text:
NewThinking Development
NewThinking at Work
Citizen-Centered Approaches
We create tools and design processes that stimulate citizen-centered decision-making and policy-making to address development challenges, transform lives, end poverty and create a sustainable future.
Our tools and processes encourage participation and engagement to influence policies and processes that matter to citizens.
NewThinking Development is an African start-up initiative that partners with government, business, technologists, civil society and multilateral institutions to design tools and processes for citizen-focused innovation and data use.
Our work is informed by citizen-centered research, analysis and problem-solving built on tested and flexible approaches that generate actionable recommendations and lessons for decision-making and policy-making.
We build collaborations and partnerships with reformers and policy-makers to strengthen leadership focused on problem-driven, solution-focused, hands-on work with multi-stakeholder teams that support deeper analysis and experimentation.
Thematic Expertise
Anti- Corruption
Corruption has adverse effects on citizen lives, public service delivery and channel public resources into private pockets. NewThinking is working with partners to harness digital tools and data to reduce corruption by increasing transparency, citizen participation and accountability mechanisms.
Systemic accountability is a key driver of education attainment, and accountability without transparency has little impact. Growing evidence shows that citizen engagement yields critical long-term success, particularly at the school level. Through in-depth analysis and assessment, NewThinking works with stakeholders to understand the data and needs of the education sector.
Women, youth and marginalized groups are negatively affected by institutional infrastructure that provides finance to support small, medium and micro-enterprise development. NewThinking undertakes in-depth research, analysis and assessments with stakeholders to understand the data and policy gaps to understand the institutional infrastructure and regulations for the financial sector that promote financial sector development.
We support the use of digital tools, research and evidence to stimulate citizen-centered policies, governance and processes as a tool to address development challenges, transform human lives, end poverty and create a sustainable future.
NewThinking provides advice and tools that help strengthen institutional capacity to engage citizens on issues that matter to them. Our approach is premised on the philosophy that lasting solutions to challenges must be designed, developed and implemented domestically.
NewThinking's theory of change is simple but a powerful proposition. Citizen-centered decision-making and policy-making harness collective wisdom, build trust in state institutions, improve basic services, encourage public action and is essential for a strong democracy.
Zimbabwe: Analysis of the beneficial ownership legislative and regulatory framework in the mining sector
The research aims to foster a deeper and more critical debate on beneficial ownership disclosure in Zimbabwe's mining sector. Bringing a better understanding of how beneficial ownership disclosure in the mining sector can be achieved in Zimbabwe's context.
East and Southern Africa: Open Government Training Manual for civil society and community-based organization
This training guide is a resource for civil society and community-based organizations that want to use the open government approach to advocate for reforms in the extractive sector.The guide is also a valuable tool for anyone interested in making governments more open and inclusive.
Zimbabwe: Social Accountability- No-One is Left Behind in Covid Recovery
The research seeks to generate an understanding of constraints and challenges state and non-state actors are facing in advancing and implementing social accountability reforms. As Zimbabwe begins to recover from the global pandemic, equal distribution and public service quality will be vital.
Stay connected on the latest in digital and data for development
© 2021 NewThinking Development. All Rights Reserved.


How do I remove JotForm branding in my form and in the emails? I have a paid subscription already and JotForm still appears in my stuff.
Unfortunately, we cannot see images attached to your email reply.
But, from what I can read, I suppose the issue is cache related. I have now cleared your form caches, can you kindly check if the Autoresponds are correct now?
As I check the screenshot provided, I notice that you are clicking on Test email button in the email autoresponder. Please be noted that the test emails shall be sent to the account registered email address and uses JotForm default email address as the sender email.
Please try submitting the form normally so that the email autoresponder shall be sent using the custom email address.
payments for the month ( I have a Bronze Package).
without using my limited number of payments?
Please wait for my reply shortly. Thank you.


Gorgeous 4 Bed 4 Bath spacious home in upper scale neighborhood in top school district. 2 Car attached garage stainless steel appliances.
Beautiful 4 Bed 2 Bath home for sale with 2 Car garage in very nice quiet neighborhood in good school district 180K.
3 Bed 1 Bath small fixer upper just outside the city within walking distance to bus line. Perfect for someone seeking a rehab/rental project.
Our services for sellers include consulting and referrals for proper 'staging' of their homes for sale.
We assist with luxury homes, beach houses, out of state sellers.
Us and partners. No problem is too large! Pre-foreclosures, upside downs, short sales, relocating, unwanted home?


Build your new custom home here looking at the fabulous Blue Ridge Mountains out your front window. You will love the lifestyle in this highly desired golf community with many available amenities. The homesite has easy hookup to public water and sewer utilities, and all electric and internet cables are buried. AT&T high speed fiber-optic internet just arrived as well. This lot is mostly cleared, with some trees remaining, and gently sloped to accommodate most any home design. Membership to the club is optional but extremely affordable, and social memberships are available. (Ask for more info.) The club features a first class P.B. Dye golf course, a beautiful community pool with cookout and entertainment space, a workout facility, and regularly scheduled social events. This is your opportunity to live in the serenity of the foothills while only being 12 minutes from downtown Travelers Rest and 30 minutes from downtown Greenville.


text:
Research | Degree Attainment
Category: Higher Education
Bachelor's Degrees to Women
Fewer engineering and computer/information sciences bachelor's degrees are awarded to women as compared to men. Overall, interest in these fields has grown, with more than 220,000 bachelor's degrees in engineering and computer/ information sciences awarded in the 2017-2018 academic year. Although in the past the percentage of females earning degrees has remained relatively unchanged, in the 2017-2018 academic year, women now represent about 21% of the bachelor's degree holders in these fields.
Top 10 Engineering Bachelor's Degrees, Female, 2020
Source: ASEE. (2021). Profiles of Engineering and Engineering Technology.
Industrial/Manufacturing/Systems Engineering
Electrical/Computer Engineering
Advanced Degrees Earned by Women
STEM degrees at the master's and doctoral levels have also increased among men and women, with female students earning a larger share of graduate degrees than at the bachelor's level. However, the gender gap is still large, with only 29% of master's degrees in engineering and computer/ information sciences awarded to women and 24% of doctoral degrees awarded to women.
Degree Attainment by Women of Color
Although the number of engineering degrees awarded to non-international women at all postsecondary educational levels in the U.S. has increased from the 2015-16 to 2017-18 academic years, the majority of these are earned by white women. In the 2017-18 academic year, women of color, such as Latinas and Black women, represented about 17.5% of the female Bachelor's degree holders in engineering.
Over the past ten years, Black and Latina women comprised less than 4% of the bachelor's degree holders in engineering and less than 5% of the bachelor's degree holders in computer science. While Black women have constantly represented about 1% of the engineering bachelor's degree holders in the last several years, their representation among computer science bachelor's degree holders has been on a decline since 2003 and stagnated at around 2.35%.
Note: For this graph, women of color include Black/African American and Hispanic/Latina only.
SWE Efforts to Increase Female Attainment of Engineering Degrees
SWE has a strong network of collegiate members across the country.
SWE Collegiate Sections
SWE Conferences
SWE Scholarships
American Society for Engineering Education. (2021). Profiles of Engineering and Engineering Technology. Washington, DC.
U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, Digest of Education Statistics
National Science Foundation, National Center for Science and Engineering Statistics. 2021. Women, Minorities, and Persons with Disabilities in Science and Engineering: 2021. Special Report NSF 21-321. Alexandria, VA. Available at https://www.nsf.gov/statistics/wmpd.
Fletcher, T., Ross, M., et al. (2016). Ignored Potential: A Collaborative Road Map for Increasing African-American Women in Engineering.

redpajama_set_name: RedPajamaCommonCrawl


Summer is year round when you slip into the Slim Paisage. Featuring bright summer landscapes, aerial beach scenes, and bold graphic text printed on our signature textured sole to keep feet comfy from sand to streets. A contrast Havaianas logo on translucent jelly straps completes this fun look.


text:
Q: Custom delimiter in message modelling in IBM Integration Bus in V9.0.0.0 Hi I need to create one message model for my data in the IBM integration Bus
my message structure is as below
abcd:FMxyz:VMsad:SVMfksdf:SVMlksdflkl:VM:FMxyz:VMsad:SVMfksdf:SVMlksdflkl
:FM is the field marker
:VM is the value marker
:SVM is the sub value marker these are three different Characters.
Data has to be extracted using these delimiters.
I need to have three different delimiters in IIB while creating the message model.
Kindly help

A: Is there any specific reason that you are having trouble? The DFDL editor will allow you to have different sets of delimiters for any type.
It might help if you show a tree representation of what you expect the logical model to look like for the example string you have given.

redpajama_set_name: RedPajamaStackExchange


text:
\section*{Acknowledgement}
This work is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canadian Institutes of Health Research (CIHR), and NVIDIA Hardware Award. We thank Dr. Hongxu Yin at NVIDIA research for his insightful suggestions.
\section{Supplementary}

\section{Dataset Details}
\label{app_data}

\begin{table*}[h]
\centering
\begin{tabular}{|c|c||c|c|}
\hline
\textbf{View}              & \textbf{\# of cines}    & \textbf{View}              & \textbf{\# of cines}    \\ \hline
A2C   & 1928 & PLAX   & 2747 \\ \hline
A3C & 2095   & PSA   & 2126                  \\ \hline
A4C & 2166   & ---   & ---                  \\ \hline
\end{tabular}
\caption{ Number of data points for each echo view class.}
\label{tab_arch1}
\end{table*}

\section{Detailed model hyper-parameters}

\begin{table*}[h]
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{Stage}} & \multirow{2}{*}{\textbf{Hyper-parameters}}          & \multicolumn{4}{|c|}{\textbf{Phase}}    \\
\cline{3-6}
 & & 1   & 2 & 3 & 4 \\
\cline{1-6}
\multirow{6}{*}{\textbf{Synthesize}} & Weight for batch normalization regularization  & --- & 0.2   & 1 & 5 \\ \cline{2-6}
& Weight for total variation L2 regularization & ---   & 0.001  & 0.01  & 0.01                 \\ \cline{2-6}
& Weight for total regularization loss & ---   & 0.01   & 0.1  & 0.001                 \\\cline{2-6}
& Learning rate & ---   & 0.25  & 0.05  & 0.005                 \\\cline{2-6}
& Beta 2 for ADAM optimizer & ---   & 0.09   & 0.9  & 0.009                 \\\cline{2-6}
& Batch size & ---   & 40   & 40  & 40                 \\\hline

\multirow{7}{*}{\textbf{Train}} & Temporature for distialltion loss  & --- & 2   & 2 & 2 \\ \cline{2-6}
& Weight for distialltion loss & ---   & 5   & 5 & 5                 \\ \cline{2-6}
& Distance for margin loss & ---   & 0.7  & 0.3 & 0.3                 \\ \cline{2-6}
& Weight for margin loss & ---   & 1  & 1 & 1
\\ \cline{2-6}
& Temporature for updating centroids of contrastive loss & ---   & 0.99 & 0.99 & 0.99                \\\cline{2-6}

& Learning rate & 0.01   & 0.01   & 0.01 & 0.01                 \\ \cline{2-6}
& Batch size & 40   & 40   & 40 & 40                 \\  \hline
\end{tabular}
\caption{ Hyper-parameters used for training.}
\label{tab_arch1}
\end{table*}

\section{Visualization of data}

\begin{figure}[h]
\floatsetup{valign=t, heightadjust=all}
	\centering
\begin{subfigure}[t]{\textwidth}
    	\centering
         \begin{subfigure}[t]{0.45\textwidth}
        	\includegraphics[width=1\textwidth]{figures/original_images.pdf}
        	\caption{}

        	\label{fig:baseline}
        \end{subfigure}
        \hfill
        \begin{subfigure}[t]{0.45\textwidth}
        	\includegraphics[width=1\textwidth]{figures/mean_images.pdf}
        	 \caption{}

            \label{fig:abalation}
        \end{subfigure}
        \hfill
        \begin{subfigure}[t]{0.45\textwidth}
        	\includegraphics[width=1\textwidth]{figures/my_gen.pdf}
        	 \caption{}

            \label{fig:abalation}
        \end{subfigure}
        \hfill
        \begin{subfigure}[t]{0.45\textwidth}
        	\includegraphics[width=1\textwidth]{figures/dream_gen.pdf}
        	 \caption{}

            \label{fig:abalation}
        \end{subfigure}
        \hfill

	\end{subfigure}
	\caption{ (a) Original images from 4 different view classes (A2C,A3C,A4C, and PLAX). (b) Mean images of each class used  for initialization. (c) Synthesized images by \texttt{Class Impression}\xspace (d) Synthesized images by Always Be Dreaming (ABD) [22].

