Wait no longer. The much anticipated Uno WiFi Rev 2 from Arduino is on sale at Mouser Electronics. The first AVR-based, natively enabled Internet of Things (IoT) board from Arduino, the Uno WiFi Rev 2 board integrates an 8-bit microcontroller, Wi-Fi module, sensors, and hardware-based security in the familiar Uno Rev 3 form factor. The new board addresses the wireless connectivity and low power demands of the growing IoT market.
The board is based on the new Microchip Technology ATmega4809 megaAVRmicrocontroller (MCU).
This MCU offers Core Independent Peripherals (CIPs) and an integrated high-speed analog-to-digital converter (ADC) with voltage reference for faster conversion of analogue signals.
It also provides more memory than on previous Uno boards, boasting 48kBytes of flash and 6kBytes of RAM, plus three UARTs to enable communication with more than one RF module.
The Uno WiFi Rev 2 includes a u-blox NINA-W13 module with integrated TCP/IP protocol stack to provide 802.11b/g/n Wi-Fi connectivity enabling access to a Wi-Fi network or for use as an access point.
It features support for over-the-air (OTA) programming for transfer of Arduino sketches or Wi-Fi firmware.
Wireless security is provided by a Microchip ECC608 CryptoAuthentication IC, which combines hardware-based key storage with hardware cryptographic accelerators to implement authentication and encryption protocols.
Other features include onboard debugging, inertial measurement unit (IMU) with 3-axis accelerometer and 3-axis gyroscope, 14 digital input/output pins (six PWM outputs), and six analogue inputs.
Designed to ease deployment of designs that need Wi-Fi connectivity, the Arduino Uno WiFi Rev 2 is ideal for prototyping IoT applications such as smart agriculture, smart home, motor control, automation, wearables, and other open source projects.


M.Pharm. in Quality Assurance is a 2- year postgraduate Pharmacy course. Quality assurance comprises a planned and systematic set of activities applied to fulfil quality necessities for a product or service. Candidates enrolled to the course are taught the essential components of quality assurance programs, such as benchmarks, goals, leadership and inspiration. The course covers the latest industry trends, standards like ISO and Six Sigma, and the importance of quality assurance in the manufacture of food, service and pharmaceuticals.
The duration of the course is 2 years spread over 3 semesters. Quality Assurance is a vital component of the pharmaceutical industry, performed with the aim of providing superior quality end products. The quality of pharmaceuticals is a global topic of concern, and drugs need to be marked as safe. Quality control has the purpose of ensuring that the grade of products is excellent and the processes employed are hygienic and viable. This department is responsible for checking all aspects surrounding a product. Quality Assurance further checks the products which have been approved by the Quality Control department.
The sales and marketing job function absorbs the maximum number of pharmacy graduates. My class of B.Pharm (batch of 2004 – 2008) consisted of 90 students. Approximately 40 – 50 students started in the sales domain (as Medical Representative aka MR) after graduation.
The number of jobs available in the sales function is plenty. The salary structure is also decent. More importantly, on good performance the incentives are really lucrative. The job is challenging of course, but rewarding as well.
You have to travel a lot, and you do get chances to travel to new places in India and abroad (sponsored by employers). The pressure of achieving targets is obviously there, but the targets are quite achievable most of the time, at least according to my batch mates, juniors and seniors.
If you are dynamic, pro-active, confident (go-getter attitude) and like talking, negotiating, travelling and making money – this role is definitely for you.
Some people think that Medical agent is just any sales job – Absolutely Wrong. You have to advise the Medical Doctors about the pharmacological effects of the drug, their optimum doses, indications for specific diseases, side-effects and contraindications. So, your subject knowledge and communication skills need to be on par.
You always need to be updated with the current market knowledge, scientific research and many other things. People might also think that even B.Sc. graduates can also do MR jobs – true.
But, if you are from B.Pharm background, then you will have various advantages during your job duties. You can get promoted from Field Officer to Area Manager level within 3 years (on average it takes 2 to 5 years). You can also move into the Product Management Team (PMT) with 4 – 7 years of experience under your belt.
If the candidates get job as a Medical Representative after B.Pharm, then they can expect to earn something between 14,000 and 25,000 Rupees per month. With experience and promotions, you can end up earning between 25K and 32K per month, after 3 years. If you switch to PMT or do an MBA after 3 years, you can get salary up to 40K to 60K per month (again excluding incentives). So monetarily, this is the most rewarding job role.


text:
UNICRI and INTERPOL launch public opinion survey on AI in law enforcement
Your voice matters! Take part in the survey by 28 February 2023
The Hague, 15 November 2022. The United Nations Interregional Crime and Justice Research Institute (UNICRI), through its Centre for Artificial Intelligence (AI) and Robotics, and the International Criminal Police Organization (INTERPOL) Innovation Centre, are conducting a public survey on the use of artificial intelligence (AI) in law enforcement.
The survey is part of UNICRI and INTERPOL's initiative to develop a Toolkit for Responsible AI Innovation in Law Enforcement. It aims to capture public concerns and views, and ensure that they are reflected in the two organizations' work to shape the guidance being provided to law enforcement on responsible AI.
"AI in law enforcement is a sensitive topic that offers both opportunities and challenges. We hope the public will express their views and help us shape the guidelines for responsible AI innovation in law enforcement," said Irakli Beridze, Head of UNICRI's Centre for Artificial Intelligence.
To encourage global participation and capture a wide range of views, the survey is open to the public and available in the six official languages of the United Nations: Arabic, Chinese, English, French, Russian and Spanish.
The survey began on 15 November 2022 and will run until 28 February 2023. It can also be accessed here. It is funded by the European Commission's Directorate-General for Migration and Home Affairs (DG HOME).
For more information contact the UNICRI AI team at aicentre.unicri@un.org or click here.
Artificial intelligence (AI) Law enforcement Toolkit for Responsible Artificial Intelligence Innovation in Law Enforcement Security through Research, Technology and Innovation Goal 16 16: Peace, justice and strong institutions
Phnom Penh, Cambodia - 2023-01-24
Uzbekistan - 2023-01-19
The Toolkit for Responsible Artificial Intelligence Innovation in Law Enforcement
UNICRI Centre for Artificial Intelligence and Robotics celebrates 5th anniversary
Artificial Intelligence and Robotics for Law Enforcement
Voices from Mali: Malian youth speak up to prevent violent extremism
Video: The nexus between transnational organized crime and terrorism

redpajama_set_name: RedPajamaCommonCrawl


The ANC lodged a formal complaint with the South African Human Rights Commission (SAHRC) on Wednesday over a tweet by a senior Democratic Alliance (DA) leader, which they say was racist.
"The chief whip of the African National Congress in Parliament, Stone Sizani, this afternoon lodged a formal complaint with the South African Human Rights Commission against individuals or institutions whose public conduct and utterances have sought to undermine the dignity and rights of over 11-million voters who elected the ANC into office on May 7 2014," the ANC said early on Wednesday evening.
When asked to respond to the ANC's move to lodge a complaint, DA national spokesperson Phumzile van Damme said: "Mike has admitted he was wrong.
"He has agreed that it was ill-considered, offensive and deleted it," she said.
The tweet was deleted soon after it was posted given the unhappiness it created, but it gained traction through screengrabs and retweets. Waters sent out a brief tweet in apology in the early hours of Tuesday after sustained pressure from Twitter users.
Hi all. If I offended anyone in any way I apologise.
The party's new leadership in Parliament were slow to react to the outrage.
Later on Tuesday, the DA's new leader in Parliament Mmusi Maimane told the SABC that the DA had accepted the apology and would be reviewing its social media policy in response to the uproar.
But the Mail & Guardian has seen a copy of the party's existing social media policy, which has been in circulation for some time within the party, which prohibits the sort of tweet sent out by Waters. It is unclear how additions to the existing social media policy would help matters.
While the DA has been slow to counteract the damage caused by Waters's tweet, the ANC has reacted rapidly, with new party spokesperson Zizi Kodwa capitalising on the misstep by calling it an insult to black South Africans.
The party in Parliament has now taken the matter one step further by approaching the SAHRC.
The ANC said the decision to lodge a complaint with the SAHRC was not taken lightly but settled on to combat "persisting racism in the country".
It also "took into consideration the ongoing attacks against, as well as the undermining of the intelligence of the majority of South Africans who exercised their constitutional right in this year's election from influential institutions and figures".
The ill-conceived tweet followed an uproar over a cartoon published by Eyewitness News last week showing ANC ministers and those who voted for them as clowns, and referring to voters as "poephols".


TSA Pre-Check, I'm a fan!
For a while now, I've been tempted to invest in TSA Pre-Check. I kept putting it off or thinking I'm fine without it. But then, I saw this article come out. Immediately my nerves increased. I already had issues with TSA (specifically Atlanta). I didn't want there to be more issues! That day I filled … Continue reading TSA Pre-Check, I'm a fan!
My "Love" Letter to the College Diabetes Network, CDN.
disclaimer- I didn't check for errors- my apologizes Currently- I'm in one of my favorite settings- a coffee shop watching the evening stroll by. Nothing like tea for LSAT Studying and finishing up a blog post. Break from LSAT studying- count em- less than two weeks before test day- I can only keep powering through … Continue reading My "Love" Letter to the College Diabetes Network, CDN.


We just learned about the Dermis.
Another part of the skin is the Hypodermis, also called the Subcutaneous tissue.
This is the skin below the Dermis, which is below the Epidermis.
The body mostly has fat cells in the hypodermis, but it also has white blood cells and blood vessels.
There is also something called deep fascia or connective tissue that connects your muscles to your skin.


Rheumatic fever was the single biggest cause of valve disease before widespread availability of good antibiotics.
Myxomatous degeneration: Age-related degeneration - It happens most often in elderly patients and commonly affects the mitral valve.
Calcific degeneration: A buildup of calcium on the aortic or mitral valves due to age or other reasons causes thickened valves which prevents them from closing well.
Congenital (genetic) disease: Irregularly-shaped valves that people are born with.
Weight loss medicines such as fen-phen and Redux: These can accelerate valvular damage.
Endocarditis: An infection in the lining of the heart's walls and valves. Sometimes infections can destroy the valves.
Coronary artery disease: This can cause functional limitations in the parts of the heart muscle which support the heart valves.
Hypertension: Can lead to degeneration (wear and tear injuries).


An iconic living room to New York's innovators and elite, Bar 44 hosts creators in media, publishing, fashion and tech. In the evening, the lounge becomes a vibrant social scene that is centered around a 20 foot fireplace and fueled by the innovative spirit of craft cocktails and bright, seasonal small plates. Lead by renowned bar tending team, Bar 44 is defined by its ability to reinvent timeless classics and its power to establish new and unique experiences.


Maleek Berry drops a fresh video for his song "Eko Miami" featuring UK artist Geko taken from his debut EP "Last Daze Of Summer".
The artistic video was directed by Tshisz Nelson for JM Films. Maleek Berry is ready to set the tone again with this colourful video.


