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HomeBritish TV PremieresMay British TV Premieres: What's New on Acorn TV for May 2020?
May British TV Premieres: What's New on Acorn TV for May 2020?
April 30, 2020 I Heart British TV British TV Premieres 0
Tomorrow marks the beginning of May, and that means we can all look forward to more great British shows on our favourite British TV streaming networks. In this post, we'll take a look at what's coming to Acorn TV in May.
If you don't already subscribe to Acorn TV, you can sign up on their website HERE. If you have a smart TV that isn't compatible with their app (like a Samsung) or you prefer to subscribe through Amazon Video, you can sign up HERE instead. Either way you do it, you get a free 7-day trial, and both offer easy online cancellation if you decide it's not for you. Both options have the same programming, though some people have reported slightly smoother streaming on Amazon (and occasionally, a delay of a few hours for new programming to appear).
Acorn TV May 2020 Schedule of New Shows…
New British TV Shows on Acorn TV in May 2020
May 4 Premieres
May 11 Premieres
What Else is Coming up on Acorn TV in 2020?
What Do You Think of the May 2020 Acorn TV Premieres?
Julia Ormond stars in Gold Digger, premiering on May 4 on Acorn TV
This BBC One twisty romantic thriller starring Emmy® winner Julia Ormond (Howard's End, Legends of the Fall) and Ben Barnes (Westworld) tells the story of a 60-year-old woman falling in love with a 35-year-old man and the effect it has on her already dysfunctional family. Mother first, wife second, woman last, Julia Day (Ormond) has spent her whole life putting the needs of others before herself, but now she falls in love with Benjamin Greene (Barnes), a man 25 years her junior.
The impact their unconventional relationship has on her family is explored and the secrets of their past are revealed — has Julia finally found the happiness she's always deserved, or is Benjamin really only after her money like they think he is? The series also stars Alex Jennings (The Crown) as Julia's ex-husband Ted, and Julia McKenzie (Agatha Christie's Marple) as Ted's mother, Hazel.
Episodes 1 & 2 – May 4
Episodes 3 & 4 – May 11
Episodes 5 & 6– May 18
You can view the trailer below:
Land Girls
Land Girls, Series 3
Following the premiere of Series 1 on April 6th and Series 2 on April 20th, Acorn brings the third and final season to the service on May 4th.
This series follows the highs and lows of the lives of four women in the Women's Land Army during WWII.
Can't wait? Watch it HERE.
Porterhouse Blue
David Jason (Only Fools and Horses, A Touch of Frost), Ian Richardson (House of Cards) and Griff Rhys Jones (Not the Nine O'Clock News) lead a distinguished cast in Malcolm Bradbury's four-part adaptation of Tom Sharpe's classic comic novel about the fictional Porterhouse college in Cambridge, noted for its rowing, ancient traditions, feasting, crusty fellows and the dismal academic standards of its students.
When the headmaster dies, the government appoints an old graduate as his successor – the politician Sir Godber Evans (Richardson), who is determined to drag this bastion of privilege into the 20th century and announces a major break with six centuries of college tradition: Porterhouse will open its doors to scholars! Head Porter Skullion (Jason) is as stunned as the Fellows and students, but the new master, egged on by his wife (Barbara Jefford), is determined to sow seeds of change in the very foundations of Porterhouse.
A Royal Tour of the 20th Century
The British royal family has been at the center of the 20th century's most iconic moments – two world wars, the end of empires, coronations, weddings, glittering tours, fashion and scandals – a century that saw the Windsors' influence and participate in a wealth of defining historical moments, firmly making their mark on this important period of modern history.
This series uses rarely-viewed archival footage to discover the untold and overlooked stories of Britain's royal family.
Seachange
Seachange, Series 3
This Australian drama from the 1990s follows a corporate lawyer whose life implodes, causing her to leave the city to go live in a small seaside town. There, she becomes the local judge and builds a new life for herself and her children.
Series 1 and 2 premiered in April, and May brings us the third and final season of the series.
Michael Smiley stars in the upcoming Dead Still
Set in 1880s Ireland in the Victorian heyday of postmortem photography, this six-part period mystery focuses on a photographer who investigates the murders of his deceased subjects. Cast members include Michael Smiley (Luther), Kerr Logan (Game of Thrones), Eileen O'Higgins (Mary Queen of Scots) and Aidan O'Hare (Jackie).
This series is an Acorn TV Original Series that they commissioned, and you can read more about it here. We also included an interview with the writer in the 2019 British TV Year in Review magazine.
The May release schedule for this one is as follows:
Episodes 1 & 2 – May 18th
Episode 3 – May 25th
There will be 6 episodes in total, and you can check out the trailer below:
Series 5 of Mount Pleasant premieres on Acorn TV in May
We've been getting questions about this one for a while, so we're pleased to see that Series 5 will finally be joining Series 1-4 on Acorn TV. No word yet on Series 6 or 7, but it's good to see they're continuing to add more.
This long-running heartwarming UK dramedy hit, which originally aired on Sky, depicts the lives of a tightly knit family in suburban Manchester, including their everyday struggles. Starring Sally Lindsay (Coronation Street, Scott & Bailey), Daniel Ryan (Black Sea), Ainsley Howard (Mum & Dad, The Village), and Neil Fitzmaurice (Going Off Big Time).
Treasure Houses of Britain
Hosted by Selina Scott, this five-episode docuseries offers rare access to five of Britain's finest stately homes – Burghley House, Chatsworth, Blenheim Palace, Holkham Hall and Boughton House, marking the first time they were filmed in 3D.
These episodes shed light on these well-loved, stately buildings, as well as the treasures they house: from the tapestries the First Duke of Marlborough commissioned to celebrate his triumph at the Battle of Blenheim, to the Italian pietra dura cabinet which the Medici gave to the owners of Burghley – the greatest Elizabethan house in England.
Can't wait? You can watch it HERE now.
On June 1st, Acorn TV will be adding the 2019 revival of Seachange (8 episodes in total). On the 15th, we can look forward to a new season of the Welsh mystery Hidden.
What's your pick of the upcoming new releases? Is there anything you've been eagerly awaiting? Though we've already had a chance to preview Dead Still (review coming soon), we're thrilled to see what everyone else thinks of this creative new series.
New Shows on Acorn TV's May 2020 Premiere List
Watch the Sketches From the BBC's Big Night In
BritBox Premieres: What's New on BritBox in May 2020?


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Q: Difference between the AdornerLayer.Update methods What exactly are the differences between AdornerLayer.Update() and AdornerLayer.Update(UIElement)?
As far as I understand it, there is exactly one adorner layer per ui element, which you can get with AdornerLayer.GetAdornerLayer(Visual). The only difference I can see is if there isn't a adorner layer for each ui element. So for example if I have the following visual tree:
UIElement1
-> UIElement2
-> UIElement3

Then somehow UIElement2 and 3 don't have their own adorner layer but use the adorner layer of UIElement1. And then Update() would update all adorners for UIElement1, 2 and 3 and Update(UIElement) would only update the adorners for the corresponding ui element.
So what are the conditions for when a UI element has its own adorner layer and when it uses the adorner layer of a UI element higher up in the visual tree?

A: Each UIElement does not automatically have its own, unique AdornerLayer. Take a look at this documentation:
Adorners Overview - Adorning a Single UIElement

GetAdornerLayer walks up the visual tree, starting at the specified UIElement, and returns the first adorner layer it finds. (If no adorner layers are found, the method returns null.)

AdornerDecorator

The AdornerDecorator specifies the position of the AdornerLayer in the
  visual tree. It is typically used in a ControlTemplate for a control
  that might host Adorner objects. For example, the ControlTemplate of a
  Window contains an AdornerDecorator so that the child elements of the
  window can be adorned. The GetAdornerLayer method returns null if you
  pass in an element that does not have an AdornerDecorator as an
  ancestor in its visual tree.

So, if you use a Window as the root element, there is guaranteed to be at least one Adorner in the visual tree, there may be more depending on what other elements you use.

redpajama_set_name: RedPajamaStackExchange


Never Give Up: Human Relationships: Are "Scraps From the Table" Enough?
Human Relationships: Are "Scraps From the Table" Enough?
The basis of human relationships is the fact of being persons and "being-together." When we forget or seek to evade this foundation, the things we try to do to help others remain shallow and don't reach their most crucial and essential needs.
Indeed, they don't do justice to their humanity.
Not that our "actions" and efforts to meet people's needs are not important, but their specific human value is founded on "presence" and expresses presence.
If we're not (in some way) trying to care for the whole scope and depth of people's needs, to enrich their lives, to help them grow as persons, to defend their rights, and to seek ways to rectify the injustice and oppression that hold them down, we are clearly not "present" with them in an appropriate, meaningful human relationship.
External actions in service to our neighbor, when they are possible within the circumstances of a relationship, are a necessary expression of interpersonal communion. They are not, however, sufficient in their material and perceivable form for the constitution of the relationship itself.
Let us consider how this applies in broad terms of human societal relations, keeping in mind that the same dynamic applies analogously to relationships, responsibilities, and needs of every kind.
Thus we know that we can address the needs of hungry people by feeding them and housing them. But we must do so in a human way. Circumstances may require that emergency priority be given to addressing the most proximate physical needs of people in desperation, and at first it may only be possible to give them the basic care necessary for protection and sustenance.
But we must not allow our concern to drift after a crisis is met, lest the marginal and precarious status of people in need becomes a long term, diminished condition of life. We must feed them, but we cannot be satisfied with this alone, with the fact that the people at the gates of our cities are not dying of hunger. The immediate physical needs of human beings are inextricably bound up with more profound exigencies proper to their value as bodily and spiritual persons.
We do not, however, have interpersonal relationships with chickens.
There are fundamental differences between persons and non-personal creatures that are evident by the way societies throughout history have considered them.
Though there is clearly something that can be (and was meant to be in God's original plan for creation) beautiful and mysterious in the interaction and even collaboration between humans and animals, it does not constitute interpersonal communion (this is a truth that Genesis 2 teaches us).
The care we consider sufficient and benevolent for chickens and even pets would be considered criminal and dehumanizing as a standard of care for humans.
People from impoverished or war-ravaged countries, who are forced to prioritize their basic needs of physical survival, have said about first-worlders: "Their dogs live better than we do!"
This is a provocation and a cry against injustice. These people do not mean to imply that their lives would be adequate and their humanity respected if they were just treated like European or American dogs.
In Jesus's relevant parable on this point (Like 16:19-31), we see that the dogs eat the scraps that fall from the rich man's table. Lazarus longs just to be able to eat those scraps as he lies starving by the gate. And though the scraps would have been better than nothing, it would hardly have been sufficient if the rich man had decided to have his servants toss some scraps at Lazarus when the meal was over.
Lazarus was his neighbor. He was a person. Our needy neighbors are persons. They need more than food. They need "not to be alone." They need relationships and communion. They need love.


