What are the best b2b ecommerce solution platforms to use? Read and discover the top recommended solutions in USA!
Did you know that the b2b ecommerce industry is 4 times bigger than the b2c market? Yes, and according to one research, in 2 years from now, the b2b market will mature into a huge market that it worth not billions but trillions of dollars.
The choice if your b2b ecommerce solution is a make or a break point. Even though there are various b2b ecommerce platforms and they all cater to different customer segments, the business-to-business ecommerce market comes to be less or more similar to b2c ecommerce standards.
The website interface, the order requirements, customer experience, payment integrations, and etc. have all come to be of common nature. So, the choice of your b2b ecommerce solution must be one that can meet your needs and contribute to stable management when the business is expanding and growing.
The market today has endless choices to choose from. Not all ecommerce platform solutions for business-to-business provide the same level of processing capabilities and experience. A well-planned strategy and a thorough analysis are necessary to compare one platform against the other so that a best solution can be found and chosen.
Contalog – This is a very popular b2b ecommerce platform that simplifies the process of inventory management, order management, and product management with minimal effort. Some of the best features of this ecommerce portal include dual order creation process, customer specific pricing, and etc. This platform also notifies users when inventory level reaches reorder levels or reaches out-of-stock situations and help you maintain your stock levels at all times.
Goecart – Another great business-to-business ecommerce solution with extra features for sales tax support, CRM, mobile API, and etc. The features and capabilities Geocart offers simplify order management, inventory management, search options, payment support, and much more.
InsiteCommerce – A catalog management solution that can help you optimize and manage complicated pricing matrix for all order processing schemes. It is designed as a dynamic tool meaning, it can meet the growing needs of both – distributors and manufacturers. It comes with in-built promotion and merchandising methods for creating your own b2b ecommerce business.
Netsuite – If you are looking for one of the best b2b ecommerce solutions on the market that provides an impeccable b2b features such as real-time inventory tracking, real-time shipment tracking, SEO focused web pages, and etc. look no further as Netsuite is the perfect tool for you. It supports a few different languages, more than 30 foreign currencies, and etc.
Intershop – This is just another great ecommerce solution for all b2b businesses out there. It works as a single platform solution that can present brands, companies, and items on a global scale. It comes fully equipped with PIM or Product Information Management, OMS or Order Management System, and WCM or Web Content Management.
Which of these b2b ecommerce solution platforms is the best one for you? Well, that is up to you to decide. Share your experience in the comments below.


text:
Jayalalithaa, Karunanidhi slam UN resolution on Sri Lanka
Coming out strongly against a UN resolution that endorsed a domestic probe in Sri Lanka over alleged war crimes, Chief Minister Jayalalithaa expressed deep disappointment and anguish while DMK chief Karunanidhi termed it shocking. MORE..
War crimes in Sri Lanka: Jaya urged to convene all-party meeting
Indian PMK leader Anbumani Ramadoss on Thursday urged Tamil Nadu chief minister J Jayalalithaa to convene an all-party meeting to exert pressure on the Centre to bring out a resolution in the UNHRC, seeking international probe into the alleged war crimes in Sri Lanka, the Times of India news agency reported. MORE...
September 24, 2015 02:49 pm|
TN assembly for international probe on Sri Lanka
A day after India and Sri Lanka decided to renew efforts to address the vexed ethnic issue in the island nation, Tamil Nadu today stepped up pressure on the Modi government for an international probe against alleged human rights violations and war crimes committed in 2009 in Northern Sri Lanka. MORE..
Pass resolution against Sri Lanka: Vaiko
India's Marumalarchi Dravida Munnetra Kazhagam general secretary Vaiko has appealed to Chief Minister Jayalalithaa to pass a resolution against Sri Lanka seeking international inquiry against alleged war crimes by Sri Lankan Army during war with LTTE. MORE...
September 6, 2015 06:50 am|
Push Sri Lanka not to arrest fishermen - Jayalalithaa
Bringing to the notice of Indian Prime Minister Narendra Modi yet another incident of arrest of Tamil Nadu fishermen by the Lankan Navy, Chief Minister J Jayalalithaa on Tuesday urged him to take immediate steps to release the 16 fishermen and their boats. MORE..
Retrieve Katchatheevu from Sri Lanka: Jayalalithaa tells Modi
Tamil Nadu Chief Minister J Jayalalithaa on Friday urged Indian Prime Minister Narendra Modi to protect the fishing rights of Indian fishermen and to retrieve the Katchatheevu islet from Sri Lanka. MORE..
August 7, 2015 05:13 pm|
Jayalalithaa takes oath Tamil Nadu CM
AIADMK supremo Jayalalithaa on Saturday was sworn in for the fifth time as Tamil Nadu chief minister, nearly eight months after she was forced to quit over corruption charges. MORE..
May 23, 2015 11:25 am|
Easter attacks: Father of Kochchikade suicide bomber further remanded

redpajama_set_name: RedPajamaCommonCrawl


Profits Before People: Not a Winning Strategy For Wells Fargo - Princeton Partners, Inc.
In my career as a marketer, I have had the opportunity to work with over a dozen banks, ranging from local community banks to regional banks, and even a well-known national player. Along the way, I became an admirer of Wells Fargo. I thought their mission, focus on understanding customers, and prioritization of customer's needs was right on the money. I also admired their marketing sophistication as they adopted technology early to get a whole customer account viewpoint. Years before other competitors, they seized on this information technology advantage and established "winning customer share of wallet" as a priority KPI.
"Winning share of wallet," or getting multiple account relationships per customer, is not an unethical goal. As the theory goes, if you satisfy customers' needs, they will reward you with additional business. In banking, a checking account is an entry point. Banks much prefer to build upon that demand deposit account (a liability on their balance sheet) with new, margin-generating assets such as a personal, home equity or business loan. So what went wrong at Wells Fargo?
It is clear that winning customer's share of wallet morphed into an out-of-control, cross-selling culture. This culture was fueled by incentives to employees and pressure to get results, even at the expense of customers. It was also stimulated by the use of the legacy business model that the largest public banks decided to continue with in order to keep profits growing and shareholders happy.
Dinging their less-sophisticated customers with fees became a profit center. But this approach only covered up deeper problems that large banks like Wells Fargo must deal with such as: a) supporting a large bureaucracy and brick and mortar infrastructure; b) adapting to lower net interest margins in a post-Great Recession rate environment; and c) investing in technology to compete with disrupters and to comply with regulatory and bank security challenges in a hacker's world.
Tough problems, indeed. But the stated values of the bank were not operationalized throughout and a "profits before people" disease has now severely compromised the brand value of the company.
Now, reports by groups such as the Consumer Financial Protection Bureau are revealing that fraudulent practices, like those used by Wells Fargo, are also being used by other banks to protect profits. It doesn't matter that Wells Fargo executives have pointed out that only a very small fraction of customer accounts had been affected; the damage to the brand is done and maybe irreversible.
Wells Fargo has fallen from high heights. In 2015, it was one of the world's largest companies in market value. It was cited by BrandFinance as the world's most valuable bank brand. Apparently, Wells Fargo got too big to value its customers.


text:
Home » Xbox » E3 2019: New Xbox Scarlett Releasing Holiday 2020
E3 2019: New Xbox Scarlett Releasing Holiday 2020
Microsoft had lots of things to talk about during E3 2019 this year, but the biggest news came in the form of an announcement at the end of its press conference. After confirming work on a next-gen console had begun at last E3, the company finally revealed the first details about it. It's codenamed Scarlett, and in fact, Scarlett might refer to multiple consoles rather than a single one. But whatever the case, Microsoft confirmed that a next-gen system will launch in the Holiday 2020 period.
Microsoft also announced Halo: Infinite will be a Scarlett launch title, which marks the first time a Halo game has been a launch Xbox game since the original Halo: Combat Evolved for the first Xbox.
Microsoft kept many of its Scarlett cards close to its chest, electing instead to mostly talk about its internal hardware. The console, which is yet to receive a final name, will be "four times" more powerful than the Xbox One X, Microsoft says. The company went on to say Scarlett contains a custom-engineered AMD processor and will be capable of both 8K resolution, 120 frames per second framerates, and variable refresh rates. It also supports ray-tracing and the system is equipped with a solid-state drive (SSD), something Sony has touted about PS5. Microsoft noted that the SSDS will be used as virtual RAM. The console has a custom AMD Zen 2 CPU, Radeon RDNA architecture, and GDDR6 RAM.
One detail absent from the Xbox press conference that we've since learned is that Scarlett has a disc drive, allowing you to play physical discs. It'll also support all your existing Xbox one games and accessories. Xbox boss Phil Spencer has also shared some thoughts in a post-conference interview where he talks about future console generations and not necessarily needing you to buy new hardware.
Scarlett was touted during the conference as a "bigger leap than any generation we've done before." Spencer also described it as the "most powerful and highest performance console we've ever designed." Microsoft also very deliberately made it clear the system is designed explicitly for gaming, a notable difference from how the Xbox One was originally unveiled.
For more on the new device, read our exhaustive look at Scarlett's hardware specs or our comprehensive article from before E3 on everything we know about Xbox Scarlett–including the rumored budget alternative. Our previous understanding of the next-gen Xbox scene was that Microsoft has more than one system coming–similar to how we have the Xbox One S and more powerful Xbox One X. Scarlett was reported to be the more powerful of the next-gen systems, and it's the only one that was discussed during the conference.
For more, stay tuned to GameSpot's E3 roundup hub.


text:
A Brief Q&A with Hadley Ruggles:
BASIS Independent Brooklyn's Head of School
Education / Private Schools /
Hadley Ruggles, Head of School at BASIS Independent Brooklyn, knows firsthand that by offering young minds phenomenal opportunities to learn, a school will see phenomenal student results. Prior to her Brooklyn appointment, Ruggles served as founding Head of School at BASIS Independent Silicon Valley and as Head of School at BASIS Scottsdale, the #2 ranked high school in the nation.
The BASIS.ed managed network of schools started in Arizona in 1998 with one charter school, 50 students, 6 teachers, and a vision for an American educational network of global quality. Since that time, the BASIS.ed network has grown to include 23 schools, both independent and charter, over 14,000 students (with more than 5,000 students on the wait list) and over 1,000 meticulously selected teachers.
In 2013, the BASIS.ed system participated for the first time in the OECD Test for Schools based on PISA (Programme for International Student Assessment), and the results were extraordinary: BASIS students ranked higher in math and reading than students in Shanghai, China, the highest ranked school system in the world.
Ms. Ruggles was kind enough to sit down with EPR Properties for an informal Q&A about how the BASIS.ed curriculum and school culture work together to bring out the exceptional in each student.
You can listen to the conversation below:
More About BASIS Independent Brooklyn Head of School Hadley Ruggles
In 2011 Ms. Ruggles was a recipient of the National Endowment for the Humanities grant, and she holds both a Master of Science degree in Education and Social Policy as well as a Master of Arts degree in American Literature from Northwestern University. As a Midwestern native, Ruggles did her undergraduate work at the University of Kansas, where she received a Bachelor of Arts degree in Religious Studies and Slavic Languages and Literature.
Her rich academic background is a striking example of the intellectual accomplishment those familiar with BASIS.ed have come to associate with the school's uniquely rigorous curriculum and its standards of excellence in the field of instruction and academic leadership.
Hadley Ruggles
https://insightcenter.eprkc.com/listen-a-brief-qa-with-basis-independent-brooklyns-head-of-school/


text:
Q: How can I get a list of Anonymous pipes opened As a follow up to the older question : How can I get a list of all open named pipes in Windows? , I am in a situation where I need to check if a process uses anonymous pipes to communicate with its children. Is there a way to do this ? I've tried pipelist and process-explorer but it looks like they display only lists of NamedPipes

