text:
The lupus susceptibility gene Pbx1 regulates the balance between follicular helper t cell and regulatory t cell differentiation
Seung Chul Choi, Tarun E. Hutchinson, Anton A. Titov, Howard R. Seay, Shiwu Li, Todd M. Brusko, Byron P. Croker, Shahram Salek-Ardakani, Laurence Morel
Pbx1 controls chromatin accessibility to a large number of genes and is entirely conserved between mice and humans. The Pbx1-d dominant-negative isoform is more frequent in CD4+ T cells from lupus patients than from healthy controls. Pbx1-d is associated with the production of autoreactive T cells in mice carrying the Sle1a1 lupus-susceptibility locus. Transgenic (Tg) expression of Pbx1-d in CD4+ T cells reproduced the phenotypes of Sle1a1 mice, with increased inflammatory functions of CD4+ T cells and impaired Foxp3+ regulatory T cell (Treg) homeostasis. Pbx1-d-Tg expression also expanded the number of follicular helper T cells (TFHs) in a cell-intrinsic and Ag-specific manner, which was enhanced in recall responses and resulted in Th1-biased Abs. Moreover, Pbx1-d-Tg CD4+ T cells upregulated the expression of miR-10a, miR-21, and miR-155, which were implicated in Treg and follicular helper T cell homeostasis. Our results suggest that Pbx1-d impacts lupus development by regulating effector T cell differentiation and promoting TFHs at the expense of Tregs. In addition, our results identify Pbx1 as a novel regulator of CD4+ T cell effector function.
https://doi.org/10.4049/jimmunol.1502283
10.4049/jimmunol.1502283
Dive into the research topics of 'The lupus susceptibility gene Pbx1 regulates the balance between follicular helper t cell and regulatory t cell differentiation'. Together they form a unique fingerprint.
T-Helper Cell Immunology and Microbiology 100%
Lupus Erythematosus Neuroscience 80%
Regulatory T Cell Immunology and Microbiology 40%
Mouse Immunology and Microbiology 30%
Transgenics Immunology and Microbiology 30%
Mice Agricultural and Biological Sciences 30%
Cell Differentiation Immunology and Microbiology 20%
Homeostasis Immunology and Microbiology 20%
Choi, S. C., Hutchinson, T. E., Titov, A. A., Seay, H. R., Li, S., Brusko, T. M., Croker, B. P., Salek-Ardakani, S., & Morel, L. (2016). The lupus susceptibility gene Pbx1 regulates the balance between follicular helper t cell and regulatory t cell differentiation. Journal of Immunology, 197(2), 458-469. https://doi.org/10.4049/jimmunol.1502283
The lupus susceptibility gene Pbx1 regulates the balance between follicular helper t cell and regulatory t cell differentiation. / Choi, Seung Chul; Hutchinson, Tarun E.; Titov, Anton A. et al.
In: Journal of Immunology, Vol. 197, No. 2, 15.07.2016, p. 458-469.
Choi, SC, Hutchinson, TE, Titov, AA, Seay, HR, Li, S, Brusko, TM, Croker, BP, Salek-Ardakani, S & Morel, L 2016, 'The lupus susceptibility gene Pbx1 regulates the balance between follicular helper t cell and regulatory t cell differentiation', Journal of Immunology, vol. 197, no. 2, pp. 458-469. https://doi.org/10.4049/jimmunol.1502283
Choi SC, Hutchinson TE, Titov AA, Seay HR, Li S, Brusko TM et al. The lupus susceptibility gene Pbx1 regulates the balance between follicular helper t cell and regulatory t cell differentiation. Journal of Immunology. 2016 Jul 15;197(2):458-469. doi: 10.4049/jimmunol.1502283
Choi, Seung Chul ; Hutchinson, Tarun E. ; Titov, Anton A. et al. / The lupus susceptibility gene Pbx1 regulates the balance between follicular helper t cell and regulatory t cell differentiation. In: Journal of Immunology. 2016 ; Vol. 197, No. 2. pp. 458-469.
@article{791c899fbb0a42a1bd803dd7f343bfbe,
title = "The lupus susceptibility gene Pbx1 regulates the balance between follicular helper t cell and regulatory t cell differentiation",
abstract = "Pbx1 controls chromatin accessibility to a large number of genes and is entirely conserved between mice and humans. The Pbx1-d dominant-negative isoform is more frequent in CD4+ T cells from lupus patients than from healthy controls. Pbx1-d is associated with the production of autoreactive T cells in mice carrying the Sle1a1 lupus-susceptibility locus. Transgenic (Tg) expression of Pbx1-d in CD4+ T cells reproduced the phenotypes of Sle1a1 mice, with increased inflammatory functions of CD4+ T cells and impaired Foxp3+ regulatory T cell (Treg) homeostasis. Pbx1-d-Tg expression also expanded the number of follicular helper T cells (TFHs) in a cell-intrinsic and Ag-specific manner, which was enhanced in recall responses and resulted in Th1-biased Abs. Moreover, Pbx1-d-Tg CD4+ T cells upregulated the expression of miR-10a, miR-21, and miR-155, which were implicated in Treg and follicular helper T cell homeostasis. Our results suggest that Pbx1-d impacts lupus development by regulating effector T cell differentiation and promoting TFHs at the expense of Tregs. In addition, our results identify Pbx1 as a novel regulator of CD4+ T cell effector function.",
author = "Choi, {Seung Chul} and Hutchinson, {Tarun E.} and Titov, {Anton A.} and Seay, {Howard R.} and Shiwu Li and Brusko, {Todd M.} and Croker, {Byron P.} and Shahram Salek-Ardakani and Laurence Morel",
note = "Funding Information: This work was supported by National Institutes of Health Grants R01 AI045050 (to L.M.) and R01 AI087734 (to S.S.-A.). Publisher Copyright: Copyright {\textcopyright} 2016 by The American Association of Immunologists, Inc.",
doi = "10.4049/jimmunol.1502283",
T1 - The lupus susceptibility gene Pbx1 regulates the balance between follicular helper t cell and regulatory t cell differentiation
AU - Choi, Seung Chul
AU - Hutchinson, Tarun E.
AU - Titov, Anton A.
AU - Seay, Howard R.
AU - Li, Shiwu
AU - Brusko, Todd M.
AU - Croker, Byron P.
AU - Salek-Ardakani, Shahram
AU - Morel, Laurence
N1 - Funding Information: This work was supported by National Institutes of Health Grants R01 AI045050 (to L.M.) and R01 AI087734 (to S.S.-A.). Publisher Copyright: Copyright © 2016 by The American Association of Immunologists, Inc.
N2 - Pbx1 controls chromatin accessibility to a large number of genes and is entirely conserved between mice and humans. The Pbx1-d dominant-negative isoform is more frequent in CD4+ T cells from lupus patients than from healthy controls. Pbx1-d is associated with the production of autoreactive T cells in mice carrying the Sle1a1 lupus-susceptibility locus. Transgenic (Tg) expression of Pbx1-d in CD4+ T cells reproduced the phenotypes of Sle1a1 mice, with increased inflammatory functions of CD4+ T cells and impaired Foxp3+ regulatory T cell (Treg) homeostasis. Pbx1-d-Tg expression also expanded the number of follicular helper T cells (TFHs) in a cell-intrinsic and Ag-specific manner, which was enhanced in recall responses and resulted in Th1-biased Abs. Moreover, Pbx1-d-Tg CD4+ T cells upregulated the expression of miR-10a, miR-21, and miR-155, which were implicated in Treg and follicular helper T cell homeostasis. Our results suggest that Pbx1-d impacts lupus development by regulating effector T cell differentiation and promoting TFHs at the expense of Tregs. In addition, our results identify Pbx1 as a novel regulator of CD4+ T cell effector function.
AB - Pbx1 controls chromatin accessibility to a large number of genes and is entirely conserved between mice and humans. The Pbx1-d dominant-negative isoform is more frequent in CD4+ T cells from lupus patients than from healthy controls. Pbx1-d is associated with the production of autoreactive T cells in mice carrying the Sle1a1 lupus-susceptibility locus. Transgenic (Tg) expression of Pbx1-d in CD4+ T cells reproduced the phenotypes of Sle1a1 mice, with increased inflammatory functions of CD4+ T cells and impaired Foxp3+ regulatory T cell (Treg) homeostasis. Pbx1-d-Tg expression also expanded the number of follicular helper T cells (TFHs) in a cell-intrinsic and Ag-specific manner, which was enhanced in recall responses and resulted in Th1-biased Abs. Moreover, Pbx1-d-Tg CD4+ T cells upregulated the expression of miR-10a, miR-21, and miR-155, which were implicated in Treg and follicular helper T cell homeostasis. Our results suggest that Pbx1-d impacts lupus development by regulating effector T cell differentiation and promoting TFHs at the expense of Tregs. In addition, our results identify Pbx1 as a novel regulator of CD4+ T cell effector function.
U2 - 10.4049/jimmunol.1502283
DO - 10.4049/jimmunol.1502283


STI Investigations & Intelligence Group is a Full Service Private Investigation And Surveillance Firm With Offices In Surrey And Victoria British Columbia. We Offer Professional Investigation Services Serving Vancouver, The Fraser Valley, And Vancouver Island With Associates Worldwide.
We Are A Dedicated Team Of Licensed Professional Private Investigators Who Are Passionate About What We Do.
Our Investigations And Surveillance Team Are Experts In Surveillance, Cyber Intelligence, And Investigations.
Our Services Are Strictly Confidential & Always Discreet.


