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text:
Family and friends mourn Ryan Jones death
By Comfort Edem | November 29, 2021
Ryan Jones Death – Cause Of Death: We learned about the passing of Ryan Jones from Riverdale, Utah through a social media publication made by School Personnel Lost To Covid on 8th November 2021, According to the post, Ryan Jones passed away on Friday 5th November 2021 at the age of 51 after battling with COVID.
Ryan was born on December 12 in the year 1969 in Cedar City, UT, to Rudy and Janis Jones. He was a longtime resident of Riverdale a city in southern Weber County, Utah, United States. He attended Bonneville High and graduated in the year 1996, and continued his study at Weber State University where he studied history and zoology.
He was a former biologist and technical writer at Hill Air Force Base where he worked part-time for the Natural Resources Department of the Environmental Directorate. He later became a history & biology teacher at Davis County Schools from August 1997 to his demise. He is survived by his wife, Lisa, his children, Jesse (Katey), Meghan, Mckay (Ty), Kirtlyn, Rylee, and Bridger; his parents, Janis and Rudy; his siblings, Rhett, Josh, and Heather (Dusty); his grandson, Flint.
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redpajama_set_name: RedPajamaCommonCrawl


text:
Q: How to share dependencies in package.json definition using yarn 2? We are using yarn workspace 2 monorepo project.
Our problem is, that there a 10 miniproject contains (for example):
  "devDependencies": {
    "typescript": "^4.0.2"
  }

Is there any stategy how to define some ROOT parent of package.json and exnted it in childs?
Like:
Parent:
  "devDependencies": {
    "typescript": "^4.0.2"
  }

Child:
  "devDependencies": {
    ...Parent, // will use "typescript": "^4.0.2"
  }

redpajama_set_name: RedPajamaStackExchange


A tropical fruit, also known as balsam pear, bitter melon is a widely cultivated in Asia, Africa and South America. The fresh juice and the extract of the unripe fruit have been found in various studies to have a blood sugar lowering effect. As such, bitter melon is extensively used in folk medicine as a treatment for diabetes.
Drinking 50-60ml of the juice has been shown to be the ideal quantity for this in clinical trials.


text:
Swedish Grants Are Abused to 'Create Islamic Parallel Society', Researchers Warn
© AP Photo / Martin Meissner
https://sputniknews.com/europe/201910301077178353-swedish-grants-are-abused-to-create-islamic-parallel-society-researchers-warn/
According to researchers, the organisation Ibn Rusdh, a major benefits recipient, spreads an activist brand of Islam, targetting newly arrived migrants and marginalised Muslims living in vulnerable neighbourhoods.
Two senior Swedish researchers and associate professors Magnus Ranstorp and Aje Carlbom have highlighted the threat of parallel societies being indirectly funded through taxpayer money in a recent joint opinion piece in the daily newspaper Expressen.
An illustrative example thereof, according to the researchers, is the student union Ibn Rushd which gets an annual SEK 23 million ($2.3 million) grant from the Adult Education Council (FR).
"By paying SEK 23 million a year to Ibn Rush, FR strengthens a politically-oriented group, one that can be linked to the Muslim Brotherhood's* ideological school", the researchers wrote, describing the latter as "an Islamic minority that has been working to gain influence over Muslims' religious thinking".
According to Ranstorp and Carlbom, this is tantamount to funding "institutionalised segregation with the aim of creating a parallel social existence for Swedish Muslims". The unilateral support, they argued, permits the creation of an Islamic "parallel society" existing alongside the majority society. This goal is made clear by Ibn Rushd's own terminology, which uses a specific term, "Muslim civil society".
Ibn Rushd's largest areas of activity are the Arabic language and the Islamic way of life, whereas its target audience is new arrivals and marginalised Muslims living in vulnerable neighbourhoods, Ranstorp and Carlbom stressed.
"Given that these target groups are at a long distance from the majority community, it is doubtful that studies in Arabic and Islam are contributing to opportunities for participation in Sweden's democratic community", they wrote, stressing that their target audience already enjoys ample opportunities to speak their native language, but lack everyday opportunities to socialise with Swedes and learn how Swedish society is structured. "Supporting activities that encourage further isolation from the norms and languages of the majority society thus becomes counterproductive", they concluded.
Ranstorp and Carlbom also stressed Ibn Rushd's long history of inviting racist and anti-Semitic speakers, something, they ventured, no other organisation would have gotten away with.
"Given that Ibn Rushd has been active as a study association since 2008, it is reasonable to assume that the association has long been aware that anti-Semitism is unacceptable in the Swedish majority society", they stressed.
Therefore, they concluded, the grants may be seen as an encouragement to spread "a certain activist interpretation of Islam" whose stated goal is to establish a "Muslim parallel society".
In 2019, professor of political science Erik Amnå published a study "When trust is tested", criticising Ibn Rushd. The organisation met the criticism by accusing the critics of Islamophobia.
Ibn Rushd derives its name from an Andalusian scholar, whose name is often westernised as Averroes.
In 2017, the Swedish Agency for Youth and Society (MUCF) refused to fund the Association of Young Muslims, an umbrella organisation with over 30 member associations, concluding that the latter failed to respect the ideas of democracy and indulged in anti-Semitism and homophobia, and urging it to pay back funding from previous years. This became the starting point of a legal process over funding.
A 2017 Pew Research report documents the Muslim population at 8.1 percent of the total population of Sweden (10 million).
Magnus Ranstorp is an associate professor of political science at the National Defence College and is seen as one of Sweden's leading researchers of terrorism, whereas Aje Carlbom is an associate professor of anthropology at Malmö University.
* The Muslim Brotherhood (also known as The Society of Muslim Brothers) is a terrorist organisation banned in Russia and several other countries.
Sweden Sees Rise of 'Middle Eastern' Thermos Bomb Trend – Reports
'Lure for Migrants': Outrage as Government Webpage Markets Sweden Abroad as 'Country of Benefits'
Former Scania CEO Warns of Upcoming Civil War in Sweden
Islam, Muslim Brotherhood, Scandinavia, Sweden
00:31Blasts Heard in Iraq's Capital of Baghad - Reports


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LUCY AND DESI (2022) – Documentary by Amy Poehler Chronicles Moving Love Story of Lucille Ball and Desi Arnaz
Posted on March 17, 2022 by marruda3
LUCY AND DESI (2022), a new Amazon Prime original documentary directed by Amy Poehler, is filled with poignant moments as it chronicles the love story of Lucille Ball and Desi Arnaz, showing how they struggled to balance the demands of show business with their family life, a struggle that ultimately led to their divorce, but one that never stopped them from loving each other.
LUCY AND DESI makes for a nice companion piece to BEING THE RICARDOS (2021), Aaron Sorkin's movie chronicling one of the most stressful weeks in the lives of the famous TV couple, starring Nicole Kidman as Lucy and Javier Bardem as Desi. I actually enjoyed LUCY AND DESI more than I did BEING THE RICARDOS, but this might be an unfair comparison, because LUCY AND DESI features the real Lucille Ball and Desi Arnaz in tons of archive footage both in character and behind the scenes, and that's something that is simply hard to beat. As such, LUCY AND DESI is a heck of a documentary.
The film basically is the love story between Lucy and Desi. We see how they first meet, how well they worked together on I LOVE LUCY (1951-1957), how things eventually fell apart, and how they continued to be in each other's lives long after their marriage had ended. In fact, the point is made that after they divorced, they actually got along better.
There are many fascinating tidbits and revelations made in this film, from the pushback they received from producers who did not want to cast Desi in I LOVE LUCY because they didn't want a Cuban lead, but a generic white American, to the genesis for the plot line of the series which featured Desi's Ricky Ricardo who was in show business but really wanted out, and his wife Lucy, who was not in show business, but really wanted in.
In real life, it was Lucy who loved to perform, and Desi who had a knack for the business side of things. It was Desi who built DesiLu Productions and turned it into one of television's largest production companies. But it was also incredibly grueling work and wore him down to the point where he eventually because of failing health and drinking, just had to quit. Lucy would buy out the company from him, but since her interests were never in business, she eventually sold the company to Paramount.
Lucy and Desi's daughter Lucie Arnaz, who is interviewed throughout the documentary, makes the poignant point that what Lucy and Desi really wanted in their lives was the perfect combination of show business and family life. They gave the world I LOVE LUCY, and so as Lucie Arnaz explains, the world got this perfect combination in the form of an everlasting TV show, but Lucy and Desi never got to enjoy it in real life, as their marriage ended in divorce. Whereas they succeeded creatively, giving the world the gift of I LOVE LUCY, they failed personally. They couldn't live out what they had created in the fictional world of television.
Other notable points include Desi's early life in Cuba, and how in the U.S., after fleeing the Cuban revolution and arriving here penniless as a refuge, he was always looking to recapture what he lost in his home country, that feeling of home, but sadly was never able to. And how Lucy and Ethel, as played by Vivian Vance, were shown as active women who were constantly plotting together and taking the lead in the storylines over their husbands, which was something unheard of in 1950s TV shows. Furthermore, Lucy's second show, THE LUCY SHOW (1962-1968), broke new ground by having Lucy and Vivian Vance play two single moms raising children on their own.
We also see how Lucy helped many performers get their starts, including Carol Burnett, who appears in interviews and speaks of how generous Lucy was to her, and how she still thinks of Lucy nearly every day.
LUCY AND DESI is also filled with tons of archival footage of both Lucy and Desi, including famous clips from I LOVE LUCY. And one of the most memorable moments comes right near the end, after the film takes us through Desi's final days, as he is sick with lung cancer, and we hear as told by Lucie Arnaz, how Lucy and Desi spoke on the phone, and how they told each other they loved each other, and how Lucy was the last person other than Lucie Arnaz who was with Desi when he died, to speak to him. Desi Arnaz died in 1986 from lung cancer. He was only 69 years old.
But the moment comes from footage from the Kennedy Center Awards in 1986, where Robert Stack reads a posthumous statement from Desi Arnaz in which he gives all the credit for the success of I LOVE LUCY to Lucy, and says I LOVE LUCY was never just a title. The audience then turns and gives an emotional Lucille Ball a standing ovation.
Lucy would pass three years later in 1989 at the age of 77.
LUCY AND DESI was written by Mark Monroe who provides a solid framework for the telling of the story of Lucille Ball and Desi Arnaz.
Amy Poehler, known of course for her comic performances on SATURDAY NIGHT LIVE and on PARKS AND RECREATION (2009-2015), also directed and starred in one of my favorite movies from last year, MOXIE (2021). Poehler succeeds once again behind the camera and does exceptional work here directing LUCY AND DESI.
And it's an important story to tell because I LOVE LUCY is not only one of the all-time best, if not the best television comedy series ever, but also one of the biggest influencers. It was the first show to use a live studio audience, the first to use film, and utilized a three-camera system developed by Desi Arnaz. It was also successful because Desi hired the best people and was comfortable delegating work out to these people, like acclaimed cinematographer Karl Freund. It featured a Latin male in the lead, two strong female characters, allowed Lucy to be pregnant in a storyline which was unheard of in the 1950s, and featured the amazing physical comedic talents of Lucille Ball, who unlike most beautiful actresses, was unafraid to make herself look ugly or foolish to get a laugh.
I still remember when Lucille Ball passed away in 1989, it was a big deal, both in my family and in the national media. Lucille Ball is easily one of the biggest stars of her generation, and she did it mostly in the smaller medium of television rather than film, which is also a testament to her talent, and she has lived on, cementing her place with the other entertainment greats in film and television history. And one of the reasons she achieved this success, was the tireless efforts of her husband Desi Arnaz, whose business sense provided I LOVE LUCY with the best people to create the show, giving Lucy her vehicle to finally display her talents to the world.
The story of Lucy and Desi is one that needs to be told, of how two people very much in love created a cultural and entertainment phenomenon with the TV show I LOVE LUCY, but sadly, they were unable to survive the pressures of doing so and eventually divorced. They both remarried and stayed married longer to their second spouses than they were with each other, but they never stopped loving each other and were there for each other until the end.
LUCY AND DESI is a tale of dreams, of hard work, of love, and the costs of trying to balance them, and how unlike in television, where happy endings abound, in reality, people are human, and stress often takes its toll, as it did with Lucy and Desi, but people are also resilient, and Lucy and Desi carried on, and they loved each to the last.
Indeed, I LOVE LUCY was never just a title. It's also an everlasting love letter from Desi to Lucy, one that we are all invited to read.
Amy Poehler biographies Comedies Desi Arnaz documentaries Lucille Ball Michael Arruda movie reviews Prime Video Uncategorized Amazon Prime VideoAmy PoehlercomediesDesi ArnazDesilu productionsdocumentaryI LOVE LUCYLucie ArnazLucille BallLUCY AND DESI (2022)LUCY AND DESI (2022) movie reviewLUCY AND DESI (2022) reviewMichael Arruda movie reviewtelevision
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WEST SIDE STORY (2021) – Spielberg's Remake on Par with 1961 Original →