A: @Deanna : As an answer to close this topic
Indeed procExplorer lists all anonymous objects. But it is not yet possible to view anonymous pipes specifically. Which leads me to assume that, one cannot determine with absolute certainty the un-named pipes used by a process

redpajama_set_name: RedPajamaStackExchange


26/02/2012�� I'm just wanting to know if there is any way of getting rid of algea with out effecting your system. My pond is in full sun which i know wont help, but it is an ornamental pond so i �... 5/04/2015�� Aquaponics remove algae Aquaponics - appropedia: the sustainability wiki, Root crops. despite growing in a rocky medium, such as clay pebbles or gravel, root crops are said to do reasonably well in an aquaponics system.
Instead of using a white or translucent water tank outdoor, buy a dark colored tank that inhibits algae growth by blocking sunlight from coming in. You won�t have to waste your weekend going through this lengthy and laborious process. Go To Tanks is the #1 storage tank supplier in the nation for water storage needs and we have a wide selection of dark colored tanks for you to choose from... Now if you are talking about a display aquarium or an ornamental fish pond, you may need to resort to chemical or UV means of killing algae along with cleaning it out of the system, however, in an aquaponics system we have to avoid chemical methods and we really don�t require the high tech of UV sterilization just to get rid of algae.
Now if you are talking about a display aquarium or an ornamental fish pond, you may need to resort to chemical or UV means of killing algae along with cleaning it out of the system, however, in an aquaponics system we have to avoid chemical methods and we really don�t require the high tech of UV sterilization just to get rid of algae.
Inland Aquatics shows how to make an acrylic tank, how Algae Turf scrubbers work, the infancy of a Tilapia farm - Aquaponics and its LOCK UP at Inland. Channel Catfish added to the Aquaponics Finally got the channel catfish added, who knows I might actually start growing something.


text:
\section{Credits}

\section{Introduction}
\label{Intro}
Modern machine learning systems require large amounts of labelled data.
For many applications, such labelled data is created by getting humans  to explicitly label each training example. A problem of perpetual interest in machine learning is reducing the tedium of such human supervision via techniques like active learning, crowd-labeling, distant supervision, and semi-supervised learning.  A limitation of all these methods is that supervision is restricted to the level of individual examples.

A recently proposed~\cite{snorkel_nips2016}  paradigm is that of Data Programming.  In this paradigm, humans provide several labeling functions  written in any high-level programming language.  Each labeling function (LF) takes as input, an example and either attaches a label to it or backs off.
We illustrate such LFs on one of the five tasks that we experimented with, {\it viz.}, that of labeling mention of  a pair of people names  in a sentence as defining the {\it spouse} relation or not.
The users construct heuristic patterns as LFs for identifying spouse relation in a sentence containing an entity pair $\left( E_1, E_2\right)$.
A LF can assign +1 to indicate that the {\it spouse} relation is true for the candidate pair ($E_1$,$E_2$), -1 to mean that no {\it spouse} relation, and 0 to mean that the LF in unable to assert anything for this example.  Specifically for the {\it spouse} relation extraction task,  Table~\ref{tab:sample} lists six LFs.

\begin{table*}[htb]
\small
\centering
\begin{tabular}{|l|l|}
\hline
\multicolumn{2}{|l|
{{\bf SpouseDict} = \{'spouse', 'married', wife', 'husband', 'ex-wife', 'ex-husband'\}} \\
\multicolumn{2}{|l|
{{\bf FamilyDict}  = \{'father', 'mother', 'sister', 'brother', 'son', 'daughter','grandfather', 'grandmother', 'uncle', 'aunt','cousin' \}$\bigotimes$\{+'',+'-in-law'\} } \\
\multicolumn{2}{|l|}{{\bf OtherDict}  =  \{'boyfriend', 'girlfriend' 'boss', 'employee', 'secretary', 'co-worker'\} }\\
\multicolumn{2}{|l|}{{\bf SeedSet}  =  \{('Barack Obama', 'Michelle Obama
 '), ('Jon Bon Jovi','Dorothea Hurley'),('Ron Howard','Cheryl Howard'),.....\} }\\
\hline
{\bf Id} &  \textbf{Description} \\
\hline
{\bf LF1} &  If some word in {\bf SpouseDict} is present between $E_1$ and $E_2$ or within 2 words of either, return 1 else return 0 \\
\hline
{\bf LF2} & If some word in {\bf FamilyDict} is present between $E_1$ and $E_2$, return -1 else return 0.\\ \hline
{\bf LF3} & If some word in {\bf OtherDict} is present between $E_1$ and $E_2$, return -1 else return 0.\\ \hline
{\bf LF4} & If both $E_1$ and $E_2$ occur in  {\bf SeedSet}, return 1 else return 0.\\ \hline
{\bf LF5}   & If the number of word tokens lying between $E_1$ and $E_2$ are less than 4, return 1 else return 0. \\ \hline
\end{tabular}
\caption{Discrete LFs based on dictionary lookups or thresholded distance for the {\it spouse} relationship extraction task}\label{tab:sample}
\end{table*}

In isolation, each LF may neither be always correct nor complete. LFs may also produce conflicting labels.
For the purpose of illustration, consider a text snippet `Michelle Obama is the mother of Malia and Sasha and the wife of Barack Obama'. For the candidate pair (`Michelle Obama', 'Barack Obama'),  LF1 and LF4 in Table~\ref{tab:sample}  assign a label 1 whereas LF2 assigns the label -1.

\citet{snorkel_nips2016} presented a generative model for consensus on the noisy and conflicting labels assigned by the discrete LFs to determine probability of the correct labels. Labels thus obtained could be used for training any supervised model/classifier and evaluated on a  test set.
In this paper, we present two significant extensions of the above data programming paradigm.

First, the user provided set of LFs might not be complete in their discrete forms. LF1 through LF3 in Table~\ref{tab:sample} that look for words in various hand-crafted dictionaries, may have incomplete dictionaries.
\begin{table*}[htb]
\small
\centering
\begin{tabular}{|l|l|l|}
\hline
\hline
{\bf Id} & {\bf Class} &  \textbf{Description} \\
\hline
{\bf LF1} & +1 & $\max $ [cosine(word-vector($u$), word-vector($v$))-0.8$]_+$: $u \in $ {\bf SpouseDict} and $v \in$ \{words between $E_1, E_2$\}. \\
\hline
{\bf LF2} & -1 & $\max $ [cosine(word-vector($u$), word-vector($v$))-0.8$]_+$: $u \in $ {\bf FamilyDict} and $v \in$ \{words between $E_1, E_2$\}.  \\ \hline
{\bf LF3} & -1 & $\max $ [cosine(word-vector($u$), word-vector($v$))-0.8$]_+$: $u \in $ {\bf OtherDict} and $v \in$ \{words between $E_1, E_2$\}. \\ \hline
{\bf LF4} & -1 & $\max $ [0.2 -  Norm-Edit-Dist($E_1,E_2$, $u,v$)$]_+$: ~$(u,v), (v,u) \in $ {\bf SeedSet}. \\ \hline
{\bf LF5} & +1 & [1 - (number of word tokens between $E_1$ and $E_2$)/5.0$]_+$ \\ \hline
\end{tabular}
\caption{Continuous LFs corresponding to some of the discrete LFs in Table~\ref{tab:sample} for the {\it spouse} relationship extraction task}\label{tab:continuoussample}
\end{table*}
A more comprehensive alternative could be to design continuous valued LFs that return scores derived from soft match between words in the sentence and the dictionary.  As an example, for LF1 through LF3, the soft match could be obtained based on cosine similarity of pre-trained word embedding vectors~\cite{word2vec} of a word in the dictionary with a word in the sentence.
This could enable an LF to provide a continuous class-specific score
to the model, instead of a hard class label  (when triggered).  In Table~\ref{tab:continuoussample}, we list a continuous LF corresponding to each LF from Table~\ref{tab:sample}.  Such
continuous LFs can expand the scope of matching to semantically similar words beyond the pre-specified words in the dictionary.  For example: in the sentence {\tt 1) $<$Allison$>$, 27, and $<$Ricky$>$, 34, wed on Saturday surrounded by friends.}, the word 'wed' is semantically similar to 'married' and would be detected by our continuous LF but missed by the discrete ones in Table~\ref{tab:sample}.

More generally across applications, human experts are very often able to identify real-valued scores that correlate strongly with the label  but find it difficult to discretize that score into a hard label.  More examples of such scores include TF-IDF match with prototypical documents in a text-classification task, distance among entity pairs for a relation extraction task,  and confidence scores from hand-crafted classifiers.

\iffalse
We will next highlight a challenge that we attempt to address while modeling continuous LFs. We illustrate the challenges through two example sentences. Beside each sentence id, we state the value of the true label ($\pm 1$). While the candidate pair in $S_1$ is an instance of the  spouse relationship, the pair in $S_2$ is not: \\
$\langle S_1,+1 \rangle$: It's no secret that Brandi Glanville's relationship with ex-husband $<$Eddie Cibrian$>$ and his wife, $<$LeAnn Rimes$>$, has been a strained one -- but Glanville tells ETonline it's getting better. \\
$\langle S_2,-1\rangle$: Afterwards, $<$Christian Brady$>$, Dean of the Schreyer Honors College and father of $<$Mack Brady$>$, whom the game is named after, addressed the lacrosse team.  \\\
\begin{table}[h!]
\centering
\begin{tabular}{| c | c | c | c | c | }
 \hline
 Sentence id  & \multicolumn{2}{c}{Discrete} & \multicolumn{2}{c}{Continuous} \\ [0.5ex]
    & LF1 (+1) & LF2 (-1) & LF1 (+1) & LF2 (-1) \\ [0.5ex]
 \hline
$S_1$ (+1) & 1 & 0 & \(1, ~1.0\) & \(1, ~0.84\)  \\
$S_2$ (-1) & 0 & 1 & \(1, ~0.75\) & \(1, ~1.0\)  \\
 \hline
\end{tabular}
\todo{OC: See if you can present more digestible scores}
\caption{Triggering of the continuous and discrete versions of functions LF1 and LF2 for the two example sentences $S_1$ and $S_2$.}
\label{table:twochallenges}
\end{table}
In Table~\ref{table:twochallenges}, we present the outputs of both discrete and continuous LFs on the examples $S_1$ and $S_2$. For $S_1$,  in the discrete case, correct consensus can easily be performed to output the true label +1 as LF1 (designed for class +1) gets triggered whereas LF2 (designed for class -1) is not triggered.
correct consensus is challenging since both LF1 and LF2 produce non-trivial scores. Let us say that we threshold the score of each continuous LF to be able to mimic the discrete case; {\em e.g.}, score above the threshold will mean that the LF is triggered and otherwise, not.  However, the threshold would depend on the particular LF (possibly 0.75 for LF1 and 0.84 for LF2), and is tricky to estimate in an unsupervised setup such as ours. To address this challenge,
\fi
We extend existing generative models~\cite{snorkel_nips2016,snorkel_icml2017} to support continuous LFs. In addition to modeling the consensus distribution over all LFs (continuous and discrete), we model the distribution of scores for each continuous LF.
In Section~\ref{sec:motivationExample}
in the supplementary material,
we illustrate through an example, why the model for continuous LFs is not a straightforward extension of the discrete counterpart.

