No matter what sounds you profess to be your music of choice, chances are, that at some point over the last five years, you will have got your rocks off to Matt Edwards. As Matthew E he fashions club-based, dancefloor slayers. Under that aegis he's updated the classic Chi-town, jackin' sound beloved of house aficionados and layered it with fractured funk, belligerent, oddball beats and mechanical soul.
If Matthew E is all about the Saturday Night Fever, then, by contrast, Rekid is the slower, more deliberate, rhythmical funk of the mid-week mayhem. Its well-intentioned muscular intensity making it perfect soulful sustenance. Quiet Village is something else all together. A collaboration with long-time musical sparring partner Joel Martin (a vinyl freak, like Mr Edwards, who works in esteemed auction house Christie's pop department), it's sample-based music which veers from drawn out disco-not-disco delirium to evocative, under-stated soundscapes. Sea Devils - you keeping up at the back? - is undeniable electronic pop brilliance. Working with Tom from Cagedbaby, it's the least niche of Matt's projects and thanks to Sea Devils alluring pop dance grooves they'll no doubt be shagging the upper reaches of the hit parade sometime soon. Last, but by no means least etc, etc, etc, is Radio Slave. Perhaps Matt's most instantly recognisable nom de guerre, thanks in part to his inspirational melding of Kylie's Can't Get You Out Of My Head to New Order's Blue Monday, this is Matt's DJ moniker and the guise under which he completes his incendiary remixes and re-edits. However, before such sonic schizophrenia gets you calling for the men in white coats, just feel the quality and consider Matt's legacy. Fired by the first wave of acid house, Matt gained his musical spurs by partying to dance music that was just that: music you danced to, irrespective of tribal factions. This was the raison d'être he acknowledged when he took to the decks, first at London's legendary Milk Bar in 1992, then onto the, then, recently opened Ministry Of Sound and then to parties across the country, most notably in Wales - where he was a regular at free parties - and in Brighton, his home since 1998. His love of re-edits, dance music's oft-forgotten backbone, led Matt into the studio himself. His own re-edits and remixes, such as the aforementioned epoch-defining re-imagining for Ms Minogue (completed with his former partner Serge Santiago), soon gave way to his own productions and since the floodgates opened there's been no stopping the man known as, erm, Matthew E, Quiet Village, Sea Devils, Rekid and Radio Slave. He's released records for Skint, Soul Jazz, Classic, and he's worked with fellow travellers, Belgium's The Glimmers. If Matt comes up with another two projects he'll have enough alter egos for every day of the week. Psychologists would probably have a field day with him; mercifully, we don't have to worry about any crisis of identity, we just get to keep on dancing.


This paper presents an advanced Bayesian analysis method to determine the appropriate non-periodic inspection intervals of fatigue-sensitive structures. The calculation procedure of the posterior distribution is improved compared to the previous methods. The method is based on assumptions about the probability density functions of the time until crack initiation, a law of crack propagation, the probability of crack detection and the failure rate before and after crack initiation. A major feature of this method is that even when there are uncertain parameters in the equations, the next inspection interval can be determined. Moreover, the probability density function of the uncertain parameters is updated according to the results of each inspection. A simulation study was conducted to evaluate the proposed method. The proposed method is evaluated and it is statistically shown that this Bayesian approach allows (a) evaluation the inspection interval accurately even with incorrect prior knowledge about the parameters and, (b) estimation of the reliability of a system accurately, even when some of the parameters are uncertain.
Related to the importance of selectivity to natural fibers that can be used and qualify as one of the raw material composite engineering substitute for Fiber Reinforced Plastics (FRP) or Fiber Reinforced Metals (FRM), one plants that have the potential of fiber and volume growth is very abundant, namely abaca. This research aims to clarify scattering in tensile strength of an abaca single fiber through statistical approach and to search a way of suppression in the scatter with intention that abaca fibers can be used as a raw material for engineering structural components. Tensile test specimens were prepared from fiber samples from two different areas, East Aceh and North Aceh, Indonesia. Specimen gauge length is 25 mm, then as the holder of a fiber made of paper (20mm x 100mm), where the specimen size for fiber tensile test in accordance with JIS standard K-760. Diameter the specimen fibers obtained varied from 0.060 mm to 0.140 mm. The tensile strength of abaca fiber varies in the wide range from 100 MPa to 900 MPa. Coefficient of variation was calculated for East Aceh abaca fibre 0.32 and North Aceh abaca fibre 0.35. It was examined whether Weibull distribution or log-normal distribution could well express scatted experimental results. The results showed that both the distribution types could well express the experimental probabilty density, but log-normal distribution could be more rational for expression of biologial and chronological effects. To reduce scattering of tensile strength, pre-screening concept was proposed.
To date, oblique impact has not been studied and few experimental data on the local damage of reinforced concrete (RC) panels exist for oblique impact of deformable projectiles. The final purpose of this study is to propose a new formula for evaluating the local damage to reinforced concrete structures caused by oblique impact based on past experimental results and simulation results. As the first step of this final purpose, we validate the analytical method by comparison with the experimental results and simulate the damage caused by oblique impact using the validated method. First, we analyze and simulate the local damage of RC panel caused by a deformable projectile owing to an impact test normal to the target structure to verify the validity of the simulation analysis. Next, we perform simulation analyses for evaluating the perforation of RC panel due to oblique impact by the deformable projectile and present the results. Various response characteristics and perforation mechanisms to be the basis of examination of oblique impact evaluation were clarified in this paper.
To develop Pu-burner high temperature gas-cooled reactor (HTGR) attaining very high burn-up around 500 GWd/t, the security and safety fuel (3S-TRISO fuel) is proposed. The 3S-TRISO fuel employs the coated fuel particle with a fuel kernel made of plutonium dioxide (PuO2) and yttria stabilized zirconia (YSZ) as an inert matrix. Especially, a zirconium carbide (ZrC) coating is one of key technologies of the 3S-TRISO, which performs as an oxygen getter to reduce the fuel failure due to internal pressure during the irradiation. R&Ds on ZrC coating directly on the dummy kernel made of YSZ which surrogates PuO2-YSZ have been started in the Japanese fiscal year 2015. As results of ZrC coating tests on YSZ particle by the bromide process developed in Japan Atomic Energy Agency, stoichiometric ZrC coatings with 18 - 21 μm of thicknesses were obtained with 0.1 kg of particle loading weight. As indicated by a thermochemical analysis on the chemical stability of YSZ against the ZrC bromide process, no deterioration of YSZ exposed by source gases of ZrC bromide process was observed through the characterization by Scanning Transmission Electron Microscope (STEM) observation. Finally, reproducibility of stoichiometric ZrC coating on YSZ particle was demonstrated by the bromide chemical vapor deposition process.
Biocoke (BIC) is a solid fuel made from various types of biomass resources. BIC is expected to be a coal substitute fuel because of its high density and high hardness characteristics. In a previous study, we have indicated the effects of formation conditions on BIC properties. In this study, the main structural components of biomass: cellulose, hemicellulose, and lignin, were investigated to systematically deduce the forming properties from the biomass components. The initial moisture content is a critical parameter due to the solid surface reactions by lignin in the formation of BIC. The initial moisture content affects the thermal softening temperature of hemicellulose and lignin as an adhesive of BIC. Thus, we investigate the effects of biomass components and the initial moisture content on compressive strength of BIC. The raw materials of this study were woody biomass (trunk from conifer trees and bark from conifer trees) and herby biomass (bagasse and rice straw). The result of compressive strength shows that trunk BIC has the highest compressive strength among BIC made from those biomass materials. The maximum compressive strength has a directly proportional relation to cellulose content. Due to the increase of the initial moisture content, the maximum compressive strength decreased. The maximum strength could be approximated by a quadratic curve for hemicellulose content.
Levitation improvement of the bending levitation performance of thin steel plate was performed. First, the point of the steel plate where the vibration is predominantly generated was estimated using an ultrathin steel plate with a thickness of 0.18 mm. Furthermore, to decrease the vibration, levitation experiments by means of changing the bending shape and using permanent magnets were performed and the standard deviation of the displacement and levitation probability were evaluated. In addition, to determine the effect of the permanent magnets, the shape of the steel plate was analyzed using the finite difference method. As a result, the main vibration generating area was confirmed to be the longitudinal central edge area of the steel plate. In order to suppress the vibration in this area, the vibration of the steel plate was decreased by making an asymmetrical bending shape. Also, the vibration of the steel plate was suppressed by placing the permanent magnets in the positions that matched with the results of the analysis.
The coupling-matrix-free iterative s-version finite element method is extended to multiple local meshes in order to model multiple local features such as holes, inclusions, and cracks. The formulation with multiple local meshes is presented, along with the stress transfer method between the local meshes. The present method does not require the generation of coupling stiffness matrices with a very sophisticated numerical integration method between the global and local meshes or between the local meshes. Instead, stress transfers between pairs of overlapping meshes are performed. Several numerical examples, such as a patch test, a two-hole problem, and a structure with many holes, are presented. The examples demonstrate that the present method is capable of representing a uniform stress distribution as well as capturing the stress concentration of multiple holes that are located in the vicinity of each other. Moreover, numerical integration methods of the global mesh and local meshes are found to have significant influences on the convergence of the iteration. In these numerical examples, the straightforward Gaussian quadrature could not achieve convergence, whereas the element subdivision technique could. Thus, sufficient element subdivisions in the global mesh and the local meshes are necessary in order to produce a converged solution.


it would be nice to have an option to make the chat box non-transparent as the text is a bit hard to read.
actually kinda relieved its releasing tomorrow now. I completely forgot it was launching this weekend!
Hello everyone! my name is Detris. I've been playing runescape since 2007, and private servers since 2014 or so. I haven't played private servers in a long time and i'm looking to really get back into the swing of it. I have helped with working on some servers before, but not much as i have no coding experience >.> but they all fell through as the dev team always ran into problems. I've met a lot of cool people through private servers in the past and hope to do the same on Arrav. I'm on east coast time and im currently in college; most likely you'll see me in the afternoon most days. I hope to see you all in Gilenor!


