been the same. I have an ecommerce site and that is another animal next to wordpress lol. Thanks for all the tips. I am sure in the summer here there will be more updates. Keep up the great work!
You are lucky enough to get out that Cherie! 🙂 Keep it up!
How we know that Google's Panda is affecting our website ?


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Welcome to Salon 1883 located in Granville Square, downtown Halifax. On a mission to create a space that is inspiring and inclusive, we operate a relaxed salon with an intimate feel where everyone is welcome.
Our team of professionals work closely with each client to understand their hair goals and fine tune those ideas to create a sophisticated, considered, elevated and detailed work of art.
Keeping up with evolving and innovative trends is important but it's just as important for us to learn from our clients. Our priority is to fulfill their visions and provide services that make them look and feel their best.
We look forward to welcoming you into our chairs at Salon 1883.
(Owner/Hair Stylist)
With over 17 years of experience, Sarah is a staple in the Halifax stylist community. She has worked alongside some of the great stylists in the area, while honing her skills to become one of the greats herself.
Sarah attended Bumble and Bumble University in New York City and continued her education specializing in Oribe and Kevin Murphy.
Not only a stylist, Sarah is an entrepreneur who goes after what she believes in and as owner of Salon 1883, has created a welcoming and inspiring destination for all your hair desires.
To Sarah, client connection means the most and she goes above and beyond to connect with each client who is lucky enough to sit in her chair. She loves to work directly with you to determine your needs, to achieve a look that enhances your natural features and helps you feel good about yourself.
Sarah is an expert in all things colour: from sun-kissed to vibrant blonde, toning down to bold brunette. If you're looking to maintain your current colour and style, or make a big change, Sarah is looking forward to welcoming you to her chair.
(Hair Stylist)
Karen is a Master Hairstylist who has 12 years of invaluable experience working in three different cities across Canada. After beginning her career in Halifax she relocated to Calgary where she spent five years, and then onto Toronto where she spent three years working at one of the top ranked salons in the city.
Working under two industry leaders, she dedicated herself to specialize her skills as a cut and colourist. With experience working on all different hair types, Karen is known for her work in blonding and colour correction services as well as creating effortless movement in the hair. Whether it be a natural beachy blonde or a dramatic platinum, she is passionate about it all and believes there is a right shade for everyone!
Her previous affiliation with the L'Oréal Academy in Toronto gave her the opportunity to learn and train with some of the best educators in Canada. Continuing education and constantly evolving is one of her top priorities.
"Having the ability to make people feel confident and beautiful in their own skin is a powerful thing."
LA-based Chris brings the latest in hair artistry to Halifax. A master at colouring, cutting and styling, he creates effortlessly chic looks that bring out the natural beauty of his clients.
Two decades ago, Chris started his career in Halifax doing freelance and salon work. He trained in New York at Aveda and Bumble & Bumble, getting a taste for travel that would later define his career.
A natural teacher and communicator, Chris has taught throughout the US and Canada. His stunning images and words of advice have been featured on the pages of Flare, Chatelaine and ManeAddicts. His work appears in Tracey Cunningham's new book, True Color.
Chris is inspired by what makes people unique. His work brings each person's beauty to the foreground, complementing their style, personality and face shape. His warmth, care and gorgeous results have built long-term relationships with clients.
Hired in 2014 by the prestigious Meche salon in Beverly Hills, Chris has worked his magic on the hair of Hollywood's rich and famous. Visit his chair and experience for yourself, a relaxing high-end salon experience
Connect with Chris on Instagram or reach out by email to book an appointment.
Dr. Sutherland
(Medical Aesthetics)
Dr. Kailee Sutherland is a Family Doctor offering medical aesthetics with a focus on natural looking upper face rejuvenation using neuromodulator therapies.
(Esthetician)
As an esthetician with 21 years experience Jennifer has a passion for the industry. She loves what she does and loves building relationships with her clients.
With a well rounded Esthetics skill set and additional training in waxing, advanced foot care, make up and skin care. Her goal is to ensure that every visit meets each clients specific needs.
Jennifer would love to let you unwind, recharge and make you look and feel your very best.