It seems once every generation A Star Is Born needs to be remade. The latest, co-written, co-produced, and directed by co-star Bradley Cooper, is the fourth film version, the first since the 1976 release starring Barbara Streisand and Kris Kristofferson. Cooper and Grammy Award-winning artist Lady Gaga contribute to a brave and emotional film that is an instant Oscar contender. At the same time, Cooper looks like he can do anything because not only can he act, write, produce and direct, but the man can also sing.
A Star Is Born is a dramatic love story which sees two musicians going in opposite directions. Jackson Maine (Cooper), while on tour, stumbles upon a drag bar and sees Ally (Lady Gaga), a server moonlighting as a singer-songwriter, performing. They meet backstage and hang out all night, their nocturnal adventures highlighted by a song Ally wrote and sang to Jackson in a grocery store parking lot. This proves to be a life-changing encounter – Jackson invites Ally to one of his shows and blindsides her by inviting her onstage to perform her song in front of thousands. This proves to be one of many touching moments in Cooper's directorial debut and takes full advantage of the chemistry the pair provides.
A Star Is Born is a film and outlet for exceptional music, so both aspects need to stand out. The music is a character, with the new original work written mainly by Lukas Nelson (son of Willie Nelson). Lady Gaga, of course, co-wrote and provided vocals, and the music is diverse and helps tell the film's story with authenticity and heart. The highlight is Shallow, a song which is a shoo-in for an Oscar nomination.
Because she's known for her music, it wasn't surprising to see Lady Gaga belting out tunes with ease. The question coming in is can she act. It's likely that only her biggest fans know this, but Gaga studied acting, so it was just a matter of when she'd be able to show off what she's learned. At one time a role reserved for Beyoncé, Gaga turns in a memorable performance, punctuated by playing a character that's stripped down, emotionally bare and hungry for her music to be heard. I found it amusing that Ally began as a no-nonsense artist when she debuted onstage with Jackson, but as her star took flight her image began to resemble Lady Gaga's real-life presence. It almost looks like Lady Gaga, through Ally, is fighting against the things that made her what she is today. She's daring us to forget what you already know and see – judge her by what you hear. Despite the art-imitating life motif, Lady Gaga is fantastic and one of the main reasons why this A Star Is Born works.
With Lady Gaga making such a lasting impression, it's up to Cooper to hold serve. Cooper took on a lot of responsibility – he co-wrote (with Eric Roth and Will Fetters); he co-starred; he directed; he co-produced; he even sang. And since Cooper's never helmed a film before it's understandable to wonder if he could pull it off. Based on the final product, he did… and did it impressively. Cooper's direction included excellent cinematography (with help from Oscar-nominated cinematographer Matthew Libatique), lasting images and artistic choices which highlighted both Lady Gaga's and his strengths. The story he helped write is a modern tale, which means younger viewers will see something relatable, while older audiences who've seen the previous incarnations of A Star Is Born will see a fresh and sincere take on this rags to riches story.
Bradley Cooper did an amazing job as director, producer and writer, but when it's all said and done he is a phenomenal actor. As Jackson Maine, Cooper successfully portrays a musician who has overcome tinnitus to become a star. But Jackson endured a tough childhood where his significantly older brother Bobby (Sam Elliott, in an award-worthy performance) raised him. Jackson's past has led to alcoholism and drug abuse, and his addictions grow as his star dims. Jackson's life spirals out of control and it affects his relationship with Ally, who's become his wife. There's a point where Noodles (Dave Chappelle), a retired musician and Jackson's close friend, tells Jackson, after finding him passed out on the sidewalk, "It's the first time I'm worried about you." The concern drops like a bomb because a longtime friend is actually worried and because Cooper is so convincing conveying his personal tragedy. Add to it Cooper's Eddie Vedder-inspired singing and Cooper has become an Oscar frontrunner. We may have witnessed Lady Gaga's birth as a movie star, but the film's true star is Cooper.
Thanks to Cooper's star power, there are a few interesting cameos. That isn't the sole reason to watch A Star Is Born but it's a nice bonus. This is an amazing feature and full of emotion, heart, pain and love. The only drawback is it's a little long. It's deliberately paced, so its 135-minute running time feels longer. This would be near-perfect had Cooper trimmed 15-20 minutes. Regardless, A Star Is Born is beautiful work.


We're here to help, connect or talk to us now.
1. Please do not include confidential account information.
2. For account related inquiries or request, please call our HL Bank Contact Centre at +65 6818 0503 to speak to our Customer Relationship Officer.
3. Kindly be informed that the bank shall revert within one (1) working day for normal enquiries/requests and four (4) working days for complaints from the date of submission.
You will get to enjoy attractive interest rates on your savings account.
HL Bank Savings Account provides you with a monthly statement that records of all your transactions including interest earned so you can see your savings grow.
You must be minimum 21 years old.
This account is applicable to individuals only.
Note: Account opening is subject to Bank's approval.
Singapore dollar deposits of non-bank depositors are insured by the Singapore Deposit Insurance Corporation, for up to S$75,000 in aggregate per depositor per Scheme member by law. Foreign currency deposits, dual currency investments, structured deposits and other investment products are not insured.
Have not found what you're looking for?
All rights reserved. Copyright 2015 HL Bank Singapore. All content on this website is governed by Singapore law and is subject to the provisions in the Terms & Conditions which is accessible via the link above.


text:
Maritime workers face risks and dangers that differ from those faced by onshore employees. The law recognizes these differences, and offers remedies other than worker's compensation to maritime workers who are injured or who suffer illnesses as a result of their work related duties.
Maritime law and unseaworthiness claims
Any maritime worker faced with an illness or injury while working on a vessel is entitled to maintenance and cure. Maintenance includes money to cover the seaman's costs of living. This includes food, rent or mortgage payments, property taxes, and utilities. Cure refers to medical care necessary to treat the injury suffered by the individual. Maintenance and cure only apply to the most basic necessities. Even things such as car payments, and phone bills are likely excluded from this form of compensation.
Maritime workers can collect maintenance and cure until they reach maximum medical improvement, which is the point when they have recovered as much as they are expected to. This means the aid ceases even if the individual does not reach the level of health they had prior to the incident, or illness.
By dictating that ship owners have a duty to provide seamen a seaworthy vessel, general maritime law also creates the ability for maritime workers to file claims for unseaworthiness. This duty is absolute and non-delegable. If a seaman who is employed on the vessel is injured on that vessel while it is in navigation and the injury is the result of a part of the vessel or the vessel's equipment not being in adequate condition to perform the function it is meant to, then the seaman has a claim against the owner of the vessel. The owner did not have to be negligent, or even have to know about the condition. The fact that the unacceptable condition existed is all that is necessary, and these claims offer some of the largest remedies to injured seamen and their families.
In addition to general maritime law, the Jones Act, and the Longshore and Harbor Worker's Compensation Act provide other remedies to qualifying maritime workers.
The Jones Act
The Jones Act allows for negligence claims to be made against an injured seaman's employer. Unlike typical negligence claims, the seaman must only prove that the employer's negligence played a slight part in the injury. It is therefore easier to prove, and easier to recover damages than in an onshore negligence claim.
The law applies specifically to the individuals who can qualify as "seamen" under the act. The definition of seamen is not included in the act itself though, so a court is left to determine whether an employee qualifies.
To qualify as a seaman, the worker must contribute to the work of a vessel that is afloat on navigable waters. Navigable waters are those that can be used for international or interstate commerce. The employee must also spend a significant amount of his or her employment time on the vessel, or on vessels that are owned by the employer. This issue can end up being somewhat complicated, but a rule of thumb often used is that 30% of the worker's employment time should be spent on the vessel.
Unlike maintenance and cure, the damages available from a Jones Act claim are not limited to basic medical expenses and room and board. The damages can include the individual's lost earnings, lost earning capacity, medical expenses, including future projected medical expenses, as well as damages for the pain and suffering that resulted from the injury or illness. These damages tend to be much higher than those available from maintenance and cure, though an unseaworthiness claim might be comparable.
Longshore and Harbor Worker's Compensation Claims
The Longshore and Harbor Worker's Compensation Act (Longshore Act) protects those maritime workers who are not crewmembers of a vessel, and who do not qualify as seamen. In addition to the obvious, longshoremen, the Longshore Act covers shipbuilders, shipbreakers, harbor-workers and ship repairmen, among others. Like other worker's compensation programs, the Longshore Act does not rely on the fault of the employer. Longshore Act claims tend to have higher compensation benefits than do onshore worker's compensation claims. They also allow for individuals to choose their own doctors, and can provide lifetime medical treatment related to the injury, unlike some state worker's compensation programs.
Maritime Law Attorneys
Maritime law is complex, and unlike onshore worker's compensation claims, maritime law has various different ways for injured employees to be compensated if they suffer from an on the job injury or illness. If you are a maritime worker who was injured on the job, you should contact an attorney to discuss the facts specific to your case, and to determine the best course of action for you.
Contact Stern Law, PLLC for A Free Consultation
At Stern Law, PLLC, we have compassionate and caring attorneys ready to work with you in order to find the best strategy for seeking compensation for your maritime employment related injuries. Contact Stern Law, PLLC today at (844) 808-7529 for a free consolation with an experienced attorney.
Maritime Injury Claims Free Case Review
General Maritime Law Claims Overview
General Maritime Law Claims Processes
Maritime Law FAQs
Maritime Law Attorney
Maritime and Offshore Injury Law: The Longshore Act and OCSLA

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}
Reinforcement Learning (RL) has become a popular framework for autonomous behavior generation from limited feedback~\cite{sutton1998introduction}. Typical RL methods learn in isolation increasing their learning times and sample complexities. Transfer learning aims to significantly improve learning by providing informative knowledge from an external source. The source of such knowledge varies from source agents to humans providing advice~\cite{erez2008does,taylor2009transfer}. In this paper, we focus on a framework referred to as action advice or the \emph{advice model}~\cite{torrey2013teaching}. Here, the agent (i.e., student), learning in a task, has access to a teacher (another agent or human) which can provide action suggestions to facilitate learning.
Given ``good-enough'' teachers, such advice models have shown multiple benefits over standard RL techniques. For example, others~\cite{torrey2013teaching,LIP610219} show reduced learning times and sample complexities for successful behavior.

These methods, however, suffer from two main drawbacks. First, validation results are empirical in nature and not formally-grounded. We do not have fundamental understanding of these methods. Consequently, it is difficult to formally comprehend why these methods work.
Second, most of these techniques require the availability of a ``good-enough'' (optimal) teacher to benefit the student. Unfortunately, access to such teachers is difficult in a variety of complex domains, reducing the applicability of policy advice in real-world settings.

In this paper, we remedy the aforementioned drawbacks by proposing a new framework for policy advice. Our method formally generalizes current single-teacher advice models to the multi-teacher setting. Our algorithm also remedies the need for optimal teachers by exploiting both the student's and the teacher's knowledge. Even if the teacher is not optimal, a student, using our algorithm, is still capable of acquiring optimal behavior in a task; a property not supported by some state-of-the-art methods, e.g., learning from demonstration. We theoretically and empirically analyze the performance of the proposed method and derive, for the first time, regret bounds quantifying the successfulness of action advice. We also provide theoretical justification for current methods (i.e., single-teacher models) as special case of our formulation in the appendix. Our contributions can be summarized as:
\begin{itemize}
	\item defining (formally) multi-teacher advice models,

	\item introducing novel algorithms leveraging teacher and student knowledge,
	\item deriving the regret analysis showing reduced sample complexities,
	\item deriving theoretical guarantees for single teacher advice models, and
	\item quantifying negative transfer under such advice model.
\end{itemize}

Interestingly, these theoretical results justify a well-known intuition inherent to advice models: ``good teachers help while bad teachers hurt.'' The results  show that students can still achieve optimal behavior when being advised by bad teachers. They, however, pay an extra cost in terms of their learning times or sample complexities, relative to an optimal teacher. This should inspire researchers to adopt high quality teacher policies or avoid ``bad teachers'' if possible.

Given our formalization, we also derive a relation to negative transfer. We quantify, for the first time, the occurrence of negative transfer in action advice models, shedding the light on failure modes of these methods. Consequently, these results yield two claims about transfer learning. First, high quality transfer knowledge may still cause negative transfer when the target algorithm is able to outperform the source knowledge. Second, expert knowledge is important for the researchers to determine whether or not to transfer because evaluation of the transfer knowledge is usually expensive (it is equivalent to evaluating the teacher policy in the target MDP).