text:
\section{Introduction} \label{intro}
Recently, the availability of large amount of labeled data has
greatly boosted the development of feature learning methods for
classification. In particular, convolutional neural networks (CNNs)
achieve great success in visual recognition/classification tasks.
Features extracted from CNNs can provide powerful global
representations for the single object recognition
problem~\cite{Alex2012,Sermanet2013,Razavian2014}. However,
conventional CNN features may not generalize well for images
containing multiple objects as the objects can be in different
locations, scales, occlusions and categories. Fig.~\ref{example-voc} shows an
example of such images. Since multi-label recognition task is more
general and practical in real world applications, many CNN related
methods~\cite{OquabCVPR2014,Oquab2014,Wei2014} have been proposed
to address the problem.

\begin{figure}
 \centering
 \includegraphics[width=0.38\textwidth]{example-voc.pdf}
 \caption{An example of a typical multi-label image, which contains several cows in different locations as well as a person.}
 \label{example-voc}
\end{figure}

A well known fact is that image-level labels can be utilized to
fine-tune a pre-trained CNN model and produce good global
representations~\cite{Sermanet2013,Razavian2014,Oquab2014}.
However, due to the diversity and the complexity of multi-label
images, classifiers trained from such global representations might
not be optimal. For example, if we use images similar to
Fig.~\ref{example-voc} to train a classifier for ``person'', the
classifier will have to account for not only hundreds of
different variations of ``person'' but also other objects contained in the images. The complexity of
multi-label images adds an extra level of difficulty for training
appropriate classifiers with the global image representations.
Furthermore, due to the large intra-class variations of
multi-label images, the global features extracted from training
images are likely to be unevenly distributed in the feature space.
A classifier trained with such features can be successful at
regions that are densely populated with
training instances, but may fail in poorly sampled areas of the
feature space~\cite{Yu2014}.

\begin{figure*}
  \centering
 \includegraphics[width=0.7\textwidth]{System.pdf}
 \caption{Overview of the proposed multi-view multi-instance framework. We transform the multi-label object recognition problem into a multi-class multi-instance learning problem by first extracting object proposals from each image using selective search. Two types of features are then extracted for each proposal. One is a low-dimensional feature from a large-margin nearest neighbor (LMNN) CNN, which is used to generate the label view by encoding the label information of $k$-NN from the candidate pool (containing ground truth objects). The other is a standard CNN feature as the feature view. These two views are fused and then used to encode a Fisher vector for each image.}
 \label{system}
\end{figure*}

To address the problems with global CNN representations, following the recent works~\cite{Oquab2014,Wei2014,OquabCVPR2014,girshick2014}, we incorporate local information via extracting object proposals using general object detection techniques such as selective search~\cite{Uijlings2013} for multi-label object recognition. By decomposing an image into
local regions that could potentially contain objects, we avoid the complex process
of directly recognizing multiple objects
in the whole image. Instead, we only need to identify whether
there exist target objects in the local regions. However, as the
local regions are noisy and of large variations (see
Fig.~\ref{selective-search}), the usual CNN representations might
not be good enough for discrimination. Therefore, we add another
level of locality by incorporating local nearest-neighbor
relationships of these local regions. In this way, the resulting features will be more evenly
distributed in the feature space. However, such relationships are not easy
to obtain only through weak supervision, i.e., image-level labels.
Fortunately, for many multi-label applications, we can exploit the
strong supervision information, i.e. ground-truth bounding boxes, which can be considered as local regions with strong labels. Then, we can
exploit the relationships between object proposals and ground-truth bounding boxes (e.g.,
nearest neighbor relationships) to help multi-label recognition.

We would like to point out that ground-truth bounding boxes have been utilized in two proposal-based methods~\cite{OquabCVPR2014,girshick2014} for multi-label object recognition. In particular,~\cite{girshick2014} makes use of ground-truth bounding boxes to train category-specific classifiers to classify object proposals. However, it requires ground-truth bounding boxes for each category of objects, which might not be available in practice. In contrast, for our proposed method, even with only partial
strong labels (e.g., bounding boxes and labels for $10$ classes in
the $20$ classes of Pascal VOC), the proposed local relationships
generalize well and can help recognize all classes (e.g., improving
recognition of the $20$ classes in VOC). \cite{OquabCVPR2014} directly uses ground-truth bounding boxes to fine tune the CNN model, but its performance is not better than other proposal-based methods~\cite{Oquab2014,Wei2014} that do not utilize ground-truth bounding boxes. In other words, an effective way to exploit ground-truth bounding boxes for multi-label object recognition is still missing, which is what we aim to provide in this paper.

Fig.~\ref{system} gives an overview of our proposed framework. We
utilize both strong and weak labels as two \emph{views}, and
propose a multi-view multi-instance framework to tackle the
multi-label object recognition task. In particular, for any image, we first
extract object proposals using general object detection
techniques. The global image and its accompanying weak (image)
label is used to fine-tune a standard CNN to generate a \emph{feature view}
representation for each proposal. Using the ground truth bounding
boxes and their strong labels, we design a large margin nearest neighbor
(LMNN) CNN architecture to learn a low-dimensional feature so that
we could extract nearest neighbor relationship between local
regions and a candidate pool formed by ground truth objects. These
local NN features are used as the \emph{label view}. When
combining both views, we can achieve a balance between global
semantic abstraction and local similarity, hence enhancing the
discriminative power of our framework. More importantly, as the
strong labels are indirectly utilized through LMNN to encode local
neighborhood relationships among labelled local regions, the
proposed framework can generalize well to the whole local region
space, even with only partial strong labels for part of the object
classes, making our framework more practical.

The main contribution of this research lies in the proposed multi-view multi-instance framework, which utilizes bounding box annotations (strong labels) to encode the label view and combine it with the typical CNN feature representation (feature view) for multi-label object recognition. Another novelty of our work is the proposed LMNN CNN which effectively extracts local information from the strong labels.

\section{Related Works} \label{related}

Our paper mainly relates to the topics of CNN based multi-label
object recognition, multi-view and multi-instance learning and
local and metric learning.

{\bf CNN based multi-label object recognition}. Recently, CNN
models have been adopted to solve the multi-label object
recognition problem. Many
works~\cite{girshick2014,Sermanet2013,OquabCVPR2014,Oquab2014,Wei2014}
have demonstrated that CNN models pre-trained on a large dataset
such as ILSVRC can be used to extract features for other
applications without enough training data. A typical way
(\cite{Sermanet2013},~\cite{Razavian2014}
and~\cite{Chatfield2014}) is to directly apply a pre-trained CNN
model to extract an off-the-shelf global feature for each image
from a multi-label dataset, and use these features for
classification. However, different from single-object images from
the ImageNet, multi-label images usually have multiple objects in
different locations, scales and occlusions, and thus global
representations are not optimal for solving the
problem~\cite{Wei2014}. More recently, two proposal-based
methods~\cite{OquabCVPR2014,girshick2014} were propose for
multi-label recognition and detection tasks with the help of
ground truth bounding boxes. These methods achieve significant
improvement over single global representations. On the other hand,
\cite{Oquab2014} and \cite{Wei2014} handle the problem in a weakly
supervised manner by max-pooling image scores from the proposal
scores. Moreover,~\cite{Simonyan2014} employs a very deep CNN,
aggregates multiple features from different scales of the image
and achieves state-of-the-art results.