text:
package parallel

import (
	"sync"
	"runtime"
)

type Worker func(input interface{}) interface{}
type ResultHandler func(index int, result interface{})

type ParallelChannel struct {
	taskChan      chan interface{}
	outputChan    chan interface{}
	quitChan      chan bool
	parallelCount int
	running       bool
	worker        Worker
	resultHandler ResultHandler
	waitGroup     sync.WaitGroup
}

func NewParallelChannel(count int, worker Worker, handler ResultHandler) ParallelChannel {
	if count == 0 {
		count = 1
	} else if count < 0 {
		count = runtime.NumCPU()
	}
	return ParallelChannel{parallelCount: count, worker: worker, resultHandler: handler, running: false}
}

func (pc *ParallelChannel) AddTask(task interface{}) {
	pc.taskChan <- task
}

func (pc *ParallelChannel) TaskChan() chan interface{} {
	return pc.taskChan
}

func (pc *ParallelChannel) OutputChan() chan interface{} {
	return pc.outputChan
}

func (pc *ParallelChannel) Stop() {
	close(pc.taskChan)
	close(pc.quitChan)
	pc.waitGroup.Wait()
}

func (pc *ParallelChannel) ForceStop() {
	close(pc.taskChan)

	for i := 0; i < pc.parallelCount; i++ {
		pc.quitChan <- true
	}
	close(pc.quitChan)
	pc.waitGroup.Wait()
}

func (pc *ParallelChannel) Run() {
	if pc.running {
		return
	}
	pc.running = true

	pc.taskChan = make(chan interface{}, pc.parallelCount)
	pc.outputChan = make(chan interface{}, pc.parallelCount)
	pc.quitChan = make(chan bool, pc.parallelCount)

	pc.waitGroup.Add(1)
	pc.startHandleResults()

	go func() {
		var wg sync.WaitGroup

		for i := 0; i < pc.parallelCount; i++ {
			wg.Add(1)

			go func() {
			LOOP:
				for {
					select {
					case <-pc.quitChan: // 用户显式的退出
						wg.Done()
						break LOOP

					case task, ok := <-pc.taskChan:
						if !ok { // 没有任务后退出
							wg.Done()
							break LOOP
						}

						result := pc.worker(task)
						pc.outputChan <- result

					default:
						wg.Done()
						break LOOP
					}
				}
			}()
		}
		wg.Wait()

		close(pc.outputChan)

		pc.running = false
		pc.waitGroup.Done()
	}()
}

func (pc *ParallelChannel) startHandleResults() {
	go func() {
		index := 0
		for {
			result, ok := <-pc.outputChan
			if !ok {
				break
			}
			pc.resultHandler(index, result)
			index += 1
		}
	}()
}

redpajama_set_name: RedPajamaGithub


text:
\section{Introduction}
\vspace{-3mm}
Generative Adversarial Networks \citep{Goodfellow2014} (GANs) are a popular framework for modeling draws from complex distributions, demonstrating success in a wide variety of settings, for example image synthesis \citep{Radford2016, Karras2018} and language modeling \citep{Li_J2017}.
In the GAN setup, two agents, the \textit{discriminator} and the \textit{generator} (each usually a neural network), are pitted against each other.
The generator learns a mapping from an easy-to-sample latent space to a distribution in the data space, which ideally matches the real data's distribution.
At the same time, the discriminator aims to distinguish the generator's synthesized samples from the real data samples.
When trained successfully, GANs yield impressive results; in the image domain for example, synthesized images from GAN models are significantly sharper and more realistic than those of other classes of models \citep{Larsen2016}.
On the other hand, GAN training can be notoriously finicky.
One particularly well-known and common failure mode is \textit{mode collapse} \citep{Che2017, Srivastava2017}: instead of producing samples sufficiently representing the true data distribution, the generator maps the entire latent space to a limited subset of the real data space.

When mode collapse occurs, the generator does not ``converge,'' in the conventional sense, to a stationary distribution.
Rather, because the discriminator can easily learn to recognize a mode-collapsed set of samples and the generator is optimized to avoid the discriminator's detection, the two end up playing a never-ending game of cat and mouse: the generator meanders towards regions in the data space the discriminator thinks are real (likely near where the real data lie) while the discriminator chases after it.
Interestingly though, if generated samples are plotted through time (as in Figure \ref{fig:gen_oscillations}), it appears that the generator can revisit previously collapsed modes.
At first, this may seem odd.
The discriminator was ostensibly trained to recognize that mode in a previous iteration and did so well enough to push the generator away from generating those samples.
Why has the discriminator seemingly lost this ability?

\begin{figure}[t]
	\centering
	\begin{subfigure}[b]{.22\textwidth}
		\centering
		\includegraphics[width=0.55\linewidth]{ewc_imgs/osc1.png}
		\caption{Iteration 11960}
		\label{fig:3520}
	\end{subfigure}
	\begin{subfigure}[b]{.22\textwidth}
		\centering
		\includegraphics[width=0.55\linewidth]{ewc_imgs/osc2.png}
		\caption{Iteration 12000}
		\label{fig:3580}
	\end{subfigure}
	\begin{subfigure}[b]{.22\textwidth}
		\centering
		\includegraphics[width=0.55\linewidth]{ewc_imgs/osc3.png}
		\caption{Iteration 12160}
		\label{fig:3660}
	\end{subfigure}
	\begin{subfigure}[b]{.22\textwidth}
		\centering
		\includegraphics[width=0.55\linewidth]{ewc_imgs/osc4.png}
		\caption{Iteration 12380}
		\label{fig:3880}
	\end{subfigure}
	\caption{Real samples from a mixture of eight Gaussians in red; generated samples in blue. (a) The generator is mode-collapsed in the bottom right. (b) The discriminator learns to recognize the generator oversampling this region and pushes the generator away, so the generator gravitates toward a new mode. (c) The discriminator continues to chase the generator, causing the generator to move in a clockwise direction. (d) The generator eventually returns to the same mode as (a). Such oscillations are common while training a vanilla GAN. Best seen as a video: https://youtu.be/91a2gPWngo8.}
	\label{fig:gen_oscillations}
\end{figure}

We conjecture that this oscillation phenomenon is enabled by \textit{catastrophic forgetting} \citep{McCloskey1989, Ratcliff1990}: neural networks have a well-known tendency to forget how to complete old tasks while learning new ones.
In most GAN models, the discriminator is a binary classifier, with the two classes being the real data and the generator's outputs.
Implicit to the training of a standard classifier is the assumption that the data are drawn independently and identically distributed (i.i.d.).
Importantly, this assumption does \textit{not} hold true in GANs: the distribution of the generator class (and thus the discriminator's training data) evolves over time.
Moreover, these changes in the generator's distribution are adversarial, designed specifically to deteriorate discriminator performance on the fake class as much as possible.
Thus, the alternating training procedure of GANs in actuality corresponds to the discriminator learning tasks sequentially, where each task corresponds to recognizing samples from the generator at that particular point in time.
Without any measures to prevent catastrophic forgetting, the discriminator's ability to recognize fake samples from previous iterations will be clobbered by subsequent gradient updates, allowing a mode-collapsed generator to revisit old modes if training runs long enough.
Given this tendency, a collapsed generator can wander indefinitely without ever learning the true distribution.

With this perspective in mind, we cast training the GAN discriminator as a continual learning problem, leading to two main contributions.
$(\RN{1})$ While developing systems that learn tasks in a sequential manner without suffering from catastrophic forgetting has become a popular direction of research, current benchmarks have recently come under scrutiny as being  unrepresentative to the fundamental challenges of continual learning \citep{Farquhar2018}.
We argue that GAN training is a more realistic setting, and one that current methods tend to fail on.
$(\RN{2})$ Such a reframing of the GAN problem allows us to leverage relevant methods to better match the dynamics of training the min-max objective.
In particular, we build upon the recently proposed \textit{elastic weight consolidation} \citep{Kirkpatrick2017} and \textit{intelligent synapses} \citep{Zenke2017}.
By preserving the discriminator's ability to identify previous generator samples, this memory prevents the generator from simply revisiting past distributions.
Adapting the GAN training procedure to account for catastrophic forgetting provides an improvement in GAN performance for little computational cost and without the need to train additional networks.
Experiments on CelebA and CIFAR10 image generation and COCO Captions text generation show discriminator continual learning leads to better generations.

\section{Background: Catastrophic forgetting in GANs}
\vspace{-3mm}
Consider distribution $p_{\rm real}(\boldsymbol{x})$, from which we have data samples $\mathcal{D}^{\rm real}$.
Seeking a mechanism to draw samples from this distribution, we learn a mapping from an easy-to-sample latent distribution $p(\boldsymbol{z})$ to a data distribution $p_{\rm gen}(\boldsymbol{x})$, which we want to match $p_{\rm real}(\boldsymbol{x})$.
This mapping is parameterized as a neural network $G_{\boldsymbol{\phi}}(\boldsymbol{z})$ with parameters $\boldsymbol{\phi}$, termed the generator.
The synthesized data are drawn $\boldsymbol{x}=G_{\boldsymbol{\phi}}(\boldsymbol{z})$, with $\boldsymbol{z}\sim p(\boldsymbol{z})$.
The form of $p_{\rm gen}(\boldsymbol{x})$ is not explicitly assumed or learned; rather, we learn to draw samples from $p_{\rm gen}(\boldsymbol{x})$.

To provide feedback to $G_{\boldsymbol{\phi}}(\boldsymbol{z})$, we simultaneously learn a binary classifier that aims to distinguish synthesized samples $\mathcal{D}^{\rm gen}$ drawn from $p_{\rm gen}(\boldsymbol{x})$ from the true samples $\mathcal{D}^{\rm real}$.
We also parameterize this classifier as a neural network $D_{\boldsymbol{\theta}}(\boldsymbol{x}) \in [0,1]$ with parameters $\boldsymbol{\theta}$, with $D_{\boldsymbol{\theta}}(\boldsymbol{x})$ termed the discriminator.
By incentivizing the generator to fool the discriminator into thinking its generations are actually from the true data, we hope to learn $G_{\boldsymbol{\phi}}(\boldsymbol{z})$ such that $p_{\rm gen}(\boldsymbol{x})$ approaches $p_{\rm real}(\boldsymbol{x})$.

These two opposing goals for the generator and discriminator are usually formulated as the following min-max objective:

\begin{equation} \label{eqn:GAN_obj}
\min_{\boldsymbol{\phi}} \max_{\boldsymbol{\theta}} \mathcal{L}^{\rm GAN}(\boldsymbol{\theta}, \boldsymbol{\phi}) =
\mathbb{E}_{\boldsymbol{x} \sim p_{\rm real}(\boldsymbol{x})} [\log D_{\boldsymbol{\theta}}(\boldsymbol{x})] + \mathbb{E}_{\boldsymbol{z}\sim p(\boldsymbol{z})} [\log (1-D_{\boldsymbol{\theta}}(G_{\boldsymbol{\phi}}(\boldsymbol{z})))]
\end{equation}

At each iteration $t$, we sample from $p_{\rm gen}(\boldsymbol{x})$, yielding generated data $\mathcal{D}^{\rm gen}_{t}$.
These generated samples, along with samples from $\mathcal{D}^{\rm real}$, are then passed to the discriminator.
A gradient descent optimizer nudges $\boldsymbol{\theta}$ so that the discriminator takes a step towards maximizing $\mathcal{L}^{\rm GAN}(\boldsymbol{\theta}, \boldsymbol{\phi})$.
Parameters $\boldsymbol{\phi}$ are updated similarly, but to minimize $\mathcal{L}^{\rm GAN}(\boldsymbol{\theta}, \boldsymbol{\phi})$.
These updates to $\boldsymbol{\theta}$ and $\boldsymbol{\phi}$ take place in an alternating fashion.
The expectations are approximated using samples from the respective distributions, and therefore learning only requires observed samples $\mathcal{D}^{\rm real}$ and samples from $p_{\rm gen}(\boldsymbol{x})$.