\section{Preliminaries}
\subsection{Online Reinforcement Learning \& Regret Model}
In RL, an agent must sequentially select actions to maximize its total expected return. Such problems are formalized as a Markov decision Process (MDP), defined as $\mathcal{M}= \langle \mathcal{S},\mathcal{A}, \mathcal{P}, \mathcal{R} \rangle$, where $\mathcal{S}$ and $\mathcal{A}$ denote the finite state and actions spaces with a total size of $|\mathcal{S}|$ and $|\mathcal{A}|$ respectively, $\mathcal{P}: \mathcal{S} \times \mathcal{A} \times \mathcal{S} \rightarrow [0,1]$ represents the probability transition kernel describing the task dynamics, and $\mathcal{R}: \mathcal{S} \times \mathcal{A} \rightarrow \mathbb{R}$ is the reward function quantifying the performance of the agent. The total expected return of an agent following an algorithm, $\mathcal{G}$, to compute the optimal action-selection rule from a starting state $\bm{s} \in \mathcal{S}$ after $T$ time steps is defined as:
\begin{equation}
\label{Eq:return}
\mathcal{R}^{\mathcal{G}} (\bm{s},T) = \mathbb{E}\left[\sum_{t=0}^{T} \mathcal{R} (\bm{s}_{t},\bm{a}_{t})\right],
\end{equation}
with $\bm{s}_{t} \in \mathcal{S}$ and $\bm{a}_{t} \in \mathcal{A}$. The goal is to determine an optimal policy, $\pi^{\star}: \mathcal{S} \rightarrow \mathcal{A}$ that maximizes the total expected return.

\textbf{Regret Model:} Similar to standard online learning, we quantify the performance of the algorithm, $\mathcal{G}$, by measuring its regret with respect to optimality. We define the regret of a state $\bm{s}$ after $T$ time steps in terms of the expected reward as:
\begin{equation}
\label{Eq:regret}
\bm{\Delta}^{\mathcal{G}}(\bm{s},T)= \bm{\lambda}^{\star}T - \mathcal{R}^{\mathcal{G}} (\bm{s},T),
\end{equation}
where $\bm{\lambda}^{\star}$ is the optimal reward acquired by following an optimal algorithm $\mathcal{G}^{\star}$ at each time step. In the general case when no reachability assumptions are imposed, it is easy to construct MDPs in which algorithms suffer high regret. Following \citeauthor{puterman2005markov} \shortcite{puterman2005markov}, we remedy this problem by considering weakly-communicating MDPs\footnote{Please note that weakly-communicating MDPs are considered the most general among subclasses of MDPs, see~\citeauthor{puterman2005markov} \shortcite{puterman2005markov}.} defined as follows.
\begin{definition}
	\label{def:weakcommun}
	An MDP is called \textit{weakly communicating} in such a case where the state set $\mathcal{S}$ can be decomposed into two subsets, $\mathcal{S}_{1}$ and $\mathcal{S}_{2}$. In $\mathcal{S}_{1}$ any state is reachable from every other state under a deterministic policy, $\pi$, while states in $\mathcal{S}_{2}$ are transient under all policies.
\end{definition}

The optimal gain, $\bm{\lambda}^{\star}$ in Equation~\ref{Eq:regret}, is state independent. That is, any $\bm{s} \in \mathcal{S}$, shares the same optimal expected reward~\cite{puterman2005markov}, which can be solved for using:
\begin{equation*}
\bm{h}^{\star}+\bm{\lambda}^{\star}\bm{e}=\max_{\bm{a} \in \mathcal{A}}\{\mathcal{R}(\bm{s},\bm{a})+\mathcal{P}^\mathsf{T}_{\bm{s},\bm{a}} \bm{h}^\star\},
\end{equation*}
where $\bm{h}^{\star}$ is an $|\mathcal{S}|$ dimensional vector typically referred to as the bias vector, $\mathcal{P}_{\bm{s},\bm{a}}$ denotes the probability to transition from $\bm{s}$ applying action $\bm{a}$, and  $\bm{e} \in \mathbb{R}^{|\mathcal{S}|}$ is a unit dimensional vector. When needed, we explicitly write the dependency of $\bm{\lambda}^{\star}$ and $\bm{h}^{\star}$ as $\bm{h}^{\star}(\bm{s}; \mathcal{M})$ and $\bm{\lambda}^{\star}(\mathcal{M})$. We also define the span of $\bm{h}$ as: $
\text{sp}(\bm{h})= \max_{\bm{s} \in \mathcal{S}} \bm{h}(\bm{s})- \min_{\bm{s} \in \mathcal{S}} \bm{h}(\bm{s}).$

Finally, we follow \citeauthor{bartlett2009regal} \shortcite{bartlett2009regal} to define reachability in weakly communicating MDPs using the \emph{one-way diameter}:
$\text{diam}_{\text{one-way}}(\mathcal{M}) = \max_{\bm{s} \in \mathcal{S}} \min_{\pi} T^{\pi}_{\bm{s}_1\to \bar{\bm{s}}}$,
with $T^{\pi}_{\bm{s}_1\to \bar{\bm{s}}}$ being the expected number of steps needed for reaching $\bar{\bm{s}}=\arg\max_{\bm{s} \in \mathcal{S}} \bm{h}^{\star}(\bm{s}; \mathcal{M})$ from $\bm{s}_{1} \in \mathcal{S}$.

\subsection{Algorithms for Weakly-Communicating MDPs}
REGAL.C is an on-line algorithm for weakly communicating MDPs developed by \citeauthor{bartlett2009regal}~\shortcite{bartlett2009regal}. The basic idea is that the REGAL.C can estimate the true MDP with high probability in order to learn an $\epsilon$-optimal policy with high probability. Let $N(\bm{s},\bm{a},\bm{s}^{\prime};t)$ be the number of state-action-state triples $(\bm{s},\bm{a},\bm{s}^{\prime})$ that have been visited at time $t$. Further, let $t_i$ to denote the initial time of the iteration $i$. For brevity, we use  $N_i(\bm{s},\bm{a},\bm{s}^{\prime})$ and  $N_i(\bm{s},\bm{a})$ to denote $N(\bm{s},\bm{a},\bm{s}^{\prime};t_i)$ and $N(\bm{s},\bm{a};t_i)$ at iteration $i$. We also use $v_i(\bm{s},\bm{a})=N_{i+1}(\bm{s},\bm{a})-N_{i}(\bm{s},\bm{a})$ to denote the number of times a state-action pair $(\bm{s},\bm{a})$ is visited during iteration $i$. For each iteration $i$, REGAL acquires a dataset $\mathcal{D}_i$ as input and updates the transition probability (see Equation~\ref{eq:updatepro}). It then constructs a set of MDPs $\mathcal{M}^i$ to select from using $\max \lambda^*(M)\text{   s.t.   }sp(h^*(M)) \leq H$, where $H$ is the upper bound on the span $\text{sp}(\bm{h}^*(\mathcal{M}))$. Given the MDP, REGAL.C uses value iteration for acquiring the optimal policy. These steps are summarized in Algorithm~\ref{alg:REGAL.C}.
\begin{algorithm}[t]
	\caption{REGAL.C: Constrained Optimization}
	\textbf{Input}: parameter $H$, dataset $\mathcal{D}_i$ and current time $T$ \\
	\textbf{Output}: $\hat{\pi}_{i+1}$
	\label{alg:REGAL.C}
	\begin{algorithmic}[1]
		\STATE $t_i=$ current time $T$
		\STATE Use $\mathcal{D}_i$ to update the state transition probabilities by \begin{equation}
		\label{eq:updatepro}
		\hat{P}^{t}_{\bm{s},\bm{a}}(\bm{s}^{\prime})=\frac{N(\bm{s},\bm{a},\bm{s}^{\prime};t)}{\max\{N(\bm{s},\bm{a};t),1\}}
		\end{equation}
		\STATE With $t=t_i$, $\mathcal{M}^i$ is the set of MDPs s.t.
		\[

		\norm{P_{\bm{s},\bm{a}}-\hat{P}^t_{\bm{s},\bm{a}}}_1\leq \sqrt{\frac{12|\mathcal{S}|\log(2|\mathcal{A}|t/\delta)}{\max\left\{N(\bm{s},\bm{a},\bm{s};t),1\right\}}}

		\]
		\STATE Select $M^i \in \mathcal{M}^i$ by following optimization equation over $\forall M \in \mathcal{M}^i$,
		\begin{align*}
		\max \bm{\lambda}^{\star}(M)\text{   s.t.   }\text{sp}(\bm{h}^*(\mathcal{M})) \leq H
		\end{align*}
		\STATE $\hat{\pi}_{i+1}$=average reward optimal policy for $M^i$ (value iteration)
		\RETURN $\hat{\pi}_{i+1}$
	\end{algorithmic}
\end{algorithm}

\subsection{Single Teacher Advice Model}
The single teacher advice model is a framework in which a student learning in an environment benefits from a teacher's advice to speed-up learning. We define such a framework as the tuple of $\langle \pi^{\mathfrak{T}}, \bm{b}, \mathfrak{S}, \bm{f}_{d} \rangle$. Here, $ \pi^{\mathfrak{T}}$ denotes the teacher's policy, $\bm{b}$ represents the budget constraining the teacher's advice, $\mathfrak{S}$ is the student, and $\bm{f}_{d}$ is a function controlling the advice from the teacher to the student. Apart from considering single teacher models, previous work assumed optimal teachers where students always execute recommended actions. It is easy to construct complex settings in which access to optimal teachers is difficult. Consequently, we extend these works to the more realistic settings of sub-optimal teachers, as we detail later.

\section{Multiple Teacher Advice Model}
In this section we start by extending the single teacher model of~\citeauthor{torrey2013teaching}~\shortcite{torrey2013teaching} to the multiple \emph{non-optimal} teacher setting. Our advice model for $m$ teachers is defined as the tuple $\langle \Pi, \mathcal{B}, \mathfrak{S}, \bm{f}_{d}\rangle$, where $\Pi=\{\pi^{\mathfrak{T}_{1}}_1,\pi^{\mathfrak{T}_{2}}_2,\dots,\pi^{\mathfrak{T}_{m}}_m\}$ is the set of $m \in \mathbb{N}$ teacher policies, and $\mathcal{B}=\{\bm{b}_1,\bm{b}_2,\dots,\bm{b}_m\}$ denotes the set of budgets. It is easy to see that in case $\Pi=\{\pi^{\mathfrak{T}}\}$ and $\mathcal{B}=\{\bm{b}\}$, we can easily recover the special case single teacher model. We also generalize the work of~\citeauthor{torrey2013teaching}~\shortcite{torrey2013teaching} by making no restrictive assumptions on the optimality of any of the teachers. We measure the performance of the teacher with respect to a base policy $\pi^{\mathfrak{B}}$ in terms of regret:
\begin{definition}
	Given a teacher's policy, $\pi^{\mathfrak{T}} \in \Pi$, and a base policy $\pi^{\mathfrak{B}}$, then the regret of following $\pi^{\mathfrak{T}}$ is related to that acquired by following $\pi^{\mathcal{B}}$ using:
	\begin{equation*}
	\bm{\Delta}^{\mathfrak{T}}(\bm{s},T)=\rho\bm{\Delta}^{\mathfrak{B}}(\bm{s},T),
	\end{equation*}
	where $\rho \geq 0$ denotes the regret ratio, $\bm{\Delta}^{\mathfrak{T}}(\bm{s},T)=\bm{\lambda}^{\star}T - \mathcal{R}^{\mathfrak{T}}(\bm{s},T)$ and $\bm{\Delta}^{\mathfrak{B}}(\bm{s},T)=\bm{\lambda}^{\star}T - \mathcal{R}^{\mathfrak{B}}(\bm{s},T).$

\end{definition}
The above definition captures the three interesting cases quantifying the performance of an advice-based algorithm. If the teacher is optimal, i.e., when $\bm{\Delta}^{\mathfrak{T}} (\bm{s},T)=0$, $\rho$ is also $0$. In case  $0 < \rho \leq 1$, then $\bm{\Delta}^{\mathfrak{T}}(\bm{s},T) \leq \bm{\Delta}^{\mathfrak{B}}(\bm{s},T)$ indicating the the teacher's policy is at least as good as the base policy $\pi^{\mathfrak{B}}$. Finally, when $\rho >1$, $\bm{\Delta}^{\mathfrak{T}}(\bm{s},T) > \bm{\Delta}^{\mathfrak{B}}(\bm{s},T)$ implying the underperformance of the teacher. Consequently, with the correct choice of the teacher by $\rho$ one can still achieve successful advice even in such a generalized setting.

