Sure, these phrases are synonymous with football, but here in Round Rock, they're also heard on a pretty regular basis when folks get to talking about our economic development strategy. And at Austin Business Journal's recent Williamson County Growth Summit, that's just the topic that had attendees buzzing.
Mayor Craig Morgan, along with Round Rock's City Manager Laurie Hadley and Councilmembers Writ Baese, Frank Leffingwell and Rene Flores, joined other leaders from across the region in attending a sold-out event focused on the incredible economic growth opportunities being seen across Round Rock and Williamson County as a whole.
Kalahari Resorts has chosen Round Rock as the location of its fourth family resort and convention center. Its proximity to Old Settlers Park and Dell Diamond – two well-established venues that together draw more than a million visitors annually – bodes well for a successful, tourism-oriented development. This is essentially a new industry for Round Rock that will provide substantial property tax revenues and diversify available employment opportunities.
A family-owned business, Kalahari Resorts delivers a "world-away" waterpark resort and conference experience beyond expectations. The authentically African-themed Kalahari Resorts feature well-appointed guest rooms, full-service amenities, fully equipped fitness centers, on-site restaurants, unique retail shops and state-of-the-art conference centers.
Mark IV oversaw development of Round Rock's first Class-A office building, The Summit at La Frontera, in 2015, and now has plans to further expand its footprint in Round Rock with a $200 million, 1 million-square-foot mixed-use development. The District is planned for 65 acres east of the Round Rock Crossing shopping center at Texas 45 and North Greenlawn Boulevard.
With projects like these queued up for Round Rock, that not only diversify our tax base (and keep residential property tax rate among the lowest in the state) but also positively impact the quality of life for residents, we're feeling pretty darn good about the future.
Hometown heroes. They are the men and women who risk their lives day-in and day-out to keep our local communities safe. And when disaster strikes hundreds of miles away? They again stand tall in the face of adversity, proud to serve their neighbors in need.
As Hurricane Harvey pummeled the Texas coast, Round Rock first responders quickly came together to answer calls for assistance. They said goodbye to their own families and worked to help save the lives of others, reconnect loved ones and began the long road of working to rebuild communities devastated by the storm. They, like they do all so often, put the lives of others before their own, without hesitation.
Often lost in the shuffle of the next big story, the effort of these men and women is many times overlooked. But not today.
To say we are extremely proud of our public safety team would be an absolute understatement.
To each and every member of those teams: You are truly deserving of the word hero. Thank you for keeping our hometown safe and thank you for making Texas, the United States, and our world a better place to call home.
September is National Preparedness Month, which is designed to increase the overall number of individuals, families and communities that engage in preparedness actions at home, work, business, school and place of worship. The recent examples of Hurricanes Harvey and Irma are good reminders of the importance of being prepared for emergencies.
While many communities in Texas and Florida are still dealing with the impact of these storms, we should all focus on ensuring we are prepared for the next emergency that could impact our community, whether large or small.
City Homeland Security and Emergency Management Division – Committed to serving the City of Round Rock before, during and after a major emergency or disaster. Find information on local threats and hazards, and ways you can become better prepared.
Warn Central Texas – A regional resource, sign up for emergency alerts and obtain preparedness information from the localities in the region.
Preparing Wilco – Williamson County Emergency Services Facebook page, and it contains a great deal of information and resources to help you prepare. You can also find out about WILCO Ready, its new preparedness app.
National Preparedness Month – Information on National Preparedness Month and the weekly themes being highlighted. There, you can access a multitude of additional information and resources that can help you on your journey.
Ready.gov – A repository of emergency preparedness information from the Federal government, you can find a variety of great information and resources on becoming prepared.
While September is designated National Preparedness Month, it is important to remember that preparedness is a year-round task. Emergencies can happen at any time, so we should all be prepared for whatever comes our way.
Hurricane Harvey recovery: How can Round Rock help?
Let's take a moment to think about the Houston area and all those along the coast who have been crippled by Hurricane Harvey. Our community has been spared the worst. We are a city that works together with great success day in and day out, so today, let's take a moment to send positive thoughts to the fellow Texans to our south. This storm has devastated communities and some families have a long, long road to recovery.