When you attempt to turn on BitLocker for the first time on the drive that Windows is installed on, you might receive an error message stating that your basic input/output system (BIOS) must be updated before you can use BitLocker. This is because the computer was manufactured with the Trusted Platform Module (TPM) security hardware (a special microchip in some newer computers that supports advanced security features), version 1.2 or higher, and your computer's BIOS needs an update to work properly with TPM.
Procedures for updating the BIOS vary depending on the manufacturer. Check the information that came with your computer or go to the computer manufacturer's website. For more information about BIOS, see BIOS: frequently asked questions.
If you've already turned on BitLocker and need to update the BIOS for another reason, such as solving a hardware compatibility problem, temporarily suspend BitLocker before updating the BIOS, and then resume BitLocker when the update is complete. This prevents BitLocker from locking the drive Windows is installed on when you restart the computer after the update.
Updating the BIOS is an advanced procedure, and if an error occurs, your computer could stop working. Be sure to follow the manufacturer's instructions exactly.
Assistive technology software that runs on Windows, such as screen reading software, cannot read BitLocker startup screens because they are displayed during BIOS startup and before Windows runs. This includes screens used when you type a PIN or recovery key, and any BitLocker error messages.


text:
\section{Introduction}
Accurate sensing of the environment is crucial for the survival of biological organisms. Bacteria, as well as animal chemoreceptor cells, can sense certain chemicals in their chemical environment with high precision, in some cases near the single-molecule detection limit \cite{BP,mao}.
The effect of the extracellular stimulus on the cell is mediated by the signal transduction pathway - a complex biochemical reaction network. The pathway is based on a sequence of transduction steps, with each subsequent step being effected by the chemical product of the previous step. The first step typically consists in the activation of a receptor protein in the cell membrane by the external stimulus, which results in an ion influx or in the production of a second messenger chemical. This leads to the activation of subsequent steps within the cell. Through molecular feedback loops, the product of a given transduction step may regulate its own production, or influence earlier (upstream) steps of the pathway. As signal transduction pathways are inherently noisy \cite{ladbury}, faithful transmission of information through the pathways requires amplification and/or adaptation. This is often accomplished through positive (amplification) and negative (adaptation) feedback built into the reaction network.

Recent years have brought the use of information-based measures to characterize the reliability of biological signal transduction \cite{tosto,cheong,bialek,tenwoldepre}. Such measures explicitly evaluate the amount of information about the stimulus that it is trasmitted through the signaling pathway. Mutual information between the stimulus and the pathway output (i.e., the product of the final transduction step) was evaluated in, e.g.\cite{cheong,bialek} , to assess the precision with which stationary stimuli of different strengths can be distinguished. In many signaling scenarios, however, it is important to faithfully transduce the temporal variations of the input stimulus, which encode biologically important information. Some recent studies have evaluated the transduction reliability for time-varying signals by computing the information transmission rate. In \cite{tenwoldepre}, prototypical signaling pathways with feedback were represented by coupled Langevin equations with additive Gaussian noise, and thefrequency-dependent  gain-to-noise ratio was computed. In the Gaussian noise approximation, this gain (together with the input power spectrum) determines the the mutual information between the time courses of the stimulus and of the output.  Some of the stochasticity within a signaling pathway, however, arises directly from the inherent stochastic dynamics of the transduction components, and cannot always betreated as additive noise; in such a case, the information calculation cannot be reduced to the evaluation of the gain-to-noise ratio.

In this work, we carry out a perurbative computation of information transfer through a simple prototypical signaling module with biochemical feedback. The module switches stochastically between two states, with switching rates governed by the stimulus (i.e., input) and by the product (i.e., output). No additive external noise is assumed. Such autoregulated stochastic modules arise within various signal transduction and gene regulation pathways (see Sec. 2).  In a previous investigation \cite{boro} negative feedback, in this module, was shown to decrease the signal-to-noise ratio (SNR) at the output, but at the same time to increase the spectral range of the response - thus yielding no obvious expectation on how feedback overall affectsinformation transmission through the module. Here, we address this question by directly quantifying the information that is gained about the external stimulus from following the module output. In order to achieve this we introduce a novel perturbative approach on a conveniently defined relative entropy for stochastic point processes \cite{Hanggi}. This information gain is well-defined for a single stimulus trajectory (i.e., it requires no averaging over stimuli as in mutual-information-based measures) \cite{Hanggi}. We obtain an explicit analytical result valid to first order in feedback strength and to second order in the strength of input. Surprisingly, the total information gained during a long time interval is found to be proportional to the total number of state-switching events, with no further dependence on the feedback strength or on the spectral distribution of the input. We compare this result to previous investigations of information transfer through some related information channels.

\section{ A two-state signaling  module with feedback}
We consider a simple signaling module based on a single protein that switches between two conformational states. These may correspond to the open and closed state of an ion channel, or to the active/inactive states of an enzyme within a larger signaling network.
We introduce the module by referring to the example of a calcium ion channel that is autoregulated by calcium-mediated feedback. When the channel is open, calcium ions flow from the extracellular space into the cell. This leads to a fast increase of the free calcium concentration in the immediate vicinity of the channel (the increase is localized as calcium buffering in the cytoplasm leads to the formation of a calcium microdomain \cite{parek08}). For certain types of calcium channels (such as the voltage-activated L-type channels \cite{pete99} or the cyclic-nucleotide-gated (CNG) channels \cite{brad05,reidl}), the cytoplasmic calcium can inactivate the channel when it binds to the channel/calmodulin complex. This implements an autoregulatory feedback loop that shortens the response to the external gating signal, and thus helps to faithfully transduce fast signal variations. Similar autoregulatory loops, in which the product of a particular step in the pathway downregulates its own production, arise in numerous signal transduction and gene regulation networks \cite{rose02}.

The stochastic switching of the channel state is governed by the opening rate (assumed to depend on the extracellular stimulus gating the channel) and the closing rate. The negative autoregulation may be effected through a calcium-dependent increase of the closing rate or decrease of the opening rate. For the CNG channels that motivated us in this study, electrophysiological data indicates that the binding of calcium to the channel/calmodulin complex increases the closing rate. We consequently make only the closing rate depend on the calcium concentration.

The signaling module is shown schematically in Fig. 1.  When the channel is in the open state, ions flow into the microdomain at a fixed rate J.
 Once inside the microdomain, the ions are cleared out through ion pumps or exchangers in the membrane, as well as by diffusion within the cytoplasm; we assume a first-order clearance kinetics with rate constant  $\lambda$ (see Fig. 1).
The dynamics of the concentration c of ions in the cell compartment is given by the following equation:
\begin{equation}
\label{raw}
\frac{dc(t)}{dt}=\frac{J}{\Delta}S(t) -\lambda c(t),
\end{equation}
where $J$ is the flow  of ions entering the cell through the open channel, $\Delta$ the volume of the  cell micro-domain and the two-valued function  $S(t)=1$ or $0$ indicates the open or closed
state of the channel.
\begin{centering}
\begin{figure}[h!]
\hspace{2cm}\includegraphics[height=3.8 cm,width=6.8 cm, angle=0]{schemIonChanFeb2013b.pdf}
\caption{The ion-channel (gray) opens  with  rate $\gamma_+$ governed by
the input  $ V(t)$ and closes with rate $\gamma_-$ that depends on the ion concentration $c(t)$ (which is considered to be the output).  Ions inside the micro-domain  (dashed box) are cleared out at rate $\lambda$.}
\end{figure}
\end{centering}
Adopting the dimensionless variable  $\sigma(t)=c(t) \lambda \Delta/J$, Eq.  (\ref{raw})  becomes
\begin{equation}
\label{dynamics}
\frac{d\sigma}{dt}=\lambda(S(t)-\sigma(t))
\end{equation}
and $\sigma(t)$ is restricted to the range $[0,1]$.
Examples of  trajectories  of $\sigma(t)$  are shown in Fig. 2.
 The switching events of the channel between  time  $0$ and $t$:
\begin{equation}
\label{pp}
0<t_1<t_2< \dots <t_n<t
\end{equation}
are the realization of a stochastic point process driving the  dynamics of $\sigma(t)$.
 In fact, fixed a sequence  $\vec{t}_n=(0,t_1,\cdots,t_n)$  of such events and the initial condition $S(0)=i$ and $\sigma(0)=\sigma_0$, an exact solution for $\sigma(t)$ follows directly from  Eq. (\ref{dynamics}):
\begin{align}
\label{sigmaev}
\nonumber & \sigma(t|\vec{t}_{2n})=i+\left[\sigma_0  -i +(-1)^{i}\sum_{j=0}^{n-1}\left(e^{\lambda t_{2j}}-e^{\lambda t_{2j+1}}\right)\right]e^{-\lambda t}\\
 &  \sigma(t|\vec{t}_{2n+1})=1-i+
\left[\sigma(t=t_{2n+1}|\vec{t}_{2n})-1+i\right]e^{-\lambda (t-t_{2n+1})}.
\end{align}

 Eqs. (\ref{sigmaev})  determine  $\sigma(t)$ after
either an even or  an odd sequence  of switching events and show how
this variable keeps a full record (memory) of these events.

We assume that the external input   $V(t)$  gates the channel by  perturbing only the opening rate,
i.e.
\begin{equation}
\tilde{\gamma}_+=\gamma_+(1 +\varepsilon_V V(t)),
\end{equation}
while  the internal ion  concentration affects the closing rate
\begin{equation}
\label{gammam}
\tilde{\gamma}_-=\gamma_-(1 +\varepsilon_{\sigma} \sigma(t)).
\end{equation}
When $\varepsilon_{\sigma} >0$, an increase in calcium concentration leads to faster closing of the channel and hence a reduced calcium influx. Therefore $\varepsilon_{\sigma} >0$  corresponds to negative autoregulation and $ \varepsilon_{\sigma} <0$ to positive feedback on the calcium dynamics (to guarantee that
 $\tilde{\gamma}_-$ remains positive in Eq. (\ref{gammam}), $\varepsilon_{\sigma} >-1$ is required). Note that the opening rate in Eq. (\ref{gammam})  depends, through Eq. (\ref{sigmaev}), on the whole history of previous openings and closings of the channel. The feedback introduced through $\varepsilon_{\sigma}$  therefore renders the channel state dynamics non-Markovian

\section{Signal-induced information gain}
As quantification of the information transfer   we adopt the information gained during the interval $[0,t]$  due to  the stimulus $V$  \cite{Hanggi}.
To introduce this measure of information transfer we start from the definition of entropy for a stochastic point process that generates switching events. Denoting by  $\Sigma_n(t,t_n,\dots,t_1,t_0)$ the probability density  that a sequence of $n$ events occurs at time $t_1, \dots, t_n$ between time $t_0$ and $t$ the entropy of the stochastic process  during this time interval can be written as
\begin{align}
\label{tauent}
S_{\Delta \tau}(t,t_0)&=-\Sigma(t,t_0) \ln \Sigma(t,t_0) -
\nonumber\sum_{n=1}^{\infty}\int_{t_0}^t dt_1\int_{t_1}^{t} dt_{2}\\ \dots& \int_{t_{n-1}}^{t}dt_n \Sigma_n(t,t_n,\dots,t_0)\ln[\Sigma_n(t,t_n,\dots,t_0)(\Delta \tau)^n]
\end{align}
Eq. (\ref{tauent})  is the natural extension of the Shannon entropy to a continuous-time process assuming a finite resolution $\Delta \tau$ with which the continuous-time variable can be measured. This $\tau$-entropy, as well as the difference of such entropies \cite{Hanggi}, depends on $\Delta \tau$ and is consequently ill-defined (not fully defined by the stochastic process). To reach a uniquely defined measure of information transfer, Goychuk and Hanggi \cite{Hanggi}, introduced the relative entropy (or information gain) $\mathcal{K}$, the deviation of the process entropy in presence of the stimulus from the entropy value in the absence of stimulus. This information gain is defined using the Kullback-Leibler divergence \cite{K-L} of the probability density $\overline{\Sigma}_n$  for events occurring in the presence of stimulus from the probability density $\Sigma_n$ for events occuring in the stationary condition without stimulus. This information measure is independent of the resolution $\Delta \tau$ and is given b
\begin{align}
\label{Ing0}
&{\mathcal K}(t,t_0)= \Sigma(t,t_0)\ln
\left(\frac{\Sigma(t,t_0)}{\overline{\Sigma}(t,t_0)}\right) + \\
&\nonumber  \sum_{n=1}^{\infty} \int_{t_0}^t
dt_1\int_{t_1}^t dt_{2} \cdots \int_{t_{n-1}}^t dt_n
\Sigma_n(t,t_n,\dots,t_0)\ln\left(\frac{\Sigma_{n}(t,t_n, \dots, t_0)}{\overline{\Sigma}_{n}(t,t_n,\dots,t_0)}\right).
\end{align}