For cosmetic use only. Do not swallow. Avoid contact with eyes. Store in a cool, shaded place, out of the reach of children. Temporary tooth sensitivity may occur. Discontinue if you experience sustained discomfort or irritation.


text:
\section{Introduction}

Food is the paramount necessity of human life. As the saying goes, \emph{we are what we eat}, food not only provides energy for life activities, but also plays a significant role in affecting human identity, social formation, history, and culture inheritance \cite{harris1998good}. In our daily life, food is intricately linked to people's convention, lifestyle, health and social activities. Nowadays, with the development of Internet and mobile applications, sharing recipes and food images on social platforms has become a widespread trend \cite{moed2007food}. Due to the massive amounts of data resource online, food computing has become a popular field, inciting numerous machine learning tasks such as ingredient recognition \cite{matsuda2012recognition,kagaya2014food}, food image retrieval \cite{wang2019learning} and recipe recommendation \cite{trattner2017investigating,sanjo2017recipe}. Among the research topics, Image-to-Recipe learning (im2recipe) is one of the most important problems due to its profound influence on health-oriented applications \cite{min2019survey}. For instance, food-health analysis applications are required to predict detailed nutrition contents and calorie information from food images, and a recipe-retrieval system is a necessary solution on this scenario.

\begin{figure}
	\centering
	\subfigure[Prior Work]{\includegraphics[width=1.5in]{figure1_b}}
	\subfigure[Our Work]{\includegraphics[width=1.5in]{figure1_a}}
	\caption{\textbf{A comparison between prior work (a) and the proposed MCEN (b).} We learn modality-consistent embeddings by capturing the interactions between images and recipes via latent variables. The dotted lines represents that the joint information is only used during training. At inference time, the embeddings are computed independently.  }
	\label{intro}
\end{figure}

Im2recipe is a challenging task since it involves highly variant foods images and expatiatory textual recipes. A typical recipe consists of a list of ingredients and cooking instructions which may not directly align with the appearance of the corresponding food image. Typically, recent efforts have formulated im2recipe as a cross-modal retrieval problem \cite{salvador2017learning,marin2018recipe1m,carvalho2018cross,zhu2019r2gan}, to align matching recipe-image pairs in a shared latent space with retrieval learning approaches. Concretely, prior work builds two independent networks to encode textual recipes (ingredients and cooking instructions) and food images into embeddings respectively. And the retrieval loss object is learned to gather matching pairs and differentiate dissimilar items. Though existing methods are expressive and powerful, there remain two major concerns. 1) Current systems encode images and texts with two different networks independently. However, such independence brings barriers between modalities, resulting in obstacles to discover latent semantic alignments across modalities. Consequently, such approaches thus could suffer from polysemous instances \cite{song2019polysemous}. 2) The recipe representations are obtained based on fixed pre-trained skip-thought vectors \cite{kiros2015skip}, leading to highly diversities between textual and image feature spaces.

To alleviate such limitations, we strive to take a step towards capturing joint information of different modalities and injecting the cross-modal alignments into the embedding learning processes on both sides. We introduce \textbf{M}odality-\textbf{C}onsistent \textbf{E}mbedding \textbf{N}etwork (MCEN) which learns joint cross-modal representations for textual recipes and dish images. The major idea is to exploit the interactions between visual and textual features explicitly and share the cross-modal information to the embedding spaces of both modalities with stochastic latent variable models. The stochastic variable is leveraged to capture the latent correlations between modalities during training, while the embeddings can still be calculated independently at test time for high efficiency and flexibility. Moreover, The randomness introduced by latent variables is also beneficial for handling polysemous instances where one recipe corresponds with multiple images.

In a nutshell, the \textbf{main contribution} of this work is threefold:

\begin{itemize}
	\setlength{\itemsep}{2pt}
	\setlength{\parsep}{0pt}
	\setlength{\parskip}{0pt}
	\item We propose a novel cross-modal retrieval framework to obtain modality-consistent embeddings by explicitly capturing the correlations between recipes and food images with latent variables.
	\item We exploit the latent alignments during training with cross-modal attention mechanism and replace it with prior condition at inference time for efficiency.
	\item We propose a task-specific encoder for textual recipes based on hierarchical attentions, which cannot only adapt to the interaction with images, but also simplify and accelerate the training and inference procedure.
\end{itemize}

We conduct experiments on the challenging benchmark Recipe1M \cite{salvador2017learning} and the results demonstrate that our model significantly outperforms all state-of-the-art approaches on the cross-modal recipe retrieval problem and requires less computational overhead.

\section{Related Work}

\paragraph{Computational Cooking.}
Food and cooking are essential parts of human life, which are closely relevant to health \cite{trattner2017investigating}, social activities, bromatology, dietary therapy and culture \cite{harris1998good}, etc., profoundly affecting the quality of life. Therefore, research involving cooking recipes has drawn considerable attention. Food and cooking provide rich attributes on multiple channels, including both visual content (e.g., dish pictures) and texts (e.g., dish descriptions and cooking instructions). Current literature leverages the attributes in various ways. Typically, recent examples in computer visions are food classification and recognition \cite{chen2016deep,liu2016deepfood,lee2018cleannet,zhou2016fine,kagaya2014food}, and retrieval of captions \cite{engilberge2018finding,chen2018deep}, ingredients \cite{chen2017cross, chen2018deep} or recipe instructions \cite{carvalho2018cross, salvador2017learning,min2017being,min2017delicious} according to dish images, while researchers from natural language processing community usually focus on such applications as recipe recommendation \cite{trattner2017investigating,sanjo2017recipe}, aligning instructions with video and speech \cite{malmaud2015s}, recipe texts generation from flow graph \cite{mori2014flowgraph2text}, workflow generation from recipe texts \cite{yamakata2016method}, cooking action tracking \cite{bosselut2018simulating}, recipe representation \cite{malmaud2014cooking}, checklist recipe generation \cite{kiddon2016globally} and recipe-based question answering \cite{yagcioglu2018recipeqa, malmaud2014cooking}. Moreover, there is also some work using machine learning approaches to connect health with food attributes, such as prediction of nutrient \cite{kusmierczyk2016online} or energy \cite{meyers2015im2calories}, and healthy recipe recommendation \cite{elsweiler2017exploiting, trattner2017investigating, yang2017yum}. All these efforts contribute to the prosperity of food computation and understanding, bridging the gap between machine learning applications and people's daily life.

Recent introductions of large-scale food-related datasets have further accelerated the research improvements on food understanding. Considering the application purpose, the datasets can be categorized into two groups: food recognition \cite{bossard2014food,matsuda2012recognition} and cross-modal recipe retrieval \cite{salvador2017learning, marin2018recipe1m, min2017being, min2017delicious, chen2016deep, salvador2017learning}. We focus on recipe retrieval task in this paper, aiming at retrieval relevant cooking recipes with respect to the image query and vise versa. Typically, the datasets for retrieval generally incorporate both food images and other information such as ingredients, structured cooking instructions and flavor attributes. Among the datasets, Recipe1M \cite{salvador2017learning} is the most well curated large-scale dataset
with preprocessed English textual information and we evaluate the effectiveness of our method on it in this paper.

\paragraph{Text-Image Retrieval.}
Our work is related to current approaches on multi-modal retrieval task, where the key problem is to measure the similarity between a text and an image. The major challenge of this issue lies in the modality-gap, which means that the feature spaces of different modalities largely diverse from each other. Text-image retrieval is at meeting point between computer vision and natural language communities, attracting research attentions over decades \cite{lew2006content}. Traditional approaches formulate this issue as either a language modeling task \cite{kiros2014multimodal} or a correlation maximization problems \cite{rasiwasia2010new,gong2014multi} using canonical correlation analysis (CCA) \cite{hotelling1936relations}. Recently, many efforts have been made to build end-to-end retrieval systems leveraging deep learning methods \cite{srivastava2012multimodal,andrew2013deep,feng2014cross,yan2015deep,peng2016cross}. Another avenue is to improve the triplet loss with hard negative mining \cite{schroff2015facenet}, such as \cite{frome2013devise,wang2016learning,faghri2017vse++}.

Despite of the progress, the above approaches encode different modalities into independent feature spaces, suffering from modality gap between heterogenetic contents. To address this issue, recent works incorporate attention mechanism to capture the latent alignment relationships between words and different image regions \cite{huang2017instance,lee2018stacked,li2019visual, wang2019camp}. Though expressive, these methods require massive computational overhead during inference since the cross-modal attention scores between a query and each item in the reference set need to be calculated, limiting the scalability to large-scale retrieval scenario. In this paper, we leverage latent variables to incorporate cross-modal attention mechanism into retrieval tasks during training but maintain independent calculations for different modalities respectively at inference time.