The updates to $G_{\boldsymbol{\phi}}(\boldsymbol{z})$ mean that $p_{\rm gen}(\boldsymbol{x})$ changes as a function of $t$, perhaps substantially.
Consequently, samples $\{\mathcal{D}^{\rm gen}_{1}, ..., \mathcal{D}^{\rm gen}_{t}\}$ come from a sequence of different distributions.
At iteration $t$, only samples from $\mathcal{D}^{\rm gen}_{t}$ are available, as $G_{\boldsymbol{\phi}}(\boldsymbol{z})$ has changed, and saving previous instantiations of the generator or samples $\{\mathcal{D}^{\rm gen}_{1}, ..., \mathcal{D}^{\rm gen}_{t-1}\}$ can be prohibitive.
Thus, $D_{\boldsymbol{\theta}}(\boldsymbol{x})$ is typically only provided $\mathcal{D}^{\rm gen}_{t}$, so it only learns the most recent distribution, with complete disregard for previous $p_{\rm gen}(\boldsymbol{x})$.
Because of the catastrophic forgetting effect of neural networks, the ability of $D_{\boldsymbol{\theta}}(\boldsymbol{x})$ to recognize these previous distributions is eventually lost in the pursuit of maximizing $\mathcal{L}^{\rm GAN}(\boldsymbol{\theta}, \boldsymbol{\phi})$ with respect to \textit{only} $\mathcal{D}^{\rm gen}_{t}$.
This opens the possibility that the generator goes back to generating samples the discriminator had previously learned (and then forgot) to recognize, leading to unstable mode-collapsed oscillations that hamper GAN training (as in Figure \ref{fig:gen_oscillations}).
Recognizing this problem, we propose that the discriminator should be trained with the temporal component of $p_{\rm gen}(\boldsymbol{x})$ in mind.

\section{Method}
\vspace{-3mm}
\subsection{Classic Continual learning}
\vspace{-2mm}
Catastrophic forgetting has long been known to be a problem with neural networks trained on a series of tasks \citep{McCloskey1989, Ratcliff1990}.
While there are many approaches to addressing catastrophic forgetting, here we primarily focus on elastic weight consolidation (EWC) and intelligent synapses (IS).
These are meant to illustrate the potential of catastrophic forgetting mitigation to improve GAN learning, with the expectation that this opens up the possibility of other such methods to significantly improve GAN training, at low additional computational cost.

\vspace{-2mm}
\subsubsection{Elastic weight consolidation (EWC)}
\vspace{-2mm}
To derive the EWC loss, \cite{Kirkpatrick2017} frames training a model as finding the most probable values of the parameters $\boldsymbol{\theta}$ given the data $\mathcal{D}$.
For two tasks, the data are assumed partitioned into independent sets according to the task, and the posterior for Task $1$ is approximated as a Gaussian with mean centered on the optimal parameters for Task $1$ $\boldsymbol{\theta}^*_1$ and diagonal precision given by the diagonal of the Fisher information matrix $F_1$ at $\boldsymbol{\theta}^*_1$.
This gives the EWC loss the following form:
\begin{align}\label{eq:ewc}
\mathcal{L}(\boldsymbol{\theta}) = \mathcal{L}_2(\boldsymbol{\theta}) + \mathcal{L}^{\rm EWC}(\boldsymbol{\theta}),
~~~ \textrm{with}~~~
\mathcal{L}^{\rm EWC}(\boldsymbol{\theta})  \triangleq   \frac{\lambda}{2} \sum_i F_{1,i} (\theta_i - \theta_{1,i}^*)^2~,
\end{align}
where $\mathcal{L}_2(\boldsymbol{\theta}) =  \log p(\mathcal{D}_2|\boldsymbol{\theta})$ is the loss for Task $2$ individually, $\lambda$ is a hyperparameter representing the importance of Task $1$ relative to Task $2$, $F_{1,i} = \big(\frac{\partial \mathcal{L}_1(\boldsymbol{\theta})}{\partial \theta_i} \big|_{\boldsymbol{\theta}=\boldsymbol{\theta}_1^*})^2$, $i$ is the parameter index, and $\mathcal{L}(\boldsymbol{\theta})$ is the new loss to optimize while learning Task $2$.
Intuitively, the EWC loss prevents the model from straying too far away from the parameters important for Task $1$ while leaving less crucial parameters free to model Task $2$.
Subsequent tasks result in additional $\mathcal{L}^{\rm EWC}(\boldsymbol{\theta})$ terms added to the loss for each previous task.
By protecting the parameters deemed important for prior tasks, EWC as a regularization term allows a single neural network (assuming sufficient parameters and capacity) to learn new tasks in a sequential fashion, without forgetting how to perform previous tasks.

\vspace{-2mm}
\subsubsection{Intelligent synapses (IS)}
\vspace{-2mm}
While EWC makes a point estimate of how essential each parameter is at the conclusion of a task, IS \citep{Zenke2017} protects the parameters according to their importance along the task's entire training trajectory.
Termed synapses, each parameter $\theta_i$ of the neural network is awarded an \textit{importance measure} $\omega_{1,i}$ based on how much it reduced the loss while learning Task $1$.
Given a loss gradient $\gv(t) = \nabla_{\boldsymbol{\theta}} \mathcal{L} (\boldsymbol{\theta}) |_{\boldsymbol{\theta} = \boldsymbol{\theta}_t}$ at time $t$, the total change in loss during the training of Task $1$ then is the sum of differential changes in loss over the training trajectory.
With the assumption that parameters $\boldsymbol{\theta}$ are independent, we have:
\begin{equation}
\int_{t^0}^{t^1} \gv(t) d\boldsymbol{\theta} = \int_{t^{0}}^{t^{1}} \gv(t) \boldsymbol{\theta}' dt = \sum_i \int_{t^{0}}^{t^{1}} g_i(t) \theta_i' dt \triangleq -\sum_i \omega_{1,i}~,
\end{equation}
where $\boldsymbol{\theta}' = \frac{d \boldsymbol{\theta}}{dt}$ and $(t^{0}, t^{1})$ are the start and finish of Task 1, respectively.
Note the added negative sign, as importance is associated with parameters that decrease the loss.

The importance measure $\omega_{1,i}$ can now be used to introduce a regularization term that protects parameters important for Task 1 from large parameter updates, just as the Fisher information matrix diagonal terms $F_{1,i}$ were used in EWC.
This results in an IS loss very reminiscent in form\footnote{\cite{Zenke2017} instead consider $\Omega_{1,i} = \frac{\omega_{1,i}}{(\Delta_{1,i})^2 + \xi}$, where $\Delta_{1,i} = \theta_{1,i} - \theta_{0,i}$ and $\xi$ is a small number for numerical stability. We however found that the inclusion of $(\Delta_{1,i})^2$ can lead to the loss exploding and then collapsing as the number of tasks increases and so omit it. We also change the hyperparameter $c$ into $\frac{\lambda}{2}$.}:
\begin{align}\label{eq:is}
\mathcal{L}(\boldsymbol{\theta})
= \mathcal{L}_2(\boldsymbol{\theta}) + \mathcal{L}^{\rm IS}(\boldsymbol{\theta}),
~~~ \textrm{with}~~~
\mathcal{L}^{\rm IS}(\boldsymbol{\theta})  \triangleq  \frac{\lambda}{2} \sum_i \omega_{1,i} (\theta_i - \theta_{1,i}^*)^2~.
\end{align}

\subsection{GAN Continual learning}
\vspace{-2mm}
The traditional continual learning methods are designed for certain canonical benchmarks, commonly consisting of a small number of clearly defined tasks (e.g., classification datasets in sequence).
In GANs, the discriminator is trained on dataset $\mathcal{D}_t = \{\mathcal{D}^{\rm real}, \mathcal{D}^{\rm gen}_t\}$ at each iteration $t$.
However, because of the evolution of the generator, the distribution $p_{\rm gen}(x)$ from which $\mathcal{D}^{\rm gen}_t$ comes changes over time.
This violates the i.i.d. assumption of the order in which we present the discriminator data.
As such, we argue that different instances in time of the generator should be viewed as separate tasks.
Specifically, in the parlance of continual learning, the training data are to be regarded as $\mathcal{D} = \{(\mathcal{D}^{\rm real}, \mathcal{D}^{\rm gen}_1), (\mathcal{D}^{\rm real}, \mathcal{D}^{\rm gen}_2), ...\}$.
Thus motivated, we would like to apply continual learning methods to the discriminator, but doing so is not straightforward for the following reasons:

\hspace{-8mm}
\begin{minipage}{1.05\linewidth}
	\begin{itemize}
		\item \textbf{Definition of a task}: EWC and IS were originally proposed for discrete, well-defined tasks.
		For example, \cite{Kirkpatrick2017} applied EWC to a DQN \citep{Mnih2015} learning to play ten Atari games sequentially, with each game being a clear, independent task.
		For GAN, there is no such precise definition as to what constitutes a ``task,'' and as discriminators are not typically trained to convergence at every iteration, it is also unclear how long a task should be.
	\end{itemize}
\end{minipage}

\hspace{-8mm}
\begin{minipage}{1.05\linewidth}
	\begin{itemize}
		\item \textbf{Computational memory}: While Equations \ref{eq:ewc} and \ref{eq:is} are for two tasks, they can be extended to $K$ tasks by adding a term $\mathcal{L}^{\rm EWC}$ or $\mathcal{L}^{\rm IS}$ for each of the $K-1$ prior tasks.
		As each term $\mathcal{L}^{\rm EWC}$ or $\mathcal{L}^{\rm IS}$ requires saving both a historical reference term $\boldsymbol{\theta}_k^*$ and either $F_k$ or $\omegav_k$ (all of which are the same size as the model parameters $\boldsymbol{\theta}$) for each task $k$, employing these techniques naively quickly becomes impractical for bigger models when $K$ gets large, especially if $K$ is set to the number of training iterations $T$.
	\end{itemize}
\end{minipage}

\hspace{-8mm}
\begin{minipage}{1.05\linewidth}
	\begin{itemize}
		\item \textbf{Continual \textit{not} learning}: Early iterations of the discriminator are likely to be non-optimal, and without a forgetting mechanism, EWC and IS may forever lock the discriminator to a poor initialization.
		Additionally, the unconstrained addition of a large number of terms $\mathcal{L}^{\rm EWC}$ or $\mathcal{L}^{\rm IS}$ will cause the continual learning regularization term to grow unbounded, which can disincentivize any further changes in $\boldsymbol{\theta}$.
	\end{itemize}
\end{minipage}

To address these issues, we build upon the aforementioned continual learning techniques, and propose several changes.