Photos by NICOLE LAPIERRE
Copyright © 2023 Salon 1883 - All Rights Reserved.


As an added bonus, showcasing and celebrating student talent, the Reflections Program increases community awareness of the importance of the arts in education.
All students attending a school with PTA in good-standing are eligible to participate.
Visit the art gallery for examples of national award-winning works.
What recognition will the participants get?
o Student Reflections' entries at a school are first recognized, judged and celebrated by the local PTA unit.
o Some selected unit entries are then chosen to represent the PTA at the council and district PTA levels.
o Winning entries at the council/district levels are then advanced to the state level for judging.
These Award of Excellence entries, in turn, represent California State PTA at the National level of the Reflections Program.
Student recipients of the National PTA Award of Excellence and Award of Merit are recognized at the annual National PTA convention and their artwork is featured as part of the program's traveling, national exhibit.
o An expense-paid trip for student honoree and one adult to the National PTA Convention.
All remaining national-round participants receive a certificate of honorable mention.


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NewsLafayette Parish
Louisiana Strong initiative to benefit communities impacted by Ida
By: KATC News
The Louisiana Strong initiative held a donation drive Friday to benefit communities affected by Hurricane Ida - and it all started with a dream.
For the relief fund, people were able to drop off school supplies, toys, water, food, and household goods at the Jefferson Street Pub.
"I've gotten phone calls from families that are directly affected its heartbreaking to hear their stories it really is, it's an emotional scene," manager Dylan Sherman said.
UL student Ryan Rodriguez said after the storm, he realized how lucky he and those around him are. He went to sleep thinking about it one night, and said he had a dream where he raised $1,000 to help those whose lives were impacted by the storm.
"And I just went from there and started to post everywhere I could."
He originally was gathering donations from the community himself, but quickly realized it'd be more efficient to partner up with the Jefferson Street Pub. So that's what he did.
"I want to get to every little place. I don't want to just go to one city and just distribute there and that's fine. I wanted to hit every community I can and spread it as widely as we can," he said. "I have already heard the messages from them and how truly appreciative they are and we haven't even gotten the supplies out to them yet."
If you'd like to drop off donations, they're being accepted at Jefferson Street Pub until 2 a.m. Saturday, then starting again at 10 a.m. Saturday until 2 a.m. Sunday.
To reach the newsroom or report a typo/correction, click HERE.
Sign up for newsletters emailed to your inbox. Select from these options: Breaking News, Evening News Headlines, Latest COVID-19 Headlines, Morning News Headlines, Special Offers


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Houston gets back in the win column
Kyle Tucker's big night helps fuel Astros to win over Angels
Chris Campise
Photo by Rich Schultz / Getty Images
After a tense two-game series against the Dodgers in Houston earlier in the week, the Astros made their first trip on the road, starting with the opener of a three-game series against the Angels in Los Angeles on Friday night. Here is how that game went:
Final Score: Astros 9, Angels 6.
Record: 4-3, first in the AL West.
Winning pitcher: Brandon Bielak (2-0, 1.69 ERA).
Losing pitcher: Matt Andriese (0-1, 4.91 ERA).
Astros jump in front with big second inning
After a scoreless first inning, the Astros had their first chance at runs in the top of the second. They loaded the bases with one out, and Kyle Tucker would drive in two with a two-RBI double to put Houston out front 2-0. Still with one out, George Springer came to the plate with the bases loaded again and was able to work a walk to bring in another run. Jose Altuve followed, and he beat out a double-play to drive in another, extending the lead to 4-0.
Pretty sweet 😋#ForTheH pic.twitter.com/xsyKkYfwJF
— Houston Astros (@astros) August 1, 2020
Both teams go back and forth, ending McCullers Jr's night at after four innings
After getting through the first two innings scoreless, Lance McCullers Jr. ran into trouble in the bottom of the third, giving up a walk and single to set up an RBI-single to get the Angels on the board. Los Angeles would load the bases with two outs, but McCullers Jr. would get out of the jam with a strikeout to end the inning.