\section{Efficient Multi-teacher Advice}\label{Sec:New}
In this section, we propose a new algorithm which combines the advice policy and the MDPs information collected so far. This allows for an accurate framework outperforming state-of-the-art techniques for policy advice. On a high level, our algorithm consists of three main steps. First, a combined policy is constructed based on multiple teachers. Second, data depending on both teacher's advice as well as MDP information is collected. Third, a new policy is computed online.

Next, we outline each of the three steps and describe our novel algorithm. Having achieved an accurate advice model, we then rigorously analyze the theoretical aspects of our method and show a decrease in sample complexities compared to current techniques.
\subsection{The Grand-Teacher}
Our method of policy advice constructs a grand teacher combining all teacher policies in a meta-policy. To construct the grand-teacher, we use an ensemble method and design two meta-policy variations: online and offline-constructions. Next, we detail each of the two variations.

\textbf{Online Grand-Teacher:}
In the \textit{online} construction, whenever the student observes an unvisited state, $\bm{s} \in \mathcal{S}$, each teacher provides its policy advice of the form $\pi_{i}^{\mathfrak{T}_{i}}$, for all $i \in \{1,\dots, m\}$ with $m$ being the total number of teachers. The student then selects and stores the majority action from all teachers for that state $\bm{s}$. As far as budget is concerned, it is easy to see that we only require to know advice for each state in $\mathcal{S}$, thus $\bm{b}_{1}=\dots=\bm{b}_{m}=|\mathcal{S}|$. Though easy to implement and test, the online construction suffers from the potentially unrealistic need for the continuous availability of online teachers.

\textbf{Offline Grand-Teacher:} To eliminates the need for an online teacher at each visit of a new state, the offline procedure traverses the states in the MDP for constructing the meta-advice policy. The main steps of this construction is summarized in Algorithm~\ref{Algo:ConstructTeacher}.

\begin{algorithm}[t]
	\caption{Offline Construction of the Meta-Teacher}
	\textbf{Input}: The set of states in the MDP, $\mathcal{S}$. \\
	\label{Algo:ConstructTeacher}
	\begin{algorithmic}[1]
		\WHILE{$\exists s\in \mathcal{S}$ is not visited}
		\STATE Follow a policy in the MDP
		\IF{Current state $\bm{s}$ is not visited}
		\STATE Query all teachers for advice and select action $\bm{a}$ using Majority Vote.
		\ENDIF
		\ENDWHILE
		\RETURN $\pi^{\text{grand-teacher}}$
	\end{algorithmic}
\end{algorithm}
Note that Algorithm~\ref{Algo:ConstructTeacher} is capable of constructing an offline meta-teacher but requires extra exploration in the MDP. We next show that $\mathcal{O}\left(|\mathcal{S}|\log\frac{|\mathcal{S}|}{\bm{\delta}}\right)$ steps are enough to explore each state in the MDP with high probability:
\begin{theorem}[Sample Complexity]
	If Algorithm~\ref{Algo:ConstructTeacher} independently and uniformly explores each state $\bm{s} \in \mathcal{S}$, then with probability of at least $1-\bm{\delta}$, $\mathcal{O}\left(|\mathcal{S}|\log\frac{|\mathcal{S}|}{\bm{\delta}}\right)$ steps are sufficient to visit each state at least one time.
\end{theorem}

\subsection{Multi-Teacher Advice Algorithm}
To improve current methods and arrive at a more realistic advice framework, we now introduce our algorithm combining the grand-teacher's policy and information attained by the student from the MDP.

Our algorithm is based on the following intuition. At the beginning of the learning process, a student requires guidance as it typically has little to no information of the task to be solved. As time progress and the student explores, the MDP can be effectively exploited for successful learning. Unfortunately, such a process is not well modeled using current methods. Here, we remedy this problem by introducing an algorithm which follows the teacher's advice at the very beginning and then switches to a policy computed by an algorithm operating within the MDP. That is, the teacher guides the student at the beginning of the learning process and as the student gathers more experience, the teacher's influence diminishes over time by switching into a policy computed by REGAL.C. The overall procedure is summarized in Algorithm~\ref{alg:learning_dagger}. Note that our algorithm is inspired by DAGGER~\cite{ross2010reduction} in the sense that policies are updated by collecting data using a mixture of action selection rules (i.e., student and teacher policies). Contrary to DAGGER, however, our method collects all trajectories opposed to only collecting inconsistent actions, allowing for more accurate and efficient updates.

\begin{algorithm}[t]
	\caption{}
	\textbf{Input}: $\pi^{\mathfrak{T}}$ = the grand-teacher policy, $\hat{\pi}_1$=any policy \\
	\textbf{Output}: $\pi$, the $\epsilon$-optimal policy
	\label{alg:learning_dagger}
	\begin{algorithmic}[1]
		\STATE $T=0$
		\FOR{$i=1$ to $m$}
		\STATE Let $\pi_{i+1}=\beta_i\pi^T+(1-\beta_i)\hat{\pi}_i$
		\STATE Follow $\pi_i$ until $T_i$-steps
		\STATE Get dataset $D_i$
		\STATE $T=T+T_i$
		\STATE $\hat{\pi}_{i+1}=REGAL.C(D_i,T)$ \COMMENT{See Algorithm \ref{alg:REGAL.C}}
		\ENDFOR
		\RETURN $\pi_{m+1}$
	\end{algorithmic}
\end{algorithm}

To leverage both the teacher's and learned policies, we set a mixed policy of the form $\pi_{i+1}=\beta_i\pi^{\mathfrak{T}}+(1-\beta_i)\hat{\pi}_i$,
for $0\leq \beta_i\leq 1$ to guide the student's dataset collection while allowing the teacher to fractionally control exploration needed to collect data at the next iteration. $\beta$ should typically be set so as to decay exponentially over time. This decreases the student's reliance on the teacher and allows it to exploit the knowledge gathered from the MDP to learn better behaving policies than that of the teacher. It is for this reason that our algorithm, contrary to other methods, does not impose any optimality restrictions on the teacher. Having collected the dataset, Algorithm~\ref{alg:learning_dagger} uses REGAL.C (Algorithm \ref{alg:REGAL.C}) to update $\hat{\pi}_{i}$.

\subsection{Theoretical Guarantees}
In this section we formally derive the regret exhibited by our algorithm. At a high level, we provide two theoretical results. In the first, we consider the general teacher case, while in the second we derive a corollary of the regret for optimal teachers. We show, for the first time, \emph{better than constant} improvements compared to standard learners.

\begin{theorem}
	\label{thm:regalregret}
	Assume Algorithm \ref{alg:learning_dagger} is running for total $T$ steps in a weakly communicating MPD $\mathcal{M}$ starting from an initial state $\bm{s} \in \mathcal{S}$. Let $H$ be a parameter such that $H \geq \text{sp}(\bm{h}^{\star}(\mathcal{M}))$. Then, with a probability of at least $1-\delta$, the total regret is given by:
	$
	\bm{\Delta}(\bm{s},T)=\mathcal{O}\left((1-\bm{\beta}+\bm{\rho\beta})H|\mathcal{S}|\sqrt{|\mathcal{A}|T\log \frac{|\mathcal{A}|T}{\delta}}\right),
	$
	where $\bm{\beta}\in[0,1]$ such that $1-\bm{\beta}=\max_{1\leq i\leq m}\{1-\beta_i\}$, and $\bm{\rho}\geq 0$ is the ratio between the teacher's regret $\bm{\Delta}^{\mathfrak{T}}$ and the regret exhibited by REGAL.C $\bm{\Delta}^{\text{REGAL.C}}$ such that $\Delta^{\mathfrak{T}}\leq \bm{\rho}\bm{\Delta}^{\text{REGAL.C}}$.
\end{theorem}
\begin{proof}
	Due to the space limits, we provide a proof sketch. The proof is based on the regret bound of REGAL.C. We introduce the regret ratio to reduce the grand-teacher's regret to the REGAL.C's regret. Then, we apply the Hoeffding's inequality to arrive at the statement of the theorem.
\end{proof}
Theorem \ref{thm:regalregret} implies that the teacher improves learning as long as it is ``good.'' Namely, if $0<\bm{\rho}\leq 1$, $1-\bm{\beta}+\bm{\rho\beta}\leq 1$, $\bm{\beta}\in[0,1]$ which implies the student can enjoy a fraction of REGAL.C's regret. However, if $\bm{\rho}> 1$, $1-\bm{\beta}+\bm{\rho\beta}> 1$, the student suffers more regret than the original REGAL.C algorithm. This justifies our intuition that good teachers assist learning while poor ones hamper learning. Moreover, if there exists prior knowledge that a teacher has poor performance, it would be better off for the student to neglect its advice as it will suffer extra regret.

If the teacher's $\bm{\rho}=0$, we have the following Corollary:
\begin{corollary}
	\label{co:regalregretoptinalteacher}
	If the teacher is optimal, then with at least a probability of $1-\delta$ the total regret is given by:
	$
	\bm{\Delta}(\bm{s},T)=\mathcal{O}\left((1-\bm{\beta})H|\mathcal{S}|\sqrt{|\mathcal{A}|T \log \frac{|\mathcal{A}|T}{\delta}}\right).
	$
\end{corollary}

\begin{remark}
	Please note that the above theoretical results are more than a constant improvement to the regret. Notice that $\bm{\beta}$ depends on the number of iterations which can be bounded by $|\mathcal{S}|$ and $|\mathcal{A}|$ of the input MDP $\mathcal{M}$ \cite{auer2009near}. Further, $\bm{\rho}$ depends on the input teacher's policy which is also an input to Algorithm \ref{alg:learning_dagger}. Consequently, it can be shown that these regret improvements exceed simple constant bounds.
\end{remark}

\section{Negative Transfer}
To formalize the relation to negative transfer, we recognize that the regret ratio can be written as:
\begin{equation}
\rho=\frac{\bm{\Delta}^{\mathfrak{T}}(\bm{s},T)}{\bm{\Delta}^{\mathfrak{B}}(\bm{s},T)}=\frac{\bm{\lambda}^{\star}T - \mathcal{R}^{\mathfrak{T}}(\bm{s},T)}{\bm{\lambda}^{\star}T - \mathcal{R}^{\mathfrak{B}}(\bm{s},T)}
\end{equation}
This suggests that we can estimate the ratio by calculating $\bm{\lambda^{\star}}$ and $\mathcal{R}^{\pi}(\bm{s},T)$, given a policy $\pi$. So, we use $$\rho(\pi_1,\pi_2,T)=\frac{\bm{\lambda}^{\star}T - \mathcal{R}^{\pi_1}(\bm{s},T)}{\bm{\lambda}^{\star}T - \mathcal{R}^{\pi_2}(\bm{s},T)}$$ to denote the regret ratio between policy $\pi_1$ and $\pi_2$ until step $T$. At this stage, we define:
\begin{itemize}
	\item Negative transfer from policy $\pi_1$ to $\pi_2$ until $T$ steps: $\rho(\pi_1,\pi_2,T)>1$.
	\item Positive transfer from policy $\pi_1$ to $\pi_2$ until $T$ steps: $\rho(\pi_1,\pi_2,T)\leq  1$
\end{itemize}