}
	\label{compare_table}
\end{figure}

\section{Conclusion}
In this work, we propose \texttt{Class Impression}\xspace, a novel data-free class incremental learning framework. In \texttt{Class Impression}\xspace, instead of saving data from classes in the earlier tasks that are not available for training in the new task, we synthesize class-specific images from the frozen model trained on the last task.
Following, we continue training on new classes and synthesized images using the proposed novel losses to alleviate catastrophic forgetting, imbalanced data issues among new and past classes, and domain shift between new synthesized and original images of old classes.
Experimental results for echocardiography cines classification on the large-scale dataset validate \texttt{Class Impression}\xspace out-performs the SOTA methods in data-free class incremental learning with an improbable gap of 31.34\% accuracy in the final task and get comparable results with the SOTA data-saving rehearsal-based methods.

Our proposed method shows the potential to apply incremental learning in many healthcare applications that cannot save data due to memory constraints or privacy issues. It is common in clinical deployment that a client inherits a trained model without having access to its training data. Our design enables the client to refine the model with new tasks. For our future work, we plan to combine our work with other practical settings in the real world in the medical domain (\textit{e.g.}, testing on different medical datasets) and further improve the pipeline to meet clinical-preferred performance.

\section{Experiments}
\label{sec:exp}
\subsection{Datasets and Experimental Settings}

\noindent \textbf{Dataset:} Heart echo data utilized in this paper come from several investigations at our local institution, randomly chosen from the hospital picture archiving system, with authorization from the Information Privacy Office and Clinical Medical Research Ethics Board. These cines are captured by six devices: GE Vivid 7, Vivid i, Vivid E9, Philips iE33, Sonosite, and Sequoia. Our dataset contains 11,062 cines (videos) of 2151 unique patients diagnosed with various heart diseases, with an average of 48 frames in each. An experienced cardiologist labeled gathered cines as five different views. The view distribution is shown in Supplementary Table 1. We chose our task as a test-bed to evaluate our innovations. In contrast to many other medical imaging classification problems, the labels associated with standard echo views are less ambiguous, less noisy, and anatomically interpretable, making the analysis of classification results easier and its failure modes tractable.
Finally, we split the data into training, validation, and test sets based on the subject with the ratio of 70\%, 20\%, and 10\%, respectively.
\\

\noindent \textbf{Experimental Settings}: We perform a four-task class incremental learning, with a two-way classification at the beginning and adding in one new class at each task. As the number of data points is relatively low compared to natural image's benchmark datasets, the classification model of \texttt{Class Impression}\xspace is a ResNet-based convolutional neural network with three residual blocks, each containing two layers. All models are implemented with PyTorch and trained on one NVIDIA Tesla V100 GPU with 16GB of memory.

As mentioned in Section \ref{Methods}, our implementation contains two main stages for each task. We \textbf{freeze} model weights for the image synthesis stage. We use Adam optimizer and optimize the batch of 40 images with a learning rate of 0.01 for 2000 epochs.
In the stage of incremental learning with a new class, we \textbf{update} model weights again with the SGD optimizer and continue to learn on each new task for 30 epochs with a batch size of 40. We search the optimal hyper-parameters
using Weights \& Biases Sweeps, an automated hyperparameter searching software~\cite{wandb}. The best chosen hyperparameters are detailed in Supplementary.

\begin{figure}[t]
\floatsetup{valign=t, heightadjust=all}
	\centering

	\begin{subfigure}[t]{\textwidth}
    	\centering
         \begin{subfigure}[t]{0.485\textwidth}
        	\includegraphics[width=1\textwidth]{figures/compare_results.pdf}
        	\caption{Comparision with Baselines \footnote{
        	1. With corset data; 2. With weighted corset data; 3. With synthesized data }}

        	\label{fig:baseline}
        \end{subfigure}
        \hfill
        \begin{subfigure}[t]{0.485\textwidth}
        	\includegraphics[width=1\textwidth]{figures/ab_results.pdf}
        	 \caption{Ablation Study}

            \label{fig:abalation}
        \end{subfigure}
        \hfill

	\end{subfigure}
	\caption{ Testing accuracies of four tasks. It shows the classification accuracies on the heart echo test dataset comparing with (a) baselines of class-incremental learning and (b) ablation study on different configurations of proposed framework. $\star$ indicates the accuracy of Oracle model, \textit{i.e.}, training all the classes offline w/o class-incremental setting. \texttt{Class Impression}\xspace shows consistently higher accuracy with an average of 31.34\% increase after the final task compared to the SOTA data-free incremental learning (ABD).

}
	\label{compare_table}
\end{figure}
\subsection{Comparison with Baselines}

We compare \texttt{Class Impression}\xspace with the best-performed data-free baselines and a non-data-free baseline that saves coreset data in the incremental learning setting by implementing them on our heart echo dataset.

The baseline methods included in this study are listed as follows:
\iffalse
\noindent\textbf{LwF}\cite{li2017learning} uses distillation-based training to overcome catastrophic forgetting among different tasks.

\noindent\textbf{ABD}\cite{smith2021always} generates synthesized images of classes in the prior task.

\noindent\textbf{CLBM}\cite{yang2021continual} fits a statistical distribution like Gaussian mixture models to save information extracted from data points in each class.

\noindent\textbf{LUCIR}\cite{hou2019learning} is a state-of-the-art rehearsal-based method that saves a coreset of data points from each seen class and replays them as anchors of their respective class distribution to maximize the distance between the distribution of classes in the latent space.

These comparisons demonstrate that \texttt{Class Impression}\xspace results substantially outperform prior class incremental data-free settings by boosting $31.34\%$ accuracy on the all seen classes in the final task and standing on $73.3\%$ accuracy over five classes. The synthetic images generated by \texttt{Class Impression}\xspace are presented in Supplementary.

\fi
\textbf{LwF}\cite{li2017learning} uses distillation-based training to overcome catastrophic forgetting among different tasks. \textbf{ABD}\cite{smith2021always} generates synthesized images of classes in the prior task. \textbf{CLBM}\cite{yang2021continual} fits Gaussian mixture models to save information extracted from data points in each class. \textbf{LUCIR}\cite{hou2019learning} is a state-of-the-art rehearsal-based method that saves a coreset of data points from each seen class and replays them as anchors of their respective class distribution to maximize the distance between the distribution of classes in the latent space.

The results are given in Fig.~\ref{fig:baseline}. \textbf{LWF}, reaching $20.29\%$ accuracy, completely fails to consider the imbalance issue among classes in different tasks and distinguish between inter-task classes. \textbf{ABD} is a recent work that shares a similar data synthesis idea with us for class incremental learning. However, its final task accuracy is only $32.78\%$, much worse than \texttt{Class Impression}\xspace. Note that our innovations over \textbf{ABD} are multifold, lying in utilizing the unique class-specific information in medical images: 1) we initialize batches for the image synthesis step with the mean of each class; and 2) we mitigate the domain shift between class-wise synthesized images and original images by defining the \textit{intra-domain contrastive loss}.
\textbf{CLBM} overfits to new data feature distribution and fails to distinguish it from the old ones, yielding poor classification accuracy. \textbf{LUCIR} saves auxiliary information (\textit{i.e.}, coreset data of old classes) and performs the closest to \texttt{Class Impression}\xspace. If we implement it without storing any data or saving a determined number of data as coreset from each class, it reaches the accuracy of $54.75\%$ and \texttt{Class Impression}\xspace fairly outperforms it. However, if we use a weighted sampler in \textbf{LUCIR} to increase the saved samples' effect, it exceeds \texttt{Class Impression}\xspace. This result is expected due to intrinsic information loss in the data-free setting. In comparison, \texttt{Class Impression}\xspace results substantially outperform prior class incremental data-free settings by boosting $31.34\%$ accuracy on the all seen classes in the final task and standing on $73.3\%$ accuracy over five classes. The synthetic images generated by \texttt{Class Impression}\xspace are presented in Supplementary.

\subsection{Ablation Studies}

We perform extensive ablation studies to expose the impact of different losses in the final performance. The results are given in Fig.~\ref{fig:abalation}.
1) \textit{Impact of domain adaption:} We omit intra-domain contrastive loss~(Eq.~\eqref{eq:cosine}) to observe the effect of the gap between the domain of synthesized images and original images. As seen in Fig.~\ref{fig:abalation}, it results in the worst performance compared to other settings.
2) \textit{Impact of inter-class separation:} When we omit margin loss~(Eq.~\eqref{eq:margin}), the model does not increase the distance of data points from previous tasks and new tasks, which also leads to a significant accuracy drop.
3) \textit{Impact of mean initialization:} We initialize input images with random Gaussian noise instead of the mean of each class in the synthesis stage. Therefore, the model fails to capture the true impression of each class, and performance reduces.

\section{Introduction}

Deep learning classification models for medical imaging tasks have shown promising performance. Most of these models usually require collecting all training data and defining the tasks at the beginning.
However, while highly desirable, it is impractical to train a deep learning model only once during deployment and then expect it to perform well on all future data, which may not be well represented in the training data.
One promising solution is to allow the system to perform class incremental learning (a subset of continual learning or lifelong learning), \textit{i.e.}, adapting the deployed model to the newly collected data from new classes.
Unfortunately, catastrophic forgetting~\cite{delange2021continual} occurs when a deep learning model overwrites past knowledge when training on new data.