Our second extension is designed to remove the
instability of existing generative training based on unsupervised likelihood.  Across several datasets we observed  that the accuracy obtained by the existing models was highly sensitive to initialization, training epochs, and learning rates.  In the absence of labeled validation set, we cannot depend on prevalent tricks like early stopping to stabilize training.  We give control to the data programmer to guide the training process by providing intuitive accuracy guides with each LF.
In the case that the labeler develops each LF after inspecting some examples, the labeler would naturally have a rough estimate of the
fraction $q$ of examples that would be correctly labeled by the LF, amongst all examples that trigger the LF. Even otherwise, it might not be too difficult for the labeler to intuitively specify some value of $q$.
We show that such a $q$ serves as a user-controlled quality guide that can effectively guide the training process.
For the case of continuous LFs, we use  $q$
as a rough estimate of the mean score of the continuous LF whenever it triggers correctly. A quality guide of $q=0.9$ for LF1 would imply that when LF1 triggers correctly the average embedding score is 0.9. This is easier than choosing a hard threshold on the embedding score to convert it to a discrete LF.   We provide an elegant method of guiding our generative training with these user-provided accuracy estimates, and show how it surpasses simpler methods like sign penalty and data constraints.  Empirically, we show our method stabilizes unsupervised likelihood training even with very crude estimates of $q$.
We study stability issues of the existing model  with respect to training epochs and demonstrate that the  proposed model is naturally more stable.
We refer to our overall approach as \mbox{CAGE}, which stands for {\bf \uline{ C}}ontinuous {\bf \uline{ A}}nd quality {\bf \uline{ G}}uided lab{\bf \uline{ E}}ling functions. In summary, \mbox{CAGE}\  makes the following contributions: \\
1) It enhances the expression power of data programming by supporting continuous labeling functions (generalizations of discrete LFs) as  the unit of supervision from humans.\\
2) It proposes a carefully parameterized graphical model that outperforms existing models even for discrete LFs, and permits easy incorporation of user priors for continuous LFs.\\
3) It extends the generative model  through quality guides, thereby increasing its stability and making it less sensitive to initialization. Its training is based on a principled method of regularizing the marginals of the joint model with user-provided accuracy guides.
\\
We present extensive experiments on five datasets, comparing various models for performance and stability, and present the significantly positive impact of \mbox{CAGE}\ .  We show that our method of incorporating user guides leads to more reliable training than obvious ideas like sign penalty and constraint based training.

\section{Our Approach: \mbox{CAGE}\ }
\label{ourapp}
Let ${\mbox{$\mathcal{X}$}}$ denote the space of input instances and ${\mbox{$\mathcal{Y}$}}=\{1,\ldots,K\}$ denote the space of labels and $P({\mbox{$\mathcal{X}$}},{\mbox{$\mathcal{Y}$}})$ denote their joint distribution. Our goal is to learn a model to associate a label $y$ with an example ${\vek{x}} \in {\mbox{$\mathcal{X}$}}$. Unlike standard supervised learning, we are not provided true labels of sampled instances during training.  Let the  sample of $m$ unlabeled instances be ${\vek{x}}_1,\ldots,{\vek{x}}_m$.  Instead of the true $y$'s we are provided a set of $n$ labeling functions (LFs)  $ \lambda_1, \lambda_2, \ldots \lambda_n $ such that each LF  $\lambda_j$ can be either discrete or continuous. Each LF $\lambda_j$ is attached with a class $k_j$ and on an instance ${\vek{x}}_i$ outputs a discrete label $\tau_{ij}=k_j$ when triggered and  $\tau_{ij}=0$ when not triggered.  If $\lambda_j$ is continuous, it also outputs a score $s_{ij} \in (0,1)$. This is a form of weak supervision that implies that when a LF is triggered on an instance ${\vek{x}}_i$, it is proposing that the true label $y$  should be $k_j$, and if continuous it is attaching a  confidence proportional to $s_{ij}$ with its labeling.

But to reliably and accurately infer the true label $y$ from such weak supervision without any labeled data,  we need to exploit the assumption that $\tau_{ij}$ is positively correlated with $y$.  We allow the programmer of the LF to make this assumption explicit by attaching a guess on the fraction $q_j^t$ of triggering of the LF where the true $y$ agrees with $\tau_{ij}$.   We show in the experiments that crude guesses suffice.
Intuitively, this says that the user expects $q_j^t$ fraction of examples for which the LF value has been triggered to be correct.  Additionally, for continuous LF the programmer can specify the quality index  $q_j^c$ denoting the average score of $s_j$ when there is such agreement.

Our goal is to learn to infer the correct label by creating consensus among outputs of the LFs.
Thus, the model of \mbox{CAGE}\  imposes a joint distribution between the true label $y$ and the values $\tau_{ij}, s_{ij}$ returned by each LF $\lambda_j$ on any data sample ${\vek{x}}_i$ drawn from the hidden distribution $P({\mbox{$\mathcal{X}$}},{\mbox{$\mathcal{Y}$}})$.
\begin{equation}
\displaystyle P_{\theta,\pi}(y,{\vek{\tau}}_{i},{\vek{s}}_i) = \frac{1}{Z_\theta} \prod_{j=1}^n \psi_\theta(\tau_{ij}, y) \left( \psi_\pi(\tau_{ij}, s_{ij}, y)\right)^{\text{cont}(\lambda_j)}
\label{eq:joint}
\end{equation}
where $\text{cont}(\lambda_j)$ is $1$ when $\lambda_j$ is a continuous LF and $0$ otherwise. And $\theta,\pi$ denote the parameters used in defining the potentials $psi_\theta, \psi_\pi$ coupling discrete and continuous variables respectively.
In this factorization of the joint distribution we make the natural assumption that each LF independently provides its supervision on the true label.  The main challenge now is designing the potentials coupling various random variables so that: (a) The parameters ($\theta,\pi$) can be trained reliably using unlabeled data alone. This partially implies that the number of parameters should be limited. (b) The model should be expressive enough to fit the joint distribution on the $\tau_j$ and $s_j$ variables across a variety  of datasets without relying on labeled validation dataset for model selection and hyper-parameter tuning. (c) Finally, the potentials should reflect the bias of the programmer on the quality in providing the true $y$.  We will show how without such control, it is easy to construct counter-examples where the standard likelihood-based training may fail miserably.

With these goals in mind, and after significant exploration we propose the following form of potentials.  For the discrete binary $\tau_{ij}$ variables, we chose these simple potentials:
\iffalse
Let  $ \Lambda_{i} \in \{0,1\}^{n} $ be the vector of $ \Lambda_{ij} \in \{0,1\} $ instantiations across all LFs $\lambda_j$ for $j=1...n$. Then,
\begin{equation}
\displaystyle P_{\theta,\pi}(y,\Lambda_{i}) = \frac{1}{Z_\theta} \prod_{j=1}^n P_{\theta,\pi}(\Lambda_{ij}, y)
\end{equation}
\begin{equation}
\label{eq:model_disc_continuous}
\displaystyle P_{\theta,\pi}(\Lambda_{ij}, y) =
\begin{cases}
P_{\theta}(\tau_{ij}, y) P_{\pi}(s_{ij} | \tau_{ij}, y) & \text{if $\lambda_j$ is continuous,} \\
P_{\theta}(\tau_{ij}, y)  & \text{if $\lambda_j$ is discrete.} \\
\end{cases}
\end{equation}

\begin{equation}
P_{\theta}(\tau_{ij}, y) =  \exp\left( \psi_{j}(\tau_{ij}, y) \right)
\end{equation}
\fi

\begin{equation}
    \psi_{\theta}(\tau_{ij},y) =
\begin{cases}
    \exp(\theta_{jy})  & \text{if $\tau_{ij}\ne 0$,} \\
    1 & \text{otherwise.}
\end{cases}
\label{eq:decoupledthetas}
\end{equation}
Thus, for each LF we have $K$ parameters corresponding to each of the class labels.    An even simpler alternative would be to share the $\theta_{jy}$ across different $y$s as used in \cite{snorkel_icml2017} but that approach imposes undesirable restrictions on the distributions it can express.  We elaborate on that in Section~\ref{sec:compare}.

For the case of continuous LFs the task of designing the potential $\psi_{\pi}(s_{ij}, \tau_{ij}, y)$  that is trainable with unlabeled data and captures user bias well turned out to be significantly harder.
Specifically, we wanted a form that is suited for scores that can be interpreted as confidence probabilities (that lie between 0 and 1), and capture the bias that $s_{ij}$ is high when $\tau_{ij}$ and $y$ agree, and low otherwise.
For confidence variables, a natural parametric form of density is the beta density.  The beta density is popularly expressed in terms of two independent parameters $\alpha > 0$ and $\beta > 0$ as $P(s|\alpha,\beta) \propto s^{\alpha-1}(1-s)^{\beta-1}$.  Instead of independently learning these parameters, we chose an alternative representation that allows expression of user prior on the expected $s$. We write the beta in terms of two alternative parameters: the mean parameter $q_j^c$ and  the scale parameter $\pi_1$.  These are related to $\alpha$ and $\beta$ as $\alpha = q_j^c\pi_1$ and $\beta = (1-q_j^c)\pi_1$.

We define our continuous potential as:
\begin{equation}
\label{eqn:contPots}
\psi_{\pi}(\tau_{ij}, s_{ij}, y) =
\begin{cases}
    \textit{Beta}(s_{ij};\alpha_a, \beta_a )  & \text{if $k_j=y ~\& ~\tau_{ij} \ne 0$,} \\
    \textit{Beta}(s_{ij};\alpha_d, \beta_d )  & \text{if $k_j \ne y ~\& ~\tau_{ij} \ne 0$,} \\
    1 & \text{otherwise}
\end{cases}
\end{equation}
where $\alpha_a=q_j^c \pi_{jy}$ and $\beta_a=(1-q_j^c)\pi_{jy}$ are parameters of the agreement distribution and $\alpha_d=(1-q_j^c)\pi_{jy}$ and   $\beta_d=q_j^c \pi_{jy}$ are parameters of the disagreement distribution, where $\pi_{jy}$ is constrained to be strictly positive. To impose $\pi_{jy}>0$ while also maintaining differentiability, we reparametrize $\pi_{jy}$ as $\exp(\rho_{jy})$.  Thus, we require $K$ parameters for each continuous LF, which is the same as for a discrete LF. The Beta distribution would normally require $2K$ parameters but we used the user provided qualify guide in that special manner shown above to share  the mean  between the agreeing and disagreeing Beta.

We experimented with a large variety of other potential forms before converging on the above.  We will elaborate on alternatives in the experimental section.

With these potentials, the normalizer $Z_\theta$ of our joint distribution (Eqn~\ref{eq:joint}) can be calculated as
\begin{equation}
\begin{split}
Z_\theta = & \sum_y \prod_j\sum_{\tau_{j} \in \{k_j, 0\}}
 \psi_\theta(\tau_{j}, y)\int_{s_j=0}^1 \psi_\pi(\tau_{j}, s_j, y)
\\
         = & \sum_{y\in{\mbox{$\mathcal{Y}$}}}\prod_j(1+\exp(\theta_{jy}))
         \end{split}{}
\end{equation}
The normalizer reveals two further facets of our joint distribution. First our continuous potentials are defined such that when summed over $s_j$-s we get a value of 1, hence the normalizer is independent of the continuous parameters $\pi$.  That is, the continuous potentials $\psi_\pi(\tau_{ij}, s_{ij}, y)$ are locally normalized Bayesian probabilities $P(s_{ij}|\tau_{ij},y)$.  Second, the discrete potentials are not locally normalized; the  $\psi_\theta(\tau_{j}, y)$ cannot be interpreted as $\Pr(\tau_j|y)$ because by normalizing them globally we were able to learn the interaction among the LFs better. We will show empirically that either the full Bayesian model with potentials $P(y), P(\tau_{ij}|y)$, and $P(s_{ij}|\tau_{ij},y)$ or the fully undirected model where the $ \psi_\pi(\tau_{ij}, s_{ij}, y)$ potential is un-normalized are both harder to train.