The Texas Fire Chiefs Association is dedicated to assisting the citizens of Texas and victims of Hurricane Harvey. Over the next several months their focus will be specifically geared towards recovery for the first responders who may have lost their homes and fire departments who may need help getting back on their feet.
United Way of Williamson County will collect items and monetary donations to help meet the immediate and long-term needs of individuals and families in nearby communities affected by the storms. It has set up two options for Williamson County residents to partner with us to provide help and hope in the areas closest to home.
Financial contributions: Monetary donations can be made online at www.unitedway-wc.org, or by texting the word WilcoCares to 91999, or via check. All checks should be made out to United Way of Williamson County-Hurricane Relief Fund and should be mailed to P.O. Box 708, Round Rock, TX 78680. One hundred percent of donations will be directed to hurricane relief efforts. United Way of Williamson County will not be collecting administrative fees for this fund. Monetary donations are tax deductible. United Way of Williamson County is working closely with the network of United Ways across Texas to identify the areas in most critical need of resources.
#WilcoCares Supply Drive: From Wednesday, Aug. 30 through Sept. 15, United Way of Williamson County is coordinating the collection of cleaning supplies and personal care items at locations in Cedar Park, Georgetown, Round Rock, and Taylor. Accepted items include bottled water, cleaning supplies, contractor grade trash bags, mops and buckets, heavy duty work gloves, hand sanitizer and more. Absolutely no clothing or bedding will be accepted due to a lack of time and resources available to sort and store those items. A detailed list of accepted donations can be found at www.unitedway-wc.org. The collection location in Round Rock is at 1111 N. IH-35, Ste. 220. Hours are Monday-Thursday: 8 a.m. to 5 p.m. and Friday: 8 a.m. to 3 p.m.
ADRN is taking donations and helping groups to set up fundraisers to meet the immediate emergency needs of survivors and help fund the long-term repairs/rebuilds of homes. Taking community volunteer sign ups.
The organization says it prefers monetary donations to food or other goods, as it can use the money to provide exactly what's needed. It's taking donations through the website, as well as by phone at 1-800-SAL-ARMY. It accepts checks, "Hurricane Harvey" should be written on them.
Register as a volunteer with National Voluntary Organizations Active in Disaster at nvoad.org.
Encouraging people to donate money on its website or to text 90999 to donate $10.
Houston's chapter of community support organization United Way has set up a Harvey recovery fund. Donate online and choose to send the money to specific counties or let the organization decide.
The Houston Food Bank is at 1-832-369-9390; its website also offers instructions to people willing to donate food and organize food drives, and takes monetary donations of as little as $1.
Register through the disaster portal of Volunteer Houston or All Hands Volunteers, which are coordinating help.
Williamson County Animal Shelter is seeking foster homes and donations.
Large plastic or metal bins with lids so we can safely store food and goods in dry places.
Round Rock is a fast-growth community, and has been for decades. To keep pace, we regularly need to beef up spending for core services such as public safety, parks and transportation. But we're not building a Dagwood, mindlessly adding extra layers of cold cuts and cheese slices … just because. Before we add new positions we evaluate whether we are utilizing current resources efficiently.
For an appetizer, we'll cover the staff additions in fiscal 2018 budget proposal. For the main course, below, we'll share ways we've cut calories by working with what's already in the cupboard.
For starters, the budget proposal contains 35 new full-time employees (FTEs), broken down below by fund type.
Thirteen are for public safety (Police and Fire); three for General Services (building and grounds maintenance); four for Parks and Recreation; five for Planning and Development Services; and four for Transportation. Note: Ten of the new Fire staff and one Parks staff are to operate projects voted for in the 2013 bond election. The General Fund is fed by sales taxes, property taxes and some fees.
Four for maintenance and inspections; and a quarter FTE to cover additional hours for a part-time customer service representative. Utility Fund revenue comes from water and wastewater customers.
Two total, a marketing and advertising coordinator, and an operations and events assistant coordinator. HOT tax revenue comes from folks staying in Round Rock hotels and motels.
Now for the meat of this post. Here's the sampler platter of ways the City has kept a close eye on its fiscal waistline in recent years.
In the next Budget Bite, we'll dish on the key ingredient to our recipe for success — the Strategic Plan.