{\bf Multi-view and multi-instance learning}. Multi-view learning
deals with data from multiple sources or feature sets. The goal of
multi-view learning is to exploit the relationship between views
to improve the performance or reduce model complexity. Multi-view
learning is well studied in conjunction with semi-supervised
learning or active learning. To combine information from
multi-views for supervised learning, fusion techniques at feature
level or classifier level can be employed~\cite{Zhang2014}.
Multi-instance learning aims at separating bags containing
multiple instances. Over the years, many multi-instance learning
algorithms have been proposed, including miBoosting~\cite{Xu2004},
miSVM~\cite{Andrews2003}, MILES~\cite{Chen2006} and
miGraph~\cite{Zhou2009}. Several works also studied the
combination of multi-view and multi-instance learning and its
application to computer vision tasks.

{\bf Local and metric learning}. Existing local learning methods
mainly vary in the way that they utilize the labelled instances
nearest to a test instance. One way is to only use a fixed number
of nearest neighbors to the test point to train a model using
neural network, SVM or just voting. The other way is to learn a
transformation of the feature space (e.g., Linear Discriminant
Analysis). In either way, the learned model can be better tailored
for the test instance's neighborhood property~\cite{Hastie1996}.
Metric learning is closely related to local learning as a good
distance metric is crucial for the success of local learning.
Generally, metric learning methods optimize a distance metric to
best satisfy known similarity constraints between training
data~\cite{Bellet2013}. Some metric learning methods learn a
single global metric~\cite{Weinberger2009}. Others learn local
metrics that vary in different regions of the feature
space~\cite{Yang2006}.

\section{Multi-Label as Multi-Instance} \label{mi}
In this section, we introduce the first level of locality by
formulating the multi-label object recognition problem as a
multi-instance learning (MIL) problem. To be specific, given a set
of $n$ training images $\left\{\vec{X}_i\right\}_{i=1}^n$, we
extract $n_i$ object proposals $\{\vec{x}_{ij}, j = 1,\dots,n_i\}$
from each image $\vec{X}_i$ using general object detection
techniques. By decomposing images into object proposals, each
image $\vec{X}_i$ becomes a bag containing several positive
instances, i.e., proposals with the target objects, and negative
instances, i.e., proposals with background or other objects. The
problem of classifying $\vec{X}_i$ is thus transformed from a
multi-label classification problem to a multi-class MIL problem.
The merit of such a transformation is that we do not need to deal
with the complex process of directly recognizing multiple objects
in multiple scales, locations and categories in a single image.
Instead, we only need to identify whether there exist target
objects in the proposals, which has been proven to be the forte of
CNN features~\cite{girshick2014}.

MIL problems assume that every positive bag contains at least one
positive instance. As extensively compared and evaluated
in~\cite{Hosang2014}, state-of-the-art general object detection
methods like BING~\cite{Cheng2014}, selective
search~\cite{Uijlings2013}, MCG~\cite{Arbelaez2014} and
EdgeBoxes~\cite{Zitnick2014} can reach reasonably good recall
rates with several hundreds of proposals. Therefore, if we sample
enough proposals from each image, we can safely assume that these
proposals can cover all objects (or at least all object
categories) in an image, thus fulfilling the assumption of
multi-instance learning.

In particular, we employ the unsupervised selective search
method~\cite{Uijlings2013} for object proposal generation.
Selective search has proven to be able to achieve a balance
between effectiveness and efficiency~\cite{Hosang2014}. More
importantly, as it is unsupervised, no extra training data or
ground truth bounding boxes are needed in this stage. Example of
proposals extracted by selective search can be found in
Fig.~\ref{selective-search}.

\begin{figure}
 \centering
 \includegraphics[width=0.35\textwidth]{example.pdf}
 \caption{An example of object proposals generated by selective search. We demonstrate $30$ randomly sampled proposals from the full $218$ proposals, which clearly cover two of the main objects: person and bike.}
 \label{selective-search}
\end{figure}

Traditionally, MIL is formulated as a max-margin classification
problem with latent parameters optimized using alternating
optimization. Typical examples include miSVM~\cite{Andrews2003}
and Latent SVM~\cite{Felzenszwalb2010}. However, although these
methods can achieve satisfactory accuracies, their limitations in
scalability hinder their applicability to current large scale
image classification tasks. For large scale MIL problem,
\cite{Wu2014} shows that Fisher
vector~\cite{Perronnin2010,Sanchez2013} (FV) can be used as an
efficient and effective holistic representation for a bag.
Thus, we choose to represent each bag $\vec{X}_i$ as an FV.

Assume we have a $K$-component Gaussian Mixture Model (GMM) with
parameters $\theta = \{\omega_k,\vec{\mu}_k,\Sigma_k,
k=1,\dots,K\}$, where $\omega_k$, $\vec{\mu}_k$ and $\Sigma_k$ are
the mixture weight, mean vector and covariance matrix of the
$k$-th Gaussian, respectively. The covariance matrices $\Sigma_k$
are assumed to be diagonal, where the corresponding standard
deviations of the diagonal entries form a vector $\vec{\sigma}_k$.
We have~\cite{Sanchez2013}:
\begin{align}
f^{\vec{X}_i}_{\vec{\mu}_k} =& \frac{1}{\sqrt{\omega_k}}\sum_{j=1}^{n_i}\gamma_j(k)\left(\frac{\vec{x}_{ij}-\vec{\mu}_k}{\vec{\sigma}_k}\right) \,, \\
f^{\vec{X}_i}_{\sigma_k} =&
\frac{1}{\sqrt{\omega_k}}\sum_{j=1}^{n_i}\gamma_j(k)\frac{1}{\sqrt{2}}\left[\frac{\left(\vec{x}_{ij}-\vec{\mu}_k\right)^2}{\vec{\sigma}^2_k}
- 1\right] \,,
\end{align}
where $\gamma_j(k)$ is the soft assignment weight, which is also
the probability for $x_{ij}$ to be generated by the $k$-th
Gaussian:
\begin{equation}
\gamma_j(k) =p(k|x_{ij},\theta) .
\end{equation}

We map all the proposals $\{\vec{x}_{ij}, j = 1,\dots,n_i\}$ in an
image $\vec{X}_i$ to an FV by concatenating
$f^{\vec{X}_i}_{\vec{\mu}_k}$ and $f^{\vec{X}_i}_{\vec{\sigma}_k}$
for all $k = 1,\dots,K$, and denoting it as $F^{\vec{X}_i}$.
$F^{\vec{X}_i}$ will be used as the final feature to train the
one-vs-all linear classifiers. Note that for simplicity, we abuse
the notation of $\vec{x}_{ij}$ for both proposal $j$ in image $i$
and its corresponding feature representation. In the next section,
we will describe how to generate the feature representation
$\vec{x}_{ij}$ for each proposal.

\section{From Global Representation to Local Similarity}
\label{lv} Once we obtain object proposals for each image, we can
naturally use CNN features to represent these proposals. Following
general practices in the literature~\cite{Sermanet2013,Razavian2014}, each proposal is fed
into a pre-trained CNN, and the output of the second last fully
connected layer (e.g. layer 7 in AlexNet~\cite{Alex2012}) is used
as the feature representation of that particular proposal. We call
this kind of representation as the feature view $\vec{f}_{ij}$ for
proposal $\vec{x}_{ij}$ from image $\vec{X}_i$. With the proposals
represented by CNN features, one baseline is to encode each image
(bag) at the feature view by Fisher vector as discussed in
Sec.~\ref{mi}. Such baseline is able to get reasonably good
results by utilizing the Fisher vector generated only from the
feature view.

However, since the proposals contain different objects as well as
random background, there exist large variances and imbalanced
distributions. As a consequence, the global representation might
not be accurate enough. Inspired by the idea of local learning,
which solves the data density and intra-class variation problems
by focusing on a subset of the data that are more relevant to a
particular instance~\cite{Bottou1992}, we propose the second level
of locality by adding local spatial configuration information as
the label view (cf. Fig.~\ref{system}) to enhance the
discriminative power of the feature.

To effectively encode the local spatial configuration of a
proposal, we need to solve two key problems: how to form a good
candidate pool for local learning and how to determine which
candidates are relevant to a particular new proposal. For the
former problem, since we have some ground truth object bounding
boxes from the strong labels, we could use them as the candidate
pool assuming all of the ground truth objects are useful. For the
latter one, we follow the common assumption that the most relevant
candidates are the nearest neighbors. In this way, the problem
becomes how to define ``nearest''.  Many
studies~\cite{Bellet2013,Weinberger2009} have shown that the
distance metric is critical to the performances of local learning.

\subsection{CNN as Metric Learning}
\label{largeNN} Metric learning studies the problem of learning a
discriminative distance metric. Conventional Mahalanobis metric
learning methods optimize the parameters of a distance function in
order to best satisfy known similarity or dissimilarity
constraints between training instances~\cite{Bellet2013}. To be
specific, given a set of $n$ labelled training instances
$\{\vec{x}_i, y_i\}_{i=1}^n$, the goal of metric learning is to
learn a square matrix $\mathbf{M}$ such that the distance mapped
between training data, represented as
\begin{equation}
 D_M(\vec{x}_i,\vec{x}_j) = (\vec{x}_i - \vec{x}_j)^T\mathbf{M}(\vec{x}_i - \vec{x}_j) \,,
\end{equation}
satisfies certain constraints. Since $\mathbf{M}$ is symmetric and
positive semi-definite, it can be decomposed as
 $\mathbf{M} = \mathbf{W}^T\mathbf{W}$,
and $D_M(\vec{x}_i,\vec{x}_j)$ can be rewritten as:
\begin{equation}
 D(\vec{x}_i,\vec{x}_j) = \left\|\mathbf{W}(\vec{x}_i - \vec{x}_j)\right\|^2 \,.
\end{equation}

We can see that learning a distance metric is equivalent to
learning linear projection $W$ that maps the data from input space
to a transformed space. In this sense, the extraction of CNN
features from the original raw pixel space can also be viewed as a
form of metric learning, while only the process is highly
nonlinear. However, the goal of CNN is usually to minimize the
classification error using loss functions such as the logistic
loss, which may not be suitable for local encoding.