\subsection{Training \mbox{CAGE}\ }
\label{sec:train}
Our training objective can be expressed as:
\begin{equation}
    \max_{\theta,\pi} LL(\theta,\pi|D) + R(\theta,\pi|\{q_j^t\})
    \label{eq:regularizedObjective}
\end{equation}
The first part maximizes the likelihood on the observed ${\vek{\tau}}_i$ and ${\vek{s}}_i$ values of the training sample $D = {\vek{x}}_1,\ldots,{\vek{x}}_m$ after marginalizing out the true $y$. It can be expressed as:
\begin{align}
     & LL(\theta,\pi|D) = \sum\limits_{i=1}^{m} \log \sum\limits_{y \in {\mbox{$\mathcal{Y}$}}} P_{\theta,\pi}({\vek{\tau}}_i,{\vek{s}}_i, y) \label{eq:nonregulariedObjective} \\
     & = \sum\limits_{i=1}^{m} \log \sum\limits_{y \in {\mbox{$\mathcal{Y}$}}}  \prod\limits_{j=1}^{n} \psi_j(\tau_{ij},y) \left(\psi_j(s_{ij}, \tau_{ij},y )\right)^{\text{cont}(\lambda_j)} - m \log Z_\theta \nonumber
\end{align}
By \mbox{CAGE}$_{-\text{G}}$, we will hereafter refer to the model in Eqn~\ref{eq:joint} that has parameters learnt by maximizing only this (first) likelihood part of the objective in Eqn~\ref{eq:nonregulariedObjective} and not the second part $R(\theta,\pi|\{q_j^t\})$. $R(\theta,\pi|\{q_j^t\})$ is a regularizer that guides the parameters with the programmer's expectation of the quality of each LF.  We start by motivating the need for the regularizer by showing simple cases that can cause the likelihood-only training to yield poor accuracy.
\paragraph{Example 1: Sensitivity to Initialization}
Consider a binary classification task where the $n$ LFs are perfect oracles  that trigger only on instances whose true label matches $k_j$.  Assume all $\lambda_j$ are discrete.  The likelihood of such data can be expressed as:
\begin{align}
    & LL(\theta) = \sum_{i=1}^m \log(\exp(\sum_{j:k_j=y} \theta_{j1}) + \exp(\sum_{j:k_j=y} \theta_{j2})) \nonumber \\ & - m\log(\prod_j(1+\exp(\theta_{j1})) + \prod_j(1+\exp(\theta_{j2})))
    \label{eq:oracle}
\end{align}
The value of the above likelihood is totally symmetric in  $\theta_{j1}$ and
$\theta_{j2}$ but the accuracy is not.  We will get 100\% accuracy only when the parameter for the agreeing case: $\theta_{jk_j}$ is larger than $\theta_{jy}$ for
$y \ne k_j$, and 0\% accuracy if $\theta_{jk_j}$ is smaller.  A trick
is to initialize the $\theta$ parameters carefully so that the
agreeing parameters $\theta_{jk_j}$ do have larger values.  However,
even such careful initialization can be forgotten in less trivial cases as we show in the next example.

\paragraph{Example 2: Failure in spite of good initialization} Consider a set S1 of $r$ LFs that assign a label of 1 and remaining set S2 of $n-r$ LFs that assign label 2. Let each true class-2 instance trigger one or more LF from S1 and one or more LF from S2.  Let each true class-1 instance trigger {\em only} LFs from S1.   When we initialize LFs in set S1 such that $\theta_{j1}-\theta_{j2}  > 0$ and LFs in set S2 have  $\theta_{j2}-\theta_{j1}  > 0$,  we can get good accuracy. However,
as training progresses the likelihood will be globally maximized when both sets of LFs favor the same class on all instances.  If we further assume that the true class distribution is skewed, the $LL(\theta)$ objective quickly converges to this useless maxima.  This scenario is not artificial. Many of the real datasets (e.g. the LFs of Spouse relation extraction data in Table~\ref{tab:sample}) exhibit such trends.

A straight-forward fix of the above problem is to impose a penalty on the sign of $\theta_{jk_j} - \theta_{jy}$. However, since the $\theta$s of LFs interact via the global normalizer $Z_\theta$ this condition is neither necessary nor sufficient to ensure that in the joint model $P_\theta(y,{\vek{\tau}})$ the values of $y$ and $k_j$ agree more than disagree. For globally conditioned models the parameters cannot be easily interpreted, and we need to constrain at the level of the joint distribution.

One method to work with the whole distribution is to constrain the conditional $P_\theta(y|{\vek{\tau}}_i)$ over the instances where the LF triggers and constrain that the accumulated probability of the agreeing $y$ is at least $q_j^t$ as follows:
\begin{equation}
\label{dataDep:prec}
R(\theta|\{q_j^t\},D) = \sum_j \text{softplus}\left(\sum_{i:\tau_{ij}=k_j} (q_j^t - P_{\theta}(\tau_i,k_j))\right)
\end{equation}
We call this the data-driven constrained training method and refer to it as \mbox{CAGE}$_{\text{dataG}}$.
However, a limitation of this constraint is that
in a mini-batch training environment it is difficult to get enough examples per batch for reliable estimation of the empirical accuracy, particularly for LFs that trigger infrequently.
Next we present our method of incorporating the user guidance into the trained model to avoid such instability.

\subsection{Data-independent quality guides in \mbox{CAGE}\ }
\label{sec:const}
Our final approach that worked reliably was to regularize the parameters so that the learned joint distribution of $y$ and $\tau_j$ matches the user-provided quality guides $q_j^t$  over all $y,\tau_{j}$ values from the joint distribution $P_{\theta,\pi}$. By default, this is the regularizer that we employ in \mbox{CAGE}.

The $q_j^t$ guide is the user's belief on the fraction of cases where $y$ and $\tau_{j}$ agree when $\tau_{j} \ne 0$  (LF $\lambda_j$ triggers).  Using the joint distribution we can calculate this agreement probability as $P_\theta(y = k_j |\tau_j = k_j)$.
This probability can be computed in closed form by marginalizing over all remaining variables in the model in Equation~\ref{eq:joint} as follows:
\newcommand{P_\theta(y=k_j|\tau_j = k_j)}{P_\theta(y=k_j|\tau_j = k_j)}
\begin{eqnarray*}
    P_\theta(y=k_j|\tau_j = k_j) &=& \frac{P_\theta(y=k_j, \tau_j = k_j)}{P_\theta(\tau_j = k_j)} \\
    &=&  \frac{{\mbox{M}}_j(k_j)\prod_{r\ne j} (1+{\mbox{M}}_r(k_j))}
    {\sum_{y \in {\mbox{$\mathcal{Y}$}}} {\mbox{M}}_j(y)\prod_{r\ne j} (1+{\mbox{M}}_r(y))}
\end{eqnarray*}
where ${\mbox{M}}_j(y)=\exp(\theta_{jy})$.  We then seek to minimize the KL distance between the user provided $q_j^t$ and the model calculated precision $P_\theta(y=k_j|\tau_j = k_j)$ which turns out to be:
\begin{equation}
\begin{split}
        R(\theta | \{q_j^t\}) = \sum_j  q_j^t \log P_\theta(y=k_j|\tau_j = k_j) \\
        + (1-q_j^t)  \log (1-P_\theta(y=k_j|\tau_j = k_j))
\end{split}
\label{eq:userSummLLL}
\end{equation}
Specifically, when the \mbox{CAGE}\  model is restricted only to discrete LFs while also incorporating the quality guide in Eqn~\ref{eq:userSummLLL} into the objective in Eqn~\ref{eq:nonregulariedObjective}, we refer to the approach as \mbox{CAGE}$_{-\text{C}}$. Further, when the quality guide in Eqn~\ref{eq:userSummLLL} is dropped from \mbox{CAGE}$_{-\text{C}}$, we refer to the approach as \mbox{CAGE}$_{-\text{C}-\text{G}}$.

\subsection{Relationship of \mbox{CAGE}\  with existing models}
\label{sec:compare}
We would like to point out that the following two simplifications in \mbox{CAGE}\  lead to existing well known models~\cite{snorkel_nips2016,snorkel_vldb2018}, {\em viz.},
(i) Coupling the $\theta_{yj}$ parameters,
(ii) Ignoring quality guides, and (iii) Not including continuous potentials.
The design used in \cite{snorkel_icml2017} is to assign a single parameter $\theta_j$ for each LF and share it across $y$ as:
\begin{equation}
\psi_{j}^\text{snorkel}(\tau_{ij},y) =
\begin{cases}
    \exp(\theta_{j})  & \text{if $\tau_{ij}\ne 0, y = k_j$,} \\
     \exp(-\theta_{j})  & \text{if $\tau_{ij}\ne 0, y \ne k_j$,} \\
    1 & \text{otherwise.}
\end{cases}
\label{eq:snorkeloriginal}
\end{equation}
After ignoring quality guides and continuous LF, we note that a choice of $\theta_{j,+1} = - \theta_{j,-1}$ makes \mbox{CAGE}\  exactly same as the model in Snorkel.
However, we found the Snorkel's method of parameter sharing incorporates an unnecessary bias that $P_\theta(\tau_{ij} = 0|y=k_j) = 1 - P_\theta(\tau_{ij} = 0|y \ne k_j)$.
Also, Snorkel's pure likelihood-based training is subject to all the sensitivity to parameter initialization and training epochs that we highlighted in Section~\ref{sec:train}.  We show in the experiments how each of the three new extensions in \mbox{CAGE}\  is crucial to getting reliable training with the data programming paradigm.

\section{Empirical Evaluation}
\label{exp}
In this section we (1) evaluate the utility of continuous LFs vis-a-vis discrete LFs, (2) demonstrate the role of the quality guides for the stability of the unsupervised likelihood training of \mbox{CAGE}\  as well as Snorkel, and (3) perform a detailed ablation study to justify various design elements of our generative model and its guided training procedure.
\subsection{Datasets and Experiment Setup}
\label{sec:setup}
We perform these comparisons on five different datasets.

\noindent
{\bf Spouse:}~\cite{spouse}
This is a relation extraction dataset that proposes to label candidate pairs of entities in a sentence as expressing a `spouse' relation or not.
Our train-dev-test splits and set of discrete LFs shown in Table~\ref{tab:sample} are the same as in~\cite{snorkel_nips2016} where it was first used.
For each discrete LF that checks for matches in a dictionary $D$ of keywords we create a continuous LF that returns $s_j$ as the maximum of cosine similarity of their word embeddings as shown in Table~\ref{tab:continuoussample}.
We used pre-trained vectors provided by Glove~\cite{glove}.

\noindent
{\bf SMS spam}~\cite{smsspam}  is a binary spam/no-spam classification dataset with 5574 documents split into 3700 unlabeled-train and
1872 labeled-test instances.
Nine LFs are created based on (i) presence of three categories of words which are highly likely to indicate spam (ii) presence of 2 categories of trigger words in certain contexts, (iii) reference to keywords indicative of first/second or third person, (iv) text characteristics such as number of capitalized characters, presence of special characters, {\em etc.} and finally a LF that is (v) associated with the negative class, always triggers and serves as the class prior. The LFs are explained in the supplementary in Section~\ref{sec:smsspamlfs}.
The  continuous LFs are created in the same way as in Spouse based on word-embedding similarity, number of capitalized characters, {\em etc.}

\noindent
{\bf CDR:}~\cite{cdr} This is also a relation extraction dataset where the task is to detect whether or not a sentence expresses a `chemical cures disease' relation.
The train-dev-test splits and LFs are the same as in \cite{snorkel_nips2016}. We did not develop any continuous LF for CDR.