text:
REVIEW-Banana-Split-1
By Alan Ng	| March 25, 2020
Before November 8, 2016, I had operated the entirety of my voting adult life in a world superintended by a leader who was not only respected but actually...
Franco Zeffirelli's "Callas Forever" spins a make-believe story in which Maria Callas is lured from her 1977 recluse existence in Paris to star in a film...
BOOBIE GIRL
A young girl on the edge of puberty prays for larger breasts and gets them in director Brooke Keesling's amusing animated short film "Boobie Girl." Boy,...

redpajama_set_name: RedPajamaCommonCrawl


text:
The UN Introduces Homelessness on the Agenda Thanks to Vincentian Advocacy
by Javier F. Chento | Mar 1, 2020 | News | 1 comment
The UN has adopted the first resolution defining homelessness and asking countries to measure it. For the first time in its 75 years, the UN is considering homelessness as a mainstream policy issue. The work of the Vincentians has been crucial in helping to achieve this watershed moment.
The resolution was adopted at the end of 58th Session of the Commission for Social Development, which focused on affordable housing and social protection to address homelessness. Vincentian advocacy has played a vital role in introducing homelessness to the UN Agenda.
The advocacy efforts by the NGO Working Group to End Homelessness made it possible for this issue to become the priority theme of the 58th Session. The Working Group, created by the Vincentian Family, brings together organisations from all over the world. Until now, the group was coordinated by Father Memo Campuzano CM. The FHA, and our partners DePaul International and the Institute of Global Homelessness are also members, and together they have advanced the "Make us count" campaign.
The basis for the definition of homelessness adopted by the UN is the IGH Framework, also adopted by FHA. All the key categories of homelessness from this document are now included in the resolution:
[…]homelessness is not merely a lack of physical housing, but is often interrelated with poverty, lack of productive employment and access to infrastructure, as well as other social issues that may constitute a loss of family, community and a sense of belonging, and, depending on national context, can be described as a condition where a person or household lacks habitable space, which may compromise their ability to enjoy social relations, and includes people living on the streets, other open spaces or in buildings not intended for human habitation; people living in temporary accommodation or shelters for the homeless, and, in accordance with national legislation, may include, among others, people living in severely inadequate accommodation without security of tenure and access to basic services."
The resolution includes a formal written encouragement for Member States to harmonise measurement and collection of data on homelessness. The Resolution strengthens the role of the UN Statistical Commission and emphasizes "the need to make concerted efforts to identify people experiencing homelessness."
During the Commissions debates, both our Coordinator Mark McGreevy and our Commission member Dame Louise Casey highlighted the need of a definition and measurement to end homelessness.
Now, the resolution will have to be approved by the UN Social and Economic Council in the summer, and then by the General Assembly in September. Our long-term aim in to introduce homelessness in the next set of Sustainable Developments Goals planned for 2030.
Source: https://vfhomelessalliance.org/
Tags: United Nations, Vincentian Family Homeless Alliance
← Lenten Video Series: Day 4, Run To God The CM Chinese Province during COVID-19: Long-distance Solidarity in Action →
PAULINAH APPIAH ANTWI on March 1, 2020 at 7:47 am
The Lord hears the cry of the poor. Thanks to our dedicated team. May they all be blessed.


text:
\section{Introduction}
In online convex games \cite{shalev2006convex,gordon2008no}, agents interact with each other in the same environment in order to minimize their individual cost functions. Many applications, including traffic routing \cite{sessa2019no} and online marketing \cite{shi2019no}, among many others \cite{hazan2019introduction}, can be modeled as online convex games. The individual cost functions depend on the joint decisions of all agents and are typically unknown but can be accessed via querying. Using this information, the agents can sequentially select the best actions that minimize their expected accumulated costs.
This can be done using online learning algorithms whose performance is typically measured using the notions of regret \cite{hazan2019introduction} that quantify the gap between the agents' online decisions and the best decisions in hindsight. Many such online learning algorithms have been recently proposed, and have been shown to achieve sub-linear regret, which indicates that optimal decisions are eventually selected; see e.g., \cite{shalev2006convex,gordon2008no,shalev2011online}.
In this paper, here we focus on online convex games for high-stakes applications, where decisions that minimize the expected cost functions are not necessarily desirable. For example, in portfolio management, a selection of assets with the highest expected return is not necessarily desirable since it may have high volatility that can lead to catastrophic losses. To capture unexpected and catastrophic outcomes, risk-averse criteria have been widely proposed in place of the expectation, such as the Sharpe Ratio \cite{sharpe1994sharpe} and Conditional Value at Risk (CVaR) \cite{artzner1999coherent}.