\textbf{Number of tasks as a rate}: We choose the total number of tasks $K$ as a function of a constant rate $\alpha$, which denotes the number of iterations before the conclusion of a task, as opposed to arbitrarily dividing the GAN training iterations into some set number of segments.
Given $T$ training iterations, this means a rate $\alpha$ yields $K = \frac{T}{\alpha}$ tasks.

\textbf{Online Memory}: Seeking a way to avoid storing extra $\boldsymbol{\theta}_k^*$, $F_k$, or $\omegav_k$, we observe that the sum of two or more quadratic forms is another quadratic, which gives the classifier loss with continual learning the following form for the $(k+1)^{\rm th}$ task:
\begin{align} \label{eqn:EWC_no_discount}
\mathcal{L}(\boldsymbol{\theta})
= \mathcal{L}_{k+1}(\boldsymbol{\theta}) + \mathcal{L}^{\rm CL}(\boldsymbol{\theta}),
~~~ \textrm{with}~~~
\mathcal{L}^{\rm CL}(\boldsymbol{\theta})  \triangleq   \frac{\lambda}{2} \sum_i  S_{k,i} (\theta_{i} - \bar{\theta}_{k,i}^* )^2~,
\end{align}
where $\bar{\theta}_{k,i}^* = \frac{P_{k,i}}{S_{k,i}}$, $S_{k,i} = \sum_{\kappa=1}^k Q_{\kappa,i}$, $P_{k,i} = \sum_{\kappa=1}^k Q_{\kappa,i}\theta_{\kappa,i}^*$, and $Q_{\kappa,i}$ is either $F_{\kappa,i}$ or $\omega_{\kappa,i}$, depending on the method. We name models with EWC and IS augmentations EWC-GAN and IS-GAN, respectively.

\textbf{Controlled forgetting}: To provide a mechanism for forgetting earlier non-optimal versions of the discriminator and to keep $\mathcal{L}^{\rm CL}$ bounded, we add a discount factor $\gamma$: $S_{k,i} = \sum_{\kappa=1}^k \gamma^{k-\kappa} Q_{\kappa,i}$ and $P_{k,i} = \sum_{\kappa=1}^k \gamma^{k-\kappa} Q_{\kappa,i}\theta_{\kappa,i}^*$. Together, $\alpha$ and $\gamma$ determine how far into the past the discriminator remembers previous generator distributions, and $\lambda$ controls how important memory is relative to the discriminator loss.
Note, the terms $S_k$ and $P_k$ can be updated every $\alpha$ steps in an online fashion:
\begin{equation}
S_{k,i} = \gamma S_{k-1, i} + Q_{k,i}, \qquad P_{k,i} = \gamma P_{k-1, i} + Q_{k,i} \theta_{k,i}^*
\end{equation}
This allows the EWC or IS loss to be applied without necessitating storing either $Q_k$ or $\boldsymbol{\theta}_k^*$ for every task $k$, which would quickly become too costly to be practical.
Only a single variable to store a running average is required for each of $S_k$ and $P_k$, making this method space efficient.

Augmenting the discriminator with the continual learning loss, the GAN objective becomes:
\begin{equation}
\min_{\boldsymbol{\phi}} \max_{\boldsymbol{\theta}} \mathcal{L}^{\rm CL}(\boldsymbol{\theta}, \boldsymbol{\phi}) = \mathcal{L}^{\rm GAN}(\boldsymbol{\theta}, \boldsymbol{\phi}) - \mathcal{L}^{\rm CL}(\boldsymbol{\theta})
\end{equation}
Note that the training of the generator remains the same; full algorithms are in Appendix A.
Here we have shown two methods to mitigate catastrophic forgetting for the original GAN; however, the proposed framework is applicable to almost all of the wide range of GAN setups.

\section{Related work}
\vspace{-3mm}
\paragraph{Continual learning in GANs} There has been previous work investigating continual learning within the context of GANs.
Improved GAN \citep{Salimans2016} introduced historical averaging, which regularizes the model with a running average of parameters of the most recent iterations.
Simulated+Unsupervised training \citep{Shrivastava2017} proposed replacing half of each minibatch with previous generator samples during training of the discriminator, as a generated sample at any point in time should always be considered fake.
However, such an approach necessitates a historical buffer of samples and halves the number of current samples that can be considered.
Continual Learning GAN \citep{Seff2018} applied EWC to GAN, as we have, but used it in the context of the class-conditioned generator that learns classes sequentially, as opposed to all at once, as we propose.
\cite{ThangTung2018} independently reached a similar conclusion on catastrophic forgetting in GANs, but focused on gradient penalties and momentum on toy problems.
\vspace{-2mm}
\paragraph{Multiple network GANs} The heart of continual learning is distilling a network's knowledge through time into a single network, a temporal version of the ensemble described in \cite{Hinton2015}.
There have been several proposed models utilizing multiple generators \citep{Hoang2018, Ghosh2018} or multiple discriminators \citep{Durugkar2017, Neyshabur2017}, while Bayesian GAN \citep{Saatchi2017} considered distributions on the parameters of both networks, but these all do not consider time as the source of the ensemble.
Unrolled GAN \citep{Metz2017} considered multiple discriminators ``unrolled'' through time, which is similar to our method, as the continual learning losses also utilize historical instances of discriminators.
However, both EWC-GAN and IS-GAN preserve the important parameters for prior discriminator performance, as opposed to requiring backpropagation of generator samples through multiple networks, making them easier to implement and train.
\vspace{-2mm}
\paragraph{GAN convergence} While GAN convergence is not the focus of this paper, convergence does similarly avoid mode collapse, and there are a number of works on the topic \citep{Heusel2017, Unterthiner2018, Nagarajan2017, Mescheder2017}.
From the perspective of \cite{Heusel2017}, EWC or IS regularization in GAN can be viewed as achieving convergence by slowing the discriminator, but per parameter, as opposed to a slower global learning rate.

\section{Experiments}
\vspace{-3mm}
\subsection{Discriminator Catastrophic Forgetting}
\vspace{-3mm}
While Figure \ref{fig:gen_oscillations} implies catastrophic forgetting in a GAN discriminator, we can show this concretely.
To do so, we first train a DCGAN \citep{Radford2016} on the MNIST dataset.
Since the generator is capable of generating an arbitrary number of samples at any point, we can randomly draw 70000 samples to comprise a new, ``fake MNIST" dataset at any time.
By doing this at regular intervals, we create datasets $\{\mathcal{D}^{\rm gen}_{1}, ..., \mathcal{D}^{\rm gen}_{T}\}$ from $p_{\rm gen}(x)$ at times $1,...,T$.
Samples are shown in Appendix B.

Having previously generated a series of datasets during the training of a DCGAN, we now reinitialize the discriminator and train to convergence on each $\mathcal{D}^{\rm gen}_{t}$ in sequence.
Importantly, we do \textit{not} include samples from $\mathcal{D}^{\rm gen}_{<t}$ while fine-tuning on $\mathcal{D}^{\rm gen}_{t}$.
After fine-tuning on the train split of dataset $\mathcal{D}^{\rm gen}_{t}$, the percentage of generated examples correctly identified as fake by the discriminator is evaluated on the test splits of $\mathcal{D}^{\rm gen}_{\leq t}$, with and without EWC (Figure \ref{fig:acc_fake}).
The catastrophic forgetting effect of the discriminator trained with SGD is clear, with a steep drop-off in discriminating ability on $\mathcal{D}^{\rm gen}_{t-1}$ after fine-tuning on $\mathcal{D}^{\rm gen}_{t}$; this is unsurprising, as $p_{\rm gen}(x)$ has evolved specifically to deteriorate discriminator performance.
While there is still a dropoff with EWC, forgetting is less severe.

\begin{figure}[!t]
	\centering
	\includegraphics[width=0.9\linewidth]{disc_degradation.png}
	\caption{Each line represents the discriminator's test accuracy on the fake GAN datasets. Note the sharp decrease in the discriminator's ability to recognize previous fake samples upon fine-tuning on the next dataset using SGD (left). Forgetting still occurs with EWC (right), but is less severe.}
	\label{fig:acc_fake}
\end{figure}

While the training outlined above is not what is typical for GAN, we choose this set-up as it closely mirrors the continual learning literature.
With recent criticisms of some common continual learning benchmarks as either being too easy or missing the point of continual learning \citep{Farquhar2018}, we propose GAN as a new benchmark providing a more realistic setting.
From Figure \ref{fig:acc_fake}, it is clear that while EWC certainly helps, there is still much room to improve with new continual learning methods.
However, the merits of GAN as a continual learning benchmark go beyond difficulty.
While it is unclear why one would ever use a single model to classify successive random permutations of MNIST \citep{Goodfellow2013}, many real-world settings exist where the data distribution is slowly evolving.
For such models, we would like to be able to update the deployed model without forgetting previously learned performance, especially when data collection is expensive and thus done in bulk sometime before deployment.
For example, autonomous vehicles \citep{Huval2015} will eventually encounter unseen car models or obstacles, and automated screening systems at airport checkpoints \citep{Liang2018} will have to deal with evolving bags, passenger belongings, and threats.
In both cases, sustained effectiveness requires a way to appropriately and efficiently update the models for new data, or risk obsolescence leading to dangerous blindspots.

Many machine learning datasets represent singe-time snapshots of the data distribution, and current continual learning benchmarks fail to capture the slow drift of the real-world data.
The evolution of GAN synthesized samples represents an opportunity to generate an unlimited number of smoothly evolving datasets for such experiments.
We note that while the setup used here is for binary real/fake classification, one could also conceivably use a conditional GAN \citep{Mirza2014} to generate an evolving multi-class classification dataset.
We leave this exploration for future work.

\subsection{Mixture of eight Gaussians}
\vspace{-3mm}
We show results on a toy dataset consisting of a mixture of eight Gaussians, as in the example in Figure \ref{fig:gen_oscillations}.
Following the setup of \citep{Metz2017}, the real data are evenly distributed among eight 2-dimensional Gaussian distributions arranged in a circle of radius 2, each with covariance $0.02I$ (see Figure \ref{fig:GAN_v_t}).
We evaluate our model with Inception Score (ICP) \citep{Salimans2016}, which gives a rough measure of diversity and quality of samples; higher scores imply better performance, with the true data resulting in a score of around 7.870.
For this simple dataset, since we know the true data distribution, we also calculate the symmetric Kullback–Leibler divergence (Sym-KL); lower scores mean the generated samples are closer to the true data.
We show computation time, measured in numbers of training iterations per second (Iter/s), averaged over the full training of a model on a single Nvidia Titan X (Pascal) GPU.
Each model was run 10 times, with the mean and standard deviation of each performance metric at the end of 25K iterations reported in Table \ref{tab:8-Gauss}.

\begin{table}[!t]
	\caption{Iterations per second, inception score, and symmetric KL divergence comparison on a mixture of eight Gaussians.}
	\label{tab:8-Gauss}
	\centering
	\scalebox{0.85}{
	\begin{tabular}{lllllll}
		\toprule
		\multicolumn{4}{l}{Model}                  \\
		\cmidrule(r){1-4}
		Method & $\alpha$ & $\lambda$ & $\gamma$ & Iter/s $\uparrow$ & ICP $\uparrow$ & Sym-KL $\downarrow$ \\
		\midrule
		\midrule
		GAN & -  & - & - & 87.59 $\pm$ 1.45 & 2.835 $\pm$ 2.325 & 19.55 $\pm$ 3.07 \\
		GAN + $\ell_2$ weight & 1 & 0.01 & 0 &  & 5.968 $\pm$ 1.673 & 15.19 $\pm$ 2.67 \\
		GAN + historical avg. & 1 & 0.01 & 0.995 & & 7.305 $\pm$ 0.158 & 13.32 $\pm$ 0.88 \\
		GAN + SN & - & - & - & 49.70 $\pm$ 0.13 & 6.762 $\pm$ 2.024 & 13.37 $\pm$ 3.86 \\
		\midrule
		\midrule
		GAN + IS & 1000 & 100 & 0.8 & 42.26 $\pm$ 0.35 & 7.039 $\pm$ 0.294 & 15.10 $\pm$ 1.51 \\