Our goal is to evaluate, for the signaling module introduced in Sec. 2, the information gained about the input stimulus $V(t)$ from the knowledge of the output $\sigma(t)$.

\begin{figure}[h!]
\hspace{2cm}\includegraphics[height=5.9 cm,width=7.5 cm, angle=0]{FinalTrag2.pdf}
\caption{Examples of time courses for the dimensionless calcium  concentration $\sigma(t)$ following from Eq. (\ref{dynamics}) for a given sequence $\vec{t}_n$ of channel state switches.  Thick, dashed and dotted lines correspond to $\lambda=10.0,1.0$ and $0.1$  respectively.
\end{figure}
 In the module of Fig 1, given an initial concentration $\sigma_0$,  to each realisation, or trajectory, of $S(t)$
corresponds univocally one trajectory  of $\sigma(t)$ (see Fig. 2).
The trajectories of  $\sigma(t)$  can be therefore grouped in four sets determined
by the initial  and  final channel state.
Let
$\Sigma_{ij}(\vec{t}_n,t)$ be the probability of having a given realisation of $\sigma(t)$  in correspondence of  $n$ opening and closing  events of the channel occurring between $0$ and $t$ (indicated with $\vec{t}_n$ for brevity),
 with  $i,j=\pm$  indicating  the initial and final  channel state respectively ($+$ meaning open and $-$ closed).
 We introduce then     the analogous probability for the unperturbed channel  dynamics $\Sigma_{ij}^0(\vec{t}_n,t)$,  i.e. corresponding to have neither stimulus nor feedback applied and we use   $\overline{\Sigma}_{ij}(\vec{t}_n,t)$

 for  the  dynamics including feedback  but not the stimulus.

For the unperturbed process  the waiting times distributions (WTDs) in each of the two states are Poissonian, i.e. $\psi_{\pm}^0(\tau)=\gamma_{\pm}e^{-\gamma_{\pm}\tau}$ and the corresponding survival probabilities $\Psi^0_{\pm}=\int_t^\infty \psi^0_{\pm}(\tau)d\tau=e^{-\gamma_{\pm} \tau}$. It follows that the  probability $\Sigma^0_{ij}(\vec{t}_{n},t)$ of  a time realisation of $\sigma(t)$   with
a sequence  $\vec{t}_n$ of channel switches  beginning in state $i$ of the channel and ending  in  state $j$ is (in absence of stimulus and feedback):
\begin{align}
\nonumber
\Sigma^0_{ij}(\vec{t}_{n},t)=P_{i}(\sigma_0)
\psi^0_{-i}(\tau_1)\psi^0_{i}(\tau_2)\cdots \psi^0_{-j}(\tau_n)\Psi^0_{j}(t-t_{n})
\end{align}
where  $\tau_k=t_k-t_{k-1}$ and $P_i( \sigma_0)$ is  the probability that at time $t=0$
the gate is in state $i$ and the initial concentration is $\sigma_0$.

Eq. (\ref{Ing0})  applied to our module of Fig. 1 with the notation introduced above, becomes
\begin{align}
\label{Ing}
&{\mathcal K}[\sigma|V]= \sum_{i,j=\pm}\left[ \delta_{ij} \Sigma_{ii}(t)\ln
\left(\frac{\Sigma_{ii}(t)}{\overline{\Sigma}_{ii}(t)}\right) +\right. \\
&\nonumber \left. \sum_{n=1}^{\infty} \int_{t_0}^t
dt_1\int_{t_1}^t dt_{2} \cdots \int_{t_{n-1}}^t dt_n
\Sigma_{ij}(\vec{t}_n,t)\ln\left(\frac{\Sigma_{ij}(\vec{t}_n,t)}{\overline{\Sigma}_{ij}(\vec{t}_n,t)}\right)\right],
\end{align}

which, since there is a one-to-one relation between the output trajectories $\sigma(t)$ and the sequences of channel switches,
 expresses the difference in uncertainty about the calcium fluctuations in the absence and presence of the input.  Note that Eq. (\ref{Ing}) does not include any averaging over inputs; rather, it is the information gain for a specific input realization $V(t)$.
  \section{Evaluation of the information gain}
In this section, we carry out a perturbative calculation of the signal-induced information gain in the presence of feedback.
 For a switching process without feedback, the information gain was derived previously in \cite{Hanggi}. To evaluate Eq. (\ref{Ing}) in our case, the probabilities $\Sigma_{ij} (\vec{t}_n,t)$ and $\overline{\Sigma}_{ij} (\vec{t}_n,t)$ must include the feedback contributions to the desired order in $\varepsilon_{\sigma}$.

Perturbation due to  either the stimulus or feedback leads
to time-dependent rates and consequently  modified
 survival probabilities $ \Psi_{\pm}(t,t_k)=\exp[{-\int_{t_k}^t \tilde{\gamma}_{\pm}dt'}]$ in each of the two channel states.

 The ensuing   processes are non-Poissonian, since the perturbed rates $\tilde{\gamma}_{\pm}$ depend on time, with  WTDs $\psi_{\pm}(t,t_k)=-d\Psi_{\pm}(t,t_k)/dt$ which  for small perturbation can be expanded as:
\begin{subequations}
\label{pwtd}
\begin{flalign}
&\psi_+(\tau,t_k)=\psi_+^0(\tau) \left[1+ \varepsilon_{V} \psi^V(\tau,t_k)+\varepsilon_{V}^2 \psi^{V^2}(\tau,t_k) \cdots \right]\\
&   \psi_-(\tau,\vec{t}_k)=\psi_-^0(\tau)\left[1+ \varepsilon_{\sigma} \psi^{\sigma}(  \tau,\vec{t}_k)+\varepsilon_{\sigma}^2 \psi^{\sigma^2}(\tau,\vec{t}_k)\cdots \right],
\end{flalign}
\end{subequations}
where $\tau=t-t_k$. The first order corrections are:
\begin{subequations}
\label{psiVs}
\begin{align}
&\psi^V(  \tau,t_k)=V(t_k+  \tau)-  \gamma_+ \int_{t_k}^{t_k+  \tau}dx V(x) \\
 & \psi^{\sigma}(  \tau,\vec{t}_k)=\sigma(t_k+  \tau|\vec{t}_k)-  \gamma_- \int_{t_k}^{t_k+  \tau}dx \sigma(x|\vec{t}_k)
\end{align}
\end{subequations}
and the corresponding expressions for higher order corrections can be analogously derived. The appearance  of $\vec{t}_k$ in (\ref{psiVs}b)
remarks the dependence of this correction on all the sequence of events up to time $t_k$.
Due to the form of Eqs. (\ref{psiVs})  and of the analogous higher order corrections, the WTDs in Eqs. (\ref{pwtd}) remain normalized at each order (the perturbative corrections integrate to zero  as can be checked by applying integration by parts on the first order corrections while using Eqs. (\ref{psiVs})).
As the integral of $\sigma(t)$ in Eq. (\ref{psiVs}b) can diverge  with $\tau$,   the validity of the small perturbation expansion for  $\psi_-$ has to be examined. The terms of the expansion of the information gain ensuing from the adoption of Eqs. (\ref{psiVs}) can be proved to be well behaved  when averaged over sequences $\vec{t}_n$  (see   Appendix, last paragraph).
Carrying out the  expansion to second order according to the Eqs. (\ref{pwtd})  and (\ref{psiVs}) leads to:

\begin{align}
\label{fundterm}
 \Sigma_{ij}(\vec{t}_n,t)
&\nonumber \simeq \Sigma^0_{i j}+\varepsilon_{V} \Sigma^V_{ij}  + \varepsilon_{\sigma} \Sigma^{\sigma}_{ij}+\varepsilon_{V}^2
\left(\Sigma^{VV}_{ij}+\Sigma^{V^2}_{ij}\right)+\\
&+\varepsilon_{\sigma} \varepsilon_{V} \Sigma^{\sigma V}_{ij}+\varepsilon_{\sigma}^2 \left(\Sigma^{\sigma
\sigma}_{ij}+\Sigma^{\sigma^2}_{ij}\right)
\end{align}
where to lighten notation we have dropped the dependence on $(\vec{t}_n,t)$  in the terms on the right-hand side.
In Eq. (\ref{fundterm}) the first order  contributions to  the expansion for small $\varepsilon_{V},\varepsilon_{\sigma} ($indicated  by superscripts $V$ and $\sigma$)
 are obtained by adding to   $\Sigma^0_{ij}(\vec{t}_n,t)$  the
corrections obtained drawing only one interval
 in the sequence $\vec{t}_n$
 with a WTD  perturbed up to  the first order correction in  Eqs. (\ref{pwtd})
  while the remaining intervals are generated   with unperturbed  WTDs $\psi^0_{\pm}(\tau)$.
 The  second order corrections to $ \Sigma_{ij}(\vec{t}_n,t)$  are obtained either by  drawing   two intervals in the sequence $\vec{t}_n$  with WTDs  corrected to first order  (superscripts $VV,\sigma V, \sigma \sigma$)
 or by drawing only  one  interval in $\vec{t}_n$,
but  with WTDs  corrected up to second  order (superscripts $V^2, \sigma^2$),  with the remaining intervals  drawn with  unperturbed WTDs $\psi^0_{\pm}(\tau)$.