In this paper, we consider online convex games with risk-averse agents that employ CVaR as risk measure.
We assume that only cost function evaluations at selected decisions are accessible.
Despite the practical utility of CVaR as a measure of risk in high-stakes applications, the theoretical analysis of risk-averse learning methods that employ the CVaR is limited, mainly since the CVaR values must be estimated from the distributions of the unknown cost functions. To avoid this problem, \cite{rockafellar2000optimization} proposes a variational definition of CVaR that allows to reformulate the computation of CVaR values into an optimization problem by introducing additional variables.
Building on this reformulation, many risk-averse learning methods have been proposed, including \cite{soma2020statistical,cardoso2019risk,curi2019adaptive}, and their convergence rates have been analysed. Specifically, \cite{soma2020statistical} provides performance guarantees for stochastic gradient descent-type algorithms in risk-averse statistical learning problems; \cite{cardoso2019risk} addresses risk-averse online convex optimization problems with bandit feedback; and \cite{curi2019adaptive} proposes an adaptive sampling strategy for CVaR learning, and transforms the supervised learning problem into a zero-sum game between the sampler and the learner.
However, all the above works focus on single-agent learning problems.

To the best of our knowledge, multi-agent risk-averse learning problems have not been analyzed in the literature, with only a few exceptions \cite{tamkin2019distributionally}, \cite{wang2022risk}. The work in \cite{tamkin2019distributionally} solves multi-armed bandit problems with finite actions, which are different from the convex games with continuous actions considered here.
Most closely related to this paper is our recent work in \cite{wang2022risk} proposing a new risk-averse learning algorithm for online convex games, where the agents utilize the cost evaluations at each time step to update their actions and achieve no-regret learning with high probability.
To improve the learning rate of this algorithm, \cite{wang2022risk} also proposes two
variance reduction methods on the CVaR estimates and gradient estimates, respectively.
In this paper, we build on this work to significantly improve the performance of the algorithm using a notion of momentum that combines these variance reduction techniques.
Specifically, the proposed new algorithm reuses samples of the cost functions from the full history of data while appropriately decreasing the weights of out-of-date samples.
The idea is that, using more samples, the unknown distribution of the cost functions needed to estimate the CVaR values can be better estimated. To do so, at each time step, the proposed  algorithm estimates the distribution of the cost functions using both
the immediate empirical distribution estimate and the distribution estimate from the previous time step, which summarizes all past cost values.
Then, to construct the gradient estimates, more weight is allocated to more recent  information, similar to momentum gradient descent methods \cite{qian1999momentum}.
To reduce the variance of the gradient estimate, we employ residual feedback \cite{zhang2020boosting}, which uses previous gradient estimates to update the current one.

Although \cite{wang2022risk} showed that reusing samples from the last iteration or residual feedback can individually improve the performance of the algorithm, it also identified that the combination of sample reuse and residual feedback is an unexplored and theoretically nontrivial problem.
In this work, we combine sample reuse and residual feedback by proposing a novel zeroth-order momentum method that reuses all historical samples, compared to samples from only the last iteration.
An additional important contribution of this work is that the proposed zeroth-order momentum method subsumes Algorithm 3 in \cite{wang2022risk} as a special case by appropriately choosing the momentum parameter. We show that our proposed method theoretically matches the best known result in \cite{wang2022risk} but empirically outperforms  other existing methods.

The rest of the paper is organized as follows. In Section~\ref{sec:problem}, we formally define the problem and provide some preliminary results. In Section~\ref{sec:preliminary}, we summarize results in \cite{wang2022risk} that are needed to develop our proposed method. In Section~\ref{sec:method}, we present our proposed method and analyze its regret. In Section~\ref{sec:simulation}, we numerically verify our method using a Cournot game example. Finally, in Section~\ref{sec:conclusion}, we conclude the paper.
\section{Preliminary Results}\label{sec:preliminary}
In this section, we summarize some results from \cite{wang2022risk} that lay the foundation for the subsequent analysis. Note that the CVaR values depend on the distributions of the cost functions, which are generally unknown. To estimate the distribution of the cost functions, and subsequently the CVaR values with only a few samples, a sampling strategy is proposed in \cite{wang2022risk} that uses a decreasing number of samples with the number of iterations. Specifically, at episode $t$, the sampling strategy used by the agents is designed as follows
\begin{align}\label{eq:sample_strategy}
    n_t = \lceil b  U^2 (T-t+1)^a \rceil,
\end{align}
where $\lceil \cdot \rceil$ is the regularized ceiling function, $T$ is the number of episodes, $U$ is the bound of $J_i$ as in Assumption \ref{assump:J_convex}, and $a,b \in(0,1)$ are parameters to be selected later.