Our desired metric should be discriminative such that all
categories are well separated, as well as compact so that we can
find more accurate nearest neighbors. Specifically, we want the
pairwise distance between instances from the same class to be
smaller than that between instances from different classes. In
order to achieve such a goal,~\cite{Weinberger2009} proposed the
large-margin distance to minimize the following objective
function:
\begin{align}
 \label{objective}
 \sum_{i,j}\eta_{ij}D(\vec{x}_i,\vec{x}_j) +& \notag \\ \alpha \sum_{i,j,l}\eta_{ij}(1-y_{il}) \bigl [1 + D(\vec{x}_i,\vec{x}_j) - D(\vec{x}_i,\vec{x}_l) \bigr ]_{+} & \,.
\end{align}
Here $\eta$ encodes target nearest neighbor information, where
$\eta_{ij} = 1$ if $\vec{x}_j$ is one of the $\hat{k}$ positive
nearest neighbors of $\vec{x}_i$; otherwise $\eta_{ij} = 0$. $y$
is the label information where $y_{il} = 1$ if  $\vec{x}_i$ and
$\vec{x}_l$ are in the same class; otherwise $y_{il} = 0$.
$[\cdot]_+ = \max(\cdot,0)$ is the hinge loss function. $\alpha$
is the trade-off parameter. The first term in Eq.~\ref{objective}
penalizes large distances between instances and target neighbors,
and the second term penalizes small distances between each
instance and all other instances that do not share the same label.
By employing such an objective function, we can ensure that the
$\hat{k}$-nearest neighbors of an instance belong to the same
class, while instances from different classes are separated by a
large margin.

In order to learn a discriminative metric, we propose to learn a
large-margin nearest neighbor (LMNN) CNN. Specifically, we replace
the logistic loss with the large margin nearest neighbor loss and
train a network with low-dimension output utilizing the strong
labels. Details of training and fine-tuning the LMNN CNN can be
found in Section~\ref{details}. The output of the proposed LMNN
network is a low-dimensional feature that shares the good semantic
abstraction power of conventional CNN feature and the good
neighborhood property of large-margin metric learning. We then
build the candidate pool with the LMNN CNN features extracted from
ground truth objects.

\subsection{Encoding Local Spatial Label Distribution as Label View} \label{encode}
To effectively incorporate local label information around a local
region, we encode its neighborhood as the label view.
Specifically, we extract features from each proposal
$\vec{x}_{ij}$ using the LMNN CNN, then find $k$ nearest neighbors
of $\vec{x}_{ij}$ in the candidate feature pool as $\vec{nn}_{ij}
= \{nn^1_{ij}, \dots,nn^k_{ij}\}$ and record their labels
$\vec{l}_{ij} = \left[l^1_{ij}\text{ }\dots\text{ }
l^k_{ij}\right]$. The label information (e.g. $l^k_{ij}$) of a
neighbor (e.g. $nn^k_{ij}$) is encoded as a $C$-dimensional binary
vector, which corresponds to $C$ categories. The $d$-th dimension
$l^k_{ij}(d) = 1$, $(d =1,\dots, C)$ if the object is annotated as
class $d$; otherwise $l^k_{ij}(d) = 0$. Therefore, $\vec{l}_{ij}$
is a $1 \times kC$ vector and it will be used as the feature for
the label view.

The merit of such indirectly utilizing ground truth bounding boxes
as the label view is the good generalization ability. As the label
view is a form of local structure representation, even for unseen
categories, i.e. no same-category strong labels, the encoding
process can naturally exploit existing semantically or visually
close categories to build local support. For example, suppose we
do not have the bounding box annotations for ``cat'' and
``train'', a proposal containing ``cat'' might have nearest
neighbors of ``dog'', ``tiger'' or other related animals, and a
proposal containing ``train'' might have nearest neighbors of
``car'', ``truck'' or other vehicles. Although lacking the exact
annotations of certain category, the label view is still able to
encode the local structure with semantically or visually similar
objects. In this way, our framework can make use of existing
strong supervision information to boost the overall performance.
The experimental results shown in Section~\ref{results} validate
this argument.

We directly concatenate the feature view and the label view to
form the final representation of each proposal $\vec{x}_{ij}$ as
$\left[\vec{f}_{ij}\text{ }\lambda \vec{l}_{ij}\right]$, where
$\lambda$ is the trade-off parameter between the feature view and
the label view.

\subsection{Network Configurations and Implementation Details} \label{details}

Our framework consists of two networks, a large-margin nearest
neighbor (LMNN) CNN and a standard CNN. Both networks'
architectures are similar to~\cite{Chatfield2014} with $5$
convolutional layers and $3$ fully-connected layers, and the
dimension of the layer-7 output is set to 2048. The main
differences of these two networks lie in the loss function and the
fine-tuning process. For LMNN CNN, its layer-8 output is a
128-dimensional feature, based on which we measure the pair-wise
distance for kNN, while the output of the standard CNN is a
$C$-dimensional score vector, corresponding to the $C$ categories.

\textbf{Data pre-processing and pre-training.} We use the ILSVRC
$2012$ dataset to pre-train both networks. Given an image, we
resize the short side to $224$ with bilinear interpolation and
perform a center crop to generate the standard $224 \times 224$
input. Each of these inputs is then pre-processed by subtracting
the mean of ILSVRC images.

\textbf{Fine-tuning.} To better adopt the pre-trained network for
specific applications, we also fine-tune these networks using task
relevant data. Unlike~\cite{Chatfield2014}, currently our
implementation does not involve any data augmentation in the
fine-tuning stage.

For the standard CNN used for feature view, we only fine-tune the
network with weak labels on the whole image. As our task is
multi-label recognition, following~\cite{Wei2014}, we use square
loss instead of the logistic loss. To be specific, suppose we have
a label vector $\vec{y}_i = \left[y_{i1}, y_{i2},\dots,
y_{iC}\right]$ for the $i$-th image. $y_{ij} = 1$ $(j =1,\dots,
C)$ if the image is annotated with class $j$; otherwise $y_{ij} =
0$. The ground truth probability vector of the $i$-th image is
defined as $\vec{p}_i = \vec{y}_i/\left\|\vec{y}_i\right\|_1$ and
the predicted probability vector is $\hat{\vec{p}}_i =
\left[\hat{p}_{i1}, \hat{p}_{i2},\dots, \hat{p}_{iC}\right]$.
Then, the cost function to be minimized is defined as
\begin{equation}
 \frac{1}{n}\sum_{i=1}^{n}\sum_{j=1}^{C}(p_{ij} - \hat{p}_{ij})^2 \,.
\end{equation}
During the fine-tuning, the parameters of the first seven layers
of the network are initialized with the pre-trained parameters.
The parameters of the last fully connected layer is initialized
with a Gaussian distribution. We tune the network for $10$ epochs
in total.

For the large-margin NN (LMNN) CNN used for label view, we execute
a three-step fine-tuning. The first step is the image level
fine-tuning similar to the process we have described above. The
second step is ground truth objects fine-tuning, where we
fine-tune the network using ground truth objects with the logistic
loss. The final step is the large-margin nearest neighbor
fine-tuning, where we fine-tune the network with the loss function
of Eq.~\ref{objective} described in Section~\ref{largeNN}. To
accelerate the process, in this final step, we fix all parameters
of the first seven layers and only fine-tune the parameters of the
last fully connected layer.

\section{Experimental Results} \label{exp}

In this section, we present the experimental results of the
proposed multi-view multi-instance framework on multi-label object
recognition tasks.

\subsection{Datasets and Baselines}

We evaluate our method on the PASCAL Visual Object Classes
Challenge (VOC) datasets~\cite{VOC}, which are widely used as
benchmark datasets for the multi-label object recognition task. In
particular, we use the VOC 2007 and VOC 2012 datasets. The details
of these datasets can be found in Table~\ref{datasets}. These two
datasets have a pre-defined split of \textsc{train}, \textsc{val}
and \textsc{test} sets. We use \textsc{train} and \textsc{val} for
training and \textsc{test} for testing. The evaluation method is
average precision (AP) and mean average precision (mAP).

\begin{table}
 \centering
 \caption{Dataset information.} \label{datasets} \vspace{0.1in}
 \begin{tabular}{c | r r r }
  \hline
  Dataset &\#TrainVal &\#Test &\#Classes \\ \hline
  \textsc{VOC 2007} &5011 &4952 &20 \\
  \textsc{VOC 2012} &11540 &10991 &20 \\ \hline
 \end{tabular}
\end{table}

We compare the proposed framework with the following
state-of-the-art approaches: \squishlist
 \item CNN-SVM~\cite{Razavian2014}. This method employed OverFeat~\cite{Sermanet2013}, which is pre-trained on ImageNet, to get CNN activations as the off-the-shelf features. Specifically, CNN-SVM employs the $4096$-d feature extracted from the $22$-nd layer of OverFeat and uses these features to train a linear SVM for the classification task.

 \item PRE~\cite{OquabCVPR2014}.  \cite{OquabCVPR2014} proposed to transfer image representations learned with CNN on ImageNet to other visual recognition tasks with limited training data. The network has exactly the same architecture as that in~\cite{Alex2012}. The network is first pre-trained on ImageNet. The parameters of the first seven layers of CNN are then fixed and  the last fully-connected layer is replaced by two adaptation layers. Finally, the adaptation layers are trained with images from the target dataset.