Considering the probability $ \Sigma_{ij}(\vec{t}_n,t)\equiv Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})$  as
function of the small parameters $\varepsilon_{\sigma}, \varepsilon_{V}$, due to the structure of Eq. (\ref{tauent})
 the information gain  is a sum
of terms with the following functional form:
\begin{equation}
\label{infgainbasic}
G_{ij}(\varepsilon_{\sigma},\varepsilon_{V})=Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V}) \ln \frac{Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{Q_{ij}(\varepsilon_{\sigma},0)}.
\end{equation}
A Taylor expansion of $G_{ij}(\varepsilon_{\sigma},\varepsilon_{V})$ to second order around $\varepsilon_{\sigma}=\varepsilon_{V}=0$ leads to:
\begin{align}
\label{taylor}
&G_{ij}(\varepsilon_{\sigma},\varepsilon_{V})=\varepsilon_{V} \frac{\partial Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial \varepsilon_{V}}|_{\varepsilon_{\sigma}=\varepsilon_{V}=0}+\varepsilon_{\sigma} \varepsilon_{V} \left[ \frac{\partial^2 Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial \varepsilon_{\sigma} \partial \varepsilon_{V}}+  \right. \\
& \nonumber \left.  \frac{ \frac{\partial Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial\varepsilon_{\sigma}}\frac{\partial Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial\varepsilon_{V}}}{Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}\right]_{\varepsilon_{\sigma}=\varepsilon_{V}=0}+\frac{\varepsilon_{V}^2}{2}\left[\frac{\partial^2 Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial \varepsilon_{V}^2}+\frac{ \left(\frac{\partial Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial \varepsilon_{V}}\right)^2}{Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}\right]_{\varepsilon_{\sigma}=\varepsilon_{V}=0}
\end{align}
with all missing second-order terms having coefficient equal to zero when evaluated at  $\varepsilon_{\sigma}=\varepsilon_{V}=0$ (for the full formal expansion see Appendix).
Replacing the expression of $Q_{ij}$ in terms of  $\Sigma_{ij}$ using Eq. ($\ref{fundterm}$),  one obtains for the second order expansion of  $G_{ij}$ the following terms:
\begin{align}
\label{expref}
\nonumber&
G_{ij}(\varepsilon_{\sigma},\varepsilon_{V})\simeq\varepsilon_{V} \Sigma_{ij}^V (\vec{t}_n,t)+\varepsilon_{\sigma} \varepsilon_{V} \Sigma^{\sigma V}_{ij}(\vec{t}_n,t)
+\\
&+ \varepsilon_{V}^2 \left[\Sigma^{V^2}_{ij}(\vec{t}_n,t)+\Sigma^{VV}_{ij}(\vec{t}_n,t)+\frac{\Sigma^V_{ij}(\vec{t}_n,t)^2}{2 \Sigma^0_{ij}(\vec{t}_n,t)}\right]
\end{align}
with the contribution of order $\varepsilon_{\sigma}^2$ being exactly zero.
As already mentioned, Eqs. (\ref{pwtd}) imply that the  corrections to the unperturbed
WTDs  integrate to exactly zero over time, so that the  WTDs remain normalized.
As a consequence $ \Sigma_{ij}^0 (\vec{t}_n,t)$, when
averaged over all possible sequences  $\vec{t}_n$ and initial and final channel state,  adds up to one, while all
the perturbative corrections of each order give a zero overall contribution.
 It follows therefore that  integrating and summing Eq. (\ref{expref})
over all possible paths $\vec{t}_n$ and initial and final state $i,j$
the linear term $\Sigma^V_{ij}$, and the second-order terms $\Sigma^{\sigma V}_{ij}$, $\Sigma^{V^2}_{ij}$ and $\Sigma^{VV}_{ij}$ give zero contribution.

Let us then analyse the remaining $2^{nd}$-order term:
$A^{(2)}_{ij}\equiv \frac{(\Sigma^V_{ij})^2}{2 \Sigma^0_{ij}}.$

Considering for example the case of initial and final closed state $i=j=-$, we obtain
\begin{align}
\label{amm1}
\nonumber A^{(2)}_{--}(\vec{t}_{n|k},t)&=A^{(2)}_{--}(t,t_n,\cdots t_k \cdots t_1,t_0)=\frac{\Sigma^V_{--}(\vec{t}_{n|k},t)^2}{2 \Sigma^0_{--}(\vec{t}_{n,k},t)}=
\\=&\frac{P_-(\sigma_0)}{2}\psi^0_+(\tau_1)\psi^0_-(\tau_2)
\cdots
{\psi}^{V}(\tau_{k+1},t_k)^2\cdots\psi^0_-(\tau_{n}) \Psi^0_+(t-t_{n}) ,
\end{align}
where
$\Psi_{\pm}^0$ are
unperturbed survival probabilities
for the channel opening/closing events.
Further subscript $k$  in $\vec{t}_{n|k}$  indicates the perturbed time interval within the set of $n$ time intervals,
with $k$ and $n$ in (\ref{amm1}) being even integers  for $i=j=-$.

For the distribution of initial conditions $P_{\pm} (\sigma_0 )$, it is natural to assume a stationary solution  for channel dynamics with feedback but no stimulus.
   In this case the initial probabilities are $P_{\pm}(\sigma_0)=\gamma_{\pm}/(\gamma_+ +\gamma_-)
 \pm \Sigma_{\sigma}$, where a linear correction $\Sigma_{\sigma}\propto
\varepsilon_{\sigma}$ is added to the stationary solution for the
 unperturbed dynamics without feedback.
It follows that the correction due to feedback in $P_{\pm}(\sigma_0)$ produces in Eq. (13) a contribution of order $ \varepsilon_{V}^2 \varepsilon_{\sigma} $, and can therefore  be neglected to second order.  Moreover, a direct calculation shows that this

correction is
$\propto \varepsilon_{V}^2 \varepsilon_{\sigma}\gamma_+ e^{-t(\gamma_++\gamma_-)} V^2(t)$ and therefore vanishes on the channel state switching time-scale.
It can consequently be neglected also to third order when evaluating information gain over long time intervals.
Averaging over all possible paths,  the second order contribution $A_{ij}$ to the information gain is:
\begin{align}
\label{hangres}
\mathcal{K}^{(2)}(t)&=\sum_{i j,n k}\int d \vec{t}_{n|k}
A^{(2)}_{ij}(\vec{t}_{n|k},t)=\\
&\nonumber=\varepsilon_{V}^2  \int d\omega d \omega^{\prime} \hat{V}( \omega)\hat{V}( \omega^{\prime})\frac{\imath \gamma_+
\gamma_-[1-e^{\imath ( \omega+ \omega^{\prime})t}]}{2( \omega+ \omega^{\prime})(\gamma_-+\gamma_+)}
\end{align}
where the sum over $n$ runs from $1$ to $+\infty$ ($k<n$) and
for convenience of calculation we introduced the Fourier transform $\hat{V}(\omega)$
\begin{equation}
\hat{V}(\omega)=\int_{-\infty}^{\infty} dt e^{-\imath \omega t} V(t)
\end{equation}
  of the stimulus $V(t)$, adopting the character $\imath$ for the imaginary unit.
Taking the time derivative
and integrating over frequencies gives the rate of information gain:
\begin{equation}
\label{finallowest}
\frac{d \mathcal{K}}{dt}=\frac{\varepsilon_{V}^2}{2} V^2(t) \frac{\gamma_+\gamma_-}{\gamma_+ + \gamma_-}
\end{equation}
which
coincides with  the result  obtained in   \cite{Hanggi} for a similar signaling module without feedback.

In order to see the contribution of the feedback one then has to expand
 Eq. (\ref{expref}) to third  order. This leads to the additional terms shown in Eq. (\ref{3order}). After summation, integration (for details see Appendix) we obtain
 \begin{align}
 &  \label{hangres2Z}\mathcal{K}^{(3)}(t)=\frac{1}{2}\sum_{ij,nkl}\int d \vec{t}_{n|kl}
A^{(3)}_{ij}(\vec{t}_{n|kl},t)= -\frac{\imath
\varepsilon_{V}^2 \varepsilon_{\sigma}}{2} \frac{\gamma^2_+ \gamma_-}{(\gamma_+ +\gamma_- )^2}\\
 \nonumber& \int d \omega d \omega^{\prime} \frac{\hat{V}( \omega)\hat{V}( \omega^{\prime})}{ \omega+ \omega^{\prime}} \frac{\gamma_+ +\lambda}{\gamma_- +\gamma_+ +\lambda} e^{\imath( \omega + \omega^{\prime})t}.
 \end{align}

We differentiate (\ref{hangres2Z}) with  respect to time, integrate over frequencies and  add up to (\ref{finallowest})  so as to obtain the information gain rate including the feedback contribution:
\begin{equation}
\label{finalstat}
 \frac{d \mathcal{K}}{dt}=\frac{\varepsilon_{V}^2}{2}V^2(t)\frac{\gamma_+ \gamma_-}{\gamma_+
+\gamma_- }\left[1+\varepsilon_{\sigma}\frac{\gamma_+ }{\gamma_+
+\gamma_- }\frac{\gamma_+ + \lambda}{\gamma_+ +\gamma_-+\lambda}\right] ,
\end{equation}
where terms vanishing on time scale $t\gg \mbox{max}(\lambda^{-1},\gamma^{-1}_{\pm})$, which include those carrying the dependence on the initial concentration $\sigma_0$,
 have been neglected.
Our calculation was carried out for the case of the input and the feedback acting on the opening and closing rate, respectively. Our approach can obviously be extended to the case of interchanged action of feedback and input on the rates, which would lead to the same result as in Eq. (\ref{finalstat}) with the simple interchange $\gamma_+ \leftrightarrow \gamma_-$. In the case of both feedback and input affecting the same rate, the leading feedback correction would again be of order $\varepsilon_{\sigma} \varepsilon_{V}^2$, but the coefficient would be different. The terms of order e.g. $\varepsilon_{\sigma} \varepsilon_{V}$  in the derivation can in this case emerge from perturbing to second order the same switching event, while in the case we analyzed they can only emerge from perturbing two switching events to first order. Derivation of the correction for this case, as well as of higher-order corrections, is beyond the scope of this paper.

\section{Discussion}
The information gain rate Eq. (\ref{finalstat}), when integrated over a time interval $[0,T]$ with $T \gg $max$(\lambda^{-1}, \gamma_+^{-1})$, gives the total information obtained about the stimulus by observing the output. It is seen that (to first order in feedback strength and second order in stimulus strength) the information gain depends only on the total power $\int_0^T dt V^2(t)$  of the stimulus, rather than on the spectral distribution of this power.

This implies that the information gain rate cannot be optimized by matching the temporal structure of the stimulus with the time scale of the feedback dynamics.

The factor $ \frac{\gamma_+ \gamma_-}{\gamma_+  + \gamma_- }$  in Eq. (\ref{finalstat}) expresses the rate of 'double flip events' (i.e., openings and successive closings of the channel) in the absence of stimulus and feedback. In the case $\varepsilon_\sigma=0$, the information gain rate is therefore simply proportional to the unperturbed rate of double flip events. \footnote{The true double flip rate in the presence of stimulus differs from $\frac{\gamma_+ \gamma_-}{\gamma_++\gamma_- }$ by a correction that is first order in $\varepsilon_V$. In Eq. (\ref{finalstat}), such a correction would result in a higher-order term $o(\varepsilon_{\sigma}^3)$, which is beyond the order in which we carried out the expansion.}To examine if an analogous relation holds generally, we evaluate the mean rate of double flip events in absence of stimulus but in presence of feedback. The mean closing rate is given by $\gamma_- (1+\varepsilon_\sigma \overline{\sigma}_+$), where $\overline{\sigma}_+$ is the average concentration when the channel is in the open state. To obtain a result valid to first order in $\varepsilon_\sigma$, it is sufficient to express $\overline{\sigma}_+$  to $0^{th}$ order; this conditional average was computed in Ref.  \cite{boro} and equals
\begin{equation}
 \overline{\sigma}_{+}=\frac{\gamma_-+\lambda}{\gamma_-+\gamma_+ +\lambda}.
 \end{equation}
 The mean rate of double flip events in the presence of feedback is then given by
\begin{align}
R&=\gamma_+ \gamma_- (1+\varepsilon_{\sigma} \overline{\sigma}_{+})/(\gamma_++\gamma_-(1+\varepsilon_{\sigma} \overline{\sigma}_{+})) \\
\nonumber &= \frac{\gamma_- \gamma_+}{\gamma_-+\gamma_+ } \left[1+\varepsilon_{\sigma} \frac{ \gamma_+}{\gamma_-+\gamma_+ }\frac{\gamma_++\lambda}{\gamma_-+\gamma_+ +\lambda}+o(\varepsilon_{\sigma}^2)\right]
\end{align}
and Eq. (\ref{finalstat}) becomes
\begin{equation}
\label{finaleq}
\frac{d K}{dt}=\frac{\varepsilon_V^2}{2} V^2 (t)R.
\end{equation}
The information gained per double flip event is therefore given only by the power of the input and does not depend on the feedback strength $ \varepsilon_{\sigma}$  or on the kinetic parameters $\gamma_+ $,$\gamma_-$,$\lambda$.