\noindent
{\bf Dedup:} This dataset{Publicly available at \url{https://www.cse.iitb.ac.in/~sunita/alias/}} comprises of 32 thousand pairs of noisy citation records with fields like Title, Author, Year {\em etc.} The task is to detect if the record pairs are duplicates. We have 18 continuous LFs corresponding to various text similarity functions (such as Jaccard, TF-IDF similarity, 1-EditDistance, {\em etc.}) computed over one or more of these fields. Each of these LFs is positively correlated with the duplicate label; we create another 18 with the score as 1-similarity for the negative class. The dataset is highly skewed with only 0.5\% of the instances as duplicate. All LFs here are continuous.

\noindent
{\bf Iris:}
Iris is a UCI dataset with 3 classes. We split it into 105 unlabeled train and 45 labeled test examples.  We create LFs from the 4 features of the data as follows: For each feature $f$ and class $y$, we calculate $f$'s mean value $\overline{f}_y$ amongst the examples of $y$ from labeled data and create a LF that returns the value $1-\text{norm}(f-\overline{f}_y)$  - where $\text{norm}(f-\overline{f}_y)$ is the normalized distance from this mean. This gives us a total of $4\times 3 = 12$ continuous LFs. Each such LF has a corresponding discrete LF that is triggered if the feature is closest to the mean of its corresponding class.

\noindent
{\bf Ionosphere:}
This is another 2-class UCI dataset, that
is split to 245 unlabeled train and 106 labeled test instances. 64 continuous LFs are created in a manner similar to Iris.

\iffalse
\noindent
{\bf ICMC:}~\cite{icmc} \todo{OC: This can be the first dataset you can take up once you are done with all standard expts}
This is a complaint classification task  data is crawled from the ICMC website~\cite{icmc}. The dataset is cleaned such that each class has a minimum number of data points. This is set as a 42 class classification problem. It has 39595 train instances and 6667 test instances.
\fi

\noindent
{\bf Training Setup} For each dataset and discrete LF we arbitrarily assigned a default discrete quality guide $q_j^t=0.9$ and for continuous LFs $q_j^c=0.85$. We used learning rate of $0.01$ and $100$ training epochs. Parameters were initialized  favorably --- so for the agreeing parameter initial $\theta_{jk_j=1}$ and for disagreeing parameter initial $\theta_{ky}=-1, y \ne k_j$. For Snorkel this is equivalent to $\theta_j=1$. Only for \mbox{CAGE}\ \ that is trained with guides we initialize all parameters to 1.  We show in Section~\ref{sec:init} that \mbox{CAGE}\  is insensitive to initialization whereas others are not.

\noindent
{\bf Evaluation Metric}
We report F1 as our accuracy measure on all binary datasets and for the multi-class dataset Iris we measure micro F1 across the classes.   From our generative model, as well as Snorkel, the predicted label on a test instance ${\vek{x}}_i$ is the $y$ for which the joint generative probability is highest, that is: $\text{argmax}_y P(y,{\vek{\tau}}_{i},{\vek{s}}_{i})$.   Another measure of interest is the accuracy that would be obtained by training a standard discriminative classifier $P_{\vek{w}}(y|{\vek{x}})$ with labeled data as the probabilistically labeled $P(y|{\vek{x}}_i) \propto P(y,{\vek{\tau}}_{i},{\vek{s}}_{i})$ examples ${\vek{x}}_i$ from the generative model.  In the first part of the experiment we measure the accuracy of labeled data produced by the generative model. In Section~\ref{sec:disc} in the Supplementary,  we present accuracy from a trained discriminative model from such dataset.
We implemented our model in Pytorch.\footnote{Code available at \url{https://github.com/oishik75/CAGE}.}

\subsection{Overall Results}
\begin{table}[!htb]
\setlength\tabcolsep{4.0pt}
{\begin{tabular}{|l|r|r|r|r|r|r|}
\hline &  Spouse &  CDR    &  SMS    &  Ion &  Iris  &  Dedup  \\ \hline
 Majority  & 0.17  & 0.53   &  0.23    &  0.79       & 0.84  & -  \\ \hline
 Snorkel  & 0.41 & 0.66   & 0.34   & 0.70       & 0.87 & -      \\ \hline
 \mbox{CAGE}$_{-\text{C}-\text{G}}$ & 0.48 & 0.69 & 0.34   & 0.81     & 0.87 & -      \\ \hline
 \mbox{CAGE}$_{-\text{C}}$ & 0.50 & 0.69 & 0.45 & 0.82      & 0.87 & -      \\ \hline
 \mbox{CAGE}\  & {\bf 0.58} & {\bf 0.69}  & {\bf 0.54}  & {\bf 0.97}  &   {\bf 0.87} & {\bf 0.79} \\ \hline
\end{tabular}}
\caption{Overall Results (F1) with predictions from various generative models contrasted with the Majority baseline.}
\label{tab:overall}
\end{table}

In Table~\ref{tab:overall}, we compare the performance of \mbox{CAGE}\  in terms of F1, against the following alternatives: (i) {\bf Majority:} This is a simple baseline, wherein, the label on which a majority of the LFs show agreement is the inferred consensus label.
(ii) {\bf Snorkel:} See Section~\ref{sec:compare}.
(iii) {\bf \mbox{CAGE}$_{-\text{C}-\text{G}}$:} Our model without continuous LFs and quality guides (See Section~\ref{sec:const}).
(iv) {\bf \mbox{CAGE}$_{-\text{C}}$:} Our model with quality guides but without continuous LFs (See Section~\ref{sec:const}).

From this table we make three important observations: (1)
Comparing Snorkel with \mbox{CAGE}$_{-\text{C}-\text{G}}$\ that differs only in decoupling Snorkel's shared $\theta_{j}$ parameters, we observe that the shared parameters of Snorkel were indeed introducing undesirable bias.
(2) Comparing \mbox{CAGE}$_{-\text{C}-\text{G}}$\ and \mbox{CAGE}$_{-\text{C}}$\ we see the gains due to our quality  guides.  (3)
Finally, comparing \mbox{CAGE}$_{-\text{C}}$\ and \mbox{CAGE}\  we see the gains because of  the greater expressibility of continuous LFs. These LFs required negligible additional human programming effort beyond the discrete LFs.  Compared to Snorkel our model provides significant overall gains in F1.  For datasets like Dedup which consist only of continuous scores, \mbox{CAGE}\ \ is the only option.

\iffalse
\subsubsection{Robustness to  dictionary incompleteness:}
Another advantage of the continuous LFs derived from word-embeddings is that it does not require an exhaustive dictionary of keywords. To test this, we randomly drop some words in the 'spouse','family' and 'other' \cite{9} lists of LFs in the spouse dataset and compare the models. For CDR dataset we dropped words from 'causal-past','treat', 'procedure','uncertain' lists\cite{10}.
In the table below, we show the performance of Snorkel Vs our method with increasing fraction of dropped words from the three dictionaries in Table~\ref{tab:sample}.
Dropping words resulted in lower scores on spouse dataset as expected. For CDR dataset the LFs with above mentioned lists seems to be hurting. When we dropped words, snorkel and our discrete model gave better scores than with full lists and smooth models gave similar results as with using full lists. This change is observed for both dev and test set.
\fi

We next present a more detailed ablation study to tease out the importance of different design elements of \mbox{CAGE}.

\subsection{Role of the Quality Guides}
\label{sec:init}
\begin{figure*}[!ht]
\centering
\begin{tabular}{ccc}
\includegraphics[width=0.33\textwidth]{spouse_f1.pdf} &
\includegraphics[width=0.33\textwidth]{sms_f1.pdf} &
\includegraphics[width=0.33\textwidth]{cdr_f1.pdf}
\\
(a)~Spouse  & (b) SMS  & (c) CDR
\end{tabular}
\caption{F1 with increasing number of training epochs compared across snorkel, \mbox{CAGE}$_{-\text{C}-\text{G}}$ and \mbox{CAGE}$_{-\text{C}}$, for three datasets. For each dataset, in the absence of guides, we observe unpredictable variation in test F1 as training progresses.}
\label{fig:prec}
\end{figure*}
We motivate the role of the quality guides in Figure~\ref{fig:prec} where we show test F1 for increasing training epochs on three datasets. In these plots we considered only discrete LFs.
We compare Snorkel and our model with (\mbox{CAGE}$_{-\text{C}}$) and without (\mbox{CAGE}$_{-\text{C}-\text{G}}$) these guides.
Without the quality guides, all datasets exhibit unpredictable swings in test F1.  These swings cannot be attributed to over-fitting since in Spouse and SMS F1 improves later in training with our quality guides.  Since we do not have  labeled validation data to choose the correct number of epochs, the quality guides are invaluable in getting reliable accuracy in unsupervised learning

Next, we show that the stability provided by the quality guides ($q^t_j$) is robust to large deviations from the true accuracy of a LF.  Our default $q^t_j$ value was 0.9 for all LFs irrespective of their true accuracy.  We
repeated our experiments with a precision of 0.8 and got the same accuracy across training epochs (Figure~\ref{fig:stable} in  Supplementary).  We next ask if knowing the true accuracy of a LF would help even more and how robust our training is  to distortion in the user's guess from the true accuracy.  We calculated true accuracy of each LF on the devset and distorted this by a Gaussian noise with variance $\sigma$. In Figure~\ref{fig:distort} we present accuracy after 100 epochs on two datasets with increasing distortion ($\sigma$).  On CDR \mbox{CAGE}'s accuracy is very robust to distorted $q^t_j$ but guides are important as we see from Figure~\ref{fig:prec}(c).   On Spouse accuracy is highest with perfect values of $q^t_j$ (Sigma=0) but it stays close to this accuracy up to a distortion of 0.4.
\begin{figure}[ht]
\centering
\includegraphics[width=0.35\textwidth]{f1_vary_sigma.pdf}
\caption{F1 with increasing distortion in the guess of the LF quality guide,  $q^t_j$.}
\label{fig:distort}
\end{figure}

\noindent
{\bf Sensitivity to Initialization:} We carefully initialized parameters of all models except \mbox{CAGE}\ .  With random initialization all models without guides (Snorkel and \mbox{CAGE}$_{-\text{G}}$) provide very poor accuracy.  Exact numbers are in the supplementary material ({\it c.f.} Figure~\ref{fig:init}).

\begin{table}[!htb]
\resizebox{\columnwidth}{!}
{\begin{tabular}{|l|l|l|l|l|}
\hline & \bf Spouse & \bf CDR    & \bf Sms    & \bf Ion \\ \hline
\mbox{CAGE}$_{-\text{C}-\text{G}+-\text{P}}$ & 0.48  & 0.69  & 0.34   & 0.81      \\ \hline
\mbox{CAGE}$_{-\text{C}-\text{G}}$ & 0.48  & 0.69  & 0.34   & 0.81      \\ \hline
\mbox{CAGE}$_{\text{-C,dataG}}$ & 0.48  & 0.69  & 0.34   & 0.81      \\ \hline
\mbox{CAGE}$_{-\text{C}}$ & {\bf 0.50} & {\bf 0.69} & {\bf 0.45} & {\bf 0.82}      \\ \hline
\end{tabular}}
\caption{Comparing different methods of incorporating user's quality guide on discrete LFs.}
\label{tab:role_of_constraints}
\end{table}
\begin{table*}[]
\end{table*}

\paragraph{Method of Enforcing Quality Guides}
In Table~\ref{tab:role_of_constraints}, we compare F1
for the following choices:
\iffalse
    (i) {\bf Snorkel} as-is.
   (ii) {\bf Sign Penalty} on snorkel's parameters $\psi_j$:
   We add a sign penalty to likelihood $LL(\theta)$ (Eqn \ref{eq:nonregulariedObjective})
   to keep $\theta_j$ s positive since that captures the user prior that each LF agrees more often than disagrees with true $y$.