 \item HCP~\cite{Wei2014}. HCP proposed to solve the multi-label object recognition task by extracting object proposals from the images. Specifically, HCP has three main steps. The first step is to pre-train a CNN on ImageNet data. The second step is image-level fine-tuning that uses image labels and square loss to fine-tune the pre-trained CNN. The final step is to employ BING~\cite{Cheng2014} to extract object proposals and fine-tune the network with these proposals. The image-level scores are obtained by max-pooling from the scores of the proposals.

 \item~\cite{Oquab2014}. \cite{Oquab2014} also handled the problem
in a weakly supervised manner. Particularly, multiple windows are
extracted from different scales of the images in the dense
sampling fashion. The scores of these windows are combined with
max-pooling from the same scale then sum-pooling across different
scales.

 \item VeryDeep~\cite{Simonyan2014}. \cite{Simonyan2014} densely extracts multiple CNN features across multi-scales of the image with very-deep networks (16-layer and 19-layer). The features from the same scale are concatenated by sum-pooling and features from different scale are aggregated by stacking or sum-pooling.  \cite{Simonyan2014} also augments the test set by horizontal flipping of the images.

 \item Hand-crafted Features. \cite{AMM2014} presented an
Ambiguity guided Mixture Model (AMM) to integrate external context
features and object features, and then used the contextualized SVM
to iteratively boost the performance of object classification and
detection tasks.~\cite{Dong2013} proposed an Ambiguity Guided
Subcategory (AGS) mining approach to improve both detection and
classification performance. \squishend

\subsection{Our Setup and Parameters}
It is difficult to make a completely fair comparison among
different CNN based methods as the CNN configurations, the data
augmentation and the pre-training could substantially influence
the results. All CNN based methods can benefit from extra training
data and more powerful networks as shown
in~\cite{OquabCVPR2014,Wei2014,Oquab2014,Chatfield2014,Simonyan2014}.
To fairly evaluate our proposed framework, we develop our system
based on the common $8$-layer CNN pretrained on ILSVRC 2012
dataset with 1000 categories. The details of the fine tuning
process has been elaborated in Section~\ref{details}. Once the
LMNN CNN and the standard CNN (see Fig.~\ref{system}) are trained,
the system is applied to map each training image into a final FV
feature. Finally, TopPush~\cite{Li2014} is chose to learn linear
one-vs-all classifiers for each category, which produces the
scores for each binary sub-problem of the multi-label datasets.
The scores are then evaluated with standard VOC evaluation
package. All the experiments are run on a computer with Intel
i7-3930K CPU, 32G main memory and an nVIDIA Tesla K40 card.

For the proposal extraction, we employ selective
search~\cite{Uijlings2013}, which typically generates around
$1500$ proposals on average from every image in the PASCAL VOC
2007 dataset using the parameters suggested
in~\cite{Uijlings2013}. Considering the computational time and the
hardware limitation, we random sample around $400$ proposals per image for training and testing.

For the parameters of Fisher vector, we
follow~\cite{Perronnin2010} to first employ PCA to reduce the
dimension of the original features to preserve around 90\% energy.
For VOC 2007 and 2012 datasets, after PCA, the standard CNN
features is reduced to around $450$-d. After PCA, we generate $128$
GMM codewords and encode each image with IFV similar to~\cite{Perronnin2010}.

For fine-tuning the LMNN CNN, we set the trade-off parameter
$\alpha=1$ (see~\eqref{objective}) and the nearest neighbor number
$\hat{k}=10$ (for training). For combining the feature view and
the label view features, we select the trade-off parameter
$\lambda$ (specified at the end of Section~\ref{encode}) from
$\{1,0.5,0.25\}$ by cross-validation. For the nearest neighbor
number $k$ used in testing, generally bigger $k$ leads to better
accuracy, but we observe there is no performance gain for $k >
50$. Thus, we set $k=50$ for testing. For faster NN search, we
employ FLANN~\cite{flann} with ``autotuned'' parameters.

\subsection{Image Classification Results} \label{results}

\begin{table*}
 \centering \caption{Comparisons of the classification results (in
 \%) of state-of-the-art approaches on VOC 2007
 (\textsc{trainval}/\textsc{test}). The upper part shows the
 results of the hand-crafted feature based methods and the CNN based methods
trained with $8$-layer CNN and ILSVRC $2012$ dataset. The lower
part shows the results of the methods trained with very-deep CNN
or with additional training data.} \label{voc2007-s} \scriptsize
 \begin{tabular}{ @{\,}c| *{20}{@{\,}c@{\,}} | @{\,}c@{\,}}
 \hline
                                &\textsc{plane} &\textsc{bike} &\textsc{bird} &\textsc{boat}   &\textsc{bottle} &\textsc{bus}  &\textsc{car}   &\textsc{cat}  &\textsc{chair} &\textsc{cow}
                                &\textsc{table} &\textsc{dog}  &\textsc{horse} &\textsc{motor} &\textsc{person} &\textsc{plant} &\textsc{sheep} &\textsc{sofa}  &\textsc{train} &\textsc{tv}    &\textsc{mAP}\\ \hline
  AGS~\cite{Dong2013}           &82.2  &83.0 &58.4 &76.1   &56.4   &77.5 &88.8  &69.1 &62.2 &61.8  &64.2 &51.3  &85.4  &80.2  &91.1   &48.1  &61.7  &67.7  &86.3  &70.9  &71.1\\
  AMM~\cite{AMM2014}            &84.5  &81.5 &65.0 &71.4   &52.2   &76.2 &87.2  &68.5 &63.8 &55.8  &65.8 &55.6  &84.8  &77.0  &91.1   &55.2  &60.0  &69.7  &83.6  &77.0  &71.3\\
  CNN-SVM~\cite{Razavian2014}   &88.5  &81.0 &83.5 &82.0   &42.0   &72.5 &85.3  &81.6 &59.9 &58.5  &66.3 &77.8  &81.8  &78.8  &90.2   &54.8  &71.1  &62.6  &87.4  &71.8  &73.9\\
  PRE-1000C~\cite{OquabCVPR2014}&88.5  &81.5 &87.9 &82.0   &47.5   &75.5 &90.1  &87.2 &61.6 &75.7  &67.3 &85.5  &83.5  &80.0  &95.6   &60.8  &76.8  &58.0  &90.4  &77.9  &77.7\\
  HCP-1000C~\cite{Wei2014}      &95.1  &90.1 &92.8 &89.9   &51.5   &80.0 &91.7  &91.6 &57.7 &77.8  &70.9 &89.3  &89.3  &85.2  &93.0   &64.0  &\bf{85.7}  &62.7  &94.4  &78.3  &81.5\\ \hline
  FeV                            &93.3  &92.8 &91.8 &86.5   &57.5   &84.3 &93.7  &90.6 &64.7 &78.9  &74.1 &90.3  &91.1  &90.7  &95.7   &67.4  &82.4  &70.8  &94.3  &83.3  &83.7\\
  FeV+LV-10                      &\bf{96.6} &93.6 &\bf{94.0} &\bf{89.5}   &59.6   &87.4 &\bf{94.8}  &91.0 &67.3 &81.4  &76.3 &91.0  &\bf{93.5}  &91.4  &96.1   &64.2  &83.4  &68.5  &95.8  &84.0  &85.0\\
  FeV+LV-20                      &95.7  &\bf{94.6} &93.9 &87.8   &\bf{62.7}   &\bf{87.9} &\bf{94.8}  &\bf{92.2} &\bf{67.5} &\bf{82.4}  &\bf{77.8} &\bf{92.0}  &93.2  &\bf{92.2}  &\bf{97.1}   &\bf{72.5}  &85.3  &\bf{73.4}  &\bf{96.2}  &\bf{85.5}
                                &\bf{86.2}\\
  \hline
  \hline
  HCP-2000C~\cite{Wei2014}  &96.0  &92.1 &93.7 &93.4   &58.7   &84.0 &93.4  &92.0 &62.8 &89.1  &76.3 &91.4  &95.0  &87.8  &93.1   &69.9  &90.3  &68.0  &96.8  &80.6  &85.2\\
  VeryDeep~\cite{Simonyan2014}  &\bf{98.9}  &95.0 &96.8 &95.4   &69.7   &90.4 &93.5  &96.0 &74.2 &86.6  &87.8 &96.0  &96.3  &93.1  &97.2   &70.0  &92.1  &80.3  &98.1  &87.0  &89.7\\ \hline
  FeV+LV-20-VD           &97.9  &\bf{97.0} &96.6 &94.6   &73.6   &93.9 &96.5  &95.5 &73.7 &90.3  &82.8 &95.4  &\bf{97.7}  &\bf{95.9}  &\bf{98.6}   &77.6  &88.7  &78.0  &98.3  &89.0  &90.6\\
  Fusion            &98.2  &96.9 &\bf{97.1} &\bf{95.8}   &\bf{74.3}   &\bf{94.2} &\bf{96.7}  &\bf{96.7} &\bf{76.7} &\bf{90.5}  &\bf{88.0} &\bf{96.9}  &\bf{97.7}  &\bf{95.9}  &\bf{98.6}   &\bf{78.5}  &\bf{93.6}  &\bf{82.4}
  &\bf{98.4}  &\bf{90.4}  &\bf{92.0}\\
  \hline
 \end{tabular}
\end{table*}