Before relating this result to findings from the previous literature, we first point out that the definition of �feedback� in communication / information theory is more restrictive than the definition used in the literature on biochemical signaling. In the biological literature, feedback arises when a product of a signal transduction step influences the upstream components in the pathway. This fits with the general definition of feedback in the early cybernetics literature: "When (this) circularity of action exists between the parts of a dynamic system, feedback may be said to be present" \cite{ashby}. In communication theory, however, feedback is typically required to act in such a way that it effectively modifies the input of the system.  The signaling module analyzed in this work contains in fact a feedback loop in the former sense; functionally, such type of feedback permits to e.g. achieve sensory adaptation to repeated stimuli \cite{reidl} and can improve the temporal resolution of signaling \cite{boro}. In the latter sense (viewed as a communication  channel), however, our module cannot be said to have feedback, as the input $V(t)$ is not combined with the output. Rather, the back-coupling implements an autoregulatory loop with the communication channel. The effect of the autoregulatory loop is to  give memory to the channel state-switching dynamics: the closing rate depends (through the instantaneous calcium concentration) on the full history of previous channel opening and closings. Our signaling module can therefore be viewed as a non-Markovian point process channel without feedback.

Following this clarification of terminology, to put  the results of Eqs.(\ref{finalstat}) and (\ref{finaleq}) into perspective,  we recall a known result from information theory
It was proved in \cite{davis, kabanov} (see also \cite{johnson}), that for any   Markov point process the channel capacity per event cannot exceed the capacity of the Poisson process. I.e., the capacity per event is not improved by memory.
 While we have calculated the information gain rate for one realization of the stimulus (rather than the channel capacity), our finding may be viewed as somewhat analogous. In our case, the channel is non-Markovian, but as the memory decays exponentially in time (akin to a Hawkes process), the system is 'near-Markov'. It is possible, however, that a different result (i.e., information gain affected by memory) would be obtained if we carried out the perturbation expansion to higher orders in $\varepsilon_{\sigma}$. For discrete-time (rather than point-process) channels, the influence of memory on channel capacity was recently analyzed in \cite{kostal,kostalb}.

In \cite{tenwoldepre}, an analysis of several prototypical signaling pathways with feedback was carried out under the additive Gaussian noise approximation. Under this restriction (see also Sec. 1), the authors were able to compute the mutual information rate for modules in which nonlinear feedback affected the activation of an upstream component. They concluded that when the feedback was mediated by the final output of the pathway, no improvement of the information transmission was obtained. This  is reminiscent of our main finding. In \cite{tenwoldepre}, an enhancement of information transmission was obtained only when the feedback was mediated by an intermediate product in the pathway, and not the final output. The module we analyzed in this paper (Fig. 1) falls outside of this class

In conclusion, we presented a novel perturbative approach that permits to analytically evaluate information transfer through non-Markovian point process channels. We applied this approach to a prototypical signaling module with biochemical feedback and showed that  to first order in feedback strength  the information gain rate is increased by negative feedback (and decresead by positive feedback). However, this change in information gain rate is fully accounted for by the feedback-induced change in the rate of signaling events (channel opening/closings). To first order in feedback strength, the information gain per signaling event is not affected by feedback.

\section{Acknowledgments}
We thank Lubomir Kostal for insightful comments on the manuscript. M.Z. acknowledges institutional support RVO:67985823 and grant support P304/12/G069 (The Czech Science Foundation).

\section*{Appendix: Expansion of the Information gain }
The Taylor expansion of Eq. (\ref{infgainbasic})  to second order  in $\varepsilon_{\sigma}, \varepsilon_{V}$ gives

\begin{align}
\label{taylor}
\tag{A1}
&G_{ij}(\varepsilon_{\sigma},\varepsilon_{V})=\varepsilon_{\sigma}\left[ \frac{\partial Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial \varepsilon_{\sigma}}
- \frac{Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{Q_{ij}(\varepsilon_{\sigma},0)}\frac{\partial Q_{ij}(\varepsilon_{\sigma},0)}{\partial \varepsilon_{\sigma}}\right]_{\varepsilon_{\sigma},\varepsilon_{V}=0}\\
\nonumber&+\varepsilon_{V} \frac{\partial Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial \varepsilon_{V}}|_{\varepsilon_{\sigma},\varepsilon_{V}=0}+\frac{\varepsilon_{\sigma}^2}{2}\left[\frac{\partial^2 Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial \varepsilon_{\sigma}^2} +\left(\frac{\partial Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial \varepsilon_{\sigma}}\right)^2 \frac{1}{ Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}\right. \\
\nonumber&\left.+\frac{ Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{Q^2_{ij}(\varepsilon_{\sigma},0)}\left(\frac{\partial Q_{ij}(\varepsilon_{\sigma},0)}{\partial \varepsilon_{\sigma}}\right)^2  -\frac{2}{ Q_{ij}(\varepsilon_{\sigma},0)}\frac{\partial Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial \varepsilon_{\sigma}}\frac{\partial Q_{ij}(\varepsilon_{\sigma},0)}{\partial \varepsilon_{\sigma}}\right.\\
\nonumber &\left. - \frac{\partial^2 Q_{ij}(\varepsilon_{\sigma},0)}{\partial \varepsilon_{\sigma}^2}\frac{Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{Q_{ij}(\varepsilon_{\sigma},0)} \right]_{\varepsilon_{\sigma},\varepsilon_{V}=0}+\frac{\varepsilon_{V}^2}{2}\left[\frac{\partial^2 Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial \varepsilon_{V}^2}+\frac{1}{Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})} \left(\frac{\partial Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial \varepsilon_{V}}\right)^2\right]_{\varepsilon_{\sigma}=\varepsilon_{V}=0}\\
\nonumber &+\varepsilon_{\sigma}\varepsilon_{V}\left[\frac{\partial^2 Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial \varepsilon_{\sigma} \partial \varepsilon_{V}}
+\frac{1}{Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})} \frac{\partial Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial\varepsilon_{\sigma}}\frac{\partial Q_{ij}(\varepsilon_{\sigma},\varepsilon_{V})}{\partial\varepsilon_{V}}\right]_{\varepsilon_{\sigma}=\varepsilon_{V}=0},
\end{align}
from which it is readily seen that the coefficient of the term $\propto \varepsilon_{\sigma}$ and $\propto \varepsilon_{\sigma}^2$ vanish when evaluated at $\varepsilon_{\sigma}=\varepsilon_{V}=0$.

Expanding Eq. (\ref{infgainbasic}) to third order, following the same procedure as in Eq. (\ref{taylor}) and converting back  $Q_{ij}$ to $\Sigma_{ij}$
leads to three additional terms:
\begin{align}
\label{3order}
\tag{A2}
&\nonumber \frac{ \varepsilon_{\sigma}^2 \varepsilon_{V} }{3}\left[\Sigma_{ij}^{\sigma \sigma V}   +\Sigma_{ij}^{\sigma^2 V}  \right]+\frac{\varepsilon_{V}^2 \varepsilon_{\sigma}}{6} \left[4
\frac{\Sigma_{ij}^{V}  \Sigma_{ij}^{\sigma
V}   }{\Sigma^0_{ij}  } -
\frac{ (\Sigma_{ij}^{V} )^2
\Sigma_{ij}^{\sigma}  }{(\Sigma^0_{ij} )^2}
\right]\\
\nonumber &+\frac{\varepsilon_V^3}{6} \left[-\frac{(\Sigma_{ij}^{V} )^3}{
(\Sigma^0_{ij} )^2} +6\Sigma_{ij}^{V}  \frac{\Sigma_{ij}^{VV}  +\Sigma_{ij}^{V^2}   }{\Sigma^0_{ij}  }\right].
\end{align}
where we have used the next order expansion of (\ref{fundterm}) i.e.
\begin{align}
\label{fundterm3}
\tag{A3}
 &\Sigma_{ij}(\vec{t}_n,t)
 \simeq \Sigma^0_{i j}+\varepsilon_{V} \Sigma^V_{ij}  + \varepsilon_{\sigma} \Sigma^{\sigma}_{ij}+\varepsilon_{V}^2
\left(\Sigma^{VV}_{ij}+\Sigma^{V^2}_{ij}\right)+\varepsilon_{\sigma} \varepsilon_{V} \Sigma^{\sigma V}_{ij}\\
&\nonumber+\varepsilon_{\sigma}^2 \left(\Sigma^{\sigma
\sigma}_{ij}+\Sigma^{\sigma^2}_{ij}\right)+\varepsilon_{\sigma}^3 \left(\Sigma^{\sigma
\sigma \sigma}_{ij}+\Sigma^{\sigma
\sigma^2}_{ij}+ \Sigma^{\sigma^3}_{ij}\right)+\varepsilon_{\sigma}^2 \varepsilon_{V} \left(\Sigma^{\sigma
\sigma V}_{ij}+\Sigma^{\sigma^2 V}_{ij}\right)\\
& \nonumber\varepsilon_{\sigma} \varepsilon_{V}^2 \left(\Sigma^{\sigma
VV}_{ij}+\Sigma^{\sigma V^2}_{ij}\right)+\varepsilon_{V}^3 \left(\Sigma^{V
VV}_{ij}+\Sigma \Sigma^{V
V^2}_{ij}+ \Sigma^{V^3}_{ij}\right)
\end{align}
The third term in Eq. (\ref{3order}) depends only on the stimulus while the first, proportional to $ \varepsilon_{\sigma}^2 \varepsilon_{V}$, is exactly
the same coefficient that would appear as the third order in the expansion in Eq.  (\ref{fundterm}) and therefore
gives zero contribution after integration over $\vec{t}_n $ and summation,  since perturbative corrections do not affect the normalization of the unperturbed part.
Therefore  the contribution with the feedback is given only by the term  proportional to $ \varepsilon_{V}^2  \varepsilon_{\sigma}$ in Eq. (\ref{3order}),
whose  first element  can be written as:
\begin{align}\label{rearrange}\tag{A4}
 &\frac{\Sigma_{ij}^{V}\Sigma_{ij}^{\sigma V} }{\Sigma^0_{ij}}=\Sigma_{ij}^{\sigma V
V}+\Sigma_{ij}^{\sigma}
\frac{\left(\Sigma_{ij}^{V}\right)^2 }{\Sigma^0_{ij}} =\left[\Sigma_{ij}^{\sigma V
V}+\Sigma_{ij}^{\sigma
V^2}\right]+\Sigma_{ij}^{\sigma}
\frac{\left(\Sigma_{ij}^{V}\right)^2 }{\Sigma^0_{ij}} -\Sigma_{ij}^{\sigma V^2}
\end{align}
The term between brackets  has been so rearranged in order to show that after summation and integration, it
gives zero contribution (since it  corresponds to the
term of order $\varepsilon_{V}^2 \varepsilon_{\sigma}$  in the
the expansion of Eq. (\ref{fundterm}), i.e. first term in last row in Eq. (\ref{fundterm3})).
The last term in (\ref{rearrange}) gives a contribution that vanishes on the time scale of the channel opening/closing dynamics.
The remaining term  combines with the second element in the feedback contribution in (\ref{3order})
taking an overall $1/2$ coefficient. The expression for this term, evaluated between initial and final closed states, is:

\begin{align}
\label{feedbackex}\tag{A5}
&A^{(3)}_{--}(\vec{t}_{n|kl},t)=\Sigma_{--}^{\sigma} \left(\frac{\Sigma_{--}^{V}}
{\Sigma^0_{--}}\right)^2=\\
\nonumber &=P_-(\sigma_0) \psi^0_+(\tau_1)\psi^0_-(\tau_2)
 \cdots {\psi}^{V}(\tau_{k+1},t_k)^2\cdots \psi^{\sigma}(\tau_{l+1}|\vec{t}_{l})
\cdots\psi^0_-(\tau_{n}) \Psi^0_+(t-t_{n})

\end{align}
where further subscripts  $k$ and $l$ in $\vec{t}_{n|k,l}$ indicate   that  the closing and the opening events affected by the stimulus and the feedback are respectively   the $k^{th}$  and the $l^{th}$ of the $n$ event  occurring between $0$ and $t$.   $\psi^{\sigma}(\tau_{l+1}|\vec{t}_{l})$
is the first order term given by Eq. (\ref{psiVs}) and  carries the dependence on the
 history of the process prior to $t_l$ (i.e. the all sequence $\vec{t}_l$ of  events up to $t_l$),
but, due to the exponential form of the WTDs,
 does not affect the convolution structure in (\ref{feedbackex}).