   We tried three variants of sign penalties: (1) $\sum\limits_{j=1}^{n} \max(0,- \theta_j)$ (2) $-\min( \sum\limits_{j=1}^{n} \theta_j , 0)$ and (3) $\sum\limits_{j=1}^{n} \log(1 + \exp(-\theta_j-k)$.
\fi
    (i) {\bf \mbox{CAGE}$_{-\text{C}-\text{G}+-\text{P}}$}: Our model with the objective in Eqn (\ref{eq:nonregulariedObjective}) augmented with the sign penalty $\max(0,\theta_{jy \ne k_j} - \theta_{jk_j})$  instead of the regularizer in Eqn (\ref{eq:userSummLLL}).
    (ii) {\bf \mbox{CAGE}$_{-\text{C}-\text{G}}$}: Our model without guides

    (iii) {\bf \mbox{CAGE}$_{\text{-C,dataG}}$}: The data-driven method of incorporating quality guides (See Section~\ref{sec:train}),
    and (iv) {\bf \mbox{CAGE}$_{-\text{C}}$}: our data independent regularizer of the model's marginals with $q_j^t$ (Eqn~\ref{eq:userSummLLL}).
From  Table~\ref{tab:role_of_constraints} we observe that  \mbox{CAGE}$_{-\text{C}}$\ is the only one that provides reliable gains. Thus, it is not just enough to get quality guides from users, we need to also design sound methods of combining them in likelihood training.

\subsection{Structure of the Potentials}
In defining the joint distribution $P_{\theta,\pi}(y,{\vek{\tau}}_{i},{\vek{s}}_i)$
(Eq~\ref{eq:joint}) we used undirected globally normalized potentials
for the discrete LFs(Eqn~\ref{eq:decoupledthetas}).  We compare with
an alternative where our joint is a pure directed Bayesian network
with potentials $Pr(\tau_j|y)=\exp(\theta_{jy}/(1+\exp(\theta_{jy})$
on each discrete LF and a class prior $\Pr(y)$.  We observe that the
undirected model is better able to capture interaction among the LFs
with the global normalization $Z_\theta$.
\begin{tabular}{|l|l|l|l|l|l|}
\hline
         & \textbf{Spouse} & \textbf{CDR} & \textbf{Sms} & \textbf{Ion} & \textbf{Iris} \\ \hline
Directed & 0.15            & 0.49            & 0.59         & 0.86                & {\bf 0.89}          \\ \hline
\mbox{CAGE}\      & {\bf 0.58}            & {\bf 0.69}         & {\bf 0.54}         & {\bf 0.97}                & 0.87          \\ \hline
\end{tabular}

\noindent
We repeated other  ablation experiments where  the continuous potentials are undirected and take various forms.  The results appear in the Supplementary in Sect~\ref{sec:contPotsAblation}
and show that local normalization is crucially important for modeling $s_j$ of continuous LFs.

\iffalse
\subsubsection{Sensitivity to Parameter Initialization}

\subsection{Synthetic Continuous Multiclass}
\fi

\subsection*{Related Work}
\label{relwork}

Several consensus-based prediction combination algorithms~\cite{gao2009graph,kulkarniaaai2018}
exist that combine multiple model predictions to counteract the effects of data quality and model bias.
There also exist label embedding approaches from the extreme classification literature~\cite{yeh2017learning}
that exploit inter-label correlation.
While these approaches assume that the imperfect labeler's knowledge is fixed, \cite{fang2012self} present a self-taught active learning paradigm, where a crowd of imperfect labelers learn complementary knowledge from each other. However, they use instance-wise reliability of labelers to query only the most reliable labeler without any notion of consensus. A recent work by~\cite{chang2017revolt} presents a collaborative crowd sourcing approach. However, they are motivated by the problem of eliminating the burden of defining labeling guidelines a priori and their approach harnesses the labeling disagreements to identify ambiguous concepts and create semantically rich structures for post-hoc label decisions.

There is work in the crowd-labeling literature that makes use of many imperfect labelers~\cite{kulkarniaaai2018,raykar2010learning,yan2011active,Dekel:2009:GLE:1553374.1553404} and accounts for both labeler and model uncertainty to propose probabilistic solutions to (a) adapt conventional supervised learning algorithms to learn from multiple subjective labels; (b) evaluate them in the absence of absolute gold standard; (c) estimate reliability of labelers. \cite{Donmez:2008:PLC:1458082.1458165} propose a \emph{proactive learning} method that jointly selects the optimal labeler and instance with a decision theoretic approach. Some recent literature has also studied the augmenting neural networks with rules in first order logic to either guide the individual layers~\cite{acl2019augmentinglogic} or  train model weights within constraints of the rule based system using a student and teacher model~\cite{Hu2016HarnessingDN}

Snorkel~\cite{snorkel_nips2016,snorkel_icml2017,snorkel_vldb2018,snorkel_acl2018,snorkel_icml2019} relies
on domain experts manually developing heuristic and noisy LFs. Similar methods that rely on imperfect sources of labels are~\cite{Bunescu:Mooney:07,Hearst:1992:AAH:992133.992154} relying on heuristics, ~\cite{Mintz:2009:DSR:1690219.1690287} on distant supervision and ~\cite{ruleensembles} on learning conjunctions discrete of (discrete) rules.
The aforementioned literature focuses exclusively on labeling suggestions that are discrete.
We present a generalized generative model to aggregate heuristic labels from continuous (and discrete) LFs while also incorporating user accuracy priors.

\paragraph{Conclusion}
\label{conc}
We presented a data programming paradigm that lets the user specify labeling functions  which when triggered on instances can also produce continuous scores.  The unsupervised task of  consolidating weak labels is inherently unstable and sensitive to parameter initialization and training epochs.  Instead of depending on un-interpretable hyper-parameters which can only be tuned with labeled validation data which we assume is unavailable, we let the user guide the training with interpretable quality guesses. We carefully designed the potentials and the training process to give the user more interpretable control.

\paragraph{\bf Acknowledgements} This research was partly sponsored by a Google India AI/ML Research Award and partly sponsored by IBM Research, India (specifically the IBM AI Horizon Networks - IIT Bombay initiative).

\bibliographystyle{aaai}

redpajama_set_name: RedPajamaArXiv


For a lot of people, Hollywood is one of the strangest places on the planet. And it's not because of all the movies, the controversies, the stories and the celebrities that it produces. Instead, it's because often, it can't make up its mind if someone deserves a second chance or not. While there are plenty of entertainers who end up lost in celebrity obscurity, there are others that get a second shot in their careers. Or in some cases, third or even fourth. A few names that come to mind are Robert Downey Jr. and Mickey Rourke.
But as many people know, Hollywood can both be a forgetful and forgiving place at the same time. That's why there are people out there that still think Ben Affleck might have a chance of one day reaching the top of his career, despite everything that he has been going through personally lately. After all, he's always been one of the biggest names in Hollywood and he has enough credits, accolades, and even friends in the business to back him up. With that being said, we've got 20 reasons why we think Hollywood will give Ben Affleck a second chance.
First and foremost, there's no denying that Ben Affleck has always been one of Hollywood's greatest talents. He's achieved all that he's achieved for a simple reason: he's the kind of actor that people do want to see on the big screen. Plus, he's been a household name for almost three decades now, along with his fellow Hollywood actor and best friend, Matt Damon. No matter what goes on with Ben Affleck from a personal standpoint, he will always be applauded for the contributions he's made to modern cinema, both as an actor and a director. That is something that no one can deny.
Ben also has his name attached to plenty of successful films from the past. He's won Academy Awards for his work on the unforgettable Good Will Hunting and for his work in 2012's Argo. He's also gotten plenty of acclaim for The Town. Sure, he's had a few flops, including Gigli, Jersey Girl, and more but for the most part Ben's seen more career winners than losers. But at the same time, Ben gets his name attached to important movies because movie studios and bosses know that he has a name that can help sell tickets at the box office.
It's without a doubt that Ben Affleck's biggest strength is his family. And his family members are also the most important people in his life that don't want to see him fail. After all, he's got three children who look up to him as their own personal hero. Critics can say what they will about Ben Affleck, but there's no denying that he wants to be the best person possible for his children. That's why he's not going to give up on himself, or on them for that matter. He's got so much to live for and there are people that remind him of this every single day.
A lot of people consider Ben Affleck to be a very lucky man. Why? Because Jennifer Garner has proven to be the kind of partner that will stand by her man – or in this case her ex – regardless of what he may be going through in his life both professionally or personally. Plus, she realizes that he is also the father of her three children, all of whom she loves dearly. His health and happiness is just as important as that of their kids. No matter how many times he falls, she will never give up on him, and might just be there to pick him up, too.
Sure, Ben Affleck has had plenty of roles in his life, but if there is one that is the most important to him, it's his role as a father. There's no denying that his three children are very important to him and that he wants to be there for them, every step of the way. Without his family, his career and other parts of his life wouldn't mean very much. "When you have kids, what's that expression, 'Your heart is outside your body,' all of a sudden you feel so vulnerable and this fear of a child being vulnerable is very very powerful," he said to People Magazine.
Even though he's not currently active in Hollywood right now, Ben does have quite a few movies that are in the pipeline, including his much-anticipated film, The Batman. He's actually a producer of the movie, which is now in pre-production. He's also the executive producer of another television series called City on a Hill, which is expected to hit the small screen in 2019. In other words, Ben just can't walk up and leave. He's got too much at stake here. He's got a team of people counting on his return, whenever that will be. Fingers crossed it will be sooner rather than later.
If that weren't enough, Ben also has quite a few movies in the works, too. He is the executive producer of two television movies, one called Thirst and another called Witness for the Prosecution. And that's something you can't say about everyone. People want to see Ben do well, even if his personal life might not be standing at the same level. Now, that's not to say that we should expect to see him on the red carpet at the awards shows. He's got his work cut out for him. It might be plenty of years before we see Ben reach that level again.