Kyle Tucker helped get the lead back to four runs at 5-1 in the top of the fourth, getting a leadoff single, stealing second, moving to third on a wild pitch, then scoring on an RBI sac fly by George Springer. However, the Angels answered right back with a big inning in the bottom half, getting an RBI-double and two-run homer off of McCullers Jr. to cut the lead to 5-4. He would finish the inning, but at 91 pitches, that would be the end of his night, making his final line: 4.0 IP, 6 H, 4 R, 4 ER, 4 BB, 6 K, 1 HR.
Both teams go back and forth, but Houston holds on
Alex Bregman led off the top of the fifth with a double, moved to third on a Michael Brantley single, then scored on a sac fly by Yuli Gurriel to make it a 6-4 lead. Brandon Bielak, who made a great debut against Seattle on Monday, was first out of the bullpen in the bottom of the fifth and worked around two walks for a scoreless inning.
He returned for another scoreless frame in the sixth, then the Astros added to their lead in the top of the seventh, loading the bases to set up a two-RBI single by Kyle Tucker, bringing his total to four on the night and expanding the advantage to 8-4. Bryan Abreu took over on the mound in the bottom of the seventh but would face only three batters, getting a strikeout while walking two. Enoli Paredes would finish the inning, but not before allowing a run to make it 8-5.
Paredes came back out for the bottom of the eighth, and despite allowing another run to come across to make it 8-6 would get a much-needed double play to send the game to the ninth. In the top of the ninth, the Astros were able to load the bases for Jose Altuve, who added an RBI-groundout insurance run to make it 9-6. Andre Scrubb would come in for the save opportunity and preserved the three-run lead for the Houston win.
Up Next: Game two of this three-game set between Houston and Los Angeles will be Saturday at 6:10 PM Central. The pitching matchup will be Griffin Canning for the Angels going against Zack Greinke for the Astros. Greinke will try to improve upon his first start of the year in which he was only able to go 3.1 innings while allowing three runs.
astros recap astros vs angels mlb lance mccullers lance mccullers jr george springer kyle tucker astros
Credit to Deshaun Watson for taking the high road once again
Will Doctor
Cougars climb up rankings after dominating UCF
Jack Mccraw
Turns out real Texans puppet master may not be Jack Easterby after all
SportsMap Staff
SportsMap Emails
Are Awesome
CANCEL THIS CULTURE
Detailing Watson's one-man fight against toxic culture and leadership void within Texans
Ken Hoffman
Jan 19, 2021, 5:03 pm
The power struggle continues. Photo by Getty Images.
Boy, with the recent blizzard of negative publicity – searing magazine cover stories with headlines blasting "Houston Has a Problem" and "The Chaplain Who Won a Power Struggle and Plunged a Franchise into Chaos" – I'll bet the Houston Texans wished they had a seasoned, respected and award-winning media director to handle damage control.
Oh yeah, that's exactly what they had in Amy Palcic, but she was fired last year. Reason: she "wasn't the right culture fit."
What exactly is the Houston Texans culture these days? Apparently the culture is players disliking and distrusting the team executive specifically charged with managing the team's culture. It's that same executive whose resume has more fudging than the Keebler Cookie Company. It's that executive who's accused of authorizing illegal practices and hiring private eyes to follow players in their private activities. It's that executive who's accused of intimidating employees who trash him to the media and threatening to sue media outlets. It's that executive who imposes his religious fervor on lower-ranked employees. It's that executive who has created a culture where gifted quarterback Deshaun Watson is said to want a trade out of Houston.
That executive is Jack Easterby - the backstabbing, butt-smooching BS'er who seems to have a Svengali hold on Texans chairman Cal McNair.
If it comes down to one stays and one has to go between Watson and Easterby … hmmm, let's see. Deshaun Watson threw for 4,823 yards and 33 touchdowns last season. Jack Easterby, zero and zero.
Last week, Texans legend Andre Johnson, who usually speaks up less than the magician Teller, tweeted: "Since Jack Easterby walk into the building nothing good has happened. For some reason someone can't seem to see what's going on. Pathetic!!!"
That "someone" would be Texans chairman Cal McNair, who continues to support Easterby despite all the accusations and revelations hurled Easterby's way.