\begin{table*}
 \centering \caption{Comparisons of the classification results (in
 \%) of state-of-the-art approaches on VOC 2012
 (\textsc{trainval}/\textsc{test}). The upper part shows the
 results of the hand-crafted feature based methods and the CNN based methods
trained with $8$-layer CNN and ILSVRC $2012$ dataset. The lower
part shows the results of the methods trained with very-deep CNN
or with additional training data.} \label{voc2012-s} \scriptsize
 \begin{tabular}{ @{\,}c| *{20}{@{\,}c@{\,}} | @{\,}c@{\,}}
 \hline
                                &\textsc{plane} &\textsc{bike} &\textsc{bird} &\textsc{boat}   &\textsc{bottle} &\textsc{bus}  &\textsc{car}   &\textsc{cat}  &\textsc{chair} &\textsc{cow}
                                &\textsc{table} &\textsc{dog}  &\textsc{horse} &\textsc{motor} &\textsc{person} &\textsc{plant} &\textsc{sheep} &\textsc{sofa}  &\textsc{train} &\textsc{tv}    &\textsc{mAP}\\ \hline
  NUS-PSL~\cite{Wei2014}    &97.3  &84.2 &80.8 &85.3   &60.8   &89.9 &\bf{86.8}  &89.3 &\bf{75.4} &77.8  &75.1 &83.0  &87.5  &90.1  &95.0   &57.8  &79.2  &\bf{73.4}  &94.5  &80.7  &82.2\\
  PRE-1000C~\cite{OquabCVPR2014}&93.5  &78.4 &87.7 &80.9   &57.3   &85.0 &81.6  &89.4 &66.9 &73.8  &62.0 &89.5  &83.2  &87.6  &95.8   &61.4  &79.0  &54.3  &88.0  &78.3  &78.7\\
  HCP-1000C~\cite{Wei2014}      &\bf{97.7}  &83.0 &\bf{93.2} &87.2   &59.6   &88.2 &81.9  &94.7 &66.9 &81.6  &68.0 &\bf{93.0}  &88.2  &87.7  &92.7   &59.0  &85.1  &55.4  &93.0  &77.2  &81.7\\ \hline
  FeV                            &96.8  &87.8 &88.7 &87.2   &63.8   &\bf{92.3} &86.2  &92.3 &72.4 &82.0  &76.0 &91.9  &90.3  &90.3  &95.2   &61.2  &82.6  &65.6  &92.8  &84.4  &84.0\\
  FeV-LV-10                      &97.3  &\bf{89.1} &\bf{91.5} &\bf{88.5}   &\bf{66.7}   &92.2 &\bf{87.2}  &94.0 &74.0 &82.7  &77.8 &91.6  &91.1  &92.7  &95.7   &66.5  &85.4  &\bf{69.4}  &\bf{95.6}  &85.4  &85.7\\
  FeV-LV-20                      &\bf{97.4}  &88.9 &91.2 &87.4   &64.2   &92.2 &86.4  &\bf{95.0} &\bf{75.1} &\bf{84.6}  &\bf{78.7} &\bf{93.1}  &\bf{91.9}  &\bf{93.1}  &\bf{96.6}   &\bf{67.3}  &\bf{86.2}  &\bf{69.4}  &95.3  &\bf{85.8}  &\bf{86.0}\\
  \hline
  \hline
  PRE-1512C~\cite{OquabCVPR2014}&94.6  &82.9 &88.2 &84.1   &60.3   &89.0 &84.4  &90.7 &72.1 &86.8  &69.0 &92.1  &93.4  &88.6  &96.1   &64.3  &86.6  &62.3  &91.1  &79.8  &82.8\\
  HCP-2000C~\cite{Wei2014}      &97.5  &84.3 &93.0 &89.4   &62.5   &90.2 &84.6  &94.8 &69.7 &90.2  &74.1 &93.4  &93.7  &88.8  &93.3   &59.7  &90.3  &61.8  &94.4  &78.0  &84.2\\
  \cite{Oquab2014}      &96.7  &88.8 &92.0 &87.4   &64.7   &91.1 &87.4  &94.4 &74.9 &89.2  &76.3 &93.7  &95.2  &91.1  &97.6   &66.2  &91.2  &70.0  &94.5  &83.7  &86.3\\
  VeryDeep~\cite{Simonyan2014}  &99.0  &89.1 &\bf{96.0} &\bf{94.1}   &74.1   &92.2 &85.3  &\bf{97.9} &79.9 &92.0  &\bf{83.7} &\bf{97.5}  &96.5  &94.7  &97.1   &63.7  &\bf{93.6}  &75.2  &97.4  &87.8  &89.3\\
  NUS-HCP-AGS~\cite{Wei2014}    &\bf{99.0}  &91.8 &94.8 &92.4   &72.6   &\bf{95.0} &\bf{91.8}  &97.4 &\bf{85.2} &\bf{92.9}  &83.1 &96.0  &96.6  &\bf{96.1}  &94.9   &68.4  &92.0  &\bf{79.6}  &97.3  &88.5  &90.3\\ \hline
  FeV+LV-20-VD           &98.4  &92.8 &93.4 &90.7   &74.9   &93.2 &90.2  &96.1 &78.2 &89.8  &80.6 &95.7  &96.1  &95.3  &97.5   &73.1  &91.2  &75.4  &97.0  &88.2  &89.4\\
  Fusion            &98.9  &\bf{93.1} &\bf{96.0} &\bf{94.1}   &\bf{76.4}   &93.5 &90.8  &\bf{97.9} &80.2 &92.1  &82.4 &97.2  &\bf{96.8}  &95.7  &\bf{98.1}   &\bf{73.9}  &\bf{93.6}  &76.8  &\bf{97.5}  &\bf{89.0}  &\bf{90.7}\\
  \hline
 \end{tabular}
\end{table*}

\textbf{Image Classification on VOC 2007}: Table~\ref{voc2007-s}
reports our experimental results compared with state-of-the-art
methods on VOC 2007. In the upper part of the table we compare
with the hand-crafted feature based methods and the CNN based
methods pre-trained on ILSVRC 2012 using $8$-layer network. To
demonstrate the effectiveness of individual components, we
consider three variations of our proposed framework: `FeV',
`FeV+LV-10' and `FeV+LV-20', where `FeV' uses only the feature
view (i.e. without the label view features), `FeV+LV-10' uses both
the feature view and the label view with 10 categories of
ground-truth bounding boxes of the training set, and `FeV+LV-20'
is the one with 20 categories of ground-truth bounding boxes.

From the upper part of Table~\ref{voc2007-s}, we can see that
using just feature view (`FeV'), we already outperform the
state-of-the-art proposal-based method (`HCP-1000C') by $2.2\%$,
which suggests that Fisher vector as a holistic representation for
bags is superior than max-pooling. With all 20 categories of
ground-truth bounding boxes of the training set, our multi-view
framework (`FeV+LV-20') achieves a further $2.5\%$ performance
gain. This significant performance gain validates the
effectiveness of the label view. Our framework shows good
performance especially for difficult categories such as
\textsc{bottle}, \textsc{cow}, \textsc{table}, \textsc{motor} and
\textsc{plant}.

If we just use the ground-truth bounding boxes from the first $10$
categories (\textsc{plane} to \textsc{cow}), our framework
(`FeV+LV-10') still outperforms single feature view (`FeV') by a
margin of $1.3\%$. As expected, using the bounding boxes of the
categories from \textsc{plane} to \textsc{cow} can boost the
performance of these categories as shown in the table. However, it
is interesting to see that the label view also improves the
accuracies of unseen categories such as \textsc{horse},
\textsc{person} and \textsc{tv}. This is mainly because the
proposed label view encoding is a form of local similarity
representation, which can generalize quite well to unseen
categories.

In the lower part of Table~\ref{voc2007-s}, we list the results of
`HCP-2000C'~\cite{Wei2014}, which uses additional $1000$
categories from ImageNet that are semantically close to VOC 2007
categories for CNN pre-training, and
`VeryDeep'~\cite{Simonyan2014}, which densely extracts multiple
CNN features from $5$ scales and combines two very-deep CNN models
($16$-layer an $19$-layer). Our framework (`FeV+LV-20') can still
outperform `HCP-2000C', but is inferior to `VeryDeep' since our
framework is based on the common 8-layer CNN.

To demonstrate the potential of our framework, we replace the
8-layer CNNs in our framework by the 16-layer CNN model
in~\cite{Simonyan2014}, which is denoted as `FeV+LV-20-VD'.
Unlike~\cite{Simonyan2014}, we do not use any data augmentation or
multi-scale dense sampling in the feature extraction stage. Our
`FeV+LV-20-VD' outperforms `VeryDeep' by nearly $1\%$. By further
averaging the scores of `VeryDeep'~\cite{Simonyan2014} and
`FeV+LV-20-VD' (denoted as `Fusion'), we achieve state-of-the-art
mAP of $92.0\%$. This suggests that our proposal-based framework
and the multi-scale CNN extracted from the whole image are
complement to each other.

\textbf{Image Classification on VOC 2012}: Table~\ref{voc2012-s}
reports our experimental results compared with those of the
state-of-the-art methods on VOC 2012. Similar to
Table~\ref{voc2007-s}, we compare with the hand-crafted feature
based methods and the CNN based methods pre-trained on ILSVRC 2012
using $8$-layer CNN model in the upper part and the methods
trained with additional data or very-deep CNN models in the lower
part.

The results are consistent with those on VOC 2007. Our framework
that uses only the feature view (`FeV') already outperforms the
state-of-the-art hand-crafted feature method (`NUS-PSL') by
$1.8\%$ and the state-of-the-art proposal-based CNN method
(HCP-1000C) by $2.3\%$. With the aid of the label view, our
`FeV+LV-20' obtains an additional $2\%$ performance again, even
outperforming the two proposal-based methods pre-trained on
additional $512$ or $1000$ categories of image data (`PRE-1512C'
and `HCP-2000C') and comparable to~\cite{Oquab2014}. By employing
just $10$ categories of bounding boxes, the mAP performance of our
`FeV+LV-10' does not degrade much.

When employed with the very-deep 16-layer CNN
model~\cite{Simonyan2014}, our framework (`FeV+LV-20-VD') achieves
similar performance as `VeryDeep'. When we averagely fuse the
scores of~\cite{Simonyan2014} and our proposal-based
representation, our method (`Fusion') achieves state-of-the-art
result, outperforming~\cite{Wei2014}.

\section{Conclusion}

In this paper, we have proposed a multi-view multi-instance
framework for solving the multi-label classification problem.
Compared with existing works, our framework makes use of the strong labels to provide another view of local information (label view) and combines it with the typical feature view information to boost the discriminative power of feature
extraction for multi-label images.
The experimental results validates the discriminative power and the
generalization ability of the proposed framework.