		GAN + IS & 100 & 10 & 0.98 & 42.29 $\pm$ 0.10 & 7.500 $\pm$ 0.147 & 11.85 $\pm$ 0.92 \\
		GAN + IS & 10 & 100 & 0.99 & 41.07 $\pm$ 0.07 & 7.583 $\pm$ 0.242 & 11.88 $\pm$ 0.84 \\

		GAN + SN + IS & 10 & 100 & 0.99 & 25.69 $\pm$ 0.09 & 7.699 $\pm$ 0.048 & 11.10 $\pm$ 1.18 \\
		\midrule
		GAN + EWC & 1000 & 100 & 0.8 & 82.78 $\pm$ 1.55 & 7.480 $\pm$ 0.209 & 13.00 $\pm$ 1.55 \\
		GAN + EWC & 100 & 10 & 0.98 & 80.63 $\pm$ 0.39 & 7.488 $\pm$ 0.222 & 12.16 $\pm$ 1.64 \\
		GAN + EWC & 10 & 10 & 0.99 & 73.86 $\pm$ 0.16 & 7.670 $\pm$ 0.112 & 11.90 $\pm$ 0.76 \\

		GAN + SN + EWC & 10 & 10 & 0.99 & 44.68 $\pm$ 0.11 & 7.708 $\pm$ 0.057 &  11.48 $\pm$ 1.12 \\
		\bottomrule
	\end{tabular}
	}
\end{table}

The performance of EWC-GAN and IS-GAN were evaluated for a number of hyperparameter settings.
We compare our results against a vanilla GAN \citep{Goodfellow2014}, as well as a state-of-the-art GAN with spectral normalization (SN) \citep{Miyato2018} applied to the discriminator.
As spectral normalization augments the discriminator loss in a way different from continual learning, we can combine the two methods; this variant is also shown.

Note that a discounted version of discriminator historical averaging \citep{Salimans2016} can be recovered from the EWC and IS losses if the task rate $\alpha=1$ and $Q_{k,i} = 1$ for all $i$ and $k$, a poor approximation to both the Fisher information matrix diagonal and importance measure.
If we also set the historical reference term $\bar{\theta}_{k}^*$ and the discount factor $\gamma$ to zero, then the EWC and IS losses become $\ell_2$ weight regularization.
These two special cases are also included for comparison.

We observe that augmenting GAN models with EWC and IS consistently results in generators that better match the true distribution, both qualitatively and quantitatively, for a wide range of hyperparameter settings.
EWC-GAN and IS-GAN result in a better ICP and FID than $\ell_2$ weight regularization and discounted historical averaging, showing the value of prioritizing protecting important parameters, rather than all parameters equally.
EWC-GAN and IS-GAN also outperform a state-of-the-art method in SN-GAN.
In terms of training time, updating the EWC loss requires forward propagating a new minibatch through the discriminator and updating $S$ and $P$, but even if this is done at every step ($\alpha = 1$), the resulting algorithm is only slightly slower than SN-GAN.
Moreover, doing so is unnecessary, as higher values of $\alpha$ also provide strong performance for a much smaller time penalty.
Combining EWC with SN-GAN leads to even better results, showing that the two methods can complement each other.
IS-GAN can also be successfully combined with SN-GAN, but it is slower than EWC-GAN as it requires tracking the trajectory of parameters at each step.
Sample generation evolution over time is shown in Figure \ref{fig:GAN_v_t} of Appendix C.

\FloatBarrier
\subsection{Image generation of CelebA and CIFAR-10}
\vspace{-3mm}
Since EWC-GAN achieves similar performance to IS-GAN but at less computational expense, we focus on the former for experiments on two image datasets, CelebA and CIFAR-10.
Our EWC-GAN implementation is straightforward to add to any GAN model, so we augment various popular implementations.
Comparisons are made with the TTUR \citep{Heusel2017} variants\footnote{\url{https://github.com/bioinf-jku/TTUR}} of DCGAN \citep{Radford2016} and WGAN-GP \citep{Gulrajani2017}, as well as an implementation\footnote{\url{https://github.com/minhnhat93/tf-SNDCGAN}} of a spectral normalized \citep{Miyato2018} DCGAN (SN-DCGAN).
Without modifying the learning rate or model architecture, we show results with and without the EWC loss term added to the discriminator for each.
Performance is quantified with the Fr\'echet Inception Distance (FID) \citep{Heusel2017} for both datasets.
Since labels are available for CIFAR-10, we also report ICP for that dataset.
Best values are reported in Table \ref{tab:image_FID_ICP}, with samples in Appendix C.
In each model, we see improvement in both FID and ICP from the addition of EWC to the discriminator.

\begin{table}[!t]
	\caption{Fr\'echet Inception Distance and Inception Score on CelebA and CIFAR-10}
	\label{tab:image_FID_ICP}
	\centering
	\scalebox{0.87}{
	\begin{tabular}{lccc}
		\toprule
		& \multicolumn{1}{c}{CelebA} & \multicolumn{2}{c}{CIFAR-10} \\
		\cmidrule(lr){3-4}
		Method & FID $\downarrow$  & FID $\downarrow$ & ICP $\uparrow$ \\
		\midrule
		\midrule
		DCGAN & 12.52 & 41.44 & 6.97 $\pm$ 0.05 \\
		DCGAN + EWC & 10.92 & 34.84 & 7.10 $\pm$ 0.05 \\
		WGAN-GP & - & 30.23 & 7.09 $\pm$ 0.06 \\
		WGAN-GP + EWC & - & 29.67 & 7.44 $\pm$ 0.08 \\
		SN-DCGAN & - & 27.21 & 7.43 $\pm$ 0.10 \\
		SN-DCGAN + EWC & - & 25.51 & 7.58 $\pm$ 0.07 \\
		\bottomrule
	\end{tabular}
	}
\end{table}

\subsection{Text generation of COCO Captions}
\vspace{-3mm}
We also consider the text generation on the MS COCO Captions dataset \citep{Chen2015}, with the pre-processing in \cite{guo2017long}.
Quality of generated sentences is evaluated by BLEU score \citep{Papineni2002}.
Since BLEU-$b$ measures the overlap of $b$ consecutive words between the generated sentences and ground-truth references, higher BLEU scores indicate better fluency. Self BLEU uses the generated sentences themselves as references; lower values indicate higher diversity.

We apply EWC and IS to textGAN \citep{zhang2017adversarial}, a recently proposed model for text generation in which the discriminator uses feature matching to stabilize training.
This model's results (labeled ``EWC'' and ``IS'') are compared to a Maximum Likelihood Estimation (MLE) baseline, as well as several state-of-the-art methods: SeqGAN \citep{yu2017seqgan}, RankGAN \citep{lin2017adversarial}, GSGAN \citep{jang2016categorical} and LeakGAN \citep{guo2017long}.
Our variants of textGAN outperforms the vanilla textGAN for all BLEU scores (see Table \ref{tab:test_bleu}), indicating the effectiveness of addressing the forgetting issue for GAN training in text generation.
EWC/IS + textGAN also demonstrate a significant improvement compared with other methods, especially on BLEU-2 and 3.
Though our variants lag slightly behind LeakGAN on BLEU-4 and 5, their self BLEU scores (Table \ref{tab:self_bleu}) indicate it generates more diverse sentences. Sample sentence generations can be found in Appendix C.

\begin{table}
	\centering
	\small
	\begin{minipage}{1.\linewidth}
		\centering
		\caption{Test BLEU $\uparrow$ results on MS COCO\vspace{-2mm}}
		\label{tab:test_bleu}
		\begin{tabular}{lcccccccc} %
			\toprule

			{Method}& MLE & SeqGAN & RankGAN & GSGAN & LeakGAN & textGAN & EWC & IS\\
			\midrule
			BLEU-2 & 0.820 & 0.820 & 0.852 & 0.810 & 0.922 & 0.926 & 0.934 & 0.933\\
			BLEU-3 & 0.607 & 0.604 & 0.637 & 0.566 & 0.797 & 0.781 & 0.802 & 0.791\\
			BLEU-4 & 0.389 & 0.361 & 0.389 & 0.335 & 0.602 & 0.567 & 0.594 & 0.578\\
			BLEU-5 & 0.248 & 0.211 & 0.248 & 0.197 & 0.416 & 0.379 & 0.400 & 0.388\\
			\bottomrule
		\end{tabular}
		\label{table:cocotest}
	\end{minipage}
	\begin{minipage}{1.\linewidth}
	\vspace{5mm}
	\end{minipage}
	\begin{minipage}{1.\linewidth}
		\centering
		\caption{Self BLEU $\downarrow$ results on MS COCO\vspace{-2mm}}
		\label{tab:self_bleu}
		\begin{tabular}{lcccccccc} %
			\toprule

			{Method}& MLE & SeqGAN & RankGAN & GSGAN & LeakGAN & textGAN & EWC & IS\\
			\midrule
			BLEU-2 & 0.754 & 0.807 & 0.822 & 0.785 & 0.912 & 0.843 & 0.854 & 0.853\\
			BLEU-3 & 0.511 & 0.577 & 0.592 & 0.522 & 0.825 & 0.631 & 0.671 & 0.655\\
			BLEU-4 & 0.232 & 0.278 & 0.288 & 0.230 & 0.689 & 0.317 & 0.388 & 0.364\\
			\bottomrule
		\end{tabular}
		\label{table:cocoself}
	\end{minipage}
\end{table}

\section{Conclusion}
\vspace{-3mm}
We observe that the alternating training procedure of GAN models results in a continual learning problem for the discriminator, and training on only the most recent generations leads to consequences unaccounted for by most models.
As such, we propose augmenting the GAN training objective with a continual learning regularization term for the discriminator to prevent its parameters from moving too far away from values that were important for recognizing synthesized samples from previous training iterations.
Since the original EWC and IS losses were proposed for discrete tasks, we adapt them to the GAN setting.
Our implementation is simple to add to almost any variation of GAN learning, and we do so for a number of popular models, showing a gain in ICP and FID for CelebA and CIFAR-10, as well as BLEU scores for COCO Captions.
More importantly, we demonstrate that GAN and continual learning, two popular fields studied independently of each other, have the potential to benefit each other, as new continual learning methods stand to benefit GAN training, and GAN generated datasets provide new testing grounds for continual learning.

\FloatBarrier

\subsubsection*{Acknowledgments}
We would like to thank Chenyang Tao, Liqun Chen, Johnny Sigman, and Gregory Spell for insightful conversations, and Jianqiao Li for code and assistance setting up experiments.

redpajama_set_name: RedPajamaArXiv


text:
Tim Hortons Faces Petition, Counter-Boycott Over Pipeline Promo
Marek Slusarczyk /Wikimedia Commons
Canada's iconic coffee chain, Tim Hortons, ran into the perils of cross-promotion last week, after an online petition site spotted a series of in-house ads it was running for Enbridge's failing Northern Gateway pipeline proposal.
A couple of weeks after the ads began appearing on video screens at 1,500 Tim's locations across the country, SumOfUs mobilized more than 28,000 supporters to protest the campaign. "Now, when you buy your morning double double at Tim Hortons, you'll have to watch Enbridge's slick public relations ads about how great the tar sands are," wrote campaigner Angus Wong. "Enbridge is desperate to get support to build a massive tar sands and pipeline project from Alberta through British Columbia—and is spending millions to try and reach Canadians and change public opinion."
"Arg," tweeted internationally-renowned author Margaret Atwood in response. "You'll have to watch Enbridge's slick tar sands ads next time you're in @TimHortons." And within a day, the company replied. "We value your feedback and the Enbridge advertisements are no longer airing on Tims TV."
But that was just the end of the first act, as Alberta Wildrose Party leader Brian Jean and federal Defence Minister Jason Kenney, an Alberta Member of Parliament, joined a boycott over the decision to pull the ads. "I'm pretty sure Tims will correct their mistake quickly, but I don't think it's appropriate what they have done," Jean said. "I'll pick up my Tims coffee again when they decide to apologize for taking jabs at our industry, which is so important to Albertans."
(Last fall, before fossil fuel jobs began to crash due to low oil prices, Clean Energy Canada reported that clean energy created more direct employment in Canada than tar sands/oil sands operations.)
In the analysis of the Tim Hortons PR debacle, advertising and marketing CEO Eric Erickson said the Enbridge partnership was a mistake from the start. "Timmy's was probably best suited to not dive into that pool in the first place," he told CBC.
in Canada, Climate & Society, Climate Action / "Blockadia", Energy Politics, Fossil Fuels, Jobs & Training, Jurisdictions, Media, Messaging, & Public Opinion, Pipelines / Rail Transport, Tar Sands / Oil Sands
Global warming means double jeopardy for sea life

redpajama_set_name: RedPajamaCommonCrawl


It's Irish. A joyful juxtaposition of the morbid and the comic that celebrates murder ballads, the Black Death and life through death.
1. Isn't It Grand, Boys?