To formalize negative transfer, our goal at this stage is to relate $\rho(\cdot)$ to a metric between source and target tasks. For that sake, we define:
$
d_t^s(\pi_s)=\hat{\mathcal{R}}^{\pi_s}_s(\bm{s},T)- \hat{\mathcal{R}}^{\pi_s}_t(\bm{s},T),
$
with $\hat{\mathcal{R}}^{\pi_s}_s(\bm{s},T)$ and $\hat{\mathcal{R}}^{\pi_s}_t(\bm{s},T)$ being the agent's estimates of the rewards in the source and the target after $T$ steps. Consequently, an estimate $\hat{\rho}$ to $\rho$ can be derived as:
\begin{align*}
&\hat{\rho}(\pi_s,\pi_t,T)\\
&=\frac{\bm{\lambda}^{\star}T - \hat{\mathcal{R}}^{\pi_s}_t(\bm{s},T)}{\bm{\lambda}^{\star}T - \hat{\mathcal{R}}^{\pi_t}_t(\bm{s},T)} \\
&=\frac{\bm{\lambda}^{\star}T +\left(\hat{\mathcal{R}}^{\pi_s}_s(\bm{s},T)- \hat{\mathcal{R}}^{\pi_s}_t(\bm{s},T)\right)- \hat{\mathcal{R}}^{\pi_s}_s(\bm{s},T)}{\bm{\lambda}^{\star}T -\hat{\mathcal{R}}^{\pi_t}_t(\bm{s},T)} \\
&=\frac{\bm{\lambda}^{\star}T +d_t^s(\pi_s)- \hat{\mathcal{R}}^{\pi_s}_s(\bm{s},T)}{\bm{\lambda}^{\star}T -\hat{\mathcal{R}}^{\pi_t}_t(\bm{s},T)}.
\end{align*}

$\hat{\mathcal{R}}^{\pi_s}_s(\bm{s},T)$ and $\hat{\mathcal{R}}^{\pi_t}_t(\bm{s},T)$ can be bounded by the Empirical Bernstein bound \cite{audibert2007tuning}. With a probability $1-\delta$, we have
\begin{equation*}
\left| \hat{\mathcal{R}}^{\pi_s}_s(\bm{s},T) -\mathbb{E}_{\pi_s}\left[\sum_{t=0}^{T} \mathcal{R}_s (\bm{s}_{t},\bm{a}_{t})\right] \right| \leq \epsilon_1,
\end{equation*}
with $\epsilon_1=\bar{\sigma}\sqrt{\frac{2\log(3/\delta)}{n_s}}+\frac{6R_{max}\log(3/\delta)}{n_s}$, $\bar{\sigma}=\sqrt{1/n_s\sum_{i=1}^{n_s} (R_i-\bar{R})^2 }$ is the standard deviation of the sample , we derive
\begin{equation}
\frac{\bm{\lambda}^{\star}T +d_t^s(\pi_s)- C_2}{\bm{\lambda}^{\star}T -C_4}\leq \hat{\rho}(\pi_s,\pi_t,T)\leq \frac{\bm{\lambda}^{\star}T +d_t^s(\pi_s)- C_1}{\bm{\lambda}^{\star}T -C_3}
\end{equation}
with $C_1, C_2, C_3,$ and $C_4$ are constants. Consequently, for negative transfer:
$$\hat{\rho}(\pi_s,\pi_t,T) \geq \frac{\bm{\lambda}^{\star}T +d_t^s(\pi_s)- C_2}{\bm{\lambda}^{\star}T -C_4}> 1.$$ Then, assuming enough samples, negative transfer occurs if:
\begin{align*}
&d_{t}^{s}(\pi_{s}) \\
&> \left\{\mathbb{E}_{\pi_s}\left[\sum_{t=0}^{T} \mathcal{R}_s (\bm{s}_{t},\bm{a}_{t})\right]-\mathbb{E}_{\pi_t}\left[\sum_{t=0}^{T} \mathcal{R}_t (\bm{s}_{t},\bm{a}_{t})\right]\right\} \numberthis \label{eq:condition}
\end{align*}

The condition sheds light on the negative transfer in the sense of metric notation and provides a formal way to determine negative transfer. First, if the condition in Eq. \ref{eq:condition} holds after evaluation, researchers should avoid the source policy $\pi_s$ to the target tasks since it may cause negative transfer. Second, if
the researchers have enough expert knowledge about their working domain and transfer information, usually they can avoid this evaluation phase in practice.
In short, Eq. \ref{eq:condition} provides a formal way to understand negative transfer and justify the intuition (adopt high quality source knowledge and avoid bad teachers ) in the transfer practice.

\section{Experimental Results}
\begin{figure*}[t]
	\centering
	\begin{subfigure}{.6\columnwidth}
		\centering
		\includegraphics[width=\columnwidth]{gridworld-eps-converted-to.pdf}
		\caption{Grid World}
		\label{fig:gridworld}
	\end{subfigure}
	\begin{subfigure}{.6\columnwidth}
		\centering
		\includegraphics[width=\columnwidth]{linearchain-eps-converted-to.pdf}
		\caption{Combination Lock}
		\label{fig:comblock}
	\end{subfigure}
	\begin{subfigure}{.6\columnwidth}
		\centering
		\includegraphics[width=\columnwidth]{blockdude-eps-converted-to.pdf}
		\caption{Block Dude}
		\label{fig:blockdude}
	\end{subfigure}

	\caption{Our method with optimal teacher has similar performance as the optimal policy. And the REGAL algorithm (no advice) outperform random teacher and worst teacher group which justifies that the poorer teachers do harm the learning. Azar's method depends on the quality of the teachers --- when the teachers are very poor, the algorithm shows no learning.}
	\label{fig:avaragereawrd}
	\vspace{-5pt}
\end{figure*}

Given the above theoretical successes, this section provides empirical validation on three domains:

\textbf{Combination Lock:} We use the domain described in Figure \ref{fig:mdpexample} which is a variation from \cite{whitehead1991complexity}. The experimental setting follows the caption description.

\textbf{Grid World} is an RL benchmark in which an agent has to navigate an $m \times m$ grid world with the goal of reaching a goal state. We employ an $11 \times 11$ grid world with a four room layout as introduced in~\citeauthor{sutton1998introduction}~\shortcite{sutton1998introduction}. The agent begins in the lower left corner of the map and navigates to the goal state being the upper right corner. To navigate, the agent has access (in each cell) to four actions transitioning it to the: north, south, west and east. Applying an action, it then transitions in that direction with a probability of 0.8 and in the other three with a probability of 0.2. In case the direction is blocked, the agent stays in the same state. Finally, the agent receives a reward of $0$ once reaching the goal state and a reward of $-1$ in all others.

\textbf{Block Dude} is a game where an agent again navigates a maze to reach a goal state. Reaching the goal directly is impossible due to the presence of blocks restricting its movement. The agent, however, can move to the left, right, and upwards. To reach the goal state, it needs to pick-up blocks and relocate them in correct positions. We use the default level 1 BURLAP \cite{burlap} in which there are two blocks and $3 \times 25$ maze. The agent receives a reward of $+1$ in the goal state and a reward of $-1$ in all other states.

\begin{figure}
	\centering
	\includegraphics[width=.7\linewidth]{cc.png}
	\caption
		There are $n+1$ states in the MDP. The last state has only one action and the rest have two. The agent receives reward $-1$ for all actions, except when taking action $a$ in state $n$, $\mathcal{R}(n,a)=1$. The agent stays in state $n$ with probability $0.9$ and goes to state $0$ with probability $0.1$. The optimal policy is to take action $a$ in each state. Since there are $n+1$ states, the budget $\mathfrak{B}$ is at least $n+1$ to achieve zero regret.
	}
	\label{fig:mdpexample}
\end{figure}

\subsection{Experimental Setup \& Results}
To construct the grand teacher, we set the total number of teachers $k=10$. For each teacher, the budget, $\bm{b}_{i}$, is set to the total number of states. In Algorithm \ref{alg:learning_dagger}, the maximum number of iterations and the size of each dataset, $\mathcal{D}_i$, were set to $10$ and  $200$, respectively. Values of $p^{i}=0.5^{i}$ for $i=1,\dots, 10$ were used to determine $\bm{\beta}_i=p^i$. For Algorithm~\ref{alg:REGAL.C}, the confidence $\bm{\delta}$ was set to $0.8$ and $H$ to $1000$. The optimal gain $\lambda^{\star}$ and the optimal bias vector $\bm{h}$ can be approximated using the value function $\mathcal{V}$ \cite{puterman2005markov}. Let $\mathcal{V}^l$ be the value function at iteration $l$, $l=0,1,\dots$. The optimal gain $\bm{\lambda}^{\star}\approx \text{sp}(\mathcal{V}^{l+1}-\mathcal{V}^{l})$, where $\text{sp}(\mathcal{V}^{l})=\max_{\bm{s}\in \mathcal{S}}\mathcal{V}^{l}(\bm{s})-\min_{\bm{s}\in \mathcal{S}}\mathcal{V}^l(\bm{s})$ and the optimal bias vector $\bm{h}^{\star}\approx \mathcal{V}^{l}-l\bm{\lambda}^{l}$, when $l$ is large enough. To smooth the natural variance in the student's performance, each learning curve is averaged over $10$ independent trials of student learning. To better evaluate our method, we adopt six experimental settings by considering different teachers and learning algorithms. For teachers, we consider three forms. The first, referred to as ``optimal teacher'' provides optimal actions and is used by the grand teachers. The second, referred to as ``worst teacher'' advices the student to take actions with the lowest Q-values, while the last randomly selects action suggestions from the set of allowed moves. We also compare our method to REGAL.C (no advice), optimal policy (without learning), and Azar's method~\cite{azar2013regret}. Please note that Azar's method can not converge to the optimal policy and suffers loss as its performance is restricted by the teacher.

Performance, measured by the average reward, is reported in Figure \ref{fig:avaragereawrd}. First, it is clear that given optimal teachers, our method exactly traces the optimal policy achieving a regret of 0. It is also important to note that in all three domains, even if the teacher was not optimal, and contrary to current techniques, our method is capable of acquiring optimal behavior. This is achievable as our method allows for learning within the multiple teacher framework.

\section{Related Work on Transfer Learning}
Few theoretical results on transfer and policy advice have been achieved. Closest to this work is that in~\citeauthor{torrey2013teaching}~\shortcite{torrey2013teaching}, where the authors only provide empirical validations to their approach without drawing on any theoretical analysis. Given the theoretical derivations in this paper, we in fact note that the method~\cite{torrey2013teaching} is a special case of ours considering only one-teacher advice models.

Another method considering advice under multiple teachers is that in~\citeauthor{azar2013regret}~\shortcite{azar2013regret}. \citeauthor{azar2013regret} propose a method capable of selecting the best policy from a set of teacher policies and derive sub-linear regret of the form $\mathcal{O}(\sqrt{T})$ with $T$ being the total number of rounds. One drawback of their method, however, is the assumption of a ``good-enough'' teacher which can guide the student to optimality. Such a method may suffer huge regret if the overall quality of teacher policies is poor. It also can not obtain better policies than those of the teacher. Our algorithm remedies these problems by allowing agents to further improve, which gives them the opportunity to surpass the teacher's performance.