For future directions, there are several possibilities to explore.
First of all, we can improve the scalability and possibly also
the performance of the framework by establishing a
proposal selection criteria to filter out noisy proposals.
Secondly, we may build a suitable candidate pool directly from the
extracted proposals to eliminate the need for strong labels.

\noindent\textbf{Acknowledgments} \small This research is supported by Singapore MoE AcRF Tier-1 Grant
RG138/14 and also partially supported by the Rapid-Rich Object
Search (ROSE) Lab at the Nanyang Technological University,
Singapore. The Tesla K40 used for this research was donated by the NVIDIA Corporation.

{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


text:
#include <iostream>
#include <Eigen/Core>
#include <Eigen/Sparse>
#include "eigen_spmat.h"

using namespace Eigen;

#include "eigen_spmat_s.cpp"
#include "eigen_spmat_d.cpp"
#include "eigen_spmat_c.cpp"
#include "eigen_spmat_z.cpp"

redpajama_set_name: RedPajamaGithub


Royal recognition of Imafidon's successes and contributions education, employment, entrepreneurship.
This has been featured in all leading media outlets worldwide including the BBC, CNBC, CNN, The Times, etc.
How the GENIUS Formula can help anyone learn quicker.
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How to identify the silver lining of Brexit for the benefit of your organization.
How to get Free CNN, Fox News, ABC, BBC, RT TV or other major media coverage for your books, products or services and explode your success in 2019.


text:
Tulian (, also Romanized as Ţūlīān, Tūleyān, Tūlīān, and Tūlīyān) is a village in Dehdasht-e Sharqi Rural District, in the Central District of Kohgiluyeh County, Kohgiluyeh and Boyer-Ahmad Province, Iran. At the 2006 census, its population was 1,393, in 253 families.

References

Populated places in Kohgiluyeh County

redpajama_set_name: RedPajamaWikipedia


Description: Activate the infusion to heal yourself for 244 life over 5 turns.
Description: Sends a telepathic attack, trying to destroy the brains of any target in the beam, doing 219.16 mind damage.
Description: Inflicts 12.56 mind damage and cripples the target's higher mental functions, reducing cunning by 1 and confusing (8% power) the target for 0 turns.
Description: Sends out a blast of telepathic static in a 2 radius, inflicting 12.56 mind damage. This attack can brainlock affected targets.
Description: Cripples the target's mind, inflicting 8.64 mind damage and reducing its Mental Save by 1 for 4 turns. This attack always hits, and the mental save reduction stacks.
Description: Puts the target into a deep sleep for 2 turns, rendering it unable to act. Every 6 points of damage the target suffers will reduce the effect duration by one turn.
Description: Targets you have slept take 2.09 mind damage each turn for five turns upon waking.
In the Dreamscape, your damage will be improved by 6%.
Converted Psi damage you take will be further reduced by 20.9% (18.4% from character level with the remainder further reduced by 3.2% from talent level).
The first talent point also increases your solipsism threshold by 20% (currently 30%), reducing your global speed by 1% for each percentage your current Psi falls below this threshold.
Learning this talent also increases your solipsism threshold by 10% (currently 30%).
The first talent point invested will also increase the amount of Psi you gain from Willpower by 0.5, but reduce the amount of life you gain from Constitution by 0.25 and will increase your solipsism threshold by 10% (currently 30%).
The first talent point also increases your solipsism threshold by 10% (currently 30%).
Description: Fire a bolt of distortion that ignores resistance and inflicts 106.58 physical damage. This damage will distort affected targets, decreasing physical resistance by 15% and rendering them vulnerable to distortion effects for two turns.
Description: Creates a distortion wave in a radius 9 cone that deals 106.58 physical damage and knocks back targets in the blast radius.
This damage will distort affected targets, decreasing physical resistance by 15% and rendering them vulnerable to distortion effects for two turns.
Description: Ravages the target with distortion, inflicting 30.61 physical damage each turn for 9 turns.
Description: Create a powerful maelstorm for 2 turns. Each turn, the maelstrom will pull in targets within a radius of 1, and inflict 2.76 physical damage.
Description: Forge a guardian from your thoughts alone. Your guardian's primary stat will be improved by 26, its two secondary stats by 13, and it will have Magic, Cunning, and Willpower equal to your own.
Description: Your thought-forms now know Lucid Dreamer, Biofeedback, and Psychometry at talent level 1.
Description: You now gain 1% mind speed while Thought-Form: Bowman is active, 2 Mindpower while Thought-Form: Warrior is active, and 0% resist all while Thought-Form: Defender is active.
Learning this talent adds 1.38 mind damage and 1.38 burning damage to your Dream Hammer strikes.
Description: Call upon the legendary resilience of the Dwarven race to increase your armour (+10), armour hardiness (+20%), spell (+15) and physical (+15) saves for 8 turns.
Description: Your Feedback decay now heals you for 0.6 times the loss, and the decay rate is reduced to 84% of the normal rate (up to 8.4% per turn). As a result, you are healed for 5.30% of your feedback pool each turn.
Description: Activate to create a resonance field that will absorb 50% of all damage you take (400 max absorption). The field will not interfere with Feedback gain.
Description: Increases your maximum Feedback by 0, and increases the Feedback you gain from damage by 0.0% (to 33.6% of damage received).
Description: Use Feedback to replenish yourself. This heals you for 28 life, and restores 3 stamina, 5 mana, 4 equilibrium, 3 vim, 1 positive and negative energies, 2 psi energy, and 1 hate.
Description: Resonate with psionic, nature, and anti-magic powered objects you wear, increasing your physical and mind power by 1.50 or 50% of the object's material level (whichever is lower).
Description: Slip into a lucid dream. While in this state, you are considered sleeping, but can still act, are immune to insomnia, inflict 8% more damage to targets under the effects of Insomnia, and your Physical, Mental, and Spell saves are increased by 8.
This powerful effect constantly drains 1.75% of your maximum Psi per turn, and is considered a psionic channel; as such it will break if you move.
Each time you are hit by non-physical, non-mind damage, you get a 12% resistance to that element for 7 turns.
If Antimagic Shield is not active, you also absorb part of the impact and use it to fuel your own powers, decreasing your equilibrium and increasing your stamina by 1.25 each hit.
Each creature whose attack on you is absorbed or reduced by the shield also takes a backlash dealing 3.27 manaburn damage (see description in the Mana Clash talent), this may only happen up to 10 times per turn and each time the damage is reduced by 25%.
Description: Drain 30 mana, 15 vim, 7 positive and negative energies from your target, triggering a chain reaction that explodes in a burst of arcane damage.
You failed to protect the lost tinker from death by rattlesnake.
It can be used to boost speed by 47% (based on Cunning), costing 50 power out of 50/50.
After moving, you gain 119% increased movement speed for 3 turns (which ends if you take any actions other than movement), and you may reload your ammo (if any).
Description: Touch a target (or yourself) to infuse it with Nature, healing it for 254 (this heal does not work on undead).
It can be used to teleport randomly (rad 23), putting all charms on cooldown for 19 turns.
It can be used to convince all non arcane users in radius 10 to turn on their spellcasting friends for 6 turns (chance increases with your Mindpower), costing 25 power out of 25/25.
100% chance to regenerate 5 equilibrium.
Angry Bumpkin wears (replacing Persistant Will): iron torque of kinetic psionic shield [power 25] (20 cooldown).
Corrupted protosentient globula casts Drain.
Angry Bumpkin converts some damage to Psi!
Neroma the corrupted mastocytic feeder's Beyond the Flesh hits Angry Bumpkin for 3 to psi, 17 physical (20 total damage).
Angry Bumpkin the level 17 dwarf solipsist was skewered to death by Neroma the corrupted mastocytic feeder on level 1 of inside a giant tentacle.
Angry Bumpkin stops regenerating health quickly.
Angry Bumpkin is not stunned anymore.
Angry Bumpkin is no longer attuned.
Angry Bumpkin deactivates Lucid Dreamer.
Neroma the corrupted mastocytic feeder's Beyond the Flesh killed Angry Bumpkin!
Talent Distortion Wave is ready to use.
Talent Dream Walk is ready to use.
Talent Aura of Silence is ready to use.
Angry Bumpkin receives 7 healing (2 psi heal).
Angry Bumpkin receives 6 healing (2 psi heal).
Angry Bumpkin receives 6 healing (1 psi heal).
There is a Passageway into the Daikara here (press '' or right click to use).