Image-to-recipe is a newly proposed task and is formulated as a cross-modal learning task by recent efforts \cite{chen2017cross,salvador2017learning}, to retrieve the relevant recipes based on image queries. Following these settings, several inspiring methods have been introduced to improve the retrieval performance by using such techniques as additional textual feature \cite{chen2018deep}, semantic information \cite{carvalho2018cross} and adversarial learning \cite{zhu2019r2gan,wang2019learning}.

\section{Modality-Consistent Embedding Network}
\subsection{Overview}
In this section we introduce the methodology of the proposed Modality-Consistent Embedding Network (MCEN).

\textbf{Problem Formulation.}
The aim of the proposed framework is to measure the similarity between food images and the relevant textual recipes. Formally, denote $\{\mathbf{v}^i,\mathbf{r}^i\}_{i=1}^N$ as a set of $N$ image-recipe pairs where an image $\mathbf{v}^i\in\mathbf{V}$ and a recipe $\mathbf{r}^i\in\mathbf{R}$. The notations $\mathbf{V}$ and $\mathbf{R}$ denote the visual and recipe spaces. It should be noted that one recipe corresponding to multiple images is allowed. A recipe $\mathbf{r}^{i}$ consists of a set of ingredients $\mathbf{X}^{ing, i}$ and a list of  cooking instructions $\mathbf{X}^{ins, i}$. An image $\mathbf{v}^i$ contains the appearance of a completed dish. Importantly, the ingredients and cooking instructions of a recipe may not directly align with the appearance of the matching image, which brings additional heterogeneity challenge compared to traditional cross-modal retrieval tasks.

Considering the information gap between modalities, we set our target to learn the mapping functions from observed data to the embedding distributions as $\mathbf{V}\rightarrow\mathbf{E}^{v}$ and $\mathbf{R}\rightarrow\mathbf{E}^{r}$, where $\mathbf{E}^{v}\in\mathbb{R}^{d}$ and $\mathbf{E}^{r}\in\mathbb{R}^{d}$ denote the distributions of $d$-dimensional image embedding and recipe embedding respectively, so that a picture is closer to the corresponding recipe than any other image in the latent space.

\begin{figure*}
	\centering
	\includegraphics[width=17.5cm]{figure2}
	\caption{\textbf{The architecture and training flow of MCEN.} The red dotted lines denote that the cross-modal attention components only work during training and are omitted at testing time. The system is comprised of three major components: a recipe encoder, an image encoder, and a modality-consistent embedding component. The interaction between images and texts is captured with latent variables and shared by both latent spaces.}
	\label{arch}
\end{figure*}

\textbf{Architecture.}
The architecture of MCEN is illustrated in Figure \ref{arch}. The system consists of three major modules: a recipe encoder, an image encoder and an embedding learning component for modality-consistent space modeling. Through the training flow, the visual feature is extracted by feeding the food picture $\mathbf{v}^i$ to the CNN-based image encoder. Meanwhile, the high-level representations of instructions and ingredients are obtained by hierarchical attention-based RNN encoders. Then these representations are then fed to cross-modal attention components to exploit the interactions between images and texts. The cross-modal correlations are then leveraged to estimate the posterior distributions of embeddings with neural variational inference \cite{kingma2013auto,rezende2014stochastic}. With this method, we can discriminate training and inference process so as to reduce cross-modal computation at prediction time. To keep modality consistency, we align the distributions of latent representations by minimizing the KL-divergence of priors of different modalities. Finally, the latent representations sampled from the posterior distributions are passed to feed-forward layers to obtain the final embeddings of images and recipes respectively. The entire model is trained end-to-end with retrieval learning object.

The major novelty of MCEN comes from the incorporation of cross-modal correlation modeling with latent variables. MCEN captures the latent alignment relationships between images and texts during training while at inference time we do not require cross-modal attention since the posterior distribution is replaced by the prior during test. Though there exists prior work that focuses on modeling correlations between modalities \cite{lee2018stacked,li2019visual}, these approaches come with high computation overhead since the alignment score between a query and each reference instance needs computing as many times as the size of reference set \cite{song2019polysemous}. Conversely, MCEN obtains embeddings of different modalities independently during inference, which significantly reduces the computational overhead. Moreover, almost all prior methods require fixed pre-trained instruction vectors for recipes while parameters for image encoding are updated with respect to the retrieval object. The isomerism in training process leads to a diversity between feature spaces of images and recipes. In this work, the architecture of MCEN recipe encoder is quite different from prior systems and can be trained end-to-end from scratch.

\subsection{Image Encoder}
Given a food picture $\mathbf{v}$, the image encoder is responsible to extract the abstract features of the input. Different from previous methods, we use the output of the last residual block (res5c) of ResNet-50 \cite{he2016deep} which consists of $7 \times 7=49$ columns of 2048 dimensional convolutional outputs, denoted by $\mathbf{H}^{v}=(\mathbf{h}_1^{v},\mathbf{h}_2^{v},\cdots,\mathbf{h}_{49}^{v},)$. To obtain the representation for the image hidden states, we propose to use an attention layer, which estimates the importance of each hidden vector. Since a dish image may contain multiple objects that are not relevant to the recipe (i.e., forks and flowers), the aim of attention model is to force the encoder to focus more on regions that may contribute to the retrieval object.

Formally, the image representation $\mathbf{s}^{v}$ is calculated with the weighted summation of convolutional states as:
\begin{equation}
	\mathbf{s}^{v}=\sum_{i=1}^{49}\alpha_{i}^{v}\mathbf{h}_{i}^{v},
\end{equation}
where $\alpha_{i}^{v}$ is the attention score at position $i$, representing the importance of this region, calculated by:
\begin{equation}
	\alpha_{i}^{v}={\rm softmax}(\mathbf{v}_v^\top{\rm tanh}(\mathbf{W}_v\mathbf{q}_v + \mathbf{U}_v\mathbf{h}_{i}^{v})),
\end{equation}
where $\mathbf{W}_v$, $\mathbf{U}_v$ and $\mathbf{v}_v$ are trainable matrices and vector. $\mathbf{q}_v$ is the attention query vector. Here, it is a trainable vector initialized from scratch. For sake of writing convenience, we call such attention layer as \emph{Attention Pooling} and the input annotations ($\mathbf{H}^{v}$) as \emph{Attention Context}.

\subsection{Recipe Encoder}
In the recipe branch, ingredients and instructions are encoded separately with similar networks. Since the ingredients or instructions of a recipe usually comprise multiple sentences, we use a hierarchical attention-based model to extract textual features. Each instruction/ingredient is first fed to a word-level bi-directional recurrent neural network (bi-RNN) with gated recurrent unit (GRU) \cite{cho2014learning} and the final word-level representations are calculated with attention pooling mechanism (Equation 1-2) where the RNN hidden states are used as the attention contexts. Denote $\mathbf{H}^{ins}=(\mathbf{h}^{ins}_{1}\cdots\mathbf{h}^{ins}_{m})$ and $\mathbf{H}^{ing}=(\mathbf{h}^{ing}_{1}\cdots\mathbf{h}^{ing}_{n})$ as the feature sequences of instructions and ingredients respectively, where $m$ and $n$ are the numbers of instructions and ingredients of a recipe, and each element $\mathbf{h}^{ins}_{t}/\mathbf{h}^{ing}$ is the abstract representation of an instruction/ingredient. To model the correlations between instructions and ingredients, we employ the attention-based RNN decoder \cite{bahdanau2014neural}, which takes $\mathbf{H}^{ins}$ as the sequential input and $\mathbf{H}^{ing}$ as the contexts respectively. The output of the RNN decoder is denoted as $\mathbf{H}^c=(\mathbf{h}_1^c,\cdots,\mathbf{h}_{m}^c)$ which contains the joint information of both instructions and ingredients. Then, $\mathbf{H}^c$, $\mathbf{H}^{ins}$ and $\mathbf{H}^{ing}$ are fed to independent sentence-level bi-RNNs and attention pooling layers to obtain the sentence-level representations, denoted as $\mathbf{s}^c$, $\mathbf{s}^{ins}$, and $\mathbf{s}^{ing}$ respectively. The final feature representation of the recipe is obtained by concatenating the three sentence representations as:
\begin{equation}
	\mathbf{s}^r=[{\mathbf{s}^{c}}^\top, {\mathbf{s}^{ins}}^\top, {\mathbf{s}^{ing}}^\top]^\top.
\end{equation}

\subsection{Modality-Consistent Embedding}
It is challenging to align feature representations of multiple modalities when the features are extracted with independent networks. To alleviate this issue, we incorporate latent variables to capture the interactions between modalities. This method converts the embedding computation into a generative process. Taking the image side for instance, the probability to generate a specific embedding $\mathbf{e}^v$ for a given image $\mathbf{v}$ is modeled as:
\begin{equation}
	p(\mathbf{e}^v|\mathbf{v})=p(\mathbf{e}^v|\mathbf{z}^v,\mathbf{v})p(\mathbf{z}^v|\mathbf{v}),
\end{equation}
where the latent vector $\mathbf{z}^v$ is assumed to capture the correlations between $\mathbf{v}$ and the corresponding recipe $\mathbf{r}$. The posterior of $\mathbf{z}^v$ should hence be conditioned on both the recipe $\mathbf{r}$ and image $\mathbf{v}$, denoted as $p(\mathbf{z}^v|\mathbf{v},\mathbf{r})$. The prior of latent variables is usually formulated as a standard Gaussian distribution, which may reduce the effectiveness in generation \cite{dai2019diagnosing}. Here we propose to estimate the prior distribution with a neural network model that jointly learns the prior knowledge and excavates cross-modal alignments based on single modality, denoted as $p(\mathbf{z}^v|\mathbf{v})$. To simplify the generative process, both prior and posterior distributions for latent variables are assumed to be Gaussian distributions. Concretely, the generative story is as follows. We sample a latent variable $\mathbf{z}^v$ from the prior Gaussian distribution as:
\begin{gather}
	\mathbf{z}^v|\mathbf{v}\sim\mathcal{N}(\boldsymbol{\mu}_v,{\rm diag}(\boldsymbol{\sigma}_v^2))\\
	\boldsymbol{\mu}_v=\mathbf{W}_\mu^v \mathbf{s}^v + \mathbf{b}_\mu^v\\
		\boldsymbol{\sigma}_v={\rm softplus}(\mathbf{W}_\sigma^v \mathbf{s}^v + \mathbf{b}_\sigma^v),
\end{gather}
where $\mathbf{W}_\mu^v$, $\mathbf{W}_\sigma^v$ and $\mathbf{b}_\mu^v$, $\mathbf{b}_\sigma^v$ are weight matrices and bias. Conditioned on the latent variable $\mathbf{z}^v$, we generate the final image embedding as:
\begin{equation}
	\mathbf{e}^v=f_v(\mathbf{z}^v),
\end{equation}
where $f_v$ is a mapping function implemented as a one-layer neural network with tanh activation.