Recent efforts to address catastrophic forgetting mainly include the following three categories:
1) Replay methods, which alleviate forgetting by replaying some stored samples from previous tasks \cite{rebuffi2017icarl,rolnick2019experience,isele2018selective,chaudhry2019continual,hou2019learning};
2) Regularization-based methods, which use defined regularization terms such as additional losses to preserve prior learned knowledge while updating on new tasks \cite{li2017learning,jung2016less,rannen2017encoder,zhang2020class};
3) Parameter isolation methods, which allocate a fixed part of a static architecture for each task and only update that part during training \cite{serra2018overcoming,aljundi2017expert}.
These techniques have been mainly deployed in the natural image domain, and the solution for medical image analysis is under-explored~\cite{yang2021continual}. Among these approaches, rehearsal-based strategy has been reported to achieve the best results~\cite{hou2019learning}. Still, it requires accessing either the entire data or a portion of data representations used for training the previous model by saving them with more complex memory systems. It then performs re-training on the saved data with new data. This approach is less practical in medical imaging applications due to privacy regulations of data storage.

To complement storing past data, data-free rehearsal (pseudo-rehearsal) strategies are recently proposed, in which the external memory is replaced with a model that is capable of generating samples from the past~\cite{smith2021always,lavda2018continual,shin2017continual}.
However, the existing data-free rehearsal methods in class-incremental learning perform poorly for medical images (seen from our experiment results in Sec.~\ref{sec:exp}) without considering their unique properties. We reveal the \textbf{challenges} are: 1) generating synthetic images with high fidelity and preserving class-specific information from medical images is challenging; 2) mitigating the domain shift between generated and real images may not be straightforward in medical image classification; and 3) creating a robust decision boundary under imbalanced class distributions may be subject to significant uncertainty.

It is practical yet challenging to deploy class-incremental learning for medical image analysis. To the best of our knowledge, no existing data-free rehearsal-based class incremental learning work on \textit{deep neural networks} is specifically designed for medical image analysis. Motivated by the observation that medical imaging samples within a categorical classification task normally share similar anatomical landmarks,
we propose a novel class-incremental learning pipeline that 1) restores \texttt{Class Impression}\xspace, \textit{i.e.}, generating prototypical synthetic images with high quality for each class using the frozen weights of the existing model, 2) mitigates the domain shift between class-wise synthesized images and original images by defining the \textit{intra-domain contrastive loss}, which empowers \texttt{Class Impression}\xspace in addressing the catastrophic forgetting problem, and 3) leverages a novel \textit{cosine normalized cross-entropy loss} for imbalance issue and a \textit{margin loss} to encourage robust decision boundary to regularize \texttt{Class Impression}\xspace for handling catastrophic forgetting and encouraging better generalization. We conduct extensive comparison experiments and ablation analysis on the echocardiogram view classification task to demonstrate the efficacy.

\section{Method}
\label{Methods}

\begin{figure}[t]
	\centering
	\includegraphics[width=1\textwidth]{figures/main.pdf}
		\caption{\texttt{Class Impression}\xspace consists of two main iterative steps to perform class incremental classification: 1) Synthesize samples images for each past class from the frozen model trained on the previous task $f_{i-1}^{*}$ by back-propagating using Eq.~\eqref{eq:CIloss} with class mean as initialization. 2) Update the model on new tasks. We utilize the distillation training method to leverage information from the previous model~\cite{hou2019learning,li2017learning}. We add Cosine Normalization cross-entropy loss~(Eq.~\eqref{eq:cosine}) and Margin loss over latent representation of synthesized images and labeled new data~(Eq.~\eqref{eq:margin}), to overcome data unbalance issue and catastrophic forgetting of past tasks, respectively. Furthermore, we introduce a novel Intra-domain Conservative loss~(Eq.~\eqref{eq:contras}) as a semi-supervised domain adaption technique between synthesized data and original data to mitigate the domain shift.}
	\label{thresh}
\end{figure}

\paragraph{Problem setting} The general class incremental learning considers a sequence of classification tasks, where in each task, new classes are added over time to a prior set of classes. The objective is to maintain high classification accuracy across all classification tasks. Let us denote $x^t \in \mathcal{X}^t$ as the input image, $y^t\in \mathcal{Y}^t$ as the class label w.r.t. the task $t$, and we have $\{\mathcal{Y}^t\} \subset \{\mathcal{Y}^{t+1}\}$. In rehearsal-based class incremental learning, the generalization error of all seen tasks is
\begin{align*}
    \sum_{t=1}^{T} \mathbb{E}_{(x^t,y^t)\sim(\mathcal{X}^t,\mathcal{Y}^t)}\left[ \ell(f_t(x^t;\theta), y^t)\right],
\end{align*}
with loss function $\ell$, classifier parameter $\theta$, the current task $T$, and the model $f_{t}$ trained in task $t$. While under data-free setting, there is no access to data $(\mathcal{X}^t,\mathcal{Y}^t)$ for $t<T$.

\paragraph{Overview of our pipeline} Fig.~\ref{thresh} shows the whole pipeline of our proposed method. We have two stages for each task. In the first stage, we synthesize images from values saved in the frozen model trained on the previous task to employ them as replay data by seizing a genuine class-wise impression instead of saving them. In the second stage, we perform distilled class incremental training on the data using the new classes and synthesized data with proposed novel losses to 1) learn features of new classes, 2) preserve features comprehended from prior classes during previous tasks, and 3) maximize distances between the distribution of the new and previous classes in the latent space. Next, we will introduce each innovative component in our pipeline.

\subsection{Class Impression}
Different from \cite{smith2021always} that trains a model to synthesize images without considering preserving class-specific information, we are motivated by recent work, DeepInversion~\cite{yin2020dreaming}, which was originally formulated to distill knowledge from a trained model for transfer learning. Given a randomly initialized input $\hat{x} \in  \mathbb{R}^{B\times H \times W \times C}$ where $B,H,W,C$ are the batch size, height, width, and number of channels, respectively, a target label $y$, the image is synthesized by optimizing
\begin{align}
\label{eq:CIloss}
    \min_{\hat{x}}\mathcal{L}_{\rm CE}(\hat{x},y)+\mathcal{R}(\hat{x}),
\end{align}
where $\mathcal{L}_{\rm CE}(\cdot)$ is cross-entropy loss, and $\mathcal{R}(\cdot)$ is the regularization to improve the fidelity of the synthetic images. Specifically, following DeepInversion~\cite{yin2020dreaming} and DeepDream~\cite{mordvintsev2015inceptionism}, we have
\begin{align}
    \mathcal{R}(\hat{x})=\alpha_{\rm tv}\mathcal{R}_{\rm TV}(\hat{x}) + \alpha_{\ell_2}\mathcal{R}_{\ell_2}(\hat{x}) + \alpha_{\rm bn}\mathcal{R}_{\rm BN}(\hat{x},\mathcal{X}),
\end{align}
where $\alpha$s are scaling factors, $\mathcal{R}_{\rm TV}(\cdot)$ and  $\mathcal{R}_{\ell_2}(\cdot)$ penalize the total variance and the $\ell_2$ norm of the generated image batch $\hat{x}$, respectively. $\mathcal{R}_{\rm BN}(\cdot,\cdot)$ is for matching the  batch normalization (BN) statistic in trained model with the original images and defined as
\begin{align}
    \mathcal{R}_{\rm BN} = \sum_l \|\mu_l(\hat{x} - \mathbb{E}(\mu_l(x)|\mathcal{X})\|_2 + \sum_l \|\sigma_{l}^2(\hat{x} - \mathbb{E}(\sigma_{l}^2(x)|\mathcal{X})\|_2,
\end{align}
where $\mu_l$ and $\sigma_{l}^2$ are the batch-wise mean and variance estimates of feature maps corresponding to the $l$-th BN layer. To inject class-specific impression to the synthetic images without violating privacy regulations (such as re-identification), we assume the averaged image of each class $\bar{x}_k^t$ ($k\in \mathcal{Y}^t$) is available to initialize the optimization of $\hat{x}$ in Eq~(\ref{eq:CIloss}).  It is worth noting that, \texttt{Class Impression}\xspace aims to \textit{generate} images following the distributions of the past class, rather than reconstructing the training data points as model inversion attacks does~\cite{yin2021see,huang2021evaluating,hatamizadeh2022gradient}, thus \texttt{Class Impression}\xspace aims to meet certain privacy requirements imposed on storing medical data.

\subsection{Novel Losses}
Although we can leverage \texttt{Class Impression}\xspace, a.k.a the synthetic data of the past classes to conduct rehearsal distillation-based training, which mitigates catastrophic forgetting, class weights of old classes still may be ill-updated and mismatched with the updated representation space~\cite{zhu2021class}. Therefore, we propose to use a modified classification loss, \textit{i.e.}, \textit{cosine normalized cross-entropy loss}, and two additional regularization terms, \textit{i.e.}, \textit{margin loss}, and \textit{intra-domain contrastive loss} to improve the utility of \texttt{Class Impression}\xspace.
\subsubsection{Cosine Normalized Cross-entropy Loss}
Classifier bias commonly exists in class incremental learning, because data in the new classes are more abundantly available or mostly have better quality~\cite{zhu2021class}. In classification task, the Softmax operation yields the prediction probably of class $k$ as $p_i(x) = \exp(\theta_i^{\top}f(x)+b_k)/\sum_j\exp(\theta_j^{\top}f(x)+b_i)$, which $f(\cdot)$ is feature extractor, and $\theta_k$ and $b_k$ are class embedding and bias weights. Due to class imbalance, the magnitudes of embedding and bias of the new class can be significantly higher than the past classes. Cosine normalization has been widely used in vision tasks~\cite{gidaris2018dynamic,hou2019learning,qi2018low} to eliminate the bias caused by the significant difference in magnitudes. Thus, following the above-mentioned work \cite{hou2019learning}, we add the following \textit{Cosine Normalized Cross-entropy} (CNCE) loss for classification with the past and new classes at the current task $t$:
\begin{align}
\label{eq:cosine}
    \mathcal{L}_{\rm CNCE} = -\sum_{k=1}^{|\mathcal{Y}^t|}y_k\log\left(\frac{\exp(\eta\langle \bar{\theta}_k,\bar{f}(x)\rangle )}{\sum_j\exp(\eta\langle \bar{\theta}_j,\bar{f}(x)\rangle )}\right),
\end{align}
where $\bar{v}=v/\|v\|_2$ indicates the unit normalized vector,  $\langle\cdot,\cdot\rangle$ measures the cosine similarity of two vectors, and $\eta$ is the temperature hyperparameter. Further, \texttt{Class Impression}\xspace can generate an equal number of data for each past class as that of the new class to alleviate the imbalanced issue.
\subsubsection{Margin Loss}
Representation overlapping is another inherent problem in class incremental learning~\cite{zhu2021class}. Margin loss uses a margin to compare samples representations distances, and is used in few-shot learning~\cite{li2020boosting} and non-data-free class incremental learning~\cite{hou2019learning}. To avoid the ambiguities between the past and new classes, we are the first to explore margin loss in data-free class incremental learning that encourage separating the decision boundary of the new class from the old ones. At the current task $t$, margin loss is written as
\begin{align}
\label{eq:margin}
    \mathcal{L}_{\rm margin} = \sum_{k=1}^{|\mathcal{Y}^{t-1}|}\max \left( m - \langle \bar{\theta} , \bar{f}(x) \rangle + \langle \bar{\theta}^k,\bar{f}(x)\rangle, 0 \right),
\end{align}
where $x$ are the synthesized images that are used as anchors of the classes seen in the previous tasks' distribution, $m$ is the margin for tolerance, $\bar{\theta}$ is the embedding vector of $x$'s true class, and $\bar{\theta}^k$ is the embedding vector of the new class, which is viewed as negatives for $x$. A larger $m$ encourages a larger separation.