2. Won't You Come with Me?
In memory of Michael Younger.
"Happy Songs of Death" is a joyful juxtaposition of the morbid and the comic that celebrates murder ballads, the Black Death and starving to death. Celtic songs, American songs - and notably several of Marc Gunn's original compositions all affirm that death is just the other side of life! Artists through the centuries have found inspiration in these same themes. Join Marc Gunn in a fresh look at all things mortal.
This album began many years ago when I was introduced to the music of Italian musician Angelo Branduardi. He had a delightful little song called "La Pulce D'Acqua". One day, I decided to translate it and found out the title meant "the flea of the water". Intriguing, I thought. So I tried translating more and a wild idea came to me. This song was about the Black Death!
In fact, it wasn't, according to some Italian friends who actually understood the meaning of it. I was disappointed by that, but thought it would be cool to write an upbeat song about death. Then while chatting with Andrew McKee, my former bandmate with the Brobdingnagian Bards, the title arose--Happy Songs of Death. It took me four years or so for the album to come about, but here it is.
The songs are an upbeat mix of traditional folk songs and original songs about death. There are murder ballads, people rising from the dead, war, my own song inspired by the Black Death, and a general celebration of the dead.
I was so excited about this album that I had TWO CD covers designed for it. One by Ingrid Houwers who desiged by Irish Drinking Songs for Cat Lovers CDs and the other by Nikki O'Shea who designed my What Color Is Your Dragon? CD and the Bards Comic.
Love this CD! Anything Marc Gunn does is magicial and very creative! The song the Foggy Dew has an interesting twist, blues/jazz! What a concept with a traditional song! A song about the Black Death! Red, Red and Black is one of my favorite songs of the album. It not really seem like a song about a catastrophic event in history! It is very bouncy and "happy" sounding. Marc Gunn has talent and this cd showcases that talent. There are no words to describe the talent of this artist. But, if I had to use words, awesome, magically, creative, sincere, heart felt, and WOW!
This is (as weird as it may sound) a fun album I have been listening to it continuously since I purchased it (specifically for Johnny Jump up) and have yet to tire of any of it! Thanks Marc for recording these bloody fine songs, I will enjoy them for many a year to come.
A celebration of death in a funny and fun way, as you look at life.
I have been listening to this work for a while now and I have come to realize that Marc put a significant amount of research into this album. There are scores of Irish songs about death and I really think that this CD captures the best of them and they are played in Marc's original and memorable style along with the robust vocals that Marc has been given by God.
He truly outdid himself with this album. Well thought out, quality recording, and definitely well played and sung.
Crows, knives and dead men, they keep me company hearing this CD full of scary but funny stories, in a perfect irish style. All served with the beautiful voice of Marc Gunn and the particular sound of his instrument.
Three of these "happy songs" are originally composed by him in his unique style and going round the web you can even find an amusing video of "Won't you come with me" with a special appearance of Marc.
An easy and funny CD to listen to.
To me this album can be a bit double, since I always had this strange bound with death. Also lucky number 13 is in for the tracks on it. Love them all, eventough might have spooked Marc a bit with pointing some things out.


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This research project investigated the relationships between continuous assessment and students performances in biology. A case study of secondary school in Ado Local Government Area of Ekiti State.To carry out the study, three research questions were reviewed in line with the purpose of the study. The study is a descriptive research design, the population of the study consisted of all students in the five selected secondary schools. 150 students were used for the study. Simple random simply techniques was used to selected five secondary schools in Ado Local Governmet Area were used for the Studies. The data collected were analyzed using both descriptive statistics such as mean, standard deviation. The findings shows that: Continuous assessment enable the teacher to group his students in terms of achievement. Continuous assessment enable the teacher to determines student's grade. Continuous assessment is guidance oriented in the sense that it provides information. Recommendation were made which include that prospective teachers should be encouraged by government to participate in the intensive and vigorous training programmes. It is therefore recommended that the concept of continuous assessment, its modalities and techniques, must be made a core subject of institution responsible for training teachers. Also, teacher must be involved in intensive and regular in intensive and regular in –service programme such as induction courses, seminars and workshops. All school principals, headmasters and education inspectors must equally be involved. Such programmes will help to reinforce their skills positively.
Title page i
Approval page ii
Certification iii
Dedication iv
Acknowledgements v
Table of Contents vii
Abstract ix
Background to the Study 1
Statement of the study 9
Purpose of the Study 11
Research Question 13
Scope of the study 13
Concept of Continuous Assessment 14
Principles of Continuous Assessment 15
Systematic Nature of Continuous Assessment 17
Comprehensive Nature of Continuous Assessment 18
Cumulative Nature of Continuous Assessment 18
Guidance-oriented Nature of Continuous Assessment 18
Problems of Continuous Assessment 19
Comparison Between school Based Assessment and
External Examination 23
Advantage of Continuous Assessment 26
Importance of Continuous Assessment 28
CHAPTER THREE: RESEARCH METHODOLOGY Research Method 29
Research Design 29
Population of the study 29
Sample and Sampling Techniques 29
Research Instrument 30
Validity of the Instrument 31
Reliability of instrument 31
Administration of the Instrument 31
CHAPTER FOUR:PRESENTATION AND ANALYSIS OF DATA
Analysis of Research Questions 33
CHAPTER FIVE:
SUMMARY, CONCLUSION AND RECOMMENDATIONS
Summary of Findings 40
Conclusion 41
Recommendation 42
Background to the Study
Examinations and assessments are an integral component of our educational structure. They are conducted both formally and informally, at practically all levels of education and serve a variety of different functions. In the narrow sense, examinations and assessments may be thought of as having predominantly educational purposes. They may be used in order to:- Assess students' attainment at the end of a course or studyProgramme, evaluate diagnostically students' academic achievement, progressand/or learning difficulties, Evaluate students' aptitude, and possibly for the purpose of assigning them to different courses or teaching set.
The national policy on education (2004) defined Continuous assessment as a mechanism whereby the final grading of a student in the cognitive, affective and psychomotor domain takes account, in a systematic way of all is performances, during a given period of schooling.
Osuncem (2008) explain that Continuous Assessment is a system of assessment which take into account the total experience and achievement of students throughout their school carrier. Okpala, Onacha and Oyedeji. (1999) stated that Continuous assessment is a system of assessment which is carried out at a period of interval for the purpose of monitoring and improving the overall performance of students and of the teaching – learning environment. Continuous Assessment of student ensures assessment of their overall abilities and provides the basis for making appropriate educational attainment, placement and selection decision about them. However, such an Assessment according to National policy on education (2004) it involves the use of great variety of instruments.
In the context of this study, Continuous Assessment is a system of assessment which is carried out in Biology at pre-determines intervals for the purpose of monitoring and improving the overall performance of students.
The performance level of students in senior secondary schools in Continuous Assessment and SSCE (WAEC) are based on different grades. The pattern of grading in Nigeria SSCE is such that distinction grade is been represented by A, credit grade is represented by C4 and C6 the ordinary pass is represented by D7 and E8 while failure represented by F9. Adeyemi (2010) mention that is only the grades of distinction and credit that are required for Nigeria University Admission and candidate must have at least credit in five subjects in a related area including English language in order to qualify for admission. (JAMB) 2007.
Most of the examinations and assessments regularly conducted by teachers and schools on an 'internal' basis tend to fulfill one or more of these educational functions. Other examinations conducted 'externally' by the recognized examining boards and certain professional associations, likewise have a primary educational purpose: to assess the attainment of a student at the end of a course of study. The primary assignment of any school is to ensure effective teaching and learning of which assessment and examination in a form of evaluation, plays a significant role especially in decision making about school programme (Ijaya, 2002). Assessment in education in general is essential and is an ongoing process. It is the basis for all educational activities. Assessment information is a vital tool in the hands of the professional teacher. It directs guides and protects both the teacher and the learner at every stage of academics (Okwudire, 2005).
Continuous Assessment process is an important component of the National Policy on Education (FME, 2004). This system of assessment and evaluation is an innovation in the Nigerian educational system which has its genesis from the first national curriculum conference held in Ibadan in 1969. The outcome of that historic conference was published. High powered committee consisting of educationist, university academics, civil servants, industrialist and intellectuals from various works of
life was set up to study the report in depth and present recommendations based on itsformulation into a new National Policy on Education (FME, 1977). Continuous assessment is considered to be a systematic determination of the extent of students' progress part of it in school subjects includes attitude and values from first day of students in school to the last day (Abadina, 2001). According to Okoro (2002), continuous assessment is a system of evaluation in which students are given a large number of tests at regular intervals rather than a single final examination at the end of the course.
Continuous assessment is very important in all educational level in Nigeria. Ogar (2007) opined that through continuous assessment, progress of each student can be measured and monitored and appropriate counselling method can be put in place as the case may demand.