text:
Bug Woman – Adventures in London
Because a community is more than just people
Bug Woman's Promise
Bugwoman Now Has a Facebook Page..
The Ninth Day of Christmas – Nine Ladies Dancing
From a greetings card sold on Redbubble and designed by Lexa Strong (https://www.redbubble.com/i/greeting-card/Nine-Ladies-Dancing-by-Loopti/29558558.5MT14)
Dear Readers, for my Mum and Dad's generation, dancing was something that everybody did. My Mum and Dad only had to hear the first few bars of 'Rock Around the Clock' on the radio to start jiving around the kitchen, Mum twirling around, Dad whistling along (tunelessly as always). They were always the first onto the dancefloor, and if Dad got tired, Mum would find someone else's boyfriend to take up the slack. No wonder she was always so thin! She could dance all night and still be fresh as a daisy in the morning. Have a look at Bill Hailey and the Comets in action and see if you can stop your toes from tapping….
https://www.youtube.com/watch?v=-eJOJhwgluE
But then Dad, in one of those strange quirks of history, discovered reggae. Years later he would spend time as a gin distiller in Jamaica, but he must have heard this song on the radio a good decade earlier. Dave and Ansell Collins would be his dance music of choice for years, although it was more of a solo pursuit – I would catch him moving around the living room in a strange shuffle, clicking his fingers and nodding his head, but only if he thought he was on his own. Here, for your delectation, is 'Double Barrel' . I'm pretty sure that this is more of a shoulder-gyrater than a toe-tapper. See what you think.
I think Mum thought that she'd be able to dance forever, and she was certainly still jumping up for a dance into her early seventies. But gradually, the peripheral neuropathy in her feet and the scoliosis in her back, coupled with Dad's bursitis and his increasing dementia, meant that their dancing days were done. It was one of the great sadnesses of Mum's life that she never had a chance to give line dancing a go. At the start of a brand new year, it really does strike me that if we want to try something new, we shouldn't keep putting it off, difficult as it is what with Covid putting the kibosh on so many things. On the other hand, how wonderful that Mum was still interested in trying new things and ready to learn even after her physical faculties were gone. How sad it would be to finish our lives with absolutely everything done and dusted, with no more hopes or plans.
Mum aged about 16
Well, that's the dancing done. But how about the Ladies bit? It won't surprise you to learn that there are more vernacular names for plants including the word 'Lady' or 'Ladies' than for any other word. You could make up an entire wardrobe out of the words for clothes alone. So let's do that! See if you can match the name to the photo of the plant. I've included the Latin name just to help you out (a bit).
Ladies Bonnets
Lady's Smock
Lady's Lockets
Lady's Slipper
Lady's Mantle
Lady's Tresses (ok, so not really an item of clothing, but definitely important for a lady).
A) Polygonatum biflorum
B) Aquilegia vulgaris
C) Spiranthes spiralis
D) Alchemilla vulgaris
E) Cardamine pratensis
F) Cypripedium calceolus
Photo A by Eric Hunt, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons
Photo B by Ivar Leidus – Own work, CC BY-SA 3.0 ee, https://commons.wikimedia.org/w/index.php?curid=26560960
Photo C by Björn S…, CC BY-SA 2.0 <https://creativecommons.org/licenses/by-sa/2.0>, via Wikimedia Commons
Photo D by Trish Steel, CC BY-SA 2.0 <https://creativecommons.org/licenses/by-sa/2.0>, via Wikimedia Commons
Photo E by Tim Green, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons
Photo F has no attribution information – CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=856627
This entry was posted in Uncategorized on January 2, 2022 by Bug Woman.
← The Eighth Day of Christmas – Eight Maids a-Milking The Tenth Day of Christmas – Ten Lords a-Leaping →
The Sunday Quiz – What's That Soup?
Air Pollution in London
A Winter Walk at Walthamstow Wetlands
Book Review – The Gospel of the Eels by Patrik Svensson
Wednesday Weed – Chamomile
Bug Woman on Air Pollution in London
Bug Woman on The Sunday Quiz – What…
Alittlebitoutoffocus on Air Pollution in London
Bugwoman on Location
LNHS
London Amphibians
London Birds
London Fungi
London Invertebrates
London Mammals
London Microbes
London People
London Places
London Plants
London Reptiles


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Malaysia's Ageing Population: What It Means for Singapore
CO20205 | Malaysia's Ageing Population: What It Means for Singapore
Jose Ricardo Sto. Domingo
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Malaysia's gradually ageing population may influence its migration flows. By 2040 and beyond, this could imply fewer migrants for a country such as Singapore, which has historically been reliant on immigrants for both its economy and its population.
IN MARCH 2020, the Malaysian government enforced the first of a series of Movement Control Orders (MCOs) to curb the spread of COVID-19 by restricting movement within the country and across its borders. This included the Johor-Singapore Causeway and the Malaysia-Singapore Second Link, effectively closing one of world's busiest borders.
Thousands of Malaysians and Singaporeans, along with businesses that rely on them, had to make difficult decisions on where they should remain in the meantime. The closing of borders by both sides has exposed both the deep personal ties and economic interdependency between the two nations.
Cross-Border Trends
Malaysia's Minister of Foreign Affairs, Hishammuddin Hussein, was quoted by The Star in July 2020, and said that between 200,000 to 250,000 people passed through these border crossings daily. In September, Johor's Menteri Besar (Chief Minister), Hasni Mohammad, expressed deep concern for some 100,000 Malaysians, who may lose their jobs due to prolonged border closure, as reported by news outlets.
Malaysians regularly travelled through these crossings to Singapore for work or school, while Singaporeans often visited Johor for day trips. Some Singaporeans also resided in Johor and commuted daily to Singapore. Businesses in Johor that were dependent on tourists from Singapore have been severely affected.
Hasni also said in September that about five per cent to 10% of businesses had stopped operating in Johor due to the border closing, and some have seen revenue declines of up to 70%.
Prolonged border control measures against COVID-19 have highlighted broader concerns for both countries' reliance on foreign labour. Although these will likely subside along with the pandemic, both face long-term demographic trends that are harder to manage.
Singapore's Dependency on Malaysian Migrants
While Malaysia is reliant on foreigners mostly for work, Singapore's far smaller population has relied on immigration not only for its economy, but also for population growth.
According to the United Nations, most of the migrants in Singapore are Malaysian. Based on their estimates, Malaysians in Singapore numbered 952,621 people (2019). They comprised 44.2% of Singapore's own foreign-born population or migrant stock, which includes naturalised citizens, permanent residents, students, work pass holders and their dependents.
The next closest to Malaysians were migrants from China at 17.6%, and Indonesia at 6.4% of Singapore's foreign population.
The high number of Malaysians in Singapore is likely due to proximity and cultural affinity, which makes it easier for Malaysians to integrate with Singaporeans. While Malaysians comprise the largest group of Singapore's migrant stock, fewer Malaysians may be headed to Singapore towards 2040 and beyond, when Malaysia becomes an aged country.
Malaysia's Ageing Population
Today, Malaysia is estimated to be an ageing nation, with seven per cent of its population aged 65 years old and above. A country is defined as "aged" when more than 14% of its population is 65 years old or higher. When this figure exceeds 20%, it is "super-aged".
By 2040, the Department of Statistics Malaysia estimated that 14.5% of its population will be 65 and older. Last month, the World Bank also released a report that estimated Malaysia to reach the aged country status by 2044. At the same time, the country's birth rate has been below replacement levels since 2012 and continues to decline gradually.
The UN projects Malaysia's total fertility rate (TFR) – the average number of children born to a woman – to eventually dip to 1.731, below replacement, between 2040 to 2045.
The country's current population is estimated at 32.7 million people, 69.7% of which are between 15-64 years old (working age group). The next biggest group is those aged 14 years old and under (dependents), who make up 23.3% of the population. They are rounded out by those 65 years old and above (the elderly), at seven per cent.
Nevertheless, Malaysia's population is still relatively youthful compared to some of its ASEAN neighbours such as Singapore, Thailand and Vietnam. Malaysia's current median age is estimated at 29.2, while Singapore's is at 41.1. By 2040, Malaysia's median age is projected to become 38.3 years old.
The Malaysian government has acknowledged that a gradual ageing and growing elderly population present serious long-term challenges, such as economic sustainability, retirement security, and adequacy of elder healthcare. Last September, Khazanah Research Institute released a report that stated that nearly 40% of Malaysian workers did not have retirement savings plans, while the World Bank's November report highlighted these challenges.
Malaysia's Ageing and its Migration Flows
A future elderly Malaysian population could also slow its migration flows. It would have a smaller portion of the working age, and a growing number of retirees.
With a smaller and older workforce, demand for labour could grow in Malaysia. The Malaysian government could respond by controlling the flow of its own emigrants and make the country a more attractive option for work.
Malaysia's ageing population could also increase the country's own inward migration flow. Indeed, the government has ramped up efforts to attract members of the Malaysian diaspora to return to the country and contribute to the economy. Their experiences and expertise would be valuable in terms of skills-sharing and knowledge-transfer.
These factors could make it difficult in the future for Singapore to attract and retain Malaysian migrants, especially the young. If Singapore continues to rely on foreign workers, the government may have to raise wages and provide further benefits for Malaysians or look elsewhere to replace the potentially lost labour.
Singapore's Own Ageing Population
The Singaporean government is also dealing with its own ageing population and has been exploring a combination of solutions to reduce its labour requirements and increase the employability and productivity of its elderly.
In fact, Malaysian migrant flows to Singapore appear to have slowed down. From 1990 to 2015, UN estimates showed that Malaysian migrants in Singapore were increasing in five-year intervals. However, when compared between 2015 and 2019, UN estimates show that total migrants (including Malaysians) in Singapore actually decreased by 15.3%.
Interestingly, Malaysian migrants decreased by 15.3% in 2019 as well. While this recent slowdown may not be due to Malaysia's ageing population, it could contribute to a trend leading towards 2040 and beyond.
Will Singapore Tap Other ASEAN Countries More?
Today, re-opening the border between Singapore and Malaysia remains a pressing issue. Malaysia and Singapore initiated a Reciprocal Green Lane (RGL) and a Periodic Commuting Agreement (PCA) in August to allow some form of minimal business travel between the two countries but the numbers were a far cry from the old border traffic.
Talks are underway between the two countries to formalise Standard Operating Procedures (SOPs) for a daily commute scheme between Singapore and Johor. This could, however, take some time.
Most states in Peninsular Malaysia were recently put under a Conditional Movement Control Order (CMCO) in November, until 6 December 2020, including Johor. However, the state's CMCO was lifted on 21 November, and the government announced a targeted CMCO within the Batu Pahat district from 29 November to 13 December.
For the longer term, Singapore would have to consider how its labour and population growth needs might evolve. Artificial Intelligence and automation could reduce future manpower needs. The government may also diversify sources of immigration and tap other ASEAN countries more. This would in turn require a review of the fundamental make-up of Singapore's population – and therefore, that of its economy and society.
Jose Ricardo S. Domingo is a Research Analyst with the Malaysia Programme at the S. Rajaratnam School of International Studies (RSIS), Nanyang Technological University (NTU), Singapore.
Categories: Commentaries / Country and Region Studies / International Political Economy / Non-Traditional Security
Malaysia's gradually ageing population may influence its migration flows. By 2040 and beyond, this could imply fewer migrants for a country such as ...