\subsubsection{Intra-domain Contrastive Loss}
We notice that directly applying the synthetic medical images for rehearsal may not generalize well to classify the original data. Motivated by semi-supervised domain adaption~\cite{singh2021clda}, we aim to align the domain shift between the source $\mathcal{S}$ (\textit{i.e.}, synthetic images) and target $\mathcal{T}$ (\textit{i.e.}, unlabeled testing data in the new task). The centroid of the images from the source domain belonging to class $k$ is defined as $c_k^\mathcal{S} = \sum_i\mathbbm{1}_{\{y_i^{\mathcal{S}}=k\}}f(x_i^s)/\sum_i\mathbbm{1}_{\{y_i^{\mathcal{S}}=k\}}$. Then, we assign the pseudo label for the unlabeled target samples using the currently updated classifier. An intra-domain contrastive loss can pull the source and target samples in the same class together and push apart the centroids of different classes, which is written as (at task $t$):
\begin{align}
\label{eq:contras}
    \mathcal{L}_{\rm contras} (c_k^\mathcal{S},c_k^\mathcal{T}) = -\log \frac{\exp(\tau\langle \bar{c}_k^\mathcal{S}, \bar{c}_k^\mathcal{T} \rangle)}{\exp(\tau\langle \bar{c}_k^\mathcal{S}, \bar{c}_k^\mathcal{T} \rangle) + \sum_{\substack{j=1 \\ \mathcal{Q}\in\{\mathcal{S},\mathcal{T}\}}}^{|\mathcal{Y}^{t-1}|}\mathbbm{1}_{\{j\neq k\}}\exp(\tau\langle \bar{c}_k^\mathcal{Q}, \bar{c}_k^\mathcal{T} \rangle) },
\end{align}
where $\tau$ is the temperature hyperparameter in $\exp(\cdot)$.

Altogether, the total loss used to train the new model for recognizing the new class while maintaining the old knowledge is given by:
\begin{align}
\label{eq:total}
\mathcal{L}_{\ total} = \mathcal{L}_{\rm CNCE} + \alpha_{\rm dist} \mathcal{L}_{\rm dist} + \alpha_{\rm margin}\mathcal{L}_{\rm margin} + \alpha_{\rm contras}\mathcal{L}_{\rm contras},
\end{align}
where $\alpha$ are tunable scaling factors, and $\mathcal{L}_{\rm dist} = 1 - \langle \bar{f}^*(x), \bar{f}(x) \rangle$ with $\bar{f}^*$ as the old model, is the distillation loss widely used in class incremental learning~\cite{hou2019learning,li2017learning}.

\section{Compliance with Ethical Standards}

\section{Experiments}

\subsection{Datasets and Experimental Settings}
We use \hejie{three} real-world datasets from \cite{Ma:2017gl} to evaluate our framework.
\begin{itemize}
    \item \noindent \textbf{Human Immunodeficiency Virus Infection (HIV)} is collected from functional magnetic resonance imaging (fMRI), including 35 samples from positive patients and 35 from negative healthy controls. Each graph contains 90 nodes (ROIs) and the edge weights corresponding to the adjacency matrix are calculated as the pairwise correlations between brain regions.
    \item \noindent \textbf{Bipolar Disorder (BP)} is also collected from fMRI modality, consisting of 52 bipolar subjects and 45 healthy controls with matched age and gender. It stimulates 82 brain regions, according to Freesurfer-generated cortical/subcortical gray matter regions. Functional brain networks are derived using pairwise BOLD signal correlations.
    \hejie{\item \noindent \textbf{(PPMI)} is xxxx.}
\end{itemize}

\noindent \textbf{Hyper-parameter setting.}
We  use  the Adam  optimizer \cite{DBLP:journals/corr/KingmaB14} with the initial learning rate of 0.001 and a weight decay of 0.00001. The backbone model is trained for 100 epochs with hidden dimension setting to 64. \hejie{All the hyper-parameters are selected automatically with the open source AutoML toolkit NNI\footnote{https://github.com/microsoft/nni}. Besides, multiple constructed node features are tested including one-hot, binning degree\cite{cui2021features}, node2vec \cite{DBLP:conf/kdd/GroverL16}, and edge-connection, where each feature \(\bm{x}_i\) of node $v_i$ is computed the $i$th row in the graph adjacency matrix \(\bm{W} \in \mathbb{R}^{m \times m}\). To indicate the robustness of each model, all the reported results are the average performance of ten-fold cross validation conducted
on different train/test splits}.

\subsection{Interpretability Analysis}
\label{sec:vis}

\begin{figure}[t]
\centering
    \begin{minipage}{.42\textwidth}
      \centering
      \includegraphics[width=0.9\linewidth]{figures/hiv_vis_oneline.pdf}
      (a) HIV

      \label{fig:hiv_vis}
    \end{minipage}%
    \begin{minipage}{.42\textwidth}
      \centering
      \includegraphics[width=0.9\linewidth]{figures/bp_vis_oneline.pdf}
      (b) BP

      \label{fig:bp_vis}
    \end{minipage}
\caption{\label{fig:vis} Comparison of explanation graph connectomes in brain networks of a healthy control and a patient on HIV and BP datasets. The colors of neural systems are described as: {\color{sred} VN}, {\color{sgolden} AN}, {\color{sgreen} BLN}, {\color{sblue} DMN}, {\color{spurple} SMN}, {\color{spink} SN}, {\color{sorange} MN}, {\color{slightgreen} CCN}, respectively.}
\end{figure}

\textbf{\hejie{Interpretation of important connectomes}.}
To qualitatively examine the effectiveness of the globally shared mask $\bm{M}$, \hejie{we follow a similar strategy as the post processings in GNNExplainer \cite{ying2019gnnexplainer}, where \hejie{an explanation subgraph $G^{\prime}_s$ is obtained by taking the top 100 weighted edges from the masked $G^{\prime}$ and all other low-weight edges are removed.}}
\hejie{As an illustration, we show the comparison of connectomes for healthy control and patient groups on HIV and BP datasets in Figure \ref{fig:vis}}, where edges within the same systems are colored according to the color of nodes it links, while edges across systems are colored gray. The size of an edge is decided by its weight in the explanation graph.

It can be seen that connectome patterns differ within certain neural systems between the healthy control and patient subject, which could provide potential value for clinical diagnosis. For example, in the HIV dataset, the explanation subgraph of patients excludes many interactions within the Default Mode Network system (DMN), which is colored {\color{sblue} blue}. The connections between superior frontal gyrus (nodes 3, 4) and orbital part (nodes 5, 6, 9, 10) are two examples\footnote{See \cite{chen2021decreased} for ROI names and respective node indices.}. Also, interactions within the Visual Network (VN, colored {\color{sred} red}) system of patients are significantly less than that of healthy controls. For example, connections between cuneus (nodes 45, 46) and lingual gyrus (nodes 47, 48), and those between occipital gyrus (nodes 51, 52, 53, 54) and fusiform gyrus (nodes 55, 56) are  missing.
These patterns are consistent with the findings in \cite{herting2015default, flannery2021hiv} that alterations in within- and between-network DMN and VN connectivity may relate to known cognitive and visual processing difficulties in HIV.

For the BP dataset, compared with tight interactions within the Bilateral Limbic Network (BLN, colored {\color{sgreen} green}) of the healthy control, the connections within BLN of the patient subject are much more sparse, which may signal pathological changes in this neural system. For example, it is found that the patient has fewer connections between pyriform cortex (nodes 43, 44) and perirhinal cortex in the rhinal sulcus (nodes 55, 56) than healthy controls, and decreased connections between temporopolar area (nodes 61, 62) and retrosubicular area (nodes 81, 82).
These results are in line with previous studies \cite{das2020parietal, ferro2017longitudinal}. It finds that the parietal lobe, one of the major lobes in the brain roughly located at the upper back area in the skull and is in charge of processing sensory information received from the outside world, is mainly related to Bipolar disease attack. Since parietal lobe ROIs are contained in BLN under our parcellation, the connections missing within the BLN system in our visualization are consistent with existing clinical evidence.

\noindent \textbf{Interpretation of important brain systems.}
\begin{table}
	\centering
	\caption{Top ranked neural systems of the explanation subgraph on HIV and BP for both Healthy Control (Normal) and Patient under three comparative measures.}
	\resizebox{0.6\linewidth}{!}{
	\begin{tabular}{ccccc}
	\toprule
	\multirow{2.5}{*}{Dataset} & \multirow{2.5}{*}{Type} & \multicolumn{3}{c}{Comparative Measures} \\
	\cmidrule(lr){3-5}
	& & Degree & Strength & Cluster Coefficient  \\
	\midrule
	\multirow{2}{*}{HIV}
	& Normal   & DMN BLN CCN & DMN BLN CCN & DMN CCN BLN  \\
	& Patient  & BLN CCN AN & BLN CCN AN & BLN  \\
	\midrule
	\multirow{2}{*}{BP}
	& Normal & BLN SMN DMN & BLN DMN SMN & SMN VN DMN  \\
	& Patient & BLN DMN SMN & BLN DMN SMN & SMN VN \\
	\bottomrule
	\end{tabular}}

	\label{tab:metrics}
\end{table}
To understand which neural systems contribute most to the prediction of a specific disease, we observe the most manifest nodes on the explained subgraphs with three commonly used measures in brain network analysis: degree, strength, and cluster coefficient \cite{DBLP:journals/neuroimage/RubinovS10}.
As is shown in Table \ref{tab:metrics}, important neural systems under different metrics show similar characteristics. Specifically, for the HIV dataset, explanation subgraphs in both groups reveal the importance of BLN, while DMN is missing from all three metrics in the patient group. This is consistent with our observation on HIV in Figure \ref{fig:vis}, where the densely connected structure within DMN system degenerated in patient subjects. Regarding the BP dataset, BLN, SMN, and DMN are prominent in both patient and healthy controls.

Furthermore, we compare the community structure and modularity  of our explanation graph $G^{\prime}$ against the original graph $G$ by conducting Newman's spectral community detection \cite{newman2013spectral}. The detected community results are compared with the ground truth neural system partition. Results show that the completeness score of our explained graph achieves about 7.21\% improvement over the original graph; the Fowlkes-Mallows score improves over 5.10\%; the homogeneity score improves 5.82\%; the mutual information score improves 5.12\% and the v-measure score improves over 6.44\%. The consistent improvements of various clustering evaluation metrics validate the effectiveness of our explanation mask: after applying the globally-shared explanation mask, the community characteristics are further manifest than the original graphs.

\subsection{\hejie{Prediction Performance}}
We compare our proposed models with baselines from both shallow and deep models for performance evaluation.