The National Policy on Education laid strong emphasis on the use of continuous assessment practice at the various levels of Nigeria educational system. Assessment is very important in teaching and learning process; through assessment feedback could be provided to both students and teachers (Dennis, 1988). We have both formative and summative assessment. Formative assessment is done during the course as the teaching and learning is in progress; this is called continuous assessment. The summative is done when the course is ended, this assessment is conducted at the end of the term or semester as the case may be. The two are important in biology assessment. According to Aladenusi (2010) assessment is a central element in the overall quality of teaching and learning.
Osokoya (2003) viewed continuous assessment as the method of finding out what the students have gained from learning activities. Assessment is an important element in biology teaching, Aina (2010) opines that teacher must regularly assess the effectiveness of the learning experiences which they have organized to enable the students achieve the earlier stated objectives. Abbas (2009) said ordinary continuous assessment is an ongoing test device which is comprehensive and include the three domains of learning. Continuous assessment must include project, internet assignment, use of community resources and many more; continuous assessments must not only be single assignment as it is being done by some teachers.
Biology is a core science subject and it is all encompassing as all other science related subjects are embedded in the subject. Biology which is the study of life is dynamic Aluko (2012) stated that Biological knowledge is dynamic which implies that sciences instructional practices of teachers should also be dynamic
Biology as an important subject in Nigerian senior secondary schools.National curriculum.The objective include
Adequate laboratory and field skill in Biology.
Meaningful and relevant knowledge in Biology
Ability to apply scientific knowledge to everybody life in matter of personal and community health and Agriculture.
Reasonable and Functional Scientific attitude.To achieve the above objectives, students are to be well grounded in the knowledge and skills in Biology.
Pearse (2002) defined performance as an action that is brought about or accomplished by effort, skill or courage.
The level of knowledge attained connotes performance in a school subject as symbolized by a score or grade on an achievement test. Lewis (1995) also stated that student performance connote the level of knowledge, skill or accomplishment in an area of Endeavour.
Okpala (2012) said that previous studies showed that student's performance in science is rather very low. This performance of students in senior secondary school certificate examination according to Adeola, Bolarinwa and Olafere (2012), has been a source of concern to government, parents, examination bodies, Teachers and Researchers.Such performance, Athson (2007) contended is dependent upon several factors among which are; instructional method the learning environment and the learner. Moemeke and Efed.(2010), asserted that excuses ranging from inadequate equipment and facilities, poor teacher pedagogical content knowledge, low job motivation, examination cantered evaluation and a host of others have been given as reasons for students poor performance in continuous Assessment and senior secondary school certificate examinations.
High academic performance is an indication of the attainment expected of senior secondary school student in Ado Local Government but unfortunately, it is observed by the researcher that the performance of the student in (SSCE, WEAC) is poor and does not show a direct relationship with the Continuous Assessment conducted in secondary schools in Ado Local Government. The performance shows that there is no consistence in the Continuous Assessment scores and the SSCE(WEAC). Whereas the continuous Assessment scores are high or at least fair, the SSC E (WEAC) score is low and poor. The result for this poor performance in external examination (SSCE/WAEC) could be attributing to so many factors which had earlier been stated by some researchers. In order to improve this situation, a lot of factors have to come to play in areas of senior secondary school examination (WAEC) and continuous assessment
In order to assist the students, teachers should improve the teaching learning situation for better and consistence performance of student in senior secondary schools in Ado Local Government area of Ekiti state, it is necessary to find out the relationship between Continuous Assessment and students performance in SSCE Biology.
Statement of the Problem
The academic performance of student in senior secondary school in both continuous assessments (internal) and senior secondary certificate Examination (external examination) depends on many factors such as the teachers, the teaching method, learning environment, inadequate equipment, Low job motivation and examination centered evaluation.
Quality education is evidenced by the performance of schools in external examination. Teachers of biology have the sole responsibility of making sure that they teach the student towards high academic performance and achievement in the performance of student in external examination (WAEC & NECO). The poor performance in external examination has been a major educational concern for the state government, ministry of education, parent and the subject teachers
It has been observed from the interaction of the researcher with some of the principles and record officers in the public secondary schools in Ado Local Government Area of Ekiti State that the performance of student in Continuous Assessment is always high while the performance is poor or low in external examinations. This showed that there is no direct relationship in the marks score by students in Continuous Assessment and external examinations Biology. Also the performance showed that there is no consistency of scores in continuous assessment and external examinations (SSCE/WAEC).
The researchers was motivated by the concerns of parents in ministry of education on the low performance in senior secondary school certificate examination as against the high performance in continuous assessment and therefore embark on this study to find out the relationship between continuous assessment and student performance in Biology in selected public secondary school in Ado Local Government Area of Ekiti State.
Purpose of the Study
The major purpose of the study is to determine the relationship between continuous assessment and student performance in SSCE Biology in Ado Local Government of Ekiti State.
Specifically the subject will seek to:
Determine the performance level of the students in SSCE continuous assessment in Biology
Find out the relationship between the performance of Continuous Assessment and SSCE Biology in Ado Local Government Area of Ekiti State.
Find out the problems confronting the effective implementation of continuous assessment in secondary schools
Significance of The Study
The findings of this study will be of benefit to teaching service commission of Ekiti State, Principals of secondary schools, teacher and students of Biology in Ekiti State. The study will provide information to the teaching services commission of Ekiti State about the relationship between the Continuous assessment in Biology. The commission could use the information to organize a seminars for Biology teachers for re-tanning in areas where they are deficiency.
The findings of the study will provide information to the secondary school principals in Ekiti State about area of teaching Biology where their teacher needs more orientations and where student need motivation. This could help them to encourage their teacher to attend seminars, workshop and long vacation courses for building their capacity
Schoolprincipals could also organize lecture time for students to encourage them for their performance in future.
The study will provide beneficial information to Biology teachers in Ekiti State. This information will help them to seek for avenues to improve their knowledge in the subject by looking for study leave with pay approval sponsor in order to go for retraining.
The findings of the study will be of benefit to Biology students in Ekiti State. When they discover their areas of weakness they would be encouraged to work and in order to have better grades in Biology and to maintain consistency in their Continuous Assessment and SSCE (WAEC) scores.
The study will answer the following research questions:
What is the level of performance of student in Continuous Assessment Biology?
Find out the relationship between the performance of students in Continuous Assessment and SSCE Biology in Ado Local Government Area of Ekiti State
What are the major problem confronting the effective implementation of continuous assessment
Scope of the Study
The study is restricted to the relationship between Continuous Assessment and student performance in SSCE Biology. It covered the public secondary schools in Ado Local Government Area. Also the study will look into the immediate past of Continuous Assessment and Senior Secondary School Certificate (WAEC) in Biology for comparison.
Information will be collected from the principals in public secondary schools, school record officers and (registrars) and the Biology teacher for senior secondary schools.
COMPARATIVE EFFECT OF STANDARD AND IMPROVISED MATERIALS ON STUDENTS ACADEMIC PERFORMANCE IN BASIC SCIENCE JUNIOR SECONDARY SCHOOLS


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LG Display's OLED TV Displays Certified, Verified as 'Flicker-Free'
OLED displays earn global recognition for being easy on the eye.
LG Display announced that its OLED TV displays have been recognized for maximum eye comfort by leading global independent inspection service TÜV Rheinland of Germany and UL, a global safety science company.
LG Display received an "Eye Comfort Display (OLED) - Low Blue Light, Flicker-Free" Certificate Mark from TÜV Rheinland and a "Flicker-Free Display (OLED)" Verified Mark from UL for all of its OLED TV displays, which range from 48 to 88 inches.
This is the first time in the industry that TV displays have received such certifications and marketing claim verification from prestigious organizations for being flicker-free.
When tested by TÜV Rheinland, LG Display's OLED TV displays were found to produce 0% non-visible flickers as OLED pixels emit light on their own.
Meanwhile, UL issued a "Flicker-Free Display (OLED)" UL Verified Mark of 1.3%, which is significantly below the standard of 9.6%. On the other hand, a typical LCD TV's LED backlight blinks very quickly as it emits light, so non-visible flickers are inevitable. Whether visible or not, flickers, or a very fast blinking on the screen, are known to cause headaches, eye fatigue, and poor eyesight upon prolonged exposure.
LG Display's OLED TV displays were previously recognized for their low blue light emissions. They were certified by TÜV Rheinland as emitting the lowest possible level of blue light, as well as being verified the highest blue light safety rating by UL, which is 50% lower than the standard set by the International Electrotechnical Commission. This blue light recognition combined with flicker-free status confirms the company's OLED displays as being exceptionally easy on the eye.
As the number of OLED TV brands has recently increased to 19, LG Display will encourage its global OLED TV customers and distributors to use the "Flicker-Free" and "Low Blue Light" certificate mark and verified mark as part of its efforts to further spread the popularity of OLED TVs.
"As people have more time to watch TV at home during the COVID-19 pandemic, eye comfort is becoming a more important factor to consider. LG Display will continuously offer the best experience to its customers through its OLED displays providing maximum eye comfort as well as unparalleled picture quality and futuristic design," said Dr. Chang-ho Oh, senior VP & head of the TV Business Unit at LG Display.