Estimation of Equation 4 can be challenging since the distributions are intractable. We leverage neural variational inference \cite{kingma2013auto,rezende2014stochastic} to optimize the evidence lowerbound (ELBO) as:
\begin{equation}
	\mathbb{E}_{q(\mathbf{z}^v|\mathbf{v},\mathbf{r})}(\log p(\mathbf{e}^v|\mathbf{z}^v,\mathbf{v}))-D_{KL}(q(\mathbf{z}^v|\mathbf{v},\mathbf{r})\Vert p(\mathbf{z}^v|\mathbf{v})),
\end{equation}
where $D_{KL}(\cdot)$ is the Kullback-Leibler divergence and $q(\mathbf{z}^v|\mathbf{v},\mathbf{r})$ is the approximate posterior, estimated as:
\begin{gather}
\mathbf{z}^v|\mathbf{v},\mathbf{r}\sim\mathcal{N}(\boldsymbol{\mu}_v^*,{\rm diag}({\boldsymbol{\sigma}_v^*}^2))\\
\boldsymbol{\mu}_v^*=\mathbf{W}_\mu^{v*} \mathbf{s}^{v*} + \mathbf{b}_\mu^{v*}\\
\boldsymbol{\sigma}_v^*={\rm softplus}(\mathbf{W}_\sigma^{v*} \mathbf{s}^{v*} + \mathbf{b}_\sigma^{v*}),
\end{gather}
where $\mathbf{W}_\mu^{v*}$, $\mathbf{W}_\sigma^{v*}$ and $\mathbf{b}_\mu^{v*}$, $\mathbf{b}_\sigma^{v*}$ are trainable matrices and bias, which are independent from the prior model. The cross-modal representation $\mathbf{s}^{v*}$ is obtained with an attention pooling layer which takes the recipe representation $\mathbf{s}^r$ as the query vector and image region features $\mathbf{H}^v$ as the attention contexts. The lowerbound of the likelihood can be optimized by minimizing the triplet loss, formalized as:
\begin{equation}
	\mathcal{L}_{ret}^v=[s(\mathbf{e}^v_a, \mathbf{e}^i_n)-s(\mathbf{e}^v_a, \mathbf{e}^i_p)+m]_+
\end{equation}
where $s(\cdot)$ expresses the cosine similarity between two vectors, and $m$ is the margin of error. Subscripts $p$, $n$ and $a$ refer to positive, negative and anchor of a triplet respectively.

Cases are similar on the recipe side and the distinction lies in the calculation of cross-modal representation $\mathbf{s}^{r*}$ for posterior approximation $q(\mathbf{z}^r|\mathbf{v},\mathbf{r})$. Here, we obtain $\mathbf{s}^{r*}$ with the similar manner to $\mathbf{s}^r$ (Equation 3) but replace the original trainable query vector with the image feature $\mathbf{s}^i$.  Formally, the final retrieval learning object is defined as:
\begin{equation}
	\mathcal{L}_{ret} + \alpha\mathcal{L}_{KL},
\end{equation}
where $\mathcal{L}_{ret}$ is the summation of the triplet losses for image-to-recipe and recipe-to-image retrieval, and $\alpha$ is a trade-off hyper-parameter. $\mathcal{L}_{KL}$ is the summation of the KL divergences on both sides:
\begin{equation}
	\begin{split}
	\mathcal{L}_{KL} = D_{KL}(q(\mathbf{z}^v|\mathbf{v},\mathbf{r})\Vert p(\mathbf{z}^v|\mathbf{v})) + \\ D_{KL}(q(\mathbf{z}^r|\mathbf{v},\mathbf{r})\Vert p(\mathbf{z}^r|\mathbf{r})).
	\end{split}
\end{equation}

Moreover, as discussed, we aim to align the distributions of both modalities. For this end, we simply push the prior embedding distributions of both modalities together by minimizing the following KL-divergence:
\begin{equation}
	\mathcal{L}_{cos}=D_{KL}(p(\mathbf{z}^v|\mathbf{v})\Vert p(\mathbf{z}^r|\mathbf{r})).
\end{equation}

\subsection{Cross-Modal Reconstruction}
Recent work \cite{zhu2019r2gan,wang2019learning} has proved the effectiveness of reconstruction loss on cross-modal recipe retrieval, since it encourages the embedding of one modality covers the corresponding information of the other modality. However, such an approach introduces additional network parameters to reconstruct the original images and recipes, which are too cumbersome for training a retrieval system. In this work we propose a much conciser method for cross-modal reconstruction. Instead of recovering the entire information of the original inputs, we only reconstruct the latent representations with the learned embeddings as:
\begin{gather}
	\mathbf{s}^{r'} = f^v_{r}(\mathbf{e}^v),\\
		\mathbf{s}^{v'} = f^v_{v}(\mathbf{e}^r),
\end{gather}
where $f^v_{r}$ and $f^v_{v}$ are mapping functions, implemented as two-layer neural networks. The formal reconstruction loss is formulated as:
\begin{equation}
	\mathcal{L}_{rec}=P(\mathbf{s}^{r'}, \mathbf{s}^{r}) + P(\mathbf{s}^{v'}, \mathbf{s}^{v}),
\end{equation}
where $P(\cdot)$ computes Pearson's correlation coefficient.

\subsection{Training and Inference}
The overall training object of MCEN is formulated as:
\begin{equation}
	\mathcal{L}=\mathcal{L}_{ret}+\alpha\mathcal{L}_{KL}+\beta\mathcal{L}_{cos}+\gamma\mathcal{L}_{rec},
\end{equation}
where $\alpha$, $\beta$ and $\gamma$ are hyper-parameters which balance the preference of different components.
The entire model can be trained end-to-end with the reparameterization trick \cite{kingma2013auto,rezende2014stochastic}. During inference, the latent variables are fixed to the expectation of prior distribution to stabilize the retrieval performance.

\section{Experiments}
\subsection{Settings}
\textbf{Dataset.}
The experiments are conducted on Recipe1M benchmark \cite{salvador2017learning}, a large-scale collection for recipe retrieval, including cooking instructions along with food images. The dataset consists of over 1M textual recipes and around 900K images. We use the same preprocessed samples provided by \cite{salvador2017learning} and we finally obtain 238,399 matching pairs of recipes and images for training, 51,119 pairs for validation and 51,303 pairs for test respectively. Moreover, it should be noted that we do not incorporate the additional semantic labels used by prior work \cite{salvador2017learning,carvalho2018cross,zhu2019r2gan}, such as food-classes and labels of commonly used ingredients.

\textbf{Metrics.}
We utilize the same metrics as the prior work \cite{salvador2017learning,carvalho2018cross,zhu2019r2gan}. Concretely, we compute median rank (MedR) and recall rate at top K (R@K) on sampled subsets in the test partition to evaluate the retrieval  performance. The sampling process is repeated for 10 times and the mean scores are reported. MedR measures the median retrieval rank position of true positives over all test samples, and the ranking position starts from 1. R@K refers to the percentage of queries for which matching instances are ranked among the top K results.