Human advice is also a good source of policy advice. Usually, this method adopts the human advice as the teacher's policy to improve the learning performance. However, these works focus on empirical validations \cite{cakmak2012algorithmic,griffith2013policy}.

Probabilistic policy reuse is similar to our method in which the algorithm follows its own knowledge with probability $1-\epsilon$ and teacher's policy with probability $\epsilon$ \cite{fernandez2006probabilistic}. However, $\epsilon$ is not decaying over time, making the algorithm divergent if teacher policies are not optimal.
\citeauthor{isbell:policy:2015} introduce a policy shaping algorithm using human teachers, but focus on providing rewards rather than action advice \cite{isbell:policy:2015}. Both of these works rely solely on empirical results.

Work on transfer for RL is also related to this paper, where we can consider policy advice as an instance of transferring from teachers to students~\cite{lazaric2012transfer}. Here, \citeauthor{ferrante2008transfer}, for instance, propose a method to transfer high quality samples from source to target tasks using bi-simulation measures~\cite{ferrante2008transfer}. Their method only transfers samples once, while our approach gradually provides advice to the student. Due to space constraints, we refer the reader to~\citeauthor{taylor2009transfer}~\shortcite{taylor2009transfer} for a comprehensive survey.

Lifelong reinforcement learning has drawn significant attention to the transfer community recently. Brunskill and Li studied online discovery problems in a lifelong learning setting~\cite{DBLP:journals/corr/BrunskillL15}. Bou-Ammar \textit{et al.} also studied such a problem and introduced constraints on the policy to compute ``safe'' policies \cite{DBLP:journals/corr/Bou-AmmarTE15}. Contrary to these works, in this paper, we focus on the single agent setting operating within one task.

Finally, Learning from Demonstration  \cite{argall2009survey} (LfD) is also related to our work, but LfD usually assumes that the expert is optimal and the student only tries to mimic the expert.

\section{Conclusion and Future Work}
\label{sec:conclusion}
In this paper, we formally defined the multi-teacher advice model and introduced a new algorithm which leverages teacher and student's own knowledge in the weakly communicating MDPs. We theoretically analyzed our algorithm and showed, for the first time, that the agent can achieve optimality even when starting from non-optimal teachers. Our results provide a theoretical justification for the intuition that ``bad'' teachers can hurt the learning process of the student. Also, we formally established the condition of negative transfer, shedding light on future transfer learning research, where for example, researchers can choose ``good teachers'' based on the Eq \ref{eq:condition} and avoid negative transfer with prior expert knowledge.

In future, we plan on adopting other online reinforcement learning algorithms (e.g., REGAL.D \cite{bartlett2009regal}, R-max \cite{brafman2003r}, or $E^3$ \cite{kearns2002near}) to replace REGAL.C. We will provide better methods to construct the ``grand-teacher'' without exploring the whole MDP. Also, extensions to large-scale MDPs may be an interesting direction for future research as well.

\section{Acknowledgements}
This research has taken place in part at the Intelligent Robot
Learning (IRL) Lab, Washington State University. IRL research
is supported in part by grants AFRL FA8750-14-1-
0069, AFRL FA8750-14-1-0070, NSF IIS-1149917, NSF IIS-
1319412, USDA 2014-67021-22174, and a Google Research
Award.

\bibliographystyle{named}

redpajama_set_name: RedPajamaArXiv


We thrive at the intersection of art and technology.
We provide specialized services in creative interactive media for business.
We develop eLearning products with gamification elements that can bridge the engagement gaps found in traditional learning media.
Pixel Dash specializes in developing custom games to be added to native apps for iOS and Android, to entertain and inform your customers in one place.
Pixel Dash offers custom software development for desktop, web, mobile and database/cloud, with a specialty in Microsoft .Net technologies.
Our latest news and media coverage from around the web.


text:
See also: Team Fortress 2 is Free to Play
Way to go, pally!
— The Scout on Valve's decision to make TF2 free-to-play
Two main Account types are offered in Team Fortress 2: Premium and Free. On June 23, 2011, with the release of the Über Update, Valve adopted the free-to-play (also known as "F2P") model for the game, creating a distinction in account types between players who purchased the game before June 23, 2011, and players who downloaded the game for free thereafter. An account is considered Free if a user obtained their copy of Team Fortress 2 on Steam after June 23, 2011, and did not use any method to upgrade their account afterwards.
1 Comparison
1.1 Item system restrictions
2 Upgrading account type
3 Demonstration
In comparison to Premium accounts, Free accounts are more restricted in their ability to trade, craft, and store items in their backpack. For instance, Free accounts limit players' backpack size to 50 slots and disable some cosmetic crafting blueprints available to Premium accounts.[1] Formerly, Free accounts could not trade away any items at all. Now, while Free players can receive any items through trading, they can only trade away items that were traded to them by other users. Any items obtained through the item drop system by a Free account (limited to standard items such as weapons and paint cans) will also be untradable, except for Tools. Items received from the drop system before players upgraded their accounts will always remain untradable. Although gameplay restrictions are not placed on Free accounts, dedicated server plugins have been developed on community servers to block players who have Free accounts from connecting. Some cosmetic Item sets are also unable to be crafted. Premium accounts used to have access to the Team Fortress 2 Beta, but this was later removed on June 7, 2013. When a Free account is upgraded to a Premium account, the player will be asked to nominate a user who helped them. Doing so will grant the selected player the Professor Speks.
As of June 16, 2020 Patch, certain newly-created TF2 accounts have been restricted in their ability to send messages on official servers, with exception of party chat. As of July 3, 2020 Patch, said accounts are now also restricted in voice chat, including sending voice commands. Accounts may also be restricted for being Limited User Accounts on Steam, which means the player has to spend at least $5 to gain free access.
Item system restrictions
Backpack 50 slots (expandable to 2750) 300 slots (expandable to 3000)
Items Standard Standard, Rare, and Cosmetic
Crafting Limited blueprints All blueprints
Trading Limited trading Full trading
Gifting Can receive gifts Can send and receive gifts
Upgrading account type
The Premium account type is obtained by:
Activating a retail or digital copy of The Orange Box.
Making a purchase of any amount from the Mann Co. Store or Mann Co. Online Store.
Using an Upgrade to Premium Gift given to them by another player.
Upon meeting one of the requirements above, the player's account is immediately upgraded to Premium, and all previously-imposed restrictions are removed. Players who had previously purchased a retail copy of Team Fortress 2 will have their account upgraded to Premium upon activation of the game on Steam, and will also receive the Proof of Purchase item.[1]
↑ a b Free-to-Play FAQ
Retrieved from "https://wiki.teamfortress.com/w/index.php?title=Account_types&oldid=3346285"


text:
Letters of Credit unwavering in the face of foreign injunctions
Insights & events> Insights>
The recent case of National Infrastructure Development Co. Ltd. v BNP Paribas [2016] EWHC 2508 (Comm) has confirmed that only in exceptional circumstances will the court prevent enforcement of on-demand securities.
Background of the case
National Infrastructure Development Co. Ltd (NIDCO), a government owned corporation, contracted with OAS Constructora (OAS), a Brazilian contractor, to progress a large highways project in Trinidad and Tobago. Pursuant to the contract OAS was required to open Standby Letters of Credit (SBLCs) for the benefit of NIDCO as security for their performance. Two of the SBLCs were issued by BNP Paribas (BNP). The SBLCs placed an "irrevocable obligation to pay NIDCO on demand" on BNP. The SBLCs were countered-guaranteed by a BNP subsidiary in Brazil, which in turn benefited from back to back guarantees from OAS.
OAS entered insolvency proceedings in Brazil and NIDCO terminated its contract with OAS leading to a dispute between parties. Concurrently NIDCO made demands under the SBLCs issued by BNP. OAS obtained interim injunctions in Brazil preventing BNP and its subsidiaries from paying NIDCO under the SBLCs. If BNP paid NIDCO under the SBLC in breach of the Brazilian interim injunctions it would be subject to a penalty fee of 10% of the value of the SBLCs.
NIDCO brought proceedings against BNP in England for payment of the sums due following the demands on the SBLCs and applied for summary judgment. The issue before the Court was whether the Brazilian injunction and its effect gave BNP any arguable defence or grounds for resisting payment under the SBLCs as a matter of English law. BNP argued that the English proceedings should be stayed until the conclusion of the Brazilian proceedings so that it would not be required to breach the Brazilian interim injunctions. Alternatively, if the Court were minded to grant summary judgment immediately, BNP argued that the Court should also grant a stay of execution.
Foxton J held BNP's submissions to be "untenable as a matter of English law". Notwithstanding its sympathy for BNP's position, the court could not refuse the application for summary judgment on procedural grounds by, for example, adjourning to a later date, for fear that it would undermine the important principle of English law protecting the sanctity of SBLCs. BNP conceded that it had no English law defence and relied solely on its exposure to penalties imposed by a Brazilian court as grounds for resisting the application. The court held it would be wrong to use a stay of execution to subvert the principles of substantive law which provide very limited defences to resist payment under SBLCs
In the absence of exceptional circumstances such as illegality or fraud the court saw no basis for refusing the application and, following precedent, the court granted summary judgment and denied the application for a stay of execution.
Further Case Law
Foxton J cited Power Curber v National Bank of Kuwait [1981] 2 Lloyds Rep 394 as a reliable Court of Appeal authority on similar facts to those found in Nidco v BNP. Power Curber (PC), an American company, shipped machinery to a Kuwaiti firm on the strength an irrevocable letter of credit of the National Bank of Kuwait (NBK). As soon as the machinery was delivered, the buyers made a counter-claim and obtained an order from the Kuwaiti court restraining NBK from making payment to PC. PC sued NBK and applied for summary judgment against NBK for the amount demanded under the letter of credit. The Court of Appeal upheld the first instance decision to grant summary judgment and overturned the decision to award a stay of execution pending appeal in Kuwait.
What can be learned from this decision?
Both Power Curber and NIDCO are a reminder that, subject to any fraud or illegality, the courts will strive to protect the longstanding principle in English law of autonomy of contract codified in UCP 500 Article 3(A): "Credits, by their very nature, are separate transactions from the sales or other contracts on which they may be based and banks are in no way concerned with or bound by such contracts". In the same vein, Kerr LJ referred to a letter of credit as the "lifeblood of international commerce" when giving judgment in RD Harbottle (Mercantile) Ltd v National Westminster Bank Ltd (1978) 1 Queen's Bench 146.
Foreign injunctions sought by one party to prevent the issuing bank paying under a letter of credit threaten this principle but the English courts seem loath to allow any disputes or obligations pertaining to the underlying contract to interfere with any payment under a letter of credit. The judgment in Power Curber demonstrates that the very purpose of issuing an irrevocable letter of credit was to protect the beneficiary's rights from counterclaims or setoff. By ensuring the enforcement of those rights Lord Denning was "upholding the world-wide practices of international commerce".
This publication is intended for general guidance and represents our understanding of the relevant law and practice as at December 2016. Specific advice should be sought for specific cases. For more information see our terms & conditions.
by Paul Gair
TLT's open banking predictions 2019


A finely detailed recreation of the pendant worn by Cersei Lannister. Measures 1.5" in height and comes with an 18" chain.
A finely detailed recreation of the pendant worn by Cersei Lannister. Measures 1.5″ in height and comes with an 18″ chain.