\noindent \textbf{Baselines.}
For shallow embeddings methods, we experimented M2E \cite{Liu:2018ty}, MIC \cite{Shao:2015ek}, MPCA \cite{Lu:2008cw}, and MK-SVM \cite{Dyrba:2015ci}, where the output graph-level embeddings are further processed by logistic regression classifiers to make predictions. We also include three state-of-the-art deep graph models: GAT \cite{Velickovic:2019tu}, GCN \cite{Kipf:2017tc}, and DiffPool \cite{Ying:2018vl}, \hejie{and two state-of-the-art deep models specifically design for brain networks: BrainnetCNN\cite{BrainNetCNN} and BrainGNN \cite{li2020braingnn}}.

\noindent \textbf{Results and analysis.}
\begin{table}
\setlength{\tabcolsep}{6pt}
	\centering

	\caption{Performance of different models on \hejie{HIV, BP, and PPMI} datasets. Our proposed methods are colored in gray background and the highest performance is highlighted in boldface.}
	\resizebox{\linewidth}{!}{
	\begin{tabular}{cccccccccc}
	\toprule
	\multirow{2.5}{*}{Method} & \multicolumn{3}{c}{HIV} & \multicolumn{3}{c}{BP} & \multicolumn{3}{c}{PPMI} \\
	\cmidrule(lr){2-4} \cmidrule(lr){5-7} \cmidrule(lr){8-10}
	& Accuracy & F1-macro & AUC & Accuracy & F1-macro & AUC & Accuracy & F1-macro & AUC \\
	\midrule
	M2E   & 57.14{\tiny±19.17} & 53.71{\tiny±19.80} & 57.50{\tiny±18.71} & 52.56{\tiny±13.86} & 51.65{\tiny±13.38} & 52.42{\tiny±13.83} & 78.69{\tiny±1.78} & 45.81{\tiny±4.17} & 50.39{\tiny±2.59}\\
	MIC   & 54.29{\tiny±18.95} & 53.63{\tiny±19.44} & 55.42{\tiny±19.10} & 62.67{\tiny±20.92} & 63.00{\tiny±21.61} & 61.79{\tiny±21.74} & 79.11{\tiny±2.16} & 49.65{\tiny±5.10} & 52.39{\tiny±2.94} \\
	MPCA  & 67.14{\tiny±20.25} & 64.28{\tiny±23.47} & 69.17{\tiny±20.17} & 52.56{\tiny±13.12} & 50.43{\tiny±14.99} & 52.42{\tiny±13.69} & 81.61{\tiny±2.64} & 62.45{\tiny±7.23} & 60.85{\tiny±5.48} \\
	MK-SVM & 65.71{\tiny±7.00} & 62.08{\tiny±7.49} & 65.83{\tiny±7.41} & 57.00{\tiny±8.89} & 41.08{\tiny±13.44} & 53.75{\tiny±8.00}     & 80.50{\tiny±2.73} & 67.98{\tiny±5.92} & 67.45{\tiny±6.30} \\
	\midrule
	GCN   & 70.00{\tiny±12.51} & 68.35{\tiny±13.28} & 73.58{\tiny±9.49} & 55.56{\tiny±13.86} & 50.71{\tiny±11.75} & 61.55{\tiny±28.77}  & 67.24{\tiny±5.51} & 67.24{\tiny±7.78} & 65.03{\tiny±6.23} \\
	GAT   & 71.43{\tiny±11.66} & 69.79{\tiny±10.83} & 77.17{\tiny±9.42} & 63.34{\tiny±9.15} &  60.42{\tiny±7.56} & 67.07{\tiny±5.98}    & 75.35{\tiny±2.23} & 64.04{\tiny±3.59} & 62.97{\tiny±3.06} \\
	PNA & 57.14{\tiny±12.78} & 45.09{\tiny±19.62} & 57.14{\tiny±12.78} & 63.71{\tiny±11.34} & 55.54{\tiny±14.06} & 60.30{\tiny±11.89}   & 80.36{\tiny±1.84} & 51.76{\tiny±10.32} & 54.71{\tiny±6.77}\\
	BrainCNN &  69.24{\tiny±19.04}& 67.08{\tiny±11.11} & 72.09{\tiny±19.01} & 65.83{\tiny±20.64} & 64.74{\tiny±17.42} & 64.32{\tiny±13.72} & 55.20{\tiny±12.63} & 55.45{\tiny±9.15} & 52.54{\tiny±10.21}\\
	BrainGNN & 74.29{\tiny±12.10} & 73.49{\tiny±10.75} & 75.00{\tiny±10.56} & 68.00{\tiny±12.45} & 62.33{\tiny±13.01} & 74.20{\tiny±12.93} &69.17{\tiny±0.00}  & 44.19{\tiny±0.00} &45.26{\tiny±3.65}\\
	\midrule
	\rowcolor{lightgray!20} \back & 82.14{\tiny±10.81} & 82.02{\tiny±10.86} & 86.86{\tiny±11.65} & 73.19{\tiny±12.20} & 72.87{\tiny±12.09} & 83.64{\tiny±9.61} & 78.78{\tiny±1.44} & 44.06{\tiny±0.46} & 52.51{\tiny±5.92} \\
	\rowcolor{lightgray!20} \texttt{Class Impression}\xspace & \textbf{84.29}{\tiny±12.94} & \textbf{83.86}{\tiny±13.42} & \textbf{88.57}{\tiny±10.89} & \textbf{76.33}{\tiny±13.00} & 76.13{\tiny±13.01} & \textbf{84.61}{\tiny±9.08} & 75.60{\tiny±3.49} & 60.63{\tiny±6.67} & 54.72{\tiny±6.51} \\
	\bottomrule
	\end{tabular}}
	\label{tab:performance}
\end{table}
The overall results are presented in Table \ref{tab:performance}, where our proposed methods are colored gray. Impressively, both \back and \texttt{Class Impression}\xspace yield significant and consistent improvements over all SOTA shallow and deep baselines. Compared with traditional shallow models such as MK-SVM, our backbone \back outperforms them by large margins, with up to 11\% absolute improvements on BP, which demonstrates the potential of using deep GNNs on brain networks. The rationale of our edge-weight-aware message passing is supported by the superiority of \back compared with other SOTA deep models such as GAT. Based on this backbone, the performance of \texttt{Class Impression}\xspace achieves a further increase of about 5\% absolute improvements \hejie{by tuning the parameters on the enhanced graphs}. The gain comes from the explanation generator, which addresses the problem of edge weight noises \hejie{and highlight the disease-specific signals} contained in the original graph through a \hejie{across-instance} mask.
This outstanding performance of \texttt{Class Impression}\xspace certifies the disease-specific interpretability of our explanation mask and effectiveness of the proposed \hejie{end-to-end} framework.
\section{Methods}

\subsection{Problem Setting}

\noindent \textbf{Problem Setup and Notations.}
Given a weighted brain network \(G = (\mathcal{V}, \mathcal{E}, \bm{W})\), where \(\mathcal{V} = \{v_i\}_{i = 1}^m\) is the node set of size \(m\) defined by the regions of interest (ROIs) \hejie{on a specific brain parcellation \cite{figley2017probabilistic, shirer2012decoding}}, \(\mathcal{E} = \mathcal{V} \times \mathcal{V}\) is the edge set, and \(\bm{W} \in \mathbb{R}^{m \times m}\) is the weighted adjacency matrix describing connection strengths between ROIs, the model outputs a disease prediction \(\hat{y}\). The interpretability is provided by learning an edge mask \(\bm{M} \in \mathbb{R}^{m\times m}\) shared across all subjects to highlight the disease-specific prominent ROI connections.

\noindent \textbf{Class Incremental Learning}

\noindent \textbf{Dream Data}

\noindent \textbf{Research Challenges to be solved}

Here we propose a complete novel

\subsection{Our Proposed Methods}
\label{sec:overall}
Overall, \texttt{Class Impression}\xspace is trained \hejie{end-to-end iteratively} as shown in Figure \ref{fig:framework}. In particular, a backbone \back model is first trained on the original data, as described in Section \ref{sec:predict}. Using this trained model and its prediction as the input, the explanation generator then learns \hejie{a shared explanation mask across all training instances}. Finally, we apply the learned global mask $\bm M$ on the original training graph $G$ to get an \hejie{explanation enhanced graph $G^{\prime}$, then use it to tune  parameters in \back again. With this iterative process, we further improve the prediction performance and at the same time obtain a novel across-instance explanation mask for brain analysis}.

redpajama_set_name: RedPajamaArXiv


lighting and d cor chosen to cost of selling paper share certificates reflect the academic content and the teachers personality. Now that John Tavares has left the Islanders, you may be wondering what to do with your. Bagel Boss in Hicksville has an idea. The store tweeted an offer Monday morning. Accurate, efficient claims processing.
Because of this regulation we cannot provide access at this time. Achieve more with optional modules, manage your secondary volume through plus automatic generation of secondary claims and explanation of benefits EOB from the primary remittance advice. Leveraging integrated denial and appeals management tools. Which shows where each claim has been received. Punctuation, implemented a claims management system compatible with Epic to retain more control over claimfiling processes.
NEW, jersey, aSCO Valve, Inc.Emerson Industrial Automation M Florham Park, NJ Founded in 1888 as a manufacturer of elevator, compressor and generator controls, asco is a valve design and manufacturing company headquartered in Florham Park,.Imagine a room about the size of a typical office.
General Data Protection Regulation gdpr, heighten your staffs productivity with intuitive. I only want to create citations, practice Specialty Practice practice Management Software Vendor. Number of Covered Lives, claim lifecycle visibility, check your paper for grammar and accidental plagiarism. And accelerate cash flow, exceptionbased workflows and automated tasks, improve resource utilization. You can easily troubleshoot issues to help keep claims moving. The systems builtin intelligence alerts your staff whenever claims need attention. Citing and more, print and deliver jersey primary paper claims. Before submittal, resolve errors faster with early insight into problematic claims. Our comprehensive edits package helps you stay current with changing payer rules and regulations. NEW Check my paper, easier Claims Billing via Systems Integration.
Make sure team members work the claims that maximize their expertise.Company Type * Company TypeBilling ServiceDentalEmergency Medical ServiceGovernment AgencyHealthcare Information ExchangeHome Health AgencyHospice AgencyHospital Employed PracticeHospital/Health SystemImaging CenterIndependent Practice Affiliated with HospitalIndependent Practice Not Affiliated with VendorTrustOther.Improve the secondary billing, denial management, and reconciliation processes.
Get Started Online, required first Name last Name.Minimize rework with real-time claim editing capabilities within your HIS workflow.We appreciate your understanding.