\begin{table*}
	\begin{center}
		\begin{tabular}{llcccccccc}
			\toprule
			\multirow{2}{0.6cm}{\textbf{Size}} & \multirow{2}{1.2cm}{\textbf{Methods}} & \multicolumn{4}{c}{\textbf{Image-to-Recipe}} & \multicolumn{4}{c}{\textbf{Recipe-to-Image}}\\
			\cline{3-10}
			& & \textbf{MedR} & \textbf{R@1} & \textbf{R@5} & \textbf{R@10} & \textbf{MedR} & \textbf{R@1} & \textbf{R@5} & \textbf{R@10} \\
			\midrule
			\multirow{8}{0.6cm}{1K} & Random & 500 & 0.1 & 0.5 & 1.0 & 500 & 0.1 & 0.5 & 1.0 \\
			& CCA \cite{salvador2017learning} & 15.7 & 14.0 & 32.0 & 43.0 & 24.8 & 9.0 & 24.0 & 35.0 \\
			& JE \cite{salvador2017learning} & 5.2 & 24.0 & 51.0 & 65.0 & 5.1 & 25.0 & 52.0 & 65.0 \\
			& ATTEN \cite{chen2018deep} & 4.6 & 25.6 & 53.7 & 66.9 & 4.6 & 25.7 & 53.9 & 67.1 \\
			& AdaMine \cite{carvalho2018cross} & 2.0 & 39.8 & 69.0 & 77.4 & 2.0 & 40.2 & 68.1 & 78.7 \\
			& R${\rm^{2}}$GAN \cite{zhu2019r2gan}& 2.0 & 39.1 & 71.0 & 81.7 & 2.0 & 40.6 & 72.6 & 83.3 \\
			& ACME \cite{wang2019learning} & 2.0 & 44.3 & 72.9 & 81.7 & 2.0 & 45.4 & 73.4 & 82.0 \\
			& MCEN (ours) & \textbf{2.0}$\pm 0.0$ & \textbf{48.2}$\pm 0.9$  & \textbf{75.8}$\pm 1.1$ & \textbf{83.6}$\pm 0.9$ & \textbf{1.9}$\pm 0.3$ & \textbf{48.4}$\pm 1.0$ & \textbf{76.1}$\pm 0.9$ & \textbf{83.7}$\pm 1.1$ \\
			\midrule
			\multirow{6}{0.6cm}{10K} & JE \cite{salvador2017learning} & 41.9 & - & - & - & 39.2 & - & - & - \\
			& ATTEN \cite{chen2018deep} & 39.8 & 7.2 & 19.2 & 27.6 & 38.1 & 7.0 & 19.4 & 27.8 \\
			& AdaMine \cite{carvalho2018cross} & 13.2 & 14.9 & 35.3 & 45.2 & 12.2 & 14.8 & 34.6 & 46.1 \\
			& R${\rm^{2}}$GAN \cite{zhu2019r2gan}& 13.9 & 13.5 & 33.5 & 44.9 & 12.6 & 14.2 & 35.0 & 46.8 \\
			& ACME \cite{wang2019learning} & 10.0 & 18.1 & 39.9 & 50.8 & 9.2 & 20.1 & 41.5 & 51.9 \\
			& MCEN (ours) & \textbf{7.2}$\pm0.4$ & \textbf{20.3}$\pm 0.3$ & \textbf{43.3}$\pm 0.3$ & \textbf{54.4 }$\pm 0.2$ & \textbf{6.6}$\pm 0.5$ & \textbf{21.4}$\pm 0.3$ & \textbf{44.3}$\pm 0.3$ & \textbf{55.2}$\pm 0.3$ \\
			\bottomrule
		\end{tabular}
	\end{center}
	\caption{\textbf{Retrieval Results of baselines.} The cross-modal retrieval performance is evaluated with MedR (lower is better) and R@K (higher is better). It should be noted that we do not incorporate pretraining embeddings and additional food-class labels which are utilized by prior approaches.}
	\label{main_result}
	\vspace{-1mm}
\end{table*}

\textbf{Implementation.}
For the image encoder, ResNet-50 \cite{he2016deep} pretrained on ImageNet \cite{deng2009imagenet} is used as the initialization weight. On the recipes side, the dimension of all hidden states is set to 300. Different from prior work, we do not use pretrained word embeddings. The entire recipe encoder is trained from scratch and the trainable parameters are initialized uniformly between $[-0.02, 0.02]$.

The dimension of final embeddings and all hidden states for neural inference is 1024. The margin of error $m$ is 0.3 and the hyper-parameters $\alpha$, $\beta$, $\gamma$ are set to 0.1, 0.002 and  0.008 respectively. The norm of gradient is clipped to be between $[-5, 5]$. We employ Adam solver \cite{kingma2014adam} with $\beta_1=0.9$, $\beta_2=0.999$ and $\epsilon=10^{-8}$ as the optimizer and the corresponding initial learning rate is set to $10^{-4}$. The model is trained end-to-end with batch-size 32.

To train the model efficiently, we utilize two training strategies. First, as it is observed by other work \cite{bowman2016generating}, the loss for sequence modeling suffers from KL-divergence vanishing. To address this issue, we initialize $\alpha$ as $10^{-4}$ and gradually increase it to 0.1 as the training progress runs. Moreover, incorporating two independent stochastic variables can reduce the convergence speed. We therefore leverage a stage-wise strategy. Specifically, we fix the latent representation on the image side $\mathbf{z}^{r}$ as the mean of prior $\boldsymbol{\mu}^{r}$ and focus on training the recipe part. Then we alternatively train the posterior parameters on the image side after several epochs. Finally, early stopping strategy is applied and the model with best R@1 score on validation set is selected for testing.

\textbf{Comparison.}
The proposed MCEN is compared against several SOTA approaches:
\begin{itemize}
	\setlength{\itemsep}{1pt}
	\setlength{\parsep}{1pt}
	\setlength{\parskip}{1pt}
	\item CCA \cite{hotelling1936relations}, the Canonical Correlation Analysis method. The results are from \cite{salvador2017learning}.
	\item JE \cite{salvador2017learning}, a method to learn the joint embedding space of images and texts with pairwise cosine loss. This method also incorporates the classification task as a regularization.
	\item ATTEN \cite{chen2018deep}, a hierarchical attention model for cross-modal recipe retrieval. This approach also incorporates title information to extract recipe features.
	\item AdaMine \cite{carvalho2018cross}, a two-level retrieval approach which injects the semantic information into the triplet object.
	\item R${\rm^{2}}$GAN \cite{zhu2019r2gan}, a GAN-based method which learns cross-modal retrieval and multi-modal generation simultaneously.
	\item ACME \cite{wang2019learning}, the state-of-the-art method on cross-modal recipe retrieval task, which improves modality alignment using multiple GAN components. In our experiments, we use the released pre-trained model and report the results on our sampled test set.
\end{itemize}

\subsection{Main Results}
The main results on cross-modal retrieval task are listed in Table \ref{main_result}. Generally, the proposed MCEN consistently outperforms all baselines with obvious margin across all evaluation metrics and test sets. On the 1K set, MCEN achieves 2.0 median rank, which matches the SOTA results. In terms of R@K, MCEN achieves promising performance, beating all baselines including the to-date best approach ACME across all metrics on both image-to-recipe and recipe-to-image tasks.

On the 10K setting, the performances of all models decrease significantly since the retrieval task becomes much harder. As the size of subset increases, the gap between MCEN and previous methods becomes larger. Compared with the SOTA ACME method, our model achieves almost 30\% improvements on MedR metric over both im2recipe and recipe2im tasks, indicating the robustness of MCEN.

\subsection{Ablation Studies}
To evaluate the contributions of different components, we conduct ablation study on several variants of architectures detailedly. We depict  the variants of MCEN in Figure \ref{ablation}. MCEN-vanilla (Figure \ref{ablation} (b)) is the simplest architecture which does not incorporate any latent variables. The final embeddings $\mathbf{e}^r$ and $\mathbf{e}^v$ are obtained by:
\begin{align}
	\mathbf{e}^{r} &= g_r(\mathbf{s}^{r}),\\
	\mathbf{e}^{v} &= g_v(\mathbf{s}^{v}),
\end{align}
where $\mathbf{s}^{r}$ and $\mathbf{s}^{r}$ are the output of recipe encoder (Equation 3) and image encoder (Equation 1) respectively. The mappings $g_r$ and $g_v$ are implemented as two-layer neural networks with tanh activations. We also propose two variant models which leverage latent variables on either image (Figure \ref{ablation} (c)) or recipe side (Figure \ref{ablation} (d)). Besides, the performance of MCEN without reconstruction component (Equation 17-18) is also reported. For all variants derived from MCEN, the modality-consistency loss (Equation 15) is removed.

 \begin{figure}
	\begin{center}
		\includegraphics[width=8cm]{figure3}
	\end{center}
	\caption{Variants of architectures derived from MCEN.}
	\label{ablation}
	\vspace{-2.1mm}
\end{figure}

The retrieval results of different variant models on 1K subset are listed in Table \ref{tab_ab}. Not surprisingly, MCEN outperforms all variants with all evaluation metrics. It can be observed that the performance of MCEN-vanilla is similar to ACME (Table 1), indicating the effectiveness of the proposed architecture of the recipe encoder. Moreover, an interesting finding is that MCEN-image outperforms MCEN-recipe. A possible reason could be that, compared with rigmarole instructions, the relative semantic weights of different regions in an image are easier to be exploited.

\begin{table*}
	\begin{center}
		\begin{tabular}{lcccccc}
			\toprule \multirow{2}{2cm}{\textbf{Methods}} & \multicolumn{3}{c}{\textbf{Image-to-Recipe}} & \multicolumn{3}{c}{\textbf{Recipe-to-Image}}\\
			\cline{2-7}
			& \textbf{R@1} & \textbf{R@5} & \textbf{R@10} & \textbf{R@1} & \textbf{R@5} & \textbf{R@10} \\
			\midrule
			MCEN-vanilla & 44.5 & 72.3 & 80.7 & 44.9 & 72.8 & 80.9 \\
			MCEN-recipe  & 45.8 & 73.1 & 81.3  & 46.1 & 73.3 & 81.5 \\
			MCEN-image & 47.6 & 75.1 & 83.0 & 47.8 & 75.4 & 83.3\\
			MCEN w/o reconstruction & 46.4 & 75.4 & 83.1 & 47.8 & 75.7 & 83.3 \\
			MCEN & \textbf{48.2}  & \textbf{75.8} & \textbf{83.6} & \textbf{48.4} & \textbf{76.1} & \textbf{83.7}\\
			\bottomrule
		\end{tabular}
	\end{center}
	\caption{\textbf{Ablation Study}. The models are evaluated in terms of R@K with 1K subset.}
	\label{tab_ab}
\end{table*}

\begin{table}
	\begin{center}
		\begin{tabular}{lccc}
			\toprule
			\multirow{2}{1.5cm}{\textbf{Methods}} & \multirow{2}{1.2cm}{\textbf{\#Para}} & \multicolumn{2}{c}{\textbf{Speed}} \\
			\cline{3-4}
			& & \textbf{Train} & \textbf{Test}\\
			\midrule
			AdaMine \cite{carvalho2018cross} &  46.3M & 117.8  & 197.9 \\
			R${\rm^{2}}$GAN \cite{zhu2019r2gan} & 89.9M & 30.3 & 195.4 \\
			ACME \cite{wang2019learning} & 98.6M & 30.7 & 111.7 \\
			\midrule
			MCEN-vanilla & 48.9M & 57.6 & 194.9\\
			MCEN-recipe & 59.3M & 45.0 & 189.1 \\
			MCEN-image & 59.3M & 45.2& 188.7 \\
			MCEN & 69.6M & 42.7 & 185.8 \\
			\bottomrule
		\end{tabular}
	\end{center}
	\caption{\textbf{Statistics of parameters, training and testing speed (pairs/second).} All models are evaluated with the same settings on a single Titan XP GPU with batch-size 32. This comparison could be unfair since all the baselines require additional computational overhead for pre-training skip-thought vectors.}
	\label{speed}
	\vspace{-2mm}
\end{table}

\subsection{Analysis}
\paragraph{Parameters and Speed.}
We list the numbers of parameters and speeds of different systems in Table \ref{speed}. We can observe that although the inference network on either side introduces about 10.4M parameters, the additional parameters do not significantly decrease the training and test speed. Compared with the current SOTA ACME \cite{wang2019learning}, MCEN contains about 30\% less parameters and generates cross-modal embeddings with almost double speed, proving the high efficiency of the proposed architecture. The major reason for the gap between MCEN and ACME is that ACME requires additional overhead for adversarial learning.