Averaging over all possible paths gives:
\begin{align}
\label{hangres2}\tag{A6}
\nonumber &  \mathcal{K}(t)=\frac{1}{2}\sum_{ij,nkl}\int d \vec{t}_{n|kl}
A^{(3)}_{ij}(\vec{t}_{n|kl},t)= -\frac{\imath
\varepsilon_{V}^2 \varepsilon_{\sigma}}{2} \frac{\gamma^2_+ \gamma_-}{(\gamma_+ +\gamma_- )^2}\\
 \nonumber& \int d \omega d \omega^{\prime} \frac{\hat{V}( \omega)\hat{V}( \omega^{\prime})}{ \omega+ \omega^{\prime}} \frac{\gamma_+ +\lambda}{\gamma_- +\gamma_+ +\lambda} e^{\imath( \omega + \omega^{\prime})t},
 \end{align}
plus terms that either  do not depend on $t$ or vanish  for time $t\ll $max$(\lambda^{-1}, \gamma_{+}^{-1})$ exceeding the channel state switching time scales.

Finally a  remark on the validity of the perturbative approach we introduced.
This approach is based on a direct perturbation of the switching rates and therefore of the WTDs, due to both input and feedback.  The only delicate  point  in following this procedure is the integral of $\sigma(x|\vec{t})$ in Eq. (\ref{psiVs}b), which can diverge  with $\tau$. It can be verified, however that this correction is well-behaved after summation over all stochastic paths and integration.
In fact  we  can replace  the contribution of this integral to the first order correction in Eq. (\ref{psiVs}b) with  the term $-\gamma_-\psi_-^0(  \tau)  \tau$,  which is the upper limit to the correction due to the integral  between $0$ and $\tau$ according to our perturbative prescription. The Laplace transform of this correction is
\begin{equation}
\label{A6}
\tag{A6}
\mathcal{ L} [-\gamma_{-}^2t e^{-\gamma_- t}]=-\int_0^{\infty}dt\gamma_{-}^2t e^{-\gamma_-t}e^{-s t } =  \left(\frac{\gamma_-}{\gamma_-+s}\right)^2=\hat{\psi}^0_-(s)^2
\end{equation}
where
\begin{equation}
\label{A7}
\tag{A7}
\hat{\psi}^0_-(s)=\mathcal{ L} [\gamma_{-} e^{-\gamma_- t}]=\int_0^{\infty}dt\gamma_{-} e^{-\gamma_-t}e^{-s t } =\frac{\gamma_-}{\gamma_-+s}.
\end{equation}
We can now replace Eq. (\ref{A6})  in the transition amplitude.  Due to the convolution structure of $\Sigma_{ij}(\tau, t)$ in terms of WTDs, this replacement adds just a  multiplicative factor in Laplace space. The final result is (for $i=j=-$)
\begin{equation}
\label{converge}
\tag{A8}
\Sigma_{--}(s)= \Sigma^0_{--}(s)(1+\varepsilon_{\sigma} \hat{\psi}^0_-(s)),
\end{equation}
where we have included only the contribution of the integral and not  the first term in Eq. (\ref{psiVs}b) since the latter is trivially well-behaved.
Due to Tauberian theorem, one can deduce the time asymptotic limit by analyising the $s\to 0$ behavior in Eq. (\ref{converge}). Since $\hat{\psi}_-(s)\to 1$ for $s \to 0$, such correction obtained after averaging over trajectories is  always limited, non-divergent and therefore well-behaved for $t \to \infty$.

\section*{References}

redpajama_set_name: RedPajamaArXiv


text:
# Synopsis

Extend AngularJS applications by injecting module dependencies  at build time (using static code transformations).

For example transforms this:
```javascript
angular.module('moduleName', ['dependency']);
```

into that

```javascript
angular.module('moduleName', ['dependency', 'additionalDependency']);
```

[![NPM](https://nodei.co/npm/angular-extender.png?downloads=true)](https://nodei.co/npm/angular-extender/)

[![Build Status](https://travis-ci.org/mariocasciaro/angular-extender.png)](https://travis-ci.org/mariocasciaro/angular-extender)
[![Coverage Status](https://coveralls.io/repos/mariocasciaro/angular-extender/badge.png)](https://coveralls.io/r/mariocasciaro/angular-extender)

## Motivation

Nowadays having a project using [Grunt](http://gruntjs.com/) (or any other similar tool) for workflow automation and asset management is not uncommon. So the idea with this small library is to provide a way to implement a simple **plugin system** for AngularJS applications using only static code transformations. This has two main advantages (compared to extending the module with dynamic tricks):
* Keeps the code clean from boilerplate
* The modules can be still declared in any order.

The main use case for such a system is when you have an AngularJS based application and you want to **allow third party modules (plugins) to exend you core app** without hardcoding those new dependencies.

## Usage

```javascript
var angularExtend = require('angular-extend');

var src = "angular.module('moduleName', ['aModule']);";
var res = angularExtend(src, {moduleName: ['aPlugin']});
/*
res.out will be:

angular.module('moduleName', ['aModule', "aPlugin"]);

res.changed is a boolean and will tell if any extension was applied at all.
*/
```

## Grunt plugin

A grunt plugin using this library will be soon available at https://github.com/mariocasciaro/grunt-angular-extender

## Limitations

The transformation will work only if you follow the convention of delaring modules like
```
angular.module(<string>, [<strings>]);
```
Using variables or functions to define the module name, will fail.

## Contributors

* [Mario Casciaro](https://github.com/mariocasciaro) - Creator/Maitainer - Twitter [@mariocasciaro](https://twitter.com/mariocasciaro)

Thanks to [olov](https://github.com/olov) and his project [ng-annotate](https://github.com/olov/ng-annotate) for the
inspiration he gave me to build this little tool.

-----

[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/mariocasciaro/angular-extender/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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text:
Q: MVVM Datagrid Binding SelectedItem not updating I'm new to WPF and MVVM and i've an applicaton that uses Entity Framework to connect to database and a datagrid to show the users of the application.
The users CRUD operations are made in a separate window and not in the datagrid.
My problems are related with the update of datagrid.
The insert operation is ok but the update is not.
View 1 (Users List):
<DataGrid Grid.Row="1"
  ItemsSource="{Binding Users, Mode=TwoWay}"
  SelectedItem="{Binding SelectedUser, Mode=TwoWay}"
  AutoGenerateColumns="False"
  CanUserAddRows="False">
</DataGrid>

ViewModel :
class UserListViewModel: NotificationClass
{
    UserDBContext _db = null;

    public UserListViewModel()
    {
        _db = new UserDBContext();

        Users = new ObservableCollection<User>(_db.User.ToList());
        SelectedUser = Users.FirstOrDefault();
    }

    private ObservableCollection<User> _users;
    public ObservableCollection<User> Users
    {
        get { return _users; }
        set
        {
            _users = value;
            OnProprtyChanged();
        }
    }

    private User _selectedUser;
    public User SelectedUser
    {
        get
        {
            return _selectedUser;
        }
        set
        {
            _selectedUser = value;
            OnProprtyChanged();
        }
    }

    public RelayCommand Edit
    {
        get
        {
            return new RelayCommand(EditUser, true);
        }
    }

    private void EditUser()
    {
        try
        {
            UserView view = new UserView();
            view.DataContext = SelectedUser;
            view.ShowDialog();

            if (view.DialogResult.HasValue && view.DialogResult.Value)
            {
                if (SelectedUser.Id > 0){
                    User updatedUser = _db.User.First(p => p.Id == SelectedUser.Id);

                    updatedUser.Username = SelectedUser.Username; //this doesn't do nothing, object is already with the new username ?!
                }
                _db.SaveChanges();
            }
        }
        catch (Exception ex)
        {
            MessageBox.Show(ex.Message);
        }
    }
}

after _db.SaveChanges(), datagrid should not be updated ?
Model:
class UserDBContext: DbContext
{
    public UserDBContext() : base("name=DefaultConnection")
    {

    }

    public DbSet<User> User { get; set; }
}

View 2 (User detail)
public partial class UserView : Window
{
    public UserView()
    {
        InitializeComponent();
    }

    private void SaveButton_Click(object sender, RoutedEventArgs e)
    {
        DialogResult = true;
    }

}

User object
class User: NotificationClass
{
    public int Id { get; set; }

    public string Username { get; set; }

    public string CreatedBy { get; set; }

    public DateTime? CreatedOn { get; set; }

}

NotificationClass
public class NotificationClass : INotifyPropertyChanged
{
    public event PropertyChangedEventHandler PropertyChanged = delegate { };
    public void OnProprtyChanged([CallerMemberName]string propertyName = null)
    {
        PropertyChanged(this, new PropertyChangedEventArgs(propertyName));
    }
}

if i close and open view 1, the new username is updated..
could someone help ? thanks

A: Just implementing INotifyPropertyChanged isn't enough, you have to explicitly invoke PropertyChanged (or in your case OnPropertyChanged) when a property changed.
See also https://learn.microsoft.com/en-us/dotnet/framework/wpf/data/how-to-implement-property-change-notification
You can do it like so
class User : NotificationClass
{
    private int _id;
    private string _username;
    private string _createdBy;
    private DateTime? _createdOn;

    public int Id
    {
        get => _id;
        set
        {
            if (value == _id) return;
            _id = value;
            OnPropertyChanged();
        }
    }

    public string Username
    {
        get => _username;
        set
        {
            if (value == _username) return;
            _username = value;
            OnPropertyChanged();
        }
    }

    public string CreatedBy
    {
        get => _createdBy;
        set
        {
            if (value == _createdBy) return;
            _createdBy = value;
            OnPropertyChanged();
        }
    }

    public DateTime? CreatedOn
    {
        get => _createdOn;
        set
        {
            if (value.Equals(_createdOn)) return;
            _createdOn = value;
            OnPropertyChanged();
        }
    }
}