SIORs work together, because they trust each other.
They know SIOR members represent the most successful practitioners in the commercial real estate industry. Chicago-based Block Associates, Inc., has been SIOR's only General Associate full-service, creative national marketing agency since 2005, and we've worked for more than 25 years to earn your trust.
At SIOR, it's not JUST about trust: it's ALL about trust.
real estate with local, regional and national brokerage, development, investment, construction, architecture, and other advisory-service firms. They have entrusted us with their marketing because of our extensive track record of successful accomplishments, in-depth knowledge of industry disciplines, vast experience in all facets of marketing, and creative approach to problems, issues, opportunities and strategic goals.
Nikki Block, MCR u President, Block Associates, Inc.


text:
Objects of Substance
Gazetting school life: another significant Grammar publication
Grammar Gazette, Issue No1, Term 4 1992
Publishing a school newspaper is not unique, nor was it new to Girls Grammar when this idea was approved by the then Principal, Mrs Judith Hancock, in 1992. Grammar girls had created 'newspapers' before this [1]. The earlier versions were often light-hearted, even irreverent, publications but there were other opportunities to publish more serious information. The School magazine has always afforded students the opportunity to report on school life and schoolgirl interests, but this 1992 concept was the chance to produce in a commercially printed, broadsheet format. Indeed, one of the earliest student reactions was that it 'looks like a real newspaper'.
Grammar Gazette was Mrs Hancock's preferred name for the publication, and this was appropriate not only because of the alliteration but also because 'gazette' had become a widely accepted term for a newspaper. During the early and middle 16th century, the first Venetian newspapers cost one gazzetta. However, the Grammar Gazette was, and is, free to readers, but cost those who made it a great deal of time, creative thinking, and serious application.
The first Grammar Gazette was released in Term 4, 1992. As student reporter, Taryn Burns explained, this concept was suggested by the 'Public Relations group' who felt that 'more in-depth coverage of current events and achievements inside the school community was required'. Mrs Hancock gave her permission and a group of Year 11 students and staff advisors set the presses in motion. The first edition was a large format, professionally printed broadsheet, eight pages in length. The front-page story was the opening of the Judith A Hancock Communications Centre, complete with an official photograph.
The original committee, charged with the production of this publication was determined to make it as professional as possible and this attitude was carried over to the organisation of personnel. The editorial group consisted of student editors and subeditors, student reporters and photographers, and two staff consultants: IT specialist and teacher; Paul Bennett, and me.
The founding student editors were Naomi Just and Joanna Sing, with sections managed by Erica Dunstan and Jo Larsen (sport), Kym Smith (arts), and Taryn Burns (features). Design components were overseen by Eva Wood (all 1993 Graduates). In addition, a student cartoonist was 'head hunted' and provided original illustrations to comment on school life and add interest and section headers. Cartoons started to appear in Issue 2, Term 1, 1993. One clever and popular cartoon appeared in Volume 2, No 3, Term 2, 1993. It accompanied an article about a program that provided Grammar girls with the opportunity to visit real-life workplaces to observe professions of interest.
1993 Gazette, Vol 2 No3. How cleverly tongue-in-cheek was this cartoon. It makes the reader smile using just one image and a few words
Heated discussions about what stories and photographs were to be given precedence on the front and back pages, as well as what should inhabit the preferred right-hand pages always eventuated. Each newspaper was then completely set up digitally by the staff consultants with lively input from the student editors before being sent out to professional printers. There were even advertisements from outside organisations and companies (just like the earliest School Magazines) to help defray printing costs.
It was a considerable commitment to fill the Gazettes with interesting and topical items, (the largest edition in 1995 was 20 pages long). Each term, the editorial committee held meetings at which various ideas were bounced around before assigning reporters and recruiting extra student and staff writers.
2022 students, Evelyn Ma (11R), Angeline Hsu (11G), Grace Paschkewitz (11O) and Jacqueline Hii (11R), viewing past Gazettes
However, it eventuated that it was not a problem for the editorial committee to identify stories to fill the pages. There was, and is, something always happening in school life: service and curriculum activities; a dynamic program of events from concerts to drama productions; from dinners to celebrations; and a packed sporting calendar. These were obvious places to start, but it soon became apparent that stories about past students and their post-school careers and lives, the activities of the parent groups, regular contributions from the Chair of the Board of Trustees and the Principal, and offerings by talented student artists and photographers appeared to contribute to the diverse content. This variety allowed readers an intimate insight into the complex, interesting, wide-ranging life of the Grammar girl and her interests and activities at School and after.
Gazette Vol 2 Issue 4, 1993 p4. One unique occasional column was written by 'The lone haranguer'. This anonymous contributor satirised behaviours that could be questioned. Their identity remained a mystery
The Gazette was not immune to that inevitable aspect of life, change. The newspaper began to include colour in select editions—in Term 3, 1993, the plans for the new 'sports centre' were displayed for the whole community. In the second half of 1999, many more colour pages appeared, and this edition also saw the advent of a more robust quality of paper used. In 2002, the publication morphed into a 12-page colour booklet format and the reins were taken over by the section of the School now called Communications and Engagement. By the Spring issue of 2012 (the 135th anniversary of the School), the Grammar Gazette had developed into a 35-page colour magazine, a structure that continues to this day.
Some of our early Gazettes
Grammar girls have always displayed a strong sense of commitment to their School and its community. Some would say that service is part of the DNA of a Grammar girl and undertaking such an ambitious publication for the benefit of all who read it is evidence of this attitude. Girls donated many hours from their busy school lives, their creativity, and their dedication to doing their very best. The result was a window into Girls Grammar life, but it was also a publication that celebrated the skills, activities, and interests of a School that assumed girls should seize opportunities, speak out, and aim high.
Ms Kristine Cooke (Harvey 1967)
[1] Refer to earlier article on Sock and Custard, which also acknowledges Vox discipulorum, an even earlier incarnation.
1992 Issue No1, Term 4 page 4
Grammar Gazette, Issue No1, Term 4 1992. Back page
Related Objects of Substance News
We Have a Library Set in Stone: The Library Foundation Stone
Objects of Substance | Friday 6 August 2021
Hats Off to Grammar Girls
Objects of Substance | Wednesday 4 November 2020
It's Only a Desk and a Chair—Does it Really Matter?
Objects of Substance | Friday 23 October 2020
View All Objects of Substance News ▻