\paragraph{Effectiveness of Cross-modal Attention.}
To better understand what has been learned by the cross-modal attention components, we visualize the intermediate results with attention. As shown in Figure \ref{fig_att}, the attention model learns to focus more on the valid regions containing food and ignore the background. Consequently, the final image embeddings are more constrained and not likely to be affected by noises (i.e. fork and  tablecloth) or polysemous instances.

On the recipe side, as shown in Figure \ref{ingr_att}, the attention model learns to focus on ingredients which can be interpreted based on visual connections with the food images. Taking the first sub-picture in Figure \ref{ingr_att} for instance, the attention model attaches highest weights to the three ingredients: \emph{steak}, \emph{ketchup} and \emph{baguettes}, which make up nearly the entire dish.
These observations demonstrate that the proposed MCEN learns to capture the semantic alignment relationships between images and recipes.

\begin{figure}
\begin{center}
	\includegraphics[width=8.5cm]{figure4}
	\caption{\textbf{Attention map of sampled images.} The darker color, the higher attention score.}
	\label{fig_att}
	\vspace{-3mm}
\end{center}
\end{figure}

\begin{figure}
	\begin{center}
		\includegraphics[width=8.5cm]{figure5}
		\caption{\textbf{Visualization of ingredient attention.} The model focuses on important ingredients with high attention scores.}
		\label{ingr_att}
		\vspace{-3mm}
	\end{center}
\end{figure}
\section{Conclusion and Future Work}
In this paper, we propose a Modality-Consistent Embedding Network, namely MCEN, for cross-modal recipe retrieval. The proposed model focuses on modeling the interactions between food images and textual recipes during training with latent variables. Concretely, the latent variables are modeled based on cross-modal attention mechanisms during training while the embeddings of different modalities are still calculated independently during inference. We conduct experiments on the challenging Recipe1M dataset and the evaluation results with different metrics demonstrate the efficiency and effectiveness of MCEN. In the future, we are interested in incorporating pretrained language models into cross-modals analysis tasks.

\section*{Acknowledge}
We would like to thank the reviewers for their detailed comments and constructive suggestions.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
Q: Final result code of at-commands does not output why does my LG L40 d170 modem not show the final result code?
ex.
AT
OK
AT+CGMI
LG Electronics

Shouldn't it be like this?
AT
OK
AT+CGMI
LG Electronics

OK

i tried 'ATV1' and 'ATQ1', but AFAIK, those are by default.

A: I cannot say why, but your expectations are correct. If it actually does not  give a final result code that is an extremely serious error.
Assuming that the implementation of the AT+CGMI command is just wrong and does not issue a final result code, I will suggest sending a command line with one (or more) command following +CGMI that hopefully will trigger sending a final result code at the end of processing the command line, e.g.
AT+CMGI; V1


text:
Avigilon Welcomes New CFO
Bradley Bardua, CA, brings extensive experience in leadership positions with fast-growing public companies
VANCOUVER, Jan. 23, 2012 /CNW/ - Avigilon (TSX:AVO), a leader in high-definition (HD) and megapixel video surveillance systems, today announced the appointment of Bradley Bardua, CA, to the position of chief financial officer effective immediately.
Mr. Bardua has extensive experience working with fast-growing public companies in sectors such as technology, gaming, hospitality and energy. Over the past two decades, he has helped companies raise hundreds of millions of dollars through equity and debt offerings in Canada and the US and has led teams responsible for mergers and acquisitions, financial management and corporate governance.
Recent positions include CFO roles at Pacific BioEnergy Corporation and Gateway Casinos, where he was a member of the senior leadership team overseeing a $250-million-a-year operation with 3,400 employees.
"Bradley is an excellent fit for Avigilon as we continue to expand our business in Canada and around the world," said Alexander Fernandes, President and CEO of Avigilon. "His operational management experience and capital markets expertise will help ensure that Avigilon effectively manages its resources and balances growth with profitability as we execute our global growth strategy."
Wan H. Jung, a director and co-founder of Avigilon, has stepped down from his role as CFO. Mr. Jung helped the company grow from a start-up in 2004 to a multinational leader offering a complete range of innovative high-definition surveillance systems.
Mr. Jung, who plans to retire from full-time work, will remain on Avigilon's board of directors and continue serving with the company as corporate secretary and in the capacity of advisor. He will also be available through to completion of the fourth quarter and year-end auditing process.
"Wan played key roles in growing Avigilon, developing our culture of innovation and customer service, completing our IPO, and positioning the company for long-term success," said Mr. Fernandes. "We are thankful for the exceptional work that he has done to date, and grateful that he will continue to play an important role going forward."
Avigilon is a leader in the design, manufacturing, and marketing of high-definition, network-based video surveillance systems and equipment for the global security market. The Avigilon surveillance system has been designed to provide high quality video capture, transmission, recording, and playback. The components of the Avigilon system include cameras, recording hardware, and software which may be sold separately or in combination to provide customers with a customizable end-to-end video surveillance solution.

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Obter o tamanho do conteudo retornado na requisicao Olá, estou utilizando um interceptor que irá interceptar antes e depois da requisição. O objetivo disso é obter o tamanho do conteúdo enviado e retornado, porém, não sei de um jeito para obter a resposta, parece que o response não tem um método para pega-lo
    @Component
public class DataUsageInterceptor extends HandlerInterceptorAdapter {

    @Override
    public boolean preHandle(
            HttpServletRequest request,
            HttpServletResponse response,
            Object handler
    ) throws Exception {
        System.out.println("Tamanho da requisicao: " + request.getContentLength());
        return super.preHandle(request, response, handler);
    }

    @Override
    public void afterCompletion(
            HttpServletRequest request,
            HttpServletResponse response,
            Object handler, Exception ex
    ) throws Exception {

        System.out.println("Tamanho da resposta: ??");

        super.afterCompletion(request, response, handler, ex);
    }
}

A: Você pode ler o content-length do Response Header para conseguir esta informação:
response.getHeader("content-length");

Esta informação corresponde ao tamanho do response compactado (se for o caso) por meio de Gzip ¹. Então um response com tamanho de 25 KB pode ter, descompactado, mais de 120 KB.
¹: Você pode conferir se tem gzip habilitado lendo o reponse header content-encoding.