A: it worked ! many thanks @nosale !
what about the change made to SelectedUser being reflected in my context ?
if i do this :
SelectedUser.Username = "test";
User updatedUser = _db.User.First(p => p.Id == SelectedUser.Id);

i was thinking that SelectedUser object has the "test" username and updatedUser has the old username, but not .. updatedUser already have "test"

redpajama_set_name: RedPajamaStackExchange


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An Taobh Tuathail Vol 3 Ben Graham , February 17th, 2010 10:46
Broadcasting Mondays to Fridays, from 11pm to 1am, An Taobh Tuathail ('The Other Side') is the leading (only?) underground music show on Raidio na Gaeltachta, the Irish language service of RTE (the Republic of Ireland's equivalent to the BBC). It's an obvious and lazy comparison, then, to call presenter Cian O Ciobhan the Irish John Peel, but his laid-back manner, combined with the eclectic and astonishing mix of sounds played on the programme, make him more worthy of the mantle than any other DJ I'm aware of in Britain or elsewhere today. Many of the tracks played on any given show will come as a welcome new discovery to even the most clued-in hipster, which can also be extremely frustrating if you're a non-Irish speaker trying to identify the track in question.
To some degree though, the programme's adventurous music policy was determined by necessity. Although uniquely free of the playlist restrictions of most Irish radio programmes, when An Taobh Tuathail began broadcasting in 1999, RnaG had a strict policy of not playing any English language recordings on the station. While this was obviously frustrating for the presenters, it also forced them to dig deeper in their search for material, leading to the show developing a distinctive sound that eschewed the obvious and favoured the abstract and instrumental - strands which, co-incidentally, would come to greater dominance even in English-speaking countries as the decade progressed. In 2005 the ban was relaxed, if only for RnaG's new youth-focussed strand Anocht FM, into which An Taobh Tuathail was incorporated, allowing them a freedom which British radio shows may take for granted but which An Taobh Tuathail saw, characteristically, as "the leeway to play absolutely any sound in the history of recorded music."
The type of music played on the show evolves gradually through the week, with reflective post-rock, dream pop, ambient electronic experiments and alt-folk dominating on Monday and Tuesday, harder electro and dubstep creeping in on Wednesdays, and then much more dancefloor-oriented programmes on Thursdays and Fridays, typical of the club sets O Ciobhan can be found playing out at the weekends. This CD focuses mainly on the kind of music that one might hear earlier in the week on the show. Mostly electronic music incorporating acoustic textures, or vice versa, it flows together into a seamless whole, conjuring up a wistful ambience of still night skies and gently shifting shadows, mildly but thrillingly disconcerting.
Opener 'Frozen Glass' by Galway's Loner Deluxe is typical in the way it quietly insinuates itself with rippling piano over a bed of muted strings and wind sounds, before a pair of soft, haunted torch songs, courtesy of Roshi featuring Pars Radio and Essie Jain, fully lull you into readiness for the hour of astral voyaging ahead. 'The Evening Station' by Dublin's Rollers/Sparkers pulses like the sombre call signal of some mysterious1970s longwave radio programme from behind the Iron Curtain, and the minimal Berlin beats of Miwon's 'Shinkansen,' skip and shuffle as the colours gradually shift around them. 'Walc' by the Polish composer Jacaszek is solid atmosphere, an inert gas, popping and crackling with lightning and dangerous static, and the smothered voices of those lost and swallowed up within.
By comparison the quirky electro-pop of 'Triple Trouble' by Galway's B-Movie Lightning, AKA Cane 141's Mike Smalle, seems almost conventional, recalling the likes of Memory Tapes but with more of a European dancefloor groove. Factory Kids are the band of one Tim Chaplin of Warwickshire, a regular listener and correspondent to the show, who has remixed their track 'Look After Everyone' under his Luminous alias. The result is a queasy drone over a fizzing drum machine, with echo-drowned vocals and the feel of 'Soon'-era My Bloody Valentine with the guitars completely scoured away.
The two acts potential listeners are most likely to be familiar with sit together towards the end of the CD. On 'False Memory Syndrome,' The Caretaker recreates a crackly 78rpm disc of the orchestra on the Titanic, holding their 50-year reunion from the rime-encrusted deck on the sea bed, while 'Loch Raven,' lifted from Animal Collective's Feels , evokes the ghostly voices of drowned surfers rising up from the wiped tapes of Smile. Suffice to say both songs are entirely at home amid the tapestry of tracks on the album, sounding neither better nor worse than the lesser-known acts alongside them. The final track is 'The Highway' by Austin's Peter and the Wolf, a regular favourite on the show since 2006 and a relatively straight-forward, strung-out ballad on piano and acoustic guitar- but a thing of fragile beauty, nevertheless.
You're left with that rare thing, a compilation that stands up to repeated listens as a whole, rather than something you cherry pick for your favourite tracks. An album for late nights and contemplation, drifting alone in the darkness, its chill beauty is at once eerily timeless, yet aches with the promise of a future yet unheard.
In Extremis - Jon Fletcher Gets To Grips With The Caretaker »
A New Column: The Finest In May's Electronic & Dance Releases »
Leyland Kirby Tears Apart The Stars On New Album »
Madness, Memory & Mindfulness: An Interview With Leyland Kirby »
Make Your Transition: Future Shocked At Krakow's Unsound Festival »
MORE FROM BEN GRAHAM
Cinema As Enema: The Butthole Surfers On Film »
Pulling At The Threads: Katie Gately's Favourite Albums »
Unquiet Spirits: Ifriqiyya Electrique Interviewed »
Spaceship - Outcrops »
Jim Jones And The Righteous Mind - COLLECTIV »
MORE: REVIEWS
Soundcarriers - Wilds »
Gonora Sounds - Hard Times Never Kill »
Silverbacks - Archive Material »
Medici Daughter - s/t »
Tanya Tagaq - Tongues »


text:
Columns, Music, Sound Tracks May 31, 2017 May 31, 2017
Sound Tracks: "We Can Always Come Back to This" on 'This Is Us'
by Kelly McCafferty
Atwood Magazine is excited to announce Sound Tracks (formerly Film Notes), a column in which we feature moments from current television and film and how they are heavily impacted and elevated by music. We all have those moments when watching our favorite movie or TV show: The ones where you find yourself unexpectedly wiping a tear from your cheek, or realizing your heart is racing just a little too fast, or even experiencing extreme yet irrational joy. This is due in part to the story and the characters, but also to the music. Music plays perhaps the largest role in actually making us feel so intensely for what we see on screen. There's a science as to how and why certain songs are placed within specific moments of a story; they exist to elevate the scene in a way that can only be done by music.
Sadly, This Is Us ended its first season recently — but have no fear, the series has already been picked up for two more seasons. If you don't watch this show, do yourself a favor and tune in. It's everything everyone says it is…and more. The stories are compelling and the characters are real, but there's another element to it that isn't getting the attention it deserves. The music. Show composer Siddhartha Khosla does an outstanding job in elevating the emotion of This Is Us with thoughtful song choices and an outstanding original score.
Perhaps the biggest stand out in the series so far was the original song penned by Khosla and songwriter Chris Pierce "We Can Always Come Back To This," airing in episode 16, titled "Memphis." A welcomed twist, this song isn't simply placed in the background to emphasize a scene. It was actually written into the script as a scene of its own, being an original song that one of the main characters, William, writes and performs with his band. "Memphis," focuses on two of the shows main characters, Randall and his birth father William, as they travel back to William's hometown of (you guessed it) Memphis so William can say goodbye before he passes away from cancer. "We Can Always Come Back To This" is a soulful ballad used twice in the episode to send one simple, powerful message: make memories with the people you love and they will stay with you forever.
The song is first introduced in a flashback during William's days of being in a band with his cousin. His cousin, played by Brian Tyree Henry, teasingly approaches him at the bar where the band often plays and a solemn William proclaims, "I've been writing and I think it might be good." And thus, "We Can Always Come Back To This" is born. Immediately his cousin hoists him to the stage and the band begins to play the first verse of the song. This first verse is very much about the bond William and his mother shared while he was growing up. In the flashback she recently left him to take care of family in Pittsburgh. He wrote "We Can Always Come Back To This" as a way of coping in her absence. It's his way of saying that they will always be there for each other, as they always have been, even miles away. Brian Tyree Henry holds zero emotion back as he sings the verse,
Standing at the station,
We don't know what to say.
Looking out the window,
As you are rolling away.
If I'm gonna be alone,
Let it be with you.
Mother don't you cry,
We are gonna be alright.
Open up your suite case when you get there tonight.
You're not alone.
I'm always always here with you.
It's filled to the brim with soul and has the signature Memphis blues sound all over it. The scene then continues with the band playing this song over a period of time. It could be months or years, but each time they play it the crowd grows larger and their faces grow happier. At the end of the sequence you're left to see how this song has turned this group from a cover band into something real. Unfortunately, things don't always work out the way you'd hope, and for young William in the flashback this was a truth he was okay with facing. He received a call from his ailing mother in Pittsburgh, and although she told him not to come, he immediately packed his bags and left his budding success in Memphis to be by her side. Yes, William was about to achieve his dreams, but how could he stay in Memphis to do that when the very song that got him to this point is based on his loyalty to his mother? What a brilliant play by the writers. By creating this song, they have let the audience in on a little secret as to why William is who he is. The lyrics of the second verse explain his actions while the bass and the keys pick up reinforcing the passionate feeling you get when thinking of someone you love,
No matter where we go from here.
No matter how the cards would fall,
I'll pick you up
and you hold me too.
So don't give up on me,
I'll never give up on you.
everything is going be alright.
As the episode goes on, the audience is able to see how deeply this song parallels between William's relationship with his mother and with his son. In present day, William takes Randall around Memphis. They go to the old bar where it all started, and William plays with his old band, giving Randall a memory he can cherish forever. A memory he can always come back to. The lyrics of the last verse mirror the present in a way young William never knew they would.
I know you believe it too.
if I'm going to be alone,
Look up not down.
It all comes around.
Even when you're gone,
We can always come back to this.
After a perfect day with his son in Memphis, the place where it all started with his mother, it becomes clear that William's time is sadly running out. He is rushed to the hospital where he is able to share his final words with Randall. These words are subtle wink to the song he wrote oh so many years ago. As William fades, Randall states with a tear, "I'll call Beth, (his wife) I'll have the girls here hugging you within hours." William quickly reply's, "No, no, no, no–I said goodbye when they were laying down. I want them to remember looking up at me not down. Up." His final words to Randall are heartbreaking, yet inspiring. "Roll all your windows down, Randall. Crank up the music, grow out that fro – let someone else make your bed… I haven't had a happy life. Bad breaks, bad choices, a life of almosts and could-haves. Some would call it sad, but I don't. Cause the two best things in my life were the person in the very beginning and the person at the very end, and that's a pretty good thing to be able to say I think." Just as the song says, it all comes around.
"We Can Always Come Back To This" is a character in itself the show created to reinforce the message that even after they're gone, your loved ones will always be with you. It's an honest and compelling reminder to cherish the people you love and to create memories with them that will last a lifetime. Whether it's going on a road trip with your dad to his hometown, or dancing in the kitchen with your mom as a child. Make the memories, because when you do, you'll always be able go back to them. The song plays one more time at the end of the episode in a stripped down acoustic version sung by Hannah Miller, allowing the feeling of loss and love to sink in. As Randall makes his drive back north, the stripped down version plays. He rolls his windows down and cranks his music up.
:: Stream/Buy "We Can Always Come Back To This" ::
Discover new music at Atwood Magazine
Kelly McCafferty
More from Kelly McCafferty
Film Notes: Leon Bridges' "River" in HBO's 'Big Little Lies'
Leon Bridges' song "River" is woven through the second episode of HBO's...
Music You Should Know: Missy Higgins, The Dazzle from Oz
Premiere: Nihla's "Lucy" Tells a Story of Hard Life & Haunting in Los Angeles
Today's Song: Beauts' New Video for "Drifters, All" Is a Love Letter to Cinema
Today's Song: Sam Skinner's "Learn" Favors Abstraction Over Narrative
Premiere: Saint Slumber Ignite with Radiant "Sleeptalk"
This Just In: Belfast's BEA Reimagines Heartbroken Memories in Debut Single "These Streets of Ours"
Previous articleConfidence, Hope, and Wearing Nothing: An Interview with Dagny
Next articlePremiere: Wooter Slip into a Daydream in "I Feel Funny"
Atwood is a digital music magazine that seeks out visionary artists and fresh voices with the goal of promoting the arts. The Atwood team is made up of individuals from all over the world that are passionate about art and innovation. We aim to showcase not only incredible creation, but also the stories behind them.
Subscribe to Atwood Magazine
Artist to Watch Ric Wilson Stuns with "Pull a James Baldwin": "Two pandemics going on – one with health, one with race"
Midwestern Glitz: Spirit of the Bear's Soaring & Stunning Eponymous Third Album
Summery Naturalness and the Comfort of Imagination: A Conversation with Buck Meek
Breaking the Record with Roan Yellowthorn, Part 46: Processing
G Flip & mxmtoon Revel in "Queen," a Fierce Anthem of Strength, Grace, and Love
Today's Song: Get Giddy on Wild Youth's New Single "Champagne Butterflies"
Rewriting History with Love and AmarElo: A Conversation with Emicida
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Conceptual Art and Homemade Samples: A Conversation with Lorana