ALLEN PARK — Eight Lions — all of them starters — did not practice today (Wednesday) in preparation to face the Jaguars in Jacksonville on Sunday.
Missing were wide receiver Calvin Johnson, who has a lingering knee injury; safety Louis Delmas, who re-injured his knee Sunday; running back Mikel Leshoure whose injury is known; Corey Williams (knee); DeAndre Levy, who missed Sunday's game with a hamstring; cornerback Bill Bentley (shoulder); defensive end Cliff Avril (back) and Amari Spievey.
Coach Jim Schwartz said on Monday that Spievey, who sustained a concussion at Chicago, still has to clear some hurdles.
Cornerback Drayton Florence, who broke his arm on Sept. 16, returned to practice for the first time. He was placed on injured reserve-designated to return and will be eligible to play on Nov. 18 against the Packers.
Wide receiver Mike Thomas, who was acquired in a deal with Jacksonville on Tuesday, was reportedly taking his physical during practice.
The Lions didn't wait until the Thursday trade deadline to make a move. On Tuesday they received veteran wide receiver Mike Thomas in a deal with the Jacksonville Jaguars. The Lions gave up draft picks in the deal.
Thomas has 13 catches for 80 yards so far this season. In his first three seasons with the Jaguars, he had 48, 66 and 44 receptions.
Thomas returned punts for the Jaguars so it could be the Lions will look at him in that role. Coach Jim Schwartz likes multi-dimensional players.
Lions wide receivers Titus Young and Ryan Broyles both stepped up in Sunday's win over Seattle and it was assumed the pair would get more reps with Nate Burleson gone for the season with a broken leg.
The Lions announced they released cornerback Justin Miller. They will not make the deal official until Thomas has passed his physical.
Oddly enough, the Lions play at Jacksonville on Sunday.
The NFL trade deadline was pushed back 48 hours until 4 p.m. on Thursday due to Superstorm Sandy.
ALLEN PARK — Detroit Lions safety Louis Delmas, who has played in just three games this season, had tests done on Monday on his left knee. He injured it in the third quarter on Sunday and didn't return to the game.
It was the same knee he had surgery on Aug. 7.
"(Delmas) had some tests today, he's day to day, he's sore,'' coach Jim Schwartz said on Monday. "I don't know if I'd classify it as a reinjury, it's a sore knee that's been bothering him through training camp and the season.
"He's a hard worker, a tough guy we'll try to work through it,'' Schwartz said at his Monday press conference.
The knee has been a lingering problem for Delmas. Schwartz said the surgery was able to alleviate some of the pain.
Schwartz had relatively good news on cornerbacks Jacob Lacey, who has missed two games with a concussion, and Drayton Florence who broke his arm in the game at San Francisco on Sept. 16.
Both will return to the practice field this week.
"We'll see how he progresses from there,'' Schwartz said of Lacey.
Florence should be back at practice this week.
"He's moving around well and his arm is healed up, he's not eligible to be back on the active roster for a couple of weeks,'' Schwartz said.
Florence had been placed on the injured reserve designated to return list, a new category this season.
Safety Amari Spievey suffered a concussion at Chicago a week ago.
In NFL news, the trade deadline has been pushed back from 4 p.m. Tuesday to 4 p.m. Thursday due to Hurricane Sandy.
It's not a lock, but there's a chance the Lions could still make the playoffs. They are grinding through the tough part of the schedule and with a win at Jacksonville on Sunday they will be at .500. Nate Burleson, in a message from his hospital room, told the team that every comeback starts with a setback. The setback was the loss at Chicago, perhaps the comeback started at Ford Field on Sunday. Here are five reasons the Lions have a chance at making the postseason for the second straight year.
1. Matthew Stafford is who we thought he was. Even with the tough start this season, teammates say he never hung his head, he just worked hard like he always does. To get down to basics, he has the arm and the smarts and along with that the respect of the team.
2. The defense played tough against the Seahawks but can play better. Due to injury it's been a mix-and-match secondary week to week. Young guys like Jonte Green are getting much-needed experience. Safety Ricardo Silva, who was signed from the practice squad on Sept. 29, pulled in his first career interception. Silva was one of four defenseman to play every snap — Chris Houston, Stephen Tulloch and Justin Durant.
3. The Lions have found a successful running game using the combination of Mikel Leshoure (10 carries, 46 yards) and Joique Bell (seven for 25 yards). Leshoure start the game and they save Bell for later in the game when his legs are fresh and the defense's legs are not. On Sunday the Lions finished with 331 passing yards and 84 rushing yards. They don't look for 50-50 balance, they want the ratio that works on each particular Sunday.
4. Between Titus Young, Ryan Broyles and the tight ends, there are guys who can fill in for Nate Burleson. Oh, he'll be missed, but Broyles will only improve with increased playing time. Young had a breakout game on Sunday that should boost the level of confidence that Stafford has in him. Tight ends Brandon Pettigrew (seven catches, 74 yards) and Tony Scheffler (four for 46 yards) had major contributions.
5. Look at the Giants last year. They finished the season 9-7. Can't remember anyone who considered them the Super Bowl favorite and yet they won it. They were 6-6, riding a four-game losing streak, with just four games left and made it to 9-7. The gate is wide open this season. There is not one dominant team.
DETROIT — Detroit Lions cornerback Bill Bentley (shoulder) was limited all week at practice and is inactive for today's game against the Seattle Seahawks.
Middle linebacker DeAndre Levy (hamstring is also out. Ashlee Palmer will start in his place.
As expected CB Jacob Lacey and safety Amari Spievey, who both have concussions, are also inactive.
Jonte Green will get his second straight start at cornerback while Ricardo Silva will start in place of Spievey.
Other inactives — coaches decisions — are quarterback Kellen Moore and offensive tackles Jason Fox and Corey Hilliard.
It's not do-or-die, but it's close. The Lions (2-4) are playing just their third home game of the season and need a win to stop the bleeding. Here are five things the Lions must do to beat the Seattle Seahawks ( 4-3) on Sunday at Ford Field.
1. Defense must play as tough as they did against the Bears on Monday night. They can't relax just because the Seahawks' offense ranks 30th in the NFL.
3. Hang onto the ball. Hang onto the ball. Hang onto the ball.
4. Get the ball to Calvin Johnson. Matthew Stafford doesn't care about statistics, just wins. That's all good. But the more Johnson gets the ball usually the more wins the Lions will accumulate. Of course, Johnson can't have a drop in the wide-open spaces either.
5. Be aggressive on offense from the get-go.
Eagles quarterback Michael Vick tried to cure his fumble-itis before facing the Lions by every day carrying a football with him all day to lunch, to meetings and beyond. It didn't work.
The Lions are not taking that approach with Mikel Leshoure who has fumbled twice in recent games including a costly red zone cough-up on Monday night at Chicago.
"We're always talking about 'Don't take an inch for granted.' But you can't do it at the expense of ball security. He was a very ball-secure player in college. He's been out for a while, whatever it was, a year out of contact,'' Lions offensive coordinator Scott Linehan said.
"You've got missing, really missing pretty much the entire training camp, except for maybe the last part of it. Those are the things that you take for granted and you find out and say 'Hey, listen, we've got to make sure that we're securing that ball,' and both of our fumbles were extra-effort fumbles,'' Linehan added.
Running back Joique Bell also fumbled at the 1-yard line trying to go for the extra few inches on a second down.
"You put the ball on the ground, it's a self-inflicted wound and it takes points off the board and both of those fumbles were in scoring position, so never a good time, but it's always pretty critical when you take away that opportunity,'' Linehan said.
"So, we'd like to pretty much get that off of our radar at this point. Last year we were a very good ball secure team, we didn't have a lot of fumbles,'' he added.
Each of those fumbles prevented a possible game-winning touchdown.
ALLEN PARK — Cornerback Bill Bentley, who injured his shoulder against Philadelphia, was back at practice today (Thursday) at least for the portion open to the media.
Bentley had limited practice all last week, but did not play on Monday at Chicago.
Calvin Johnson (knee) who wouldn't have participated on Wednesday if the Lions practiced was on the field for the media portion.
Missing from practice Thursday were Cliff Avril (back), DeAndre Levy (hamstring), along with Jacob Lacey and Amari Spievey who have concussions.
Also, Lions cornerback Drayton Florence tweeted Thursday that he expects to be back at practice next week. He broke his right arm in the game against the 49ers and was placed on injured reserve with the ability to return.
ALLEN PARK — Ndamukong Suh has set himself apart from other NFL players.
The Detroit Lions defensive tackle has been rated the least-liked NFL player with just 19 percent of fans saying they like him in a new poll of NFL fans by Forbes magazine.
Suh is considered a dirty player by many. He didn't help matters when he stomped on Green Bay's Evan Dietrich-Smith last year in the Thanksgiving Day game. He was suspended two games without pay by the NFL. Leading up to that incident he had already been fined $42,500 by the NFL in less than two seasons in the league.
When he came into the league, he said the NFL had never had a player like him.
Suh, the second overall pick in 2010, had no comment on Wednesday about the negative tweets Bears wide receiver Brandon Marshall issued about Suh's sack of quarterback Jay Cutler on Monday night. Cutler said he thought it was a clean hit.
The poll was a collaboration between Nielsen and E-Poll Market Research to find out which NFL players fans liked and which ones they didn't like.
ALLEN PARK — Various reports have wide receiver Brian Robiskie signing with the Lions who lost Nate Burleson for the season after he broke his right leg on Monday night.
The team has not confirmed the move.
Robiskie, a second-round pick by the Cleveland Browns in 2009, has 39 career receptions for 441 yards.
He was cut by the Browns on Nov. 1, 2011, and picked up by the Jacksonville Jaguars the next day. He was released by the Jaguars on Sept. 10.
Robiskie, who is 6-foot-3 and 209 pounds, played college football at Ohio State.
The Lions game wasn't over when it was over Monday night. After the Lions 13-7 loss to the Bears, there was some action on Twitter.
Bears wide receiver Brandon Marshall (@BMarshall) took to Twitter to express his displeasure about Ndamukong Suh's takedown of quarterback Jay Cutler.
Suh was not whistled for a roughing the passer penalty for the sack. Cutler's right shoulder was ground into the turf. He went down in pain, the trainers came out and he went off the field.
He took just one play off and was back. It was reported that he had bruised ribs.
It's possible the NFL could disagree with the on-field officials and slap Suh with a fine.
Not only did the Detroit Lions' record fall to 2-4 after a 13-7 loss to the Chicago Bears on Monday night, they lost at least one key player.
Wide receiver Nate Burleson is done for the season after breaking his right leg when it appeared to lock when he made a reception.
He was helped off the field by trainers, and then carted to the locker room late in the first half.
It's a huge loss for the Lions. Burleson has 27 catches for 240 yards in the first six games. He's the only receiver who has at least two touchdowns.
He's in his 10th season but not slowing down. In 2011 he had a career-high 73 receptions.
It's not just the statistics, Burleson is very much a leader off the field. Calvin Johnson is the quiet sort, Burleson is the opposite.
His leadership is key and it's never the same when the player is sidelined.
Also, safety Amari Spievey was carted off late in the second half. He had a head injury and was being tested for a concussion.
The slow-to-start Detroit Lions, would like to see that change tonight when they play the Chicago Bears on Monday Night Football.
If ever there's a stadium not conducive to comeback wins for the opponent, it's Soldier Field.
Last year the Lions fell behind early — they were down 20-6 at the half — and never caught up, losing 37-13.
This season they just have not been able to score touchdowns in the first half. The offense moved the ball, then has to settle for field goals.
"We've scored, we've just been scoring threes, just trying to get sevens on the board that's obviously important,'' quarterback Matthew Stafford said. "I'll take our opening drives just one play here, one play there we get seven instead of three changes some things.
The Lions have scored 73 points in the fourth quarter and 50 in the first three quarters combined. It's one reason why they are 2-3.
In Philadelphia last week it was like Stafford found his inner Superman in the fourth quarter.
Ron Jaworski, an ESPN analyst, said that in the first three quarters Stafford looked like he couldn't hit water if he jumped out of a boat and in the fourth quarter played like Joe Montana.
"Obviously I want to play like Joe Montana all four quarters, just doing whatever it takes to win, that's what I'm all about,'' Stafford said.
Offensive coordinator Scott Linehan certainly is trying to find ways to score touchdowns early.
All the Lions — coaches and players — know one thing. It doesn't matter when the points are scored.
It's a huge game in the NFC North. Here are five things the Lions (2-3) need to do to beat the Chicago Bears (4-1) at Soldier Field on Monday Night Football.
1. Don't turn the ball over. The Bears' defense has scored five touchdowns off opponents turnovers. The forecast is for rain which could make the ball slick, but it will be the same for both teams. Matthew Stafford threw four interceptions last year due to a broken finger and major wind gusts.
2. Score touchdowns early. The Lions' offense has actually moved the ball well early in games, it just doesn't seem like it because they've mostly had to settle for field goals instead of touchdowns. They have just one first-half touchdown in five games.
3. Detroit's defensive line must get after quarterback Jay Cutler early and often. The Bears' offensive line is usually a weak area and the Lions have to take advantage. This is key because with two injured cornerbacks (Jacob Lacey is out and Bill Bentley is doubtful) rookie Jonte Green is expected to get his first start.
4. The run game must continue to play well. At the Eagles last week, the Lions had 138 rushing yards which helped open up the passing game. Mikel Leshoure had 70 yards (4.7 yards per carry) and looked as good as he did in his debut when he hit the 100-yard mark on the nose.
5. Get the ball to Calvin Johnson, perhaps in the first half. It's not like Matthew Stafford is not trying, but he has to take what defenses give him. Charles Tillman is not going to make this task easier. Johnson and Stafford have not connected for a touchdown in the first five games. Last year at this point they had nine.
ALLEN PARK — Defensive tackle Corey Williams, who practiced Thursday and Friday, did not participate today (Saturday). Williams had knee surgery on Sept. 25 and it's uncertain if he'll play on Monday night at Chicago.
Also, cornerback Jacob Lacey did not practice. He hasn't participated all week after sustaining a concussion on Sunday in Philadelphia.
Cornerback Bill Bentley, who injured his shoulder at Philadelphia, was back at practice Saturday for the portion open to the media. He was limited on Friday.
This will be the first time the Lions have played on Monday Night Football at Soldier Field. Last year the Lions boosted their record to 5-0 with a Monday night win at Ford Field over the Bears.
The two NFC North teams are meeting for the 165th time overall with the Bears holding a 94-65-5 edge. The Bears have also won seven of the last eight meetings with the Lions.
ALLEN PARK — Rookie cornerback Bill Bentley returned to practice today (Friday) for the first time since injuring his shoulder in Sunday's win over the Eagles. He was on the field in a limited role during the portion of practice open to the media.
This does not mean he will be ready to play on Monday night in Chicago, but it's a step in the right direction.
This is the healthiest the Lions have been in a while. The only player absent was cornerback Jacob Lacey who sustained a concussion on Sunday.
It's possible the Lions will start rookie Jonte Green at right cornerback alongside Chris Houston. It would be Green's first NFL start. Alphonso Smith, who was brought back on Thursday, could start at nickel back. Safety Amari Spievey can also play nickel.
Kyle Vanden Bosch and Louis Delmas rested on Thursday and were back at practice Friday.
ALLEN PARK — Corey Williams, who had knee surgery on Sept. 25, returned to practice for the first time today (Thursday).
If the veteran defensive tackle doesn't have a setback, it's possible he could play on Monday night at Chicago.
Also, cornerback Alphonso Smith was at practice after signing with the Lions. He's wearing No. 27, his old number.
Cliff Avril, DeAndre Levy and Nate Burleson, who all missed Wednesday's practice, were back in action in the portion of practice open to the media.
Missing were Kyle Vanden Bosch (rest day) and Louis Delmas (possible rest day).
Also out were cornerbacks Jacob Lacey (concussion) and Bill Bentley (shoulder). Both were injured in Sunday's game.
ALLEN PARK — Alphonso Smith, who played the last two seasons for the Lions, is back in the fold. The Lions announced this morning that they have signed the cornerback. To make room on the roster, they released running back Keiland Williams.
Smith could be a short-term solution. He was in for a workout on Wednesday.
The Lions are down to two healthy cornerbacks in Chris Houston and rookie Jonte Green. Amari Spievey, a safety who used to be a corner, can fill in at nickel.
Cornerbacks Jacob Lacey (concussion) and rookie Bill Bentley (shoulder) were injured in Sunday's game. Bentley said on Wednesday he wasn't sure when he would be ready to play. With a concussion, there's no timetable for Lacey.
Neither one practiced on Wednesday.
Smith played in a dozen games last season and started one. He had three interceptions and returned one for a touchdown.
It's a bit of surprise that Williams, who also plays special teams, would be the running back released. He played in half the special teams snaps and had one carry for one yard against the Eagles.
At Philadelphia, veteran running back Kevin Smith, who does not play special teams, was inactive.
ALLEN PARK — With two cornerbacks injured in Sunday's win at Philadelphia, the Lions are looking for help at the position.
Former Detroit Lions cornerbacks Alphonso Smith and Justin Miller were in for workouts on Wednesday morning. They left the facility about 1:15 p.m. Both were released in the final round of cuts after training camp on Aug. 31.
He admitted it was a little odd working out for the Lions, a team he was with for two seasons (2010 and 2011). "Borderline uncomfortable,'' Smith said.
He did have a workout with the Miami Dolphins.
"That went well,'' Smith said with a smile.
Smith played in a dozen games for the Lions last season with one start. He finished with three interceptions with one returned for a touchdown.
In Sunday's win at Philadelphia, cornerback Jacob Lacey left the game with a concussion while rookie Bill Bentley was knocked out with a shoulder injury.
Coach Jim Schwartz didn't offer any specifics on either injury in his press conference on Monday.