text:
Home / Nollywood, Movies / New short film portraying plight of rape victims screens in Lagos
New short film portraying plight of rape victims screens in Lagos
For Queen Rasheed, a 24-year-old media entrepreneur, film is the most powerful tool when it comes to fighting social causes.
This was what motivated her collaboration with a popular filmmaker, Rola Olawore, to produce a short advocacy film 'Don't Beg Now'.
The film portrays the plight of rape victims in Nigeria. It also seeks to end the culture of silence with regard to rape.
Ms Rasheed, who owns QRTV, presented the film to the media at 355, Restaurant and Lounge in Victoria Island, Lagos, on Monday.
The short movie, which stars relatively new actors, Kuchi Chris, Anthony Umeh, and Sandra Iwajomo, showcases the psychological struggles and the years of regret that rape victims are subjected to in a lawless society.
She explained that the film graphically showcases the psychological struggles that rape victims in Nigeria are being subjected to.
Although the film's plot is characteristic of what is obtainable in Nigeria, she said the theme of rape is universal and as such, relevant in any part of the world.
Even though the self-funded movie (Don't Beg Now) was shot months before the infamous COZA rape scandal broke out, Ms Rasheed debunked reports that it inspired the plot of her movie.
Also Read: Celebrate Your Heroes, US Urges Nigerian Filmmakers.
She said the coincidence is a pointer to the fact that the subject of rape will forever be a topical issue.
She noted that since the biggest sex scandal broke out this year, more Nigerian women have come forward to share their personal stories of sexual assault, rape, and violence against women.
I'm happy that Nigerians and even policy leaders have begun to notice of the menace of rape in our society" said Rasheed.
Popular filmmaker, Rola Olawore, directed 'Dont Beg Now'.
But, according to her, more still needs to be done. Hence the need to shoot the advocacy film.
"We shot this film to portray the plight of rape victims in Nigeria and also enable the society to understand that rape is not what the victims can just get over, what with the stigma attached to it. We want people to know that rape can drastically change or outrightly destroy an individual's life and people need to understand the gravity of what rape is."
"People who have no idea about what rape trauma feels or even looks like, they will get a sense of how a dark topic like rape can be addressed. In the short movie, we hope to achieve that whilst even shedding light on the dangers of rape and the lingering effect it has on the victims".
Also Read: Kunle Afolayan's new movie, 'The Citation' bags deal in 3 countries.
A visibly passionate Ms Rasheed also lamented that the plight of rape victims are inadequately represented in Nigerian movies.
She noted, "I had to come up with a project that would tell a deep story and enlighten the Nigerian and global audience at the same time. We are not trying to monetise the release of the movie as the movie is free to watch on Vimeo. This is why we want the Nigerian media to work with us on this social advocacy project because together we can win the war."
Queen Rasheed is one of the producers of the short adovacy film on rape 'Dont Beg Now'
Explaining the production process, the director revealed that the team embarked on several months of research and planning, in order to get the plot and artistic direction right.
"We interviewed a lot of rape survivors to gain insight about their plight and to determine the artistic direction of the movie. The film is intense and carefully thought out. We focused on the trauma and what happens in the aftermath of a rape incident," he stated.
Also Read: Nollywood: NFVCB Chief, two others honoured at TINFF in Canada
On the reason why they settled for relatively new actors, he said, "We held an open audition and narrowed our search to the best actors for the film. But yes, we did plan to feature bigger actors but their schedule wasn't favorable to us, so we sought a different route and glad because we got the best actors for the project.
"You really need to see the film to appreciate the amount of effort that has gone into it."
'Don't Beg Now' is available to watch for free on Vimeo.
Attend SEMP in Global Business Strategy by MSBM UK in September Principles of mindfulness and happiness took center stage at The Joy Masterclass London Funmi Iyanda's adaptation of 'Walking with Shadows' premieres at the BFI London Film Festival Royal Norwegian embassy announces 'The Lost Café' film screening NFC set to host first Berlin/Lagos Archival Film Festival. Abbesi Akhamie, Melissa Adeyemo and Lala Akindoju's film 'Still Water Runs Deep' set to premiere at Toronto International Film Festival Short And Long Hair Problems From Jiji Short Executive Masters Programme in Global Business Strategy.
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Here Is The Top 'Should I...' Question Googled In Michigan In 2021, AT&T ReportsA new report from AT&T looks into the most "Should I..." question searched in 2021.
Attorney Representing Abuse Survivors Speaks Out Following UM $490 Settlement"This settlement is the largest known academic institution settlement where such a large percentage of the survivors were men and such a large percentage of the survivors were black men," said Attorney Parker Stinar of Wahlberg, Woodruff, Nimmo and Sloane, LLP.
Toyota Donates Winter Boots And Socks To Detroit Families, $15,000 To Salvation ArmyToyota Motor North America donated boots and socks to more than 150 Detroit families during its 11th annual "Walk In My Boot" community outreach program.
12:00 PMFamily Feud
Watch for "Eye on Detroit" segments weekdays during "CBS This Morning" at 7 a.m. featuring unique and positive stories from the Motor City.
Higher Hopes! is a 501(c)(3) non-profit organization that serves families with children enrolled in Early Head Start Child Care Partnership Programs in Metro-Detroit. They provide a bounty of food in nutritious, multiple-meal food kits. Our goal is to inspire independence, relieve food anxiety and encourage family meals together.
Read more Eye on Detroit articles.
Eye On Detroit - Detroit Holiday MarketsThe Downtown Detroit Markets and Cadillac Lodge are in full swing in downtown Detroit. Local stores are set up and ready for those last minute gifts now through December 31st.
Eye On Detroit - Connect DetroitConnect Detroit today helps Detroit-area nonprofit organizations and governments work together to solve local problems.
Eye On Detroit - Good Day SelfieGood Day Selfie Museum is an immersive self-serve photography studio located in downtown Ferndale. Featuring 20-plus unique selfie opportunities, you'll leave with a smile on your face.
Eye On Detroit - GDYT DetroitGDYT provides meaningful summer employment opportunities for Detroit youth, ages 14 to 24.
Eye On Detroit - Life Remodeled 6 Day ProjectLife Remodeled's 2021 Six Day Project was held October 4-9, 2021. They focused on clearing blight from the front yards of vacant properties, and with an army of 3,822 volunteers from 173 businesses, churches and other organizations helped clear 150 blocks in the four square miles of Detroit neighborhoods surrounding the Durfee Innovation Society.
Eye On Detroit - Detroit Parade CompanyLisa Germani takes us on a behind-the-scenes tour of the Detroit Parade Company and what it takes to put on the annual Thanksgiving Day Parade in Detroit.
Eye On Detroit Canterbury HalloweenCanterbury Village transforms to provide a true Halloween setting throughout that has something for all ages. Halloween Stroll is a family friendly event hosted all outdoors where you stroll through the quaint Village that features multiple encounters and exhibits with characters along a path lit by orange glowing lamp posts and hand-carved jack-o-lanterns. Passing through pumpkin trees, the spooky carousel house, scarecrow row, singing and dancing skeletons, the tunnel of fun and much more. After your stroll, comfort yourself with hot chocolate, cider and donuts, snacks, and various food options while enjoying the afterparty on the C Pub patio. Variety of Oktoberfest beers, hard ciders, wine, and cocktails featured as well. The Annual Halloween Stroll at Canterbury Village is a memorable, must-see experience that will become your Halloween tradition.
Eye On Detroit - STEMi MobileOakland Schools is designing, developing and deploying a mobile lab to engage k – 12 students, teachers and families in Industry 4.0 experiences. This unique and exciting program will launch in the spring of 2021! OSEF is looking for organizations interested in helping to promote STEM careers and addressing the skills gap to sponsor the program.
Eye On Detroit - Citizen YogaAfter struggling with her own mental health, coupled with the loss of her sister who took her own life, Citizen founder Kacee Must began a search for meaning. Seeking answers and purpose, Kacee packed up and went to India to spend 3 years living at a formal academy studying Vedanta philosophy. A lifelong practitioner of Yoga, it wasn't until the academy needed someone to lead the daily 30-minute Yoga session that Kacee stepped into the role of the teacher, and discovered she was born to teach. Upon returning to Detroit, Kacee started Citizen Yoga in 2013 with a mission to improve mental health and prevent suicide, offering yoga as a tool for handling the stresses of life, as inspired by her sister, Miya. Citizen is grounded in tradition, and is focused on alignment of body, mind and intellect. Always a student, Kacee embraces all schools of yoga, building upon her deep Vedanta foundation finding truth in different traditions, methodologies, and lenses such as Chinese Medicine and Ayurveda. Kacee brings to Citizen, and her teaching, the unique gift of helping you redirect your mind to gain insight through asana.
ALS of Michigan Walk 'N RollALS of Michigan is a voluntary not-for-profit organization, solely dedicated to the fight against ALS. Their mission is dedicated to helping pALS (people with ALS), their families and caregivers live life as fully as possible. Karen Carter spent the morning at this year's Walk 'N Roll for ALS at Kensington Metro Park and served as Emcee. Hundreds of supporters turned out for this year's walk.
Eye On Detroit - Teachers PetEvery week Teacher's Pet strives to teach at-risk youth each of these invaluable character traits—traits that will empower them to lead highly influential and successful lives. To achieve these goals, the intervention program pairs troubled youth with hard-to-adopt shelter dogs for multi-week workshops.
Eye On Detroit - Blue AngelsThe Blue Angels is a flight demonstration squadron of the United States Navy. Formed in 1946, the unit is the second oldest formal aerobatic team in the world, after the French Patrouille de France formed in 1931.
Eye On Detroit - 9/11 MemorialThe Brighton Area Fire Authority is holding their 20th Anniversary ceremony in remembrance of those who sacrificed their lives to protect others on 9-11. First Responders recall 9/11 and share memories from the last 20 years.
Eye On Detroit - McDuffy ParkMayor, City partners officially dedicate newly expanded neighborhood park after late community leader Antonio McDuffy McDuffy was beloved resident and champion of Piety Hill neighborhood who passed away in 2019 Project is partnership between City, Project EverGreen, Rocket Community Fund and others Park will double in size when construction is completed later this year
Eye On Detroit - TSAAs what is predicted to be a very busy summer travel season ramps up, officials with the Transportation Security Administration (TSA) are offering helpful tips so travelers who may not have flown since the start of the pandemic know what to expect at Detroit Metropolitan Wayne County Airport. They're also reminding those who are planning to travel with a firearm how to do so properly to avoid potential criminal and civil penalties. TSA is prepared to handle the increased travel volume this summer, and the agency is committed to supporting a healthy and secure environment for airline passengers, TSA employees, and airport personnel.
Sidewalk DetroitSIDEWALK DETROIT EXISTS TO ADVANCE PUBLIC LIFE AND STRONG SOCIAL INFRASTRUCTURE THROUGH THE LENS OF ARTS, CULTURE, COLLABORATIVE DESIGN AND DEEP ENGAGEMENT WITH RESIDENTS.
Eye On Detroit - Robot GarageThe Robot Garage was founded in 2011 to teach engineering, coding, and robotics to kids. We believe that every child has the potential to improve the world if we nurture their self-esteem, creativity, and problem-solving skills. Our custom curriculum is designed to do just that: meet national science standards (NGSS), and provide an "aha" moment with every project.
Eye On Detroit - Motor City CrawlMotor City Car Crawl, an all-new event in downtown Detroit produced by the Detroit Auto Dealers Association (DADA) and the Downtown Detroit Partnership (DDP), will showcase new vehicles from metro Detroit area dealers throughout six downtown parks, including Beacon Park, Cadillac Square, Campus Martius, Capitol Park, Grand Circus Park and Spirit Plaza. In addition to seeing a wide assortment of vehicles, attendees can enjoy numerous downtown establishments, all within walking distance, as well as local food trucks, beverage stations and family entertainment in various locations throughout the city during the weekend.
Eye On Detroit - Rebrand Detroithe Rebrand Detroit project is the brainchild of Hajj Flemings, Founder of Brand Camp University, and is funded by the Knight Foundation as one of the inaugural winners of the 2015 Knight Cities Challenge. The project objective is to bring the innovation economy to Detroit neighborhoods. The project strategy is designed to help revitalize these communities with a tech and brand enabled strategy that is a catalyst for neighborhoods to become innovation districts.
Eye On Detroit - Planted DetroitPlanted Detroit believes technology and farming are a perfect match. They've gathered a team of uniquely talented individuals to bring them together to sustainably grow tastier, healthier, and more accessible greens.
Eye On Detroit - US CItizenshipThe mission of the Office of Citizenship is to provide federal leadership, tools, and resources to proactively foster civic integration. To facilitate this process, the Office of Citizenship engages and supports partners to welcome immigrants; promote English language learning and education on the rights and responsibilities of citizenship; and encourage U.S. citizenship.
Eye On Detroit - Mr. October FoundationEstablished in 1997 in CA – Founded by Reginald Martinez "Reggie" Jackson, a retired Major League Baseball right fielder who played from 1967 to 1987 for the Oakland A's, Baltimore Orioles, New York Yankees and Los Angeles Angels. Jackson, nicknamed "Mr. October," is a 14-time All-Star, 5-time World Series champion, and 2-time World Series MVP. He was inducted into the Major League Baseball Hall of Fame in 1993.
Eye On Detroit Joe Louis KitchenJoe Louis Kitchen in Detroit.
Eye On Detroit - River RaisinRiver Raisin National Battlefield Park preserves, commemorates, and interprets the January 1813 battles of the War of 1812 and their aftermath in Monroe and Wayne counties in SE Michigan. The Battle resulted in the greatest victory for Tecumseh's American Indian confederation and the greatest defeat for the U.S. The resulting rally cry "Remember the Raisin" spurred support for the rest of the war.
Eye On Detroit - Detroit Holiday Markets