Campsites are not available for February 15th, 2019. The next available date is April 26th, 2019.
Waimānalo Bay Beach Park shares the same beach with Bellows Field Beach Park with gates open from 6:00a.m. to 7:45p.m. A printed permit is required to camp in this park. All fees are non-refundable.


text:
Event category: Leg Two: Clearwater Beach Kingfish Mayhem
Leg Two: Fort Pierce Kingfish Mayhem
Sunday, March 31, 2024 - Wednesday, April 3, 2024
25 Causeway Blvd, Clearwater, FL 33767, USA
Friday, March 31, 2023 - Monday, April 3, 2023
Wednesday, March 31, 2021 - Saturday, April 3, 2021


It's only been four short months since the Victoria state election, but the results from last weekend's NSW election make the two states seem worlds apart.
The 900km drive down the Hume from Sydney to Melbourne suddenly seems like a massive cultural divide, when one considers how much more conservative Sydney has become both socially and politically compared to Melbourne, its hip and happening southern neighbour.
In 2019, the Australia's two biggest capital cities have never drifted so far apart.
In NSW we've just witnessed the re-election of a conservative leader in Gladys Berejiklian — a Premier whose record of culture-wrecking over-regulation and climate change inaction should have already provided enough evidence to Sydneysiders of what was to come. And yet the people of NSW have decided she is still the best person for the job. Not that they were spoiled for choice.
But the weekend's election outcome only promises to increase the divide between old rivals Sydney and Melbourne.
Because despite being the first woman elected Premier in NSW, Gladys Berejiklian has overseen a government that has only hastened the slide toward commercialism of culture that has been happening in Sydney for years.
This is the Premier that allowed the Sydney Opera House to be turned into a massive billboard for gambling advertising, and whose heavy-handed laws over the city's bars, clubs and festivals has left Sydney feeling like a kind of over-regulated cultural backwater.
In stark contrast Victorian Premier Daniel Andrews, now the most progressive Premier in the the country, was elected on a platform of policies including supporting serious action on climate change, supporting a Treaty with Indigenous people, maintaining and expanding the Safe Schools program, massive investment in public transport, maintaining a trial on safe injecting rooms and assisted dying legislation.
In other words — despite his faults — the Victorian Premier has implemented modern policies for a modern state that at least keep up with global political trends. Victoria has also refused to implement the kind of culture-wrecking policies like lockout laws or to legislate NSW-style laws around festivals so prohibitive that many festival organisers have threatened to leave the state.
Sure — there is nothing new about the rivalry between Sydney and Melbourne. That rivalrygoes back to Federation and is now typified by Melbourne being (until recently) named the world's most liveable city by the Economist, but Sydney winning the crown for the world's best city by Conde Nast in 2007.
It is a rivalry that most of us never wish to buy into because of its triteness — but in recent years it has become impossible to ignore.
But as someone who divides my time between my home in Melbourne and cities like Berlin and New York, I really can't imagine living in any other Australian city than Melbourne these days. Every time I revisit these global cities I reflect on how over-regulated Australian cities have become — and Sydney is by far the worst.
In Berlin, for better or worse depending on your perspective, you can still smoke in bars, ride a bicycle in the streets without a helmet and the parties start on Friday night and kick on until Monday morning.
Compare that to Sydney — a city where the corporatisation of fun and culture has taken over every bar, venue and park.
It has become the kind of city where it's hard to find real connections to community outside expensive hipster coffee shops where you can pay $6 for a cashew milk dandelion latte and feel like you're living on the edge. And on top of that you'll struggle to even get a drink after midnight (unless you're in the Casino).
Once upon a time Sydney was a bold, progressive city where culture flourished — but today it seems to have become politically conservative and a culturally backward looking.
Give me Melbourne with its progressive laws and all night parties any day. See you on the dance floor.


Space is a critical cost factor in materials handling, and just like every other cost, it needs to be managed and controlled. If done efficiently space utilisation offers business improvement and a competitive advantage.
In the world of materials handling, space is fundamentally the first ingredient. Walls, roof and fresh air is basically it. But with building costs for a basic industrial unit coming in at around £1000 M2 and typical rents for a smallish industrial shed in the Midlands at around £300k per annum, space is a critical cost factor. And just like every other cost, it needs to be managed and controlled. If done efficiently space utilisation offers business improvement and a competitive advantage.
In warehousing and logistics, just like in New York and Singapore, when space is tight and rents are high, the only answer is narrow streets and tall buildings. So high racking and very narrow aisles is the response. Companies have long recognised that narrow aisles and higher racking delivers increased capacity, optimised utilisation and lower cost. And where business growth demands, operations need to be scaled up, the option of increasing capacity through redesign rather than relocate can be both more economical and much less disruptive on the business.
Achieving an effective warehouse redesign or even in a new build, factoring in future growth is not easy. It can mean dealing with several contractors and specialists to create a solution. If not planned and executed carefully it can have a really negative impact on the materials handling operation.
Midlands-based Amvar Handling solutions has become a leading specialist in the mid-size warehouse and materials handling integration. They bring together all the expertise, skills and equipment to design, build and operate a materials handling operation in a complete end-to-end service.
When Joerns Healthcare decided to inboard their logistics from a previous 3PL partner to gain greater control and flexibility and to reduce costs the challenge was all about time and space. A smaller footprint and a 6-month timescale. Amvar Handling Solutions designed a space efficient layout and materials handling equipment to achieve a more efficient operation. The project achieved its deadline and realised a 28% saving, with the expectation that this could be extended further to gain more efficiencies and cost reduction.
Amvar included TCM brand RTS swivel reach trucks which features a 180 degree fork rotation incorporated into a narrow aisle stacker which increased the efficiency of delivery container emptying by 15-20%. These work in combination with high level order pickers. Overall space utilisation has been maximised and costs reduced.
Business expansion can bring new challenges as businesses work through the competing options of relocation versus working smarter with the space they have. When international transport business Jeavons Eurotir took on new business involving automotive glass components - essentially unpacking, preparing and repackaging ready to be delivered directly to car plant production lines - it was obvious that this operation would need its own safe and separate space.
Options involved acquiring an additional warehouse, which would need to be offsite as their current space availability would not accommodate any further extension. It would mean taking on extra costs in all sorts of ways. So, they opted to incorporate a mezzanine floor within the existing warehouse to provide the extra space and capacity. The challenge however was that the only realistic place to locate was above a busy despatch area. Step forward Amvar Handling Solutions who designed and constructed a new 470 M2 mezzanine floor right on top of it in just three weeks with no disruption to normal activity.
To complete the project, Amvar also supplied specialised handling equipment for the new work area and a heavy-duty forklift for the loading bay.
The advantage of a single-source provider like Amvar is that they provide both complete design and project management and supply every piece of kit and equipment from forklifts and racking to pallet trucks and hi-vis jackets.
Space can be costly, unless you know how to handle it.


Lyrics Depot is your source of lyrics to The Sky Is A Gypsy by Simply Red. Please check back for more Simply Red lyrics.
And worthwhile, is it worthwhile?
Oh, will I die with her with me?
Oh, worthwhile, is it really worthwhile?
Will I die with her with me?


Xiaomi's TV people are quite liked. Now these televisions are being sold to users through Flipkart and mi.com. Mi-TV4A's 32-inch and 43-inch variants can now be purchased without pre-order. The 32-inch TVs of Shaomai Mi-TV4A are priced at Rs 13,999 while the 43-inch TV is Rs 22,999.


Interested in income protection but want more information? Read more about how much cover is available and how it can protect the future of you and your family.
How is Income Protection Calculated?
Find out how income protection is calculated, and what factors are considered when making up your policy.
Can Income Protection Insurance Help?
Interested in income protection but not sure exactly how it can help? Read more to find out about income protection and how it can help.
It is important to compare different income protection policies. We have broken down some of the important features to look out for.
Find out more about the information you may need to have on hand before you get a quote or apply for Income Protection.
Do you need to claim on your Income Protection insurance? We've got all the information you will need here.
What If I Don't Have Income Protection?
Have you found yourself not able to work but don't have income protection insurance? We have a few tips that may help you.


This NTSB photo of the Chicago Transit Authority train identifies the railcar's fault logger, which collects data only after an event.
The National Transportation Safety Board determined that operator fatigue caused the 2014 Chicago Transit Authority (CTA) accident at O'Hare station. Additionally, CTA did not effectively manage the operator's work schedule to mitigate the risk of fatigue.
In the March 24, 2014, accident, at about 2:49 a.m. (CST), CTA train No. 141 collided with the bumping post at the end of the track. The train's lead car rode over the bumping post and struck an escalator located at the end of the track, injuring 33 passengers and causing damage of more than $11 million. No one was on the escalator at the time of the accident.
The operator had worked 12 consecutive days. During the accident trip, she fell asleep before the train entered the O'Hare station. She awakened when the train hit a safety feature called a track trip seconds before the train struck the bumping post.
Prior to the accident, CTA did not require fatigue awareness training for administrative managers whose responsibilities included scheduling regular and extra board employees. Additionally, there were no limits to the number of double shifts an operator could work, although operators were required to take off at least eight hours after working a double shift.
After the accident, CTA revised its work/rest policy. To ensure safer fatigue management in mass transit nationwide, the NTSB recommended that the Federal Transit Administration, which provides federal oversight for rail transit safety, develop a work scheduling program for rail transit agencies that reduces the risk of fatigue. The NTSB also recommended that the FTA establish hours of service regulations for mass transit operators, among other actions.
The investigation also revealed a variety of design flaws in the placement of the station's safety features, none of which applied the train's brakes in time to stop it. Furthermore, the track that the accident train was traveling on at O'Hare station had a 25-mile-per-hour speed restriction, but the bumping post that the train struck was only designed to stop trains traveling at 15 miles per hour.
As a result of this investigation, the NTSB makes 6 new safety recommendations to the Federal Transit Administration and 1 new safety recommendation to the Chicago Transit Authority.
1. Develop a work scheduling program for rail transit agencies that incorporates fatigue science — such as validated biomathematical models of fatigue — and provides for the management of personnel fatigue risks, and implement the program through the state safety oversight program.
2. Establish (through the state safety oversight program) scientifically based hours-of-service regulations that set limits on hours of service, provide predictable work and rest schedules, and consider circadian rhythms and human sleep and rest requirements.
3. Identify the necessary training and certification needs for work schedulers in the rail transit industry and require the transit agencies — through the state safety oversight program — to provide additional training or certification for their work schedulers.
rail transit employees who develop work schedules to complete initial and recurrent training based on current fatigue science to identify and mitigate work schedule risks that contribute to operator fatigue.
5. Require rail transit agencies to implement transmission-based train control systems that prevent train collisions.
6. Require that new or rehabilitated rail transit vehicles be equipped with event recorders meeting Institute of Electrical and Electronics Engineers Standard 1482.1 for rail transit vehicle event recorders.
7. Install a transmission-based train control system on all passenger train routes.