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HOME ≫ Researchers
● Takashi KUROSAKI (Director of the Institute of Economic Research and RRC, Hitotsubashi University)
● Kazuhiro KUMO (Professor, Institute of Economic Research, Hitotsubashi University) (Research Director of the RRC)
● Ichiro IWASAKI (Professor, Institute of Economic Research, Hitotsubashi University)
● Kazuko KAWAMOTO
● Yuko ADACHI (Professor, Faculty of Foreign Language, Sophia University)
● Andrey BELOV (Professor, Faculty of Economics, Fukui Prefectural University)
● Yukiko HAMA (Associate Professor, Graduate School of International Relations, University of Shizuoka)
● Koji HITACHI (Visiting fellow, Eurasia 21 Research Institute)
● Takeo HIDAI (Associate Professor, Faculty of Economics and Business, Wako University)
● Norio HORIE (Professor, Vice Director of the Center, CENTER for FAR EASTERN STUDIES (CFES), University of Toyama)
● Noriko IGARASHI (Professor, Faculty of International Studies, Tenri University)
● Kieko Kamitake (JSPS Research Fellow (PD), Faculty of Music, Tokyo University of the Arts)
● Masaaki KUBONIWA (Emeritus Professor, Hitotsubashi University)
● Kensaku KUMABE (President, Russia-Eurasia Research Institute)
● Mayu MICHIGAMI (Associate Professor, Faculty of Economics, Niigata University)
● Yasushi NAKAMURA (Professor, International Graduate School of Social Sciences, Yokohama National University)
● Yoshiaki NISHIMURA (Professor Emeritus, Hitotsubashi University)
● Koichi NOBE (Professor, School of Economics, Senshu University)
● Shigeki ONO (Professor, Faculty of Economics, Asahikawa University)
● Yoshisada SHIDA (Associate Senior Research Fellow, ERINA)
● Keiko SUGANUMA (Associate Professor, College of Bioresource sciences, Nihon University)
● Tadashi SUGIMOTO (Director, Euro-Asian Research Institute; Visiting professor, Far Eastern State University)
● Fumikazu SUGIURA (Professor, Faculty of Economics, Teikyo University)
● Taku SUZUKI (Associate Professor, Faculty of Economics, Teikyo University)
● Shin-ichiro TABATA (Professor, Slavic Reseach Center, Hokkaido University)
● Yuka TAKEDA (Associate Professor, Faculty of Arts and Science, Kyushu University)
● Masahiro TOKUNAGA (Professor, Faculty of Commerce, Kansai University)
● Yuko TSUDA (Research Fellow, Center for Research and Development Strategy Japan Science and Technology Agency)
● Akira UEGAKI (Professor, Faculty of Economics, Seinan Gakuin University)
● Kazuho YOKOGAWA (Associate Professor, Faculty of Economics, Kanagawa University)
● Eriko YOSHIDA
RRC Staffs and Post-graduate students


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What is CPRA and How Data Masking Can Help You Comply
Michael Cunningham - December 27, 2022
In November 2020, California voters passed Proposition 24, the California Privacy Rights Act (CPRA) (full text here). To be clear, the CPRA does not replace the existing California Consumer Privacy Act (CCPA). Instead, it's more accurate to describe the CPRA as an amendment of the CCPA, providing additional context and closing some of the ambiguity and loopholes found in the original.
The CPRA goes into effect on January 1, 2023. Let's review the CPRA and focus on what ERP customers need to know.
From CCPA to CPRA: What's the Difference?
The CPRA builds on the privacy framework put in place by the CCPA and is being implemented as an amendment. The new law states that it amends the existing provisions of the California Civil Code, also known as the CCPA, to add new provisions. It also includes establishing a dedicated enforcement agency for consumers, tripling fines against companies that violate kids' data privacy, and making it harder to weaken privacy laws in the future.
A couple of the more notable additions in the CPRA are that the law expands the right to opt-out of sharing of information and establishes new rights to limit how businesses use "sensitive personal information," a new term defined broadly to include, among other things: information about health conditions, genetic data, race and ethnicity, sexual orientation, precise geolocation, and more.
ERP applications already store an abundance of personally identifiable information, such as Social Security Numbers, driver's licenses, or passport numbers. This new data classification adds to the effort of identifying and classifying information necessary to remain in compliance.
How Pathlock Enables CCPA/CPRA Compliance
Successful organizations will invest in technologies that monitor user behavior around data access and usage. However, in today's multi-application environment, where data is distributed and shared between applications, it can be challenging to implement data privacy policies consistently.
This is where Pathlock becomes an essential tool for compliance. It expands native ERP logging capabilities to capture contextual details like what data was accessed, where it was accessed from, user IDs, IP addresses, pages accessed, actions performed, and more – information paramount for compliance reporting. This contextual information can play a key role in helping security and compliance teams orchestrate policies controlling access to sensitive data.
Enhance CPRA Compliance with Pathlock's Data Masking
Pathlock's dynamic Data Masking module allows you to dynamically mask and anonymize data at the field level and the point of access, allowing you to easily enforce data governance policies beyond simple role-based controls. In addition, Pathlock offers a cross-application solution that enables you to enforce policies across multiple applications using a single interface.
The module's dynamic masking capability provides organizations with fine-grained controls that can mask sensitive data fields based on the user and the context of the access, such as location, IP address, time, data sensitivity, and more. By implementing a full or partial mask to a data record, Pathlock minimizes the risk of a data breach and fulfills privacy mandates imposed by regulatory acts like CRPA.
Pathlock goes a step further to also provide granular controls like click-to-view and in-line multi-factor authentication at the field and transaction level. This allows you to grant access to essential sensitive data while ensuring that the access is logged. Security teams can also use this feature to set up alerts.
Cross-App Access Control is Key
In summary, the California Privacy Rights Act is a significant development in data security and privacy law that will have implications for businesses operating and doing business in California. Compliance with the act will require companies to implement appropriate measures to protect personal data, such as data masking, and to provide additional rights to consumers.
Businesses will also need to look at their entire application landscape as a whole and avoid using siloed app-specific solutions. Not only will it help implement policies consistently, but it will also simplify the audit process. With proactive controls in place, businesses can avoid risking exposure of sensitive data, implement fine-grained policies, provide a clear audit trail, and keep up with CRPA and future regulations.
Talk to our compliance experts to learn how you can use data masking to enhance your CPRA compliance.
December 16, 2022 3 Key SAP Cloud Security Trends from the SAPins...
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Catskill Eats is a news website covering the Catskill Mountain region of upstate New York. Founded in 2015, it publishes stories about farms, restaurants and food and craft beverage producers, with an emphasis on local growers, restaurateurs and artisans. Its coverage area encompasses five counties: Sullivan, Ulster, Delaware, Greene and Schoharie.
All original editorial text and photos appearing here are fully copyright protected and may not be reproduced without written permission. Photos reprinted from other sources are credited to their original source.
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Read more about Catskill Eats' mission. | Read our most popular stories.
Join in on Facebook, Twitter & Instagram. | Email tips and feedback.
Catskill Eats LLC is owned and operated by Jennifer Strom, a veteran journalist whose 30-year career spans food writing, investigative reporting and management in traditional, alternative and online media. Her projects include creating and launching the Watershed Post's print and online Catskills Food Guide; covering Manhattan restaurants for news site/print magazine The Lo-Down; and overseeing restaurant and agriculture coverage as managing editor at North Carolina's award-winning Indy Week. Strom lives in Delaware County and buys most of her groceries from Catskill farmers. She adores restaurants of all kinds, from dive bars to five stars.
Comments are moderated at the sole discretion of Catskill Eats. Positive, productive contributions are strongly encouraged. Personal attacks, self-promotional rants and other non-constructive comments will not be published.
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Google I/O has already brought a number of exciting new announcements from the Mountain View, California-based tech giant. While Day 1 of the conference saw Google come up with a number of interesting hardware and software-related announcements, one of the most eagerly-anticipated was the one regarding Google's virtual reality platform, 'Daydream'. Of course, with just about all tech companies worth their salt betting big on VR, one could not possibly have expected Google to stay far behind, but now that the company has made its plans regarding Daydream official, many Android OEMs are expected to latch onto the platform and announce their entry into the sector that's expected to be the next big thing in consumer technology going forward.
That being the case, China's Huawei Technologies has now announced its intention to embrace the new platform for its maiden foray into the exciting new world of virtual reality. The company on Wednesday came out with a press release to the effect, saying that it is "looking forward to working with Google and other global industry leaders to build an ecosystem and bring rich and compelling Daydream experiences to users through transformative innovations in VR devices, applications and content". The company has also revealed that it plans to unveil VR devices based on Daydream by the end of this year. The devices will generally be powered by HiSilicon Kirin chipsets that are designed in-house by the Chinese telecom giant, but curiously, the company also says that it will be using high-end Qualcomm chips in some of those devices.
For all those who're yet to get up to speed with everything that has been going on at the Shoreline Amphitheatre in Mountain View, Daydream is the search giant's all-new virtual reality platform that will look to standardize mobile-based VR experience. The platform encompasses both the hardware and software aspects of VR, and the company expects Daydream-based VR headsets and controllers to be ready for primetime in a few weeks' time. Either way, Huawei may be one of the very first companies to have officially announced its intention of joining the Daydream bandwagon, but it certainly isn't the first one. Samsung, LG, Asus, HTC and a whole host of other companies have already jumped on the Daydream bandwagon, and more are expected to do so in the near future.


Crafting With Craig: Hunkydory DT project.
This gift voucher envelope has been made using Hunkydorys latest collection - "Window to the heart" collection.
A magical christmas topper sheet.
5mm ivory satin ribbon - American Crafts.