This doesn't sound tragic at all!
If this means yet more Matt Murdock manpain I will be MOST annoyed. He's about two seconds away from playing that blasted Evanescence song.
Also, I found out that my cat is dying today, and so I have much less sympathy for this kind of melodramatic nonsense than I would usually.


Do you remember what your "resolutions" were last year?
If you answered No… How important could they have been?
If you answered Yes… Then are they not the same intentions you've carried, year after year, maybe even a lifetime?
Aren't the life-consuming desires for change the ones you could never forget, anyway? And, aren't the truly important intentions the kind you make any day of the year?
The goals that matter most don't need to be tied to "New Year's Resolutions."
I'll share a secret with you: the concept of resolutions are a huge driving force in my line of work and in the lives of my clients… I, personally, do not do them.
For many of us, there's a bit of an energetic stink to the concept, isn't there? The language itself can doom and damn even the best efforts.
What if you threw away the concept of "New Year's Resolutions" this year? What if you just do the things you intend to do? What if you just get on with it?
Every day you have a choice to make, over and over again, to take another step toward your dream or to let it slide until tomorrow.
There are ways to approach your goals that will give them a better chance at success.
Find a buddy or a group. Accountability and social support are enormously powerful. Your friends and family may get tired of playing your coach, but someone who's working toward the same goal will be eager to share common experiences. With the Internet and social media, there's no excuse for not connecting other people who share your goals.
Talk about your experiences. If you're going to write down your "resolutions," then keep on writing. Talk about your goals daily, or at least weekly, with the other people who are on the same path. Start a journal. Keep a log book. Blog about it. Post about it on Facebook. When you find a few people who are eager to comment and discuss, start a special invitation only Group or Page. Or join one that already exists.
Low-ball your efforts. A series of small successes builds confidence and generates momentum. The opposite is also true: a goal that's too unwieldy is one bad day away from failure, deflation, and a domino effect down to defeat.
Make every day a Start date. We often put too much pressure on that auspicious First Day, when really it's just one of many. Don't psyche yourself out. Each morning, it's the same choice in front of you -- to do it again.
Simplify your goals. Psyche yourself up for success. Break your big goals down into the simplest possible action steps, especially ones that can be repeated. Make them stupid-easy to pull off. And give yourself credit for every little inch of progress.
Admittedly, the components of the "New Year's Resolution" concept are all here, but these are the good parts -- the practical down-to-earth pieces that work.
Let's live our lives with the best intentions and fight with all our will to make our dreams come true.
But, let's forget the language that has become encoded with cliches and failure -- so, no more New Year's Resolutions.


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BusinessPsychedelics
5 Key Ingredients For Success In Psychedelics Business
Dave HodesAugust 31, 20228min2270
How does a psychedelics company stay fiscally safe and still operate successfully in this space?
Every young industry eventually experiences jostling for market dominance. In psychedelics, that journey has just begun for a handful of companies.
How they get there in this new industry means working on new, and sometimes novel, strategies for building the team. And for now, that's been a hit-and-miss proposition.
While the CEO of the company makes the standard moves for company development, enticing investors or looking at mergers and acquisitions, there's not much else to work with inside the company until a product starts generating revenue.
So how does a psychedelics company stay fiscally safe and still operate successfully in this space?
Here's a five-part recipe:
1. Start with – and lean on – the heavy hitter. Generally, this means the money guy, a CEO or founder or both, who can help the business weather the hits of development ups and downs.
With that money person behind the business, a million-dollar clinical trial loss won't kill the business. These are novel drugs. There is an amazing multibillion-dollar future. Failure is expected along the way.
Example: Billionaire Peter Thiel invested heavily in both Atai and Compass Pathways. GoDaddy cofounder Bob Parsons and SpaceX founder Elon Musk also are rumored to be sniffing around the industry as well.
2. Mix in a great medical officer, the new psychedelics business power player. Get someone on board with a deep background in bioscience discovery and development, with a proven business development acumen.
This is an evolving position in the medical field, an important partner of the CEO, requiring greater strategic business building skills that some physicians find challenging but ultimately lead to greater success.
Example: In October, Cybin announced the addition of Dr. Amir Inamdar, who was formerly with AstraZeneca where he led a global program in substance use disorder, successfully progressing a small molecule from a preclinical asset to a first in-human clinical trial. He was awarded a multimillion-dollar grant by the National Institute on Drug Abuse for a project in substance use disorder. At the same time, Cybin announced a new research and development leader with deep experience in clinical trials, a new general counsel, and a communications and public relations firm.
3. Add a patent protection guru who understands the bioscience world. This could be a lawyer on retainer or even a full-time executive. Issues come up all the time in an industry that is still defining itself, and new psychedelic drug innovations or inventions are not easily understood.
Example: There are more than just the usual intellectual property issues about who owns a certain patent, which can by itself take up a lot of time and resources. There are also ethical considerations about some of the psychedelic plants that this guru has to address. Those can be particularly thorny and are just part of the complicated patent process for psychedelics.
4. Sprinkle in a group of clinical trial clinicians and developers. These are not do-it-yourself trials. Far from it. Psychedelic clinical trials need to be carefully structured and carried out, with a focus on safety, especially when experimenting with a drug for the central nervous system, exploring such concepts as consciousness and mystical experiences.
These are uncharted waters for the most part. Vetting of participants is extremely important. Protocol and scientific rigor are essential. Set and setting are important keys to success. Expectations can be unrealistic.
Example: Most psychedelic clinical trial producers are still members of academia, such as the teams at Johns Hopkins Center for Psychedelic and Consciousness Research and the Icahn School of Medicine Center for Psychedelic Psychotherapy and Trauma. And that's a good thing.
There is a movement to bring more physicians into the clinical trial settings and deepen clinical trial partnerships with various institutions outside of academia as standardization and clinical trial management objectives are created. One study reported that clinical research contributes to the expanding knowledge base of medicine and "provides physicians an opportunity to offer patients latest cutting-edge therapies. Participation opens their eyes to medical innovation, and they are benefited by satisfying intellectual curiosity, increasing research provisions and assisting career advancement."
5. Adjust adjust adjust. As the industry develops, it's becoming clearer that investors want better financial direction and better financial scrutiny of what is going on inside the company.
Cash burn appears to be endemic in this industry. That scares investors. But who is calling the shots? And what is really at stake? That has to be spelled out because this is where things can get messy.
Example: A letter from investors, which included the company's co-founder and former chief medical director Scott Freeman, noted that MindMed's management had divided its attention among too many different projects resulting in the delayed development of MindMed's core drugs. They advised selling a recent acquisition, eliminating some positions within the company and reducing board compensation. The next day, MindMed, appearing to be focused on the long-term instead, announcing two new independent additions to "strengthen" the board: Suzanne Bruhn, a research and development specialist with executive leadership chops; and Dr. Roger Crystal, inventor of the Narcan nasal spray who also holds several related patents.
Following this recipe won't automatically make your company a market leader, but it will definitely give you a leg up on the competition.
Dave Hodes
David Hodes is a business journalist based in the Washington D.C. metropolitan area. He has contributed feature articles to several cannabis and psychedelics publications, as well as general business/lifestyle publications, on a variety of topics. Hodes was selected as 2018 Journalist of the Year by Americans for Safe Access. He is a member of the National Press Club, and the deputy booking agent for the National Press Club Headliners Committee.
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Kathleen Clawson's multi-faceted career includes education, directing, and performing. She has served on the faculty of the Department of Theatre and Dance since 1997, where she teaches courses in musical theatre, directs, and is the Department publicity director. She has worked for the Santa Fe Opera for over eighteen seasons and is the Associate Director of their Apprentice Singer Program and Director of the Young Voices of the Santa Fe Opera. This season's highlights include returns to Dayton Opera as stage director for Rigoletto and Salome as well as stage direction of UnShakeable, a collaboration between the Santa Fe Opera and Chatter.
For UNM she most recently directed The 25th Annual Putnam County Spelling Bee. Other productions at UNM include Spring Awakening as well as the regional premieres of RENT, Zanna, Don't!, and Urinetown the Musical. Miss Clawson performed nationally as a classical singer including productions with The Santa Fe Opera and the Dallas Opera. Equally at home in musical theater, she has appeared as guest artist with many companies and toured nationally and internationally in The Sound of Music. Clawson trained at the Curtis Institute of Music, the University of Southern California, and the University of New Mexico. She attended the Banff Centre for Fine Arts Opera Program and was a member of the Merola Program of the San Francisco Opera.