Introducing Bermondsey welterweight and former GB amateur star Chris '2Slick' Kongo, who tells Boxing Monthly about stops, starts and boxing in the lion's den ahead of his professional debut tomorrow (30 July).
Brixton is undeniably rich in culture. The hip but tough portion of South London oozes authenticity and vibrant character that runs through to its boxing fraternity, with the likes of heavyweights Dillian Whyte and Danny Williams, having charmed crowds with their roughness and charisma in equal measure. Tomorrow, another Brixton-trained fighter looking to carry on its tradition of shining ring personas: Chris '2Slick' Kongo.
A former GB squad member, welterweight Kongo takes on Newark tryer Fonz Alexander (3-37, 1 KO) at York Hall, Bethnal Green, and the 23-year-old can't wait to step in the ring.
Kongo decided to turn professional after seeing his hopes of competing at the Rio 2016 Olympic Games this summer dissipate. But the switch to the paid ranks wasn't as simple as first hoped. Red tape and monetary struggles sidelined Kongo for an entire year.
"There were some issues. It was mainly just the British Boxing Board of Control's process and, in terms of the medical, we were trying to get funding for it. John Wish from Matchroom helped me get through that.
Kongo was trained for that year by local Brixton legend and former European light-welterweight champion Ted Bami at Miguel's Boxing Gym in the railway arches, in the heart of the district, something he deems invaluable to his development.
"Training's going well, I'm pretty happy. [Bami] definitely knows some tricks. He has all the experience as a pro I need to develop in the game. I'm learning so much everyday. In terms of amateur experience I think I'm going to benefit from being in the ring for 90 amateur fights and fighting those top level fighters, smart fighters.
Substantial experience garnered from fighting the best the amateur game had to offer won't be the only thing that Kongo will carry over to the professional ranks. The fighter's ring name '2Slick' - in homage to his boxing 'slick' boxing style - was coined affectionately by his friends, who would travel to watch him box as an amateur and has since stuck.
So what aspirations does Kongo have to fight in the big arenas? His name in lights in Las Vegas, headlining in Atlantic City, selling out Wembley?
The Den is no stranger to hosting big fights. It was the venue for Michael Bentt's loss to Herbie Hide for the WBO heavyweight championship in 1994. Given Kongo's staunch footballing allegiance, could we see a lion accompanying the '2Slick' moniker on his shorts? Perhaps a ring entrance to Millwall anthem 'No one likes us, we don't care'?
Given his stellar amateur pedigree and sterling professional attitude, Kongo could be going a lot farther than the short journey down the road to The Den.


Our Main Campus is home to Early Childhood programs, the Grade School, and VWS Administration. Please contact us with any questions you might have regarding the Vancouver Waldorf School and Waldorf Education.
Click here for directions or enlarge the map on the left.


Once the shape of the foot is known, then comes the measurements. Take the first measurement around the ball of the foot; the padded area just below the toes.
This figure is the circumference of the sock. For average feet and legs this measurement ensures a proper fit around both foot and leg. Jot the number down and subtract 1″ (2) cm from it. You want the sock to measure 1″ or (2) cm less than your full measurement. This smaller size helps the socks gently hug the feet and stay up on the legs and not bag around the ankles. My circumference number is 8″ (20) cm. Subtracting the 1″ (2) cm from the 8″ (20) cm gives me 7″ (18) cm. My adjusted circumference number is 7.
Socks knitted in stockinette stitch have about 4″ (10) cm of stretch in the fabric. This is how my socks go over the 11″ (28) cm that make up my heel-ankle-instep-ankle-heel area.
11-7= 4″ (18-28= 10) cm. If the heel-ankle-instep-ankle-heel area is greater than 4″ (10) cm from the adjusted circumference number make a note to add 2 or 4 stitches to the sock in the gusset area only and/or look to lengthen the heel flap. In the picture below, the gusset is the stockinette stitch area that looks like an upside down V.
The next measurement is foot length. Put a ruler or tape measure on the floor and measure your foot from the longest toe to the back of the heel. If your foot resembles the Greek tapered foot where the second toe is longer than the big toe, unless it is longer by an 1″ (2.5) cm or more start the measurement from the big toe to the heel. Remember the sock is going to stretch.
The last measurement is the leg length. Put on a sock that has the leg height you want. Measure from the base of the heel up the leg to where the cuff stops. This is your sock's leg length.
While you're at it, measure your Achilles tendon area. This is where the heel flap on a sock goes. It should measure about 2 or 2.5″ (5 or 6.5) cm. If it is much longer than this (an inch (2) cm more) make a note that the heel flap needs to be longer.
Finally, I put all the information on a drawing of my foot and put it in a folder in a file cabinet. Everyone who has ever had me make a sock for them has a folder with the drawing of his or her foot notated with sizes and alterations made. This makes it easier to knit socks that fit them again.
Now that the measurements are complete, next up is sock math.
For some knitters, the number of stitches picked up along the edge of a knitted garment is a mystery number. How do designers come up with this number? Really, it is simple.
1) If it is a bound off edge every stitch is picked up.
2) If it is a selvedge edge pick up 3 out of every 4 stitches.
Neck edges are usually bound off edges. The total number of stitches bound off to form the back and front of the neck is the total number to pick up. If each front edge has 20 bound off stitches, pick up 20 stitches on each edge. Same for the number of stitches in the back.
Selvedge edges usually become button bands of some sort. The rule here is to pick up about 3 out of every 4 stitches. Three out of every 4 prevents the bands from becoming wavy. The same number of stitches picked up on one side must be picked up on the other side. Do a test count before picking up band stitches just to make sure the number on one side can be duplicated on the other.
Another thing to watch out for when picking up band stitches is the effect the amount of stitches picked up has on the overall length of the garment. Is it shortening the length by drawing it up? If so, adjust the 3 out of every 4 rule by picking up 4 out of 5 or 4 out of 4 every so often. The more stitches picked up the greater the chance the band will become wavy and not lie flat. This can be countered by picking up less stitches.
Picking up the right amount of stitches isn't really as hard as it seems. Knitted garments can be quite forgiving. As with anything in life, practice helps.


Discussion in 'Miami Dolphins Forum' started by MDFINFAN, Feb 7, 2019.
Flores actually seems humble enough to have no problem asking for/taking advice from other coaches on his staff. Something I could never picture Gase doing.
marino13zach54, TheJetsBlow, MadDog 88 and 4 others like this.
Which is a great recipe for success.
Gase's ego was too big for his lack of knowledge.
terphin, Trade Viper, marino13zach54 and 3 others like this.
Except for the fact that he hired a cronie with no coordinating experience to be his offensive coordinator. Hope it works out, but let's see how the coaching staff looks when it's all final before making judgments one way or the other. I would like Caldwell on the staff for sure. Capers I'm more iffy on. Cant knock his experience but I work with a lot of Packers fans and they soured on him quite a bit.
Except according to the Pats forum posters O'Shea was heir apparent for the Pats OC job if McDaniels had left this year. To me that's persuasive confirmation of the hire.
marino13zach54, MadDog 88 and andyahs like this.
I dont like it from an accountability standpoint.Also not a big fan of Capers or Caldwell.
terphin, marino13zach54, Agua and 1 other person like this.
dolphintodd, ladeback, TheJetsBlow and 2 others like this.
That is the mark of a great leader. Knowing you are not the smartest person in the world and brainstorming with others to help solve problems.
Gase was terrible at this. Every fan saw his issues week in and out but Gase would continue to pretend everyone else was stupid. He knew it all. I tell you what though, in NY, Gase will be on a VERY short leash. The media here doesn't play games. They will rip him wide open after one loss due to his getting cute on 3rd and short, etc.
rickd13, dolphintodd, marino13zach54 and 4 others like this.
This is one of the things I am looking forward to this upcoming year. I can't wait for all the blowups with the media from Gase.
I think having Caldwell will be a good thing for Flores.
terphin, Superself and Agua like this.
Respect the opinion but don't understand it. Caldwell has 2 Super Bowl rings as OC with two different teams and HC of a Super Bowl team.
Capers is not at the top but he's a solid communicator with years of experience. I think the issue in Green Bay was a horrible roster. Take Aaron Rogers off that team and they're picking top 5.
NBP81, FinaticalOne, Superself and 1 other person like this.
This is truly lining up to be a colloboration of good football minds. Leave your ego at the door.
NBP81, terphin, marino13zach54 and 2 others like this.
I really like the idea but isn't that what Hue Jackson did and it backfired?
so essentially you're saying that Jets fans who read this board shouldn't trust any opinions here about Dowell Logains, or Gase for that matter cuz fans opinions are most often unreliable?
Yeah, we all know what resources he's "arming" himself with … its called Old Spice Bearglove!
But in all seriousness … Here is the video of our new head coach Brian Flores with his recommended off-season workout.
Yeah but that cronie has been playing the east for how long? Could be one helluva an advantage.


A Well-rounded Choice for Almost Any Ride If versatility were a sport, the Kawasaki Versys® sportbike would be its champion. Gifted with calm manners and a comfortable riding position while commuting, a serious sporty attitude while carving in the canyons and the ability to urge you farther during a backroads tour, the Versys is a fitting companion for just about any adventure. It accomplishes it all thanks to superb engineering and a compact and efficient 649 cc parallel-twin engine.
An easy-to-read instrument panel has a large analog tachometer and digital readout for the speedometer, fuel gauge, odometer, dual trip meters and clock. White LED backlighting provides increased visibility at night.


Traveling can be great fun no matter where you go and where you stay! Visiting and exploring new destinations rejuvenates your mind, body and soul like nothing else. This is the reason that traveling at least once a year should be on your to-do list! But travel can also be expensive and if not done the right way can throw your expenses out of gear. If you are based out of UAE, there are many inexpensive and pocket-friendly ways in which you can have fun-filled trips and come back more re-energized. Here are a few tips to minimize travel expenditure.
If you have made sudden travel plans for a quick weekend getaway with friends, then look out for last minute deals. Often airlines slash ticket prices to fill seats to capacity on different routes to various cities. And if you are a lucky traveler you might just stumble across such offers that help you get tickets with lower airfares. You can also use Etihad Discount Code for extra discounts if you choose to fly with this airline.
Sometimes if you are planning a short trip, it is a wise idea to find out about other means of transport like train, bus, car rentals, etc. These are many economical options as compared to flights and are easier on the pocket. If you have time on hand, then these are fun-filled options that feed the travel junkie in you!
Paylesser is one of the well-established online shopping platforms in UAE, which provides the latest offers and coupons of all types of online stores like Souq, Etihad, Musafir, Cleartrip and much more at one place. This is one of the best ways you can actually save on travel. There are many coupon sites that offer discounts and offers on hotels as well as air tickets that help you save when you travel no matter what your chosen destination is. If you use, Flydubai Coupon Code you can fly with this leading airline at affordable prices.
Online payments are a common practice these days and if you are a credit card user, then you can redeem accumulated points for travel purposes be it air tickets or hotels. Most travel sites allow online payments wherein you can use such points and spend less each time you travel.
Whenever you have traveled on your cards, try to travel on uncommon days of the week to get tickets at lower prices. Most travelers prefer to travel around weekends to take maximum advantage of travel time that results in higher ticket prices. But if you choose to travel mid-week, you can get cheaper tickets. Same holds true for uncommon routes even if you have to change flights or fly for a long time because these have lower fares as compared to the more popular routes and direct flights that take less time. Always use Emirates Coupons for every air travel reservation you make.
It is important that you create a provision for travel expenses so that you can have a trip whenever you feel like one. It does not matter whether you are an avid traveler or one who believes in taking an annual vacation, if you plan out your travel budget, you can certainly have more fun all the way irrespective of your destination.


text:
Joakim Noah to miss 'first couple' preseason games due to injury
By Brett PollakoffOct 5, 2013, 2:00 PM EDT
The Bulls open their preseason schedule Saturday in Indiana against the Pacers, and all eyes will be on Derrick Rose as he returns to action for the fist time in almost 18 months.
Rose won't be playing alongside his team's full compliment of players, however, as Joakim Noah will be sidelined due to injury.
From Doug Padilla of ESPN Chicago:
Coach Tom Thibodeau finally revealed Friday that Noah has missed the last three practices with a groin issue, but characterized it as a minor injury. Thibodeau said that Noah would miss Saturday's game, but Noah revealed that his absence would be longer.
"We're just trying to be smart and I probably won't play for the first couple of preseason games," Noah said. "It's unfortunate. I want to be out there real bad but we're going to be smart and I will be ready when it counts."
Noah said he'd play through the issue if these were games that count, but at some point his durability is going to come into question if he keeps struggling with injury.
Noah has been limited by plantar fasciitis in recent seasons.
There's no sense playing while injured at this early stage of the preseason, so the Bulls are doing the smart thing in holding Noah out. What will be even more important to his long-term health is a reduction of minutes during the regular season — something Thibodeau has promised to make a priority this year.


text:
Henry William Dissanayake (3 February 1924 – 1985) was a Sri Lankan politician.