By the way, Easterby has not sued any media outlet that is publishing stories about his bullying and sneakiness. And he won't sue because that last thing he wants is to be put in a witness chair and swear to tell the truth.
In the past 12 months, with Easterby sticking his nose in McNair's ear, the Texans have managed to alienate and infuriate superstar Watson: trade All-Pro receiver DeAndre Hopkins and create a losing, uninspired clubhouse that has favorite son J.J. Watt wanting a trade to leave his beloved Houston.
It's not like Easterby is some mad genius who somehow produces spectacular results despite his unorthodox tactics. The Texans finished 2020 with a disastrous 4-12 record, with little to show for it, not even a top draft pick to honor their futility. The Texans are clearly in need of divine intervention, and not from huckster Easterby, whose degree is in sports management from Newberry College. Easterby is only dimples and wavy hair short of being a TV preacher.
You can't deny that Easterby is inspiration. He recently inspired a public protest on the sidewalk outside NRG Stadium and signs swaying over Southwest Freeway with the same message: #FireJackEasterby. Watson asked his supporters not to attend the rally due to COVID precaution.
Then there's the case of Deshaun Watson v. Cal McNair.
Watson was born into an economically disadvantaged family and has worked for, and deserves, every penny he is paid. He is a champion.
Cal McNair found the Houston Texans under his Christmas tree in 2018 after his father Texans original owner Bob McNair died.
Watson is an extremely bright and sensitive man who is deeply involved in social issues off the field. Last year, during the summer of racial upheaval in America, he led the charge to have the name of a former slave owner removed from a building on his alma mater Clemson's campus.
McNair hardly ever speaks in public and his stumbling, confused performance at a press conference to announce the hire of general manager Nick Caserio showed why. It's rare when a team owner has to apologize after making what should have been a happy statement promising fans a better future. However, if a stage production of the Beverly Hillbillies ever goes to Broadway, we've got our Jethro.
Many times when a player gets into a public spat with a team owner, it's a dumb jock player vs. the super-smart businessman who owns a billion-dollar company. It's usually over money. And the public typically thinks, "just get rid of the ungrateful, overpaid and greedy player."
Not this time. Watson already got his – four years at $156 million. This is a war of morality. Watson is the hero here, McNair the fool being played by Easterby, who like Cassius is Shakespeare's play Julius Caesar, "has a lean and hungry look."
If it came to a public vote between Watson and McNair (Easterby), Watson's landslide win would rival Kim Jong-Un in North Korea … or LeAnn Rimes on The Masked Singer.
It's unfair to call McNair and Easterby polarizing figures because polarizing implies that there are two sides to the issue.
There is only one side. Houston loves Deshaun Watson and wants McNair to sell the team, right after he fires Easterby.
Seemingly the only defender rushing to Easterby's side is a Twitter account allegedly owned by Easterby under a fake name. If it is a burner account, Easterby has a whole lot of faith in himself.
Although football insiders say that Watson is all but out the door at NRG Stadium, there is still a chance that McNair could save the day, and do what is needed to keep Watson in Texans' gear. And that would be to fire Easterby. Now.
Sadly, given McNair's repeated pledges of loyalty to Easterby and insistence that criticism of Easterby is unwarranted, Watson's leaving Houston gets more likely each day. Andre Johnson had it right … "pathetic!!!"
Three exclamation points.
A powerful ally has emerged in fight against Jack Easterby ... ›
Jack Easterby's influence has ruined Texans organization - SportsMap ›
Texans puppet master may not be Jack Easterby after all - SportsMap ›
Report: Texans Player Was Bothered by Jack Easterby's Use of ... ›
texans houston texans nfl deshaun watson jack easterby cal mcnair


During the week of March 7-13, the Tartan Marketplace will be open weekdays from 7:30 a.m. - 3:00 p.m. and Saturday from 7:30 a.m. - 4:00 p.m.
The Main Street Cafe, Sub Shop, Snack Shop 13 and Starbucks will be CLOSED.
Please note that the Sub Shop in Building 3 will close permanently on Friday, March 4.