text:
Visit our new News website at GPBNews.org
Wednesday, October 23, 2013 - 12:00pm
Young Arts Journalists Get Tips from the Pros
Steve Murray
On Oct. 22 at Macon's Museum of Arts and Scientists, I moderated the first of six free, public symposia about the changing nature of arts and criticism in the post-print, Internet world for Art Matters: Engaging the Community through Embedded Arts Journalists.
A collaboration of the Macon Arts Alliance and Mercer University's Center for Collaborative Journalism (in which GPB is a partner), the program is funded by the National Endowment for the Arts and the Knight Foundation.
The project's chief component is to "embed" young arts journalists in the Macon and Middle Georgia area with artists and arts organizations, giving these reporters longer time and more access than typical for traditional arts journalism.
The idea? To find varying forms of storytelling – long, short, macro and micro -- and to get those stories out through multiple platforms: blog posts, podcasts, newspapers, magazines, tweets, radio spots, etc.
Another component of the program, continuing through June 30, is a series of six public symposia. In each, at least one professional artist in a particular discipline will be matched with a professional critic of the same medium, to discuss the evolving nature of their roles.
The disciplines will include film, theater, literature, pop music, classical music, and the subject of the Oct. 22 symposium, visual arts.
The Atlanta-based panelists included:
- Susannah Darrow, executive director and co-founder of BURNAWAY, an Atlanta-based nonprofit arts organization dedicated to covering the arts and stimulating dialogue in Atlanta and the Southeast through an online publication, public programs, and annual print edition.
- Shara Hughes, a painter represented by American Contemporary in New York City, Michael Andersen Gallery in Copenhagen and Berlin, and Metroquadro in Turin, who had her first solo show in Atlanta at the Atlanta Contemporary Arts Center in April of 2013
- Rachel Reese, Communications Manager of the Atlanta Contemporary Arts Center, an independent curator and arts writer, former editor of BURNAWAY and contributor to vidual arts magazines and journals including Bomb Magazine online, BOMBlog, and her writing has also appeared in Temporary Art Review, ArtSlant, ART PAPERS, and TWELV Magazine.
Topics included the collapse of mainstream print media and the loss of staff writers and arts experts; the blurring of lines between the artist and the critic; and the difficulties in encouraging people to get out of the house and experience live, practical, loud, life-size art at a time when so much can be "experienced" digitally via small screens and earbuds.
The next symposium, on literature, is scheduled for Nov. 18. Details will be announced the week of Oct. 28.
Steve Murray is Critic-in-Residence for Art Matters: Engaging the Community through Embedded Arts Journalists, a collaboration between the Macon Arts Alliance and Mercer University's Center for Collaborative Journalism. Fritz and other writers in the program will be spending time with arts and arts organizations in the Macon area through June, reporting what they discover, and fostering ongoing conversations about the arts in Middle Georgia.
Tags: Adam Ragusea | BURNAWAY | Center for Collaborative Journalism | Macon Arts Alliance | Steve Murray


The Women's Action & Information Group (WAIG) had the wonderful luck to have the famous NZ pop star Dinah Lee as their guest speaker at their September 3rd meeting in the Lane Cove Library.
Dinah regaled her audience with stories of her life in the entertainment business, sparkling along with tales about her meteoric rise to the top of the pop charts in NZ and Australia in the 1960's, with hit songs such as "Reet Petite" and Don't You Know Yockomo".
Her new fashioned "mod" look and feisty strutting on the stage, so unlike other women singers at the time, made her a powerful female presence in the music industry. She travelled to the US and England where she met with such famous (and infamous) people as Robert Mitchum, John Wayne and Glen Campbell, with whom she sang a duet on the "Shindig" show, the American equivalent of Australia's "Bandstand".
In London she performed alongside a very young mod called David Jones, who later changed his surname to Bowie. "He was a beautiful boy" she commented wistfully.
Dinah also did three tours to Vietnam during 1966 and 1967, entertaining the troops from stages constructed from oil drums and from the backs of trucks. She remembers being stranded by a break-down between Da Nang and Saigon….. spending the night watching mortars streaking overhead. Now a member of the Vietnam Veterans Association, she has her own leather jacket with the VVA emblem on the back.
These days, Dinah tells us, she is still working, voice as strong as ever. "I can still move across a stage" she says. "I'm just a bit slower" – and has just completed a video clip featuring one of her new songs "Cathedral Square" which is up on YouTube and is available with the rest of her new album on iTunes or wherever you download your music.
WAIG hopes to invite Dinah back for another visit to talk about the book she plans to publish. It is an autobiographical and pictorial account of her life in the pop world in the 60's and 70's, and sure to appeal to anyone who grew up around that era.
WAIG is a women's group which values the diverse community of women and what women can achieve through collective experience, companionship and action. WAIG meets every Monday during school term in the Lane Cove Library.


For the third Duke Otto of Bavaria, see Otto I Wittelsbach, Duke of Bavaria.
Otto III (11 February 1261 – 9 November 1312), a member of the Wittelsbach dynasty, was the Duke of Lower Bavaria from 1290 to 1312 and the King of Hungary and Croatia between 1305 and 1307. His reign in Hungary was disputed by Charles Robert of the Angevin dynasty.
Otto was born in Burghausen, the son of Henry XIII, Duke of Bavaria, and Elizabeth of Hungary.
Otto succeeded his father in 1290 as duke of Lower Bavaria, together with his younger brothers, Louis III and Stephen I. He was in opposition to Habsburg and tried to regain Styria which Bavaria had lost in 1180. Otto supported Adolf, King of Germany against Habsburg and fought on his side in the Battle of Göllheim. The Hungarian crown was offered to Otto, a grandson of Béla IV of Hungary, in 1301 but he did not accept before 1305.
In August 1305, his opponent, Wenceslaus III of Bohemia, who had inherited Bohemia from his father, renounced his claim to Hungary on behalf of Otto III. Since the Habsburg Albert I of Germany was blocking the way through Austria, Otto disguised himself as a merchant, and reached Buda in November 1305.
He was then crowned with the Holy Crown of Hungary in Székesfehérvár by the Bishops of Veszprém and Csanád on 6 December. He named himself Béla V after his grandfather. However, Otto was not able to strengthen his rule. In the course of 1306, Otto's second opponent Charles of Anjou occupied Esztergom, Szepes Castle, Zólyom and some other fortresses in the northern parts of the kingdom, and in the next year he also occupied Buda. In June 1307, Duke Otto III visited the powerful Voivode of Transylvania, Ladislaus Kán, but the latter imprisoned him. On 10 October 1307, the magnates presented at the assembly in Rákos proclaimed Charles king, but the most powerful aristocrats (Matthew III Csák, Amadé Aba and Ladislaus Kán) ignored him as well. At the end of the year, Ladislaus Kán set Otto free who then left the country, but the Voivode of Transylvania still denied to hand over the Holy Crown of Hungary to Charles, whose legitimacy could be questioned without the coronation with the Holy Crown.
Otto abdicated the Hungarian throne in 1308. Otto's involvement in Austrian and Hungarian affairs weakened his position in Bavaria and finally led to failure due to financial problems. In Hungarian historiography he is noted as an anti-king during the interregnum of 1301–1310.
During his presence in Hungary 1305–1308 Lower Bavaria was ruled by his brother Stephen I. In 1310 a new war against Habsburg devastated Burghausen. Otto died in 1312 and was succeeded in Lower Bavaria by his son Henry XV, who shared power with his cousins, Henry XIV and Otto IV, both sons of Stephen I. John I, a son of Henry XIV, was the last duke of Lower Bavaria before Louis IV, Holy Roman Emperor inherited the country and reunited the duchy in 1340.
In January, 1279, Otto married Catherine, a daughter of Rudolph I of Germany and Gertrude of Hohenburg. Their twins, Henry and Rudolph, were born in 1280 and died the same year.
Henry XV, Duke of Bavaria (28 August 1312 – 18 June 1333).


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Rosa Hart Theatre
Christmas in Louisiana: Once Upon a Time school shows are December 12th & December 13th at 9:30 a.m.
Online reservations are now closed for our school shows. Tickets will be sold at the door in the Lakeside Lobby.
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Saturday Matinee & Gala Tickets
LCCB has entertained the community for 50 years, and we strive to achieve a total dance theatre experience by providing a place for experimentation in choreography, composition, and design. None of it would be possible without the participation of the local community offering their support. Help us continue the legacy by becoming a sponsor today!
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Assemblé 2020, The Sleeping Beauty - Gala Performance
The Sleeping Beauty is the classic tale of Princess Aurora, cursed into a deep sleep, and awakened by the kiss of a prince. Based on the Kirov Ballet, this LCCB production boasts original choreography by Artistic Director, Lady Holly Hathaway Kaough and several Civic Ballet alumni. Accompanying the dancers of LCCB is the Lake Charles Symphony, under the direction of guest conductor William G. Rose. With stunning sets by Fred Stark, lavish costumes by the late Raedelle Robbins, and live performance of the Tchaikovsky score, this is a must-see event!
WHEN March 21, 2020 at 6:00 pm - 8:00 pm
WHERE Rosa Hart Theatre
Assemblé 2020, The Sleeping Beauty - Matinee Performance
LCCB Spring Performance
Lake Charles Civic Ballet is proud to present its Spring Performance. Join us as we showcase the students of Lady Leah Lafargue School of the Dance in ballet, tap, and jazz. Dancers of Lake Charles Civic Ballet will perform select works from an extensive repertoire. A memorable event recognized by art patrons which spotlights the professional training of our area's future artists.
WHEN May 17, 2020 at 10:00 am - 12:00 pm
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