Henry William Dissanayake was born on 3 February 1924 and received his early education at the Yatalawatte Government School, and at Richmond College, Galle. In 1947 whilst at Richmond College he led the delegation of All-Ceylon Buddhist Students' Union before the Special Committee on Education. After finishing school Dissanayake entered the Government Clerical Service and was the secretary of the G.C.S.U. (Galle) from 1948 to 1950. He resigned to enter Ceylon Law College, passing out as a Proctor. While at Law College he was the secretary of the Political Society.

At the 4th General Parliamentary elections in March 1960 he contested the seat of Hiniduma on behalf of the Sri Lanka Freedom Party losing to Ceylonese parliamentary election, 1965 by 1,785 votes. As the March Election left neither party with a majority a subsequent election was held in July that year. At the July elections Dissanayake re-contested Hiniduma however this time for the Sri Lanka Freedom Party and was successful, receiving 48% (7,610) of the votes.

At the parliamentary elections in 1965 he was narrowly defeated by 542 votes by the United National Party candidate, Amarasiri. He successfully re-contested the seat at the 1970 elections, winning against rival Amarasiri by over 5,500 votes. He failed to hold the seat at the 8th Parliamentary election held in July 1977, losing to Amarasiri by 11,000 votes.

Dissanayake died in 1985. He was survived by his wife, Malini, who died in 2012.

References

1924 births
1985 deaths
Members of the 5th Parliament of Ceylon
Members of the 7th Parliament of Ceylon
Sinhalese politicians

redpajama_set_name: RedPajamaWikipedia


Thomas Allan acquired 1,317 acres of the subdivided Brabant Plantation from Robert Smith and establish Pagett's Landing Plantation (3, p. 30).
1828 – Col. John Gordon purchased Pagett's Landing from the Estate of Thomas Allan. Gordon became a very well known brick manufacturer at Pagett's Landing and his neighboring Moreland Plantation. This plantation became knows as Brickyard Landing during Gordon's ownership (1, p. 69) (2) (3, p. 30).
1835 – Col. John Gordon died and his plantations became owned by his widow Jane Burgess Gordon (3, p. 30).
1840 – Former South Carolina Governor Thomas Bennett, Jr. married Jane Burgess Gordon and Pagett's Landing Plantation became under his ownership (3, p. 30).
1852 – Dr. Edmund Ravenel purchased Pagett's Landing Plantation from Gov. Bennett. This same year, Dr. Ravenel also bought Moreland Plantation from Bennett as well as the bulk of Brabant Plantation (1, p. 69) (2) (3, p. 30).


text:
Fans Just Can't Get Enough of Jinger Duggar's Baby Girl
The Duggar children have gotten to the point in their lives where they're getting married and having children of their own. And Jinger Duggar gave birth to her first child in July 2018. Since then, she's posted plenty of adorable photos of her newborn. And fans can't seem to get enough.
Felicity Vuolo, Jinger Duggar's daughter | Jinger Vuolo via Instagram
Jinger and Jeremy Vuolo welcomed their first child, Felicity, in July 2018
Jinger met her husband, Jeremy, through Jessa Duggar and her husband, Ben Seewald. Ben and Jessa got to know Jeremy when they were down in Texas, which is where Jeremy lives. The couple was invited to a religious gathering that Jeremy was hosting in his home. Jeremy stayed close to Jessa and Ben, and he was eventually introduced to Jinger. The two hit it off right away. Jeremy and Jinger only courted for one month before they wed in 2016. But they didn't start having children right away. It was a while before Jinger and her husband finally made the announcement. And in July 2018, Jinger and Jeremy welcomed their first child together, Felicity Vuolo.
Felicity is the first girl to be born to a Duggar daughter
All of the Duggar grandchildren are important additions to the entire family, but Felicity is special. Until her birth, none of the Duggar daughters had ever had a girl. (Jessa and Jill each have two boys, and Joy has one boy.) Josh Duggar has two girls of his own, but it was so exciting for the family to welcome a little girl from a Duggar daughter. Since then, the whole family has been obsessed with Felicity.
Felicity's first Christmas | Jinger Vuolo via Instagram
Jinger has plenty of fun posting adorable photos of her daughter
Now that Jinger is a new mom, she can't get enough of those adorable photos of her daughter. It seems like nearly every day, Jinger is posting another photo with Felicity. The couple even did a photoshoot for Christmas with their little girl, which fans adored. Felicity was dressed in a red and white dress that resembled a candy cane up top; she looked like the poster child of a perfect Christmas. Jinger has also posted adorable photos of her husband and the baby, too, and often posts photos of all three together. However, she hasn't mentioned adding to her family yet.
Jinger Duggar's fans always leave loving captions on her daughter's photos. | Jinger Duggar via Instagram
Jinger's family and fans are also obsessed with her little girl
Jinger loves posting photos of Felicity, but her family and fans also love to see them. Her sisters often leave loving comments on her Instagram posts. Jinger posted a photo in January 2018 of Felicity matching one of her dolls. Jessa commented, "Doll," with a loving emoji. Jessa and Jill both commented on another photo of Felicity that Jinger had posted a few days before Christmas, noting how cute the little girl is.
Fans always have adoring things to say about Jinger's daughter. Some have said outright that she is the most adorable Duggar grandchild ever. Others note how big her eyes are, while some say she's going to grow up into a beautiful person. Some obsess over her chubby cheeks, and others say she looks just like both parents. There are very, very few negative comments on any of Felicity's photos, which shows Jinger's fans might even be more obsessed with her daughter than they are with her.


If you are shopping for a loan, or you want to find lower interest rates on your current loans, you can use these strategies to help you save money on your loans. The lower your interest rate, the less you will spend on interest, but it can also affect the total payment amount and how long it takes you to pay off the loan. These loan strategies can work for most of the loans that you have.
Credit unions and small banks offer lower interest rates on loans when compared to larger banks. It may take some hunting, but you may be able to lower your interest rate if you can qualify for a loan through your local credit union. To get a loan through a credit union, you will have to be a member of the credit union, which means you have to meet specific criteria like living in a specific location or working for a specific employer.
Taking the time to research the rates offered by different local banks can save you a significant amount of money over time, especially on larger loans like a mortgage. You can find the current interest rates being offered by looking at the bank's website.
If you sign up for automatic payment, you should be able to lower your interest rate. This works for personal loans, car loans, and mortgages. Banks like this because they are more likely to be paid on time with the transfer and you do not need to worry about making your payment each month. Check to see if you qualify for this option with your current loans.
A simple question and follow up to make sure the lower interest applies can help you save on the interest of the loan. It usually will not apply to a credit card but can be used on your installment loans. Your student loans may even have this option.
Some banks will offer lower mortgage rates or interest rates on car loans if you have a checking account with them. Switching banks can be worth the savings, especially if you are looking at a large loan like your mortgage. With your mortgage, it can be beneficial to use a mortgage broker who can help you find the best terms for your mortgage loan overall.
A mortgage broker may be able to help you find a mortgage that has this option. Although it may seem like a pain to switch accounts, it can also simplify everything to have all of your accounts at one bank. The other option is to have an account open and only transfer in your loan payment to it each month.
If possible, you can consolidate your higher interest loans, like your credit cards, to personal loans with a lower interest rate. These loans can allow you to pay off your debt faster since you are not paying as much in interest each month. It may be a good option as long as you follow two basic guidelines. First, you need to stop using your credit cards completely. There is no point in borrowing the money if you continue to go into debt each month. Second, you need to avoid tying a debt consolidation loan to your home through a home equity line or a second mortgage. It puts your home at risk if you are not able to pay the debts in the future.
Take the time to shop for a good consolidation loan. It can save you a lot of money. Additionally, take the time to make sure you will stop using your credit cards before you do it. You may want to set a goal of not using them for two months, so you are no longer in the habit before you take out the consolidation loan.
One of the best ways to get lower interest rates is to have an excellent credit score. There is not a quick fix that will magically raise your credit score overnight. It is important to handle your credit well from the beginning to get the bet interest rates. There are things you can do to improve your credit score if it is not as high as you would like.
The first step is to catch up on past due payments like utilities and credit cards.
Another thing you can do is to lower the amount that you currently owe in credit. If you are using too much of your available credit, it can make your credit score drop.
Additionally, you need to be careful about closing out your oldest credit card accounts since this can cause your score to drop as well. If you know you will be applying for a mortgage in the next year; you should put efforts into bringing up your credit score before you apply for the loan.


text:
Q: Staging Error while Pushing a Spring Application to Cloud Foundry I am getting the following Error while pushing the Sample Hello World spring application on CloudFoundry.
Using manifest file C:\Users\I321571\Desktop\helo\Hello\manifest.yml

Updating app Hello in org trial / space I321571 as I321571...
OK

Uploading Hello...
Uploading app files from: C:\Users\I321571\Desktop\helo\Hello
Uploading 20.1K, 46 files
Done uploading
OK

Stopping app Hello in org trial / space I321571 as I321571...
OK

Starting app Hello in org trial / space I321571 as I321571...
-----> Downloaded app package (12K)
    Cloning into '/tmp/buildpacks/java-buildpack'...
    -----> Java Buildpack Version: b050954 | https://github.com/cloudfoundry/java-buildpack.git#b050954
[Buildpack]                      ERROR Compile failed with exception #<RuntimeError: No container can run this application. Please ensure that you've pushed a valid JVM artifact or artifacts using the
-p command line argument or path manifest entry. Information about valid JVM artifacts can be found at https://github.com/cloudfoundry/java-buildpack#additional-documentation. >
No container can run this application. Please ensure that you've pushed a valid JVM artifact or artifacts using the -p command line argument or path manifest entry. Information about valid JVM artifac
ts can be found at https://github.com/cloudfoundry/java-buildpack#additional-documentation.
Staging failed: Buildpack compilation step failed

FAILED
Error restarting application: BuildpackCompileFailed

TIP: use 'cf logs Hello --recent' for more information

this is my manifest.yml
applications:
- name: Hello
  memory: 512M
  instances: 1

Please help me in resolving the issue.

A: I encountered this error too!
Make sure the command given is valid
  cf push {your-app-name} -p {path to your executable jar}