Recently I had the privilege of sitting in on a workshop conducted by Ewan McIntosh (http://www.notosh.com) who took us through a process where he helped us (the students) become better "problem finders." He then challenged us to go out and help our students become better "problem finders." Giving up control is often hard, especially for educators, but I realized I wanted to move this way. Thus, I thought of a group of forth graders I was going to work with the following week.
These forth graders come to see me twice a week for enrichment and I decided to see what would happen. So on our first visit, I copied Ewan and took these ten year olds through an exercise to help them learn whatever they wanted to learn about. The only stipulation I gave them was that they had to choose a science related topic, since this was my field of expertise. After two thirty-minute sessions with these students they came up with two very good problems to be solved.
They fell into two distinct groups and they came up with two VERY different questions: The first group wanted to figure out how to solve the problem of groundwater pollution. Once they settled on their topic, they realized they didn't really know very much about groundwater pollution. In fact, their understanding of what groundwater pollution is was incorrect. I allowed this misconception to persist and encouraged them to learn all that they could about groundwater pollution. Once they understood what groundwater was, they realized that the best way to "solve" the problem of groundwater pollution was to prevent it. As of this post, these students have decided to make a 2-3 min video where they are going to educate people about the dangers of groundwater pollution. They hope to put it on youtube and educate the world.
The other group came up with the question: What is it about the human brain that causes it to make the decisions that it makes? This question was truly advanced for a bunch of forth graders. As they began their research they had a daunting task. After about four sessions, they turned to me and said that this answer is not out their on google. They knew some things, but they needed help. One young man told me we needed to ask a "super smart scientist." So I told him we should do that. He then replied, "Who would want to talk to a group of ten year olds?" And I told him you might be surprised.
So I encouraged them to reach out on the web and look for some neuroscientists to skype in and help them. And you can probably predict what happened. They are now looking forward to skypeing in with a professor of cognitive neurology from MIT this coming Monday.
My take-away's from these two events is that we do need to give up the control of the learning to our students. They will pursue things with passion if we only give them the chance.


"What Goes Around ..." is about the love life of a friend of Timberlake, Trace Ayala, and his relationship with Elisha Cuthbert. It also is rumored that the song talks about the relationship the two had in the TV series 24.
The song was retitled "What Goes Around... Comes Around" for its December 19, 2006, release as the third single from FutureSex/LoveSounds. It became Timberlake's third consecutive number-one hit on the Billboard Hot 100. Internationally the song was also successful, reaching the top ten in countries such as the United Kingdom, Canada, New Zealand, Germany, and Australia. The song was later certified two-times platinum in Australia, gold in the United States and gold in New Zealand. The song won Best Male Pop Vocal Performance at the 50th Grammy Awards, where it also received a nomination for Record of the Year.


text:
Olamide - King Shii | Download Music MP3
Download Olamide - King Shii
Track Title: King Shii
Album Name: Baddest Guy Ever Liveth Album
With the production credit to Pheelz, here comes a record named "King Shii" performed by Olamide.
Prominent Nigerian rapper and songwriter, Olamide, updates our playlist with this powerful record captioned "King Shii" a record, off his much-anticipated body of work tagged "Baddest Guy Ever Liveth". Released in the year 2013.
Among all the interesting records, listed in the album. Olamide picked "King Shii" as the sixteen-track of the project, with boost production from Pheelz, released under the imprint of YBNL Nation Records.
Olamide, versatility in the music scene, is something to reckon on. The young dude really knows this music of a thing well. King Shii, is an impressive record, you can't get tired of listening to, promises to reach the taste of your music.
Enjoy and experience the world of Baddo, Olamide with "King Shii". The record is a great masterpiece record.
Listen to Olamide - King Shii Mp3 Here!
DOWNLOAD Olamide - King Shii MP3
Share Olamide - King Shii Mp3 with others on;


text:
March 10, 2017 crea
By Caroline Rea
On Wednesday March 8th people around the world celebrated International Women's Day which is a part of National Women's History Month every year. In honor of that the Topeka RoadRunners want to celebrate women and girls in hockey all over the world. We want young girls in Topeka to know that they can play hockey too, because hockey is for everyone. It doesn't matter if you're a girl or boy. If you can play, you can play.
So to celebrate National Women's History Week we have reached out to a few female hockey players from all over the country. Some girls play professionally and some just play for fun, but they all have one thing in common and that is their passion for hockey.
The two major professional women's hockey leagues are the CWHL or Canadian Women's Hockey League and the NWHL or the National Women's Hockey League.
The CWHL was founded in 2007 and is comprised of 5 teams: the Boston Blades, Brampton Thunder, Calgary Inferno, Les Canadiennes, and Toronto Furies. The NWHL, on the other hand, is in their second season as a professional hockey league and is comprised of four eastern teams: the Buffalo Beauts, Boston Pride, New York Riveters, and Connecticut Whale.
One of the players we had the chance to talk to was Katie Fitzgerald. She is a professional goalie for the New York Riveters in the National Women's Hockey League. She started playing hockey when she was 5 playing in a mini mite league with all the boys in her town and continued playing with them through high school. After graduating she moved on to play in the NCAA for four years with the St. Cloud State University Huskies. During her last season with St. Cloud she would lead all goaltenders in the WCHA conference in shots faced. Fitzgerald made her NWHL debut for the Riveters in October of 2015 and in November of 2016 she recorded a 4-0 shutout marking the first shutout in Riveters history.
When we talked to Katie about her experience with the newly formed NWHL she said "I think the NWHL is amazing. I wouldn't be playing hockey anymore if it wasn't for them. Sure, there are some growing pains and struggles going with being pioneers, but we signed up for that. We like to think of the big picture, that we're doing this for the young girls that come to our games."
The other professional hockey player that we had the opportunity to talk to was Rebecca Russo. Russo started playing hockey when she was 3 years old when she saw her brother playing and wanted to be just like him so she strapped on some skates and never looked back. She played in the NCAA for the Boston University Terriers and set the BU record for most assists in one game with five against Yale.
Her senior year she ended the season with a career high of 38 points (15 goals, 23 assists) in 39 games. In that time she also maintained a scoring streak from Jan 23 – Feb 14. She joined the New York Riveters this past season and scored two of the four goals in the game that would be marked as the first shut out in Riveters history.
When we asked Russo what she thought about the NWHL she said "We are trying to establish something very special here with professional women's hockey and I think we have achieved that in the first two seasons with the NWHL. For me, being an undrafted rookie I had to prove to not only myself but others around me that I too can play in this league with the best women hockey players in the world."
We asked both Fitzgerald and Russo what advice they would give to girls who are interested in playing hockey. Fitzgerald said "I would tell young girls to never give up. It sounds cliché, but there is nothing more admirable than someone who refused to give up or listen to the voices who told her she couldn't. Proving everyone wrong is the ultimate payback."
Russo's response was very similar. She said "My advice to young girls would be to work hard and follow your dreams. It is a lot of hard work that got me to the place I am today and I wouldn't be where I am today if I didn't put in the effort both on and off the ice. My commitment to the sport, my dedication to off-ice training and my hard work in the classroom all helped me fulfill my dream. But it all comes down to what you put in. It takes hard work to achieve your goals, but if you set your mind to it and are willing to put in whatever it takes, you will get the outcome you deserve."
Although right now professional women's hockey isn't as well-known as men's hockey it looks to have a promising future. Fitzgerald says "Where we are right now is where the NHL was when it first started 100 years ago. It took them a long time to reach where they are and that's thanks to the men who agreed to be the first. Seeing the progress the WNBA and women's soccer league have made makes the next decade of growth very promising for the talented girls growing up who can dream of being the next Amanda Kessel instead of the next Patrick Kane. They now have women to look up to and see the paths they took to get to the top and learn from them."
It's not just professional hockey though. Girls play hockey at all levels. One Topeka native, Holly Ives, is a goalie and has been playing hockey since she was 11. She is currently attending the University of Eau Claire in Wisconsin to study Athletic Training, but she's still always ready to lace up her skates and get back in the net just for fun.
Holly's advice for girls who are interested in playing hockey is, "Don't be afraid to fail, that's the first step toward being good at something. It doesn't matter what kind of team you play on whether it's girls, coed, house, or travel. Just keep playing the game and working toward getting better."
Hockey is one of the greatest sports in the world and it is meant to be enjoyed by everyone. The Topeka RoadRunners are dedicated to spreading the love of the game to the kids in the Topeka community.
Anyone between the ages of 3-15 interested in learning to play hockey can reach out to us for information on our free Learn To Skate and Learn To Play Hockey programs that will start up again next season. We will even look into supplying gear, provided advanced notice, to kids who are interested in giving hockey a try. It doesn't matter if you're a girl or a boy, if you can play, you can play.
Happy International Women's Day to all our female hockey players and hockey fans out there! The Topeka RoadRunners support you!
To see the full interview with both Katie Fitzgerald and Rebecca Russo click HERE.
Previous PostInterviews with Katie Fitzgerald and Rebecca RussoNext PostSt Patrick's Day Parade


text:
Headphones / March 19, 2021
Sony MDR-1AM2 | Every Music Lovers' Favorite
Sony MDR-1AM2
Sony was always known for its outstanding products especially if you're looking for a new pair of headphones, you may have come across Sony, which has become well known for high-quality noise-canceling headphones and wireless earphones. Moreover, Sony never cuts corners when it comes to sound quality and pushes its audio technology as far as they can with the promise to deliver only to the best audio reproduction to the audiophiles and the average consumer. With this goal in mind, Sony has been making the best headphones in the market for a long time and now they are slowly branching towards the wireless and truly wireless section of the market. Furthermore, not only are they branching, but they are also succeeding to keep their promise to market only the best headphones for the best listening experiences.
With Sony headphones. You will always find the right combination of comfort and sound quality. Whether you're just listening to music or working on a project, there is a headphone from Sony ready to give the service you expect from the Japanese giant of the headphone industry.
We use these headphones as a means to escape from our day-to-day life, from boredom, and stress. At this point in our lives, headphones have become a commodity and we need these headphones very badly. You can only use headphones for escapism when they are comfortable and sounds stellar. With Sony headphones, you get just that.
Furthermore, with the new active noise-cancellation feature, you can truly achieve musical nirvana with Sony headphones. The ANMC features let you drown out all the ambiance sound and lets you reside in a world where only you and your favorite track exists.
Sony produces headphones that are always on the top ten list of headphones. They have made a headphone that defeated the pioneer of the ANC, Bose. Not only that, to ensure that you can enjoy music and also work, these headphones come with ambient mode, which lets you tune down your music just by raising your hand to one of the cans.
That's not all, the headphones come packed with eight microphones, designated to shut down the world, and increase the quality of your voice during calls. Even in the wired department of headphones, Sony thrives without a doubt. They have headphones at all price points for all sorts of use. Whether you want to enjoy music or use these to mix and tune music, you will find the right pair of headphones from Sony. And, with every release, they are bringing in new technology to the table. The Sony MDR-1AM2 is such a headphone that will fulfill all your average and professional musical needs.
The detailed sound and wide range clarity of the MDR-1AM2 headphones steal every music lovers' heart everywhere, the headphone features a lightweight build and comfy cushion with foams made for comfort guarantees listening to music for an extended period isn't an issue.
If you're an audiophile you will love these headphones as these come packed with aptX HD and Sony's LDAC. This is one of the best combinations for listening to HI-RES audio. Despite being old headphones, the revamped version as the name implies, M2, offers a ton of new features over the older models. Moreover, the closed-back design does a great job of isolating you from crowded places and the overall sound reproduction of the headphones is unbelievable at this price point.
Design and Quality
As equipped with a 40 mm driver, it may not feel like a lot but you will be shocked with its impressive sound quality right out of the gate. The drivers are protected by a grille designed specifically for the 1AM2, with a pattern that minimizing the sound damping of higher frequencies, and you can listen to every detail. However, design-wise, there are very few differences. Color aside, the headphones look and feel amazing. The Sony MDR-1AM2 sounds better, brighter, cleaner, and detailed than its predecessors. The previous MDR-1A came with warm and dark audio reproduction, with punchier bass and minor lacking in mids and highs. It isn't possible to say if the improvement is worth considering.
The balanced cable helps to separate the left and right audio channels. Aside from that, Sony added a ground connection to lower the cross-talk and increase the efficiency of the audio. With the 3.5mm cable, you're getting superior sound quality which is enough if you're trying to DJ or work professionally with these. Moreover, the headphones feature a 360-surround sound. The mic on the headphone is of decent quality.
The MDR-1AM2 comes with many accessories. In the box, you will find a soft carry pouch along with two cables. The 3.5mm jack attached with an inline remote control which houses a microphone for playback control and managing calls when connected to your phone. The other cable is a balanced cable from Sony, the 4.4mm balanced connector. Sadly, there are only a few devices that have an input port for a balanced cable. If you're looking for wireless headphones then you should be fine with the 1AM2.
Also, check out High Fidelity Headphones In 2021
Highs are resoundingly clean and enough warm for enjoyment, but by no means tiring. The bass reproduction is sufficient enough. However, you will not find an excessive amount of bass. Bass heads might be pleased but it's nothing astounding. The mid-department is where the 1AM2 shines. The mids are clear and detailed. You will also find clear and constant treble and voice reproduction, which sounds nice to hear. At this price point, it's amazing what the 1AM2 can reproduce. Owing to its warm and clear sound profile, you can use these as your primary headphones when you're doing a gig. Besides the treble and voice reproduction, you will also find amazing work of instruments when listening to instrumentals and classical songs through this pair of headphones.
Sony WH-XB900N
Sony WI-SP600N Sports Wireless Noise Canceling
Sony IER-M9
If you're looking for inexpensive headphones that will meet all your needs, then the Sony MDR-1AM2 is perfect for you. Whether you just want to blast some music or play professionally, this headphone has your back.
MDR-1AM2 Sony
Behringer HA400 | The Best Behringer Headphone Amp >


Copyright 1992, Kevin C. Killion. All Rights Reserved.
The magic on a long-distance train trip isn't only to be found in the panorama of America outside the windows. Many fond memories of trips on Amtrak are of the people met and the conversations sparked along the way. Some are upscale travelers who use the time for working or correspondence in glorious privacy in their compartments, people coming from smaller towns that are not served well by air, economy-minded travelers in coach, and retirees. Many are people who just do not need, or do not want, to cross the country in a blink: they want to see what's in between, as well. Train travel attracts a unique breed, whose stories are as compelling as the unfolding scenery.
The next bedroom to ours on the Southwest Chief was that of an older couple who were taking a retirement train trip to California, followed by a cruise. The trip was a retirement present from his company, an office equipment supplier. He had been in charge of a dwindling group that repaired manual typewriters and adding machines. As an additional retirement gift, his company was letting him take that part of the business with him to start a new venture! They didn't want it anymore, and he figured that the business would wind down at a rate equivalent to the amount he would want to be working in his "retirement" years.
Also on the Southwest Chief, we had lunch with a young couple who had gotten on in New Mexico. They were returning from a visit with his sister, and it had been profoundly distressing for him. The sister had gotten thoroughly enmeshed in the new age culture of Santa Fe. The young fellow said that she was squandering money she had gotten from a divorce settlement on various crystals, therapies and lectures. But what was worse, he said, was that she no longer seemed able to carry on a normal conversation: Every line of discussion, every expressed thought, led to some new age reference or belief. Several times he told us that he felt that he had lost a sister.
On the City of New Orleans, on the way to Memphis, I enjoyed dinner with a 17-year-old boy who just graduated from high school in McHenry. He was on his way to visit a buddy in Mississippi on his first trip ever out of the state of Illinois. I found that his main interest in life was fire: He had been an Eagle scout, earning badges in fire prevention, he volunteered at the firehouse, and when he started community college in the fall he was going to major in fire safety and EMT training. His Dad was a fireman, but, no, that isn't where he got his interest: he got his Dad the job! (Hanging around the firehouse, he heard of the opening, and told his Dad who had been laid off.) He asked me what I did, and inquired as to my travels. I told him I travelled on business to California, Washington, New YorkŠ At that point, he interrupted and said, "New York! Oh, New York! I'd just love to visit New York someday! I hear they have some amazing ways of fighting fires!"
On the Broadway Limited, I had dinner with an Episcopal priest who was on a two-week vacation to help collect himself after having been discharged by his Times Square congregation.
On the Lake Shore Limited, I had a long conversation with a man who was returning from a presentation at the Federal Aviation Administration in Washington. He was a career-long seasoned expert in aircraft navigation and control electronics, and was part of a team that had been to the FAA to promote a new system for radar-directed landings at major airports. He never flies on business, he always takes the train.
Another trip on the Lake Shore found a young couple, recently married, who were moving to Chicago from New York to a new city, new jobs and a new life together. Their possessions were in transit, so the train was a good way of taking a mini-vacation at the same time. We spent a good deal of time talking about their new city's attractions.
The dome car on the Capitol Limited always is a gathering place for some of the most interesting train travelers. On one trip, a man and his son were taking a rare trip alone, away from Mom and the other siblings. It was a special time for the two of them, as father and son talked endlessly about the train, what they were seeing outside and about just everything in their lives.
Also in the Capitol's dome car, on another trip, a lively conversation started between two men who I judged were unlikely to have met in other circumstances. One was a mellow-looking and genial man in his sixties, the other was a long-haired, somewhat scruffy fellow around 25. The younger man was a musician in a metal rock band, but his heart was in jazz, which was also a passion of the older gentleman's. For an hour they talked of jazz history, jazz classics and great musicians.
For every person with an unusual story, there are several with more ordinary but nonetheless delightful reports: the new grandparents on their way to see their grandchild for the first time, businesspeople on short trips to clients for industries I hadn't heard of before, legislators and bureaucrats traveling to Springfield or Washington, and simple vactioners. And the price of admission to all of these stories, plain or unusual, is a ticket on a train.
In mid-December, every small town whose main street crosses the tracks is showing its Christmas finery.
The endless vastness of Montana -- From awaking in the morning to a frost-covered farmland through passing an inn late at night: this was a Sunday that I'll remember always.


text:
Per un amico è il secondo album in studio del gruppo musicale italiano Premiata Forneria Marconi del 1972, pubblicato dalla Numero Uno. È stato rimasterizzato e ristampato nel 2001 dalla BMG.

Nel giugno del 2015 la rivista Rolling Stone ha collocato l'album alla diciannovesima posizione dei 50 migliori album progressive di tutti i tempi.

Il disco
Questo album, uscito dopo Storia di un minuto, ma nello stesso anno, si caratterizza per un piano musicale più complesso rispetto al primo ed ha ottenuto un successo minore. L'album è stato classificato tra i migliori album di rock progressivo di sempre.

Il testo della title track, scritto da Mauro Pagani, è rivolto a Claudio Rocchi.

Tracce
Lato A

Lato B

 Brano Generale sul vinile originale (lato A) il brano è attribuito a: Mauro Pagani, Franco Mussida, Flavio Premoli; nella ristampa su CD a: Franco Mussida e Flavio Premoli

Formazione
 Franco Mussida - chitarra acustica, chitarra a 12 corde, chitarra elettrica, mandoloncello, voce solista
 Flavio Premoli - pianoforte, clavicembalo, organo Hammond, spinetta, mellotron, campane, voce
 Mauro Pagani - flauto, flauto contralto, piccolo, violino, voce
 Giorgio Piazza - basso, voce
 Franz Di Cioccio - batteria, percussioni, voce
Note aggiuntive
 Scritto, arrangiato e prodotto dalla Premiata Forneria Marconi con la collaborazione di Claudio Fabi
 Amplificazione Romano Lombardi
 Ringraziamo anche le Ditte Pari, Monzino, U.F.I.P. e Jenko per le attrezzature forniteci
 Un ringraziamento particolare al tecnico del suono Gaetano Ria
 Un ringraziamento anche a Ivor Barnett
 Copertina di Caesar Monti, Giannici e Spinello

Note

Collegamenti esterni

  (Pubblicazione LP: novembre 1972)

redpajama_set_name: RedPajamaWikipedia


Tyne Tees Television : Runner, then General Production Assistant.
I work Full time in the media and have amassed considerable experience on both sides of the camera, I am a Walk On Artist, Stand In / Body Double (for a Number of "A" list Actors & Presenters) Voice Over Artist, Assistant Director or Supporting Artist.
Additionally I have Access to a considerable number of Genuine Costumes / Uniforms, Including Period & Historical pieces, please see photos.
Please let me know of Your Individual Specific requirements, i promise i will do my best to help, and I look forward to working with you.
I would be delighted if you would add me as one of your favorites!
Occasionally I also work as a Catwalk Model, for various Wedding Shows, usually taking the role as Father of the Bride, for a very large High Street Clothing Retailer.
In addition to London, I also have accommodation in Brighton Sussex, Birmingham, Manchester, and Newcastle upon Tyne. I am very happy to travel and open to any offers of work.
I would be delighted if you add me as one of your Star Now favorites!
I am a Professional Stand In / Body Double (for a Number of "A" list Actors & Presenters) Assistant Director or Walk Artist. I have Considerable Film crew & Stage experiance, including thousands of Live Shows, including the 02 arena, and Live Television, 2 Whole series of the Tube for Channel 4.
Minimal photography experience, However I am a Professional Stand In / Body Double (for a Number of "A" list Actors & Presenters) Assistant Director or Walk Artist.


Ok, yes, a clipboard is a mundane and possibly boring item. However, Caleb Harris shows us that they can, in fact, be beautiful too.
Starting from lumber, Caleb shows us how he steps through the process of creating these beautiful pieces. It involves quite a bit of forethought and planning to get the result he's after. He messes up a bit when he glues up some pieces and takes the time to teach a lesson on thinking ahead in regards to your grain. These are the kinds of skills that you just have to accumulate from practice because, despite him explaining it, I'm pretty sure I'll make that mistake a time or two before I learn.
It is fun to watch this go from one giant hunk of wood down to a pile of beautiful pieces. I surely wouldn't mind taking boring notes on this beautiful clipboard.


Bald Eagle Release - Lake Tahoe Wildlife Care, Inc.
After spending the summer rehabilitating at LTWC, this young bald eagle soars away, ready to enjoy its life as a healthy bird.
Lake Tahoe Wildlife Care strives for successful releases back to the wild — like this one — with every injured and orphaned animal we receive.
Help make more animal releases possible!
Release day photos (click on any to open full-size in new tab).