Using the cost evaluations at each episode, zeroth-order methods can be employed to estimate the CVaR gradients and then update the agents' actions. Specifically, at episode $t$, the agents calculate the number of samples $n_t$ according to \eqref{eq:sample_strategy}. Then, each agent perturbs the current action $x_{i,t}$ by an amount $\delta u_{i,t}$, where $u_{i,t} \in \mathbb{S}^{d_i}$ is a random perturbation direction sampled from the unit sphere $\mathbb{S}^{d_i}\subset \mathbb{R}^{d_i}$ and $\delta$ is the size of this perturbation. Next the agents play their perturbed actions $\hat{x}_{i,t}=x_{i,t}+\delta u_{i,t} $ for $n_t$ times, and obtain $n_t$ cost evaluations which are utilized to update their actions.
To facilitate the theoretical analysis, we define the $\delta$-smoothed function $C_i^{\delta}(x)= \mathbb{E}_{w_i \sim \mathbb{B}_i,u_{-i} \sim \mathbb{S}_{-i}}[C_i(x_i+\delta w_i,x_{-i}+\delta u_{-i})]$, where $\mathbb{S}_{-i}=\Pi_{j \neq i} \mathbb{S}_j$, and $\mathbb{B}_i$, $\mathbb{S}_i$ denote the unit ball and unit sphere in $\mathbb{R}^{d_i}$, respectively. The size of the perturbation $\delta$ here is related to a smoothing parameter that controls how well $C_i^{\delta}(x)$ approximates $C_i(x)$.
As shown in \cite{wang2022risk}, the function $C_i^{\delta}(x)$ satisfies the following properties.
\begin{lemma}\label{lemma:cdelta_property}
Let Assumptions \ref{assump:J_convex} and \ref{assump:J_Lips} hold. Then we have that
\begin{enumerate}
    \item $C_i^{\delta}(x_i,x_{-i})$ is convex in $x_i$,
    \item $C_i^{\delta}(x)$ is $L_0$-Lipschitz continuous in $x$,
    \item $|C_i^{\delta}(x)$-$C_i(x)|\leq \delta L_0 \sqrt{N}$,
    \item $\mathbb{E}[ \frac{d_i}{\delta} C_i(\hat{x}_t) u_{i,t}] = \nabla_i C_i^{\delta}(x_t)$.
\end{enumerate}
\end{lemma}
The last property in Lemma \ref{lemma:cdelta_property} shows that the term $\frac{d_i}{\delta} C_i(\hat{x}_t) u_{i,t}$ is an unbiased estimate of the gradient of the smoothed function $C_i^{\delta}(x)$.
Next we present a lemma that helps bound the distance between two CVaR values by the distance between the two corresponding CDFs. The proof can be found in \cite{wang2022risk}.
\begin{lemma}\label{lemma:CVaR_bound}
Let $F$ and $G$ be two CDFs of two random variables and suppose the random variables are bounded by $U$. Then we have that
\begin{align*}
    |{\rm{CVaR}}_{\alpha}[F]-{\rm{CVaR}}_{\alpha}[G]| \leq \frac{U}{\alpha}  \mathop{\rm{sup}}_{y} |F(y)-G(y)|.
\end{align*}
\end{lemma}

\section{Problem definition}\label{sec:problem}
We consider a repeated game $\mathcal{G}$  with $N$ risk-averse agents. Each agent selects an action $x_i$ from the convex set $\mathcal{X}_i \subseteq \mathbb{R}^{d_i}$, and then receives a stochastic cost $J_i(x_i,x_{-i},\xi_i): \mathcal{X} \times \Xi_i \rightarrow \mathbb{R}$, where $x_{-i}$ represents all agents' actions except for agent $i$, and $\mathcal{X}=\Pi_{i=1}^N\mathcal{X}_i$ is the joint action space.
We sometimes instead write $J_i(x,\xi_i)$ for ease of notation, where $x=(x_i,x_{-i})$ is the concatenated vector of all agents' actions. The cost $J_i(x_i,x_{-i},\xi_i)$ is stochastic, as $\xi_i \in \Xi_i$ is a stochastic variable.
We assume that the diameter of the convex set $\mathcal{X}_i$ is bounded by $D_x$ for all $i=1,\ldots,N$.
In what follows, we make the following assumptions on the class of games we consider in this paper.
\begin{assumption}\label{assump:J_convex}
The function $J_i(x_i,x_{-i},\xi_i)$ is convex in $x_i$ for every $\xi_i \in \Xi_i$ and bounded  by $U$, i.e., $|J_i(x,\xi_i)|\leq U$, for all $i=1,\ldots,N$.
\end{assumption}
\begin{assumption}\label{assump:J_Lips}
$J_i(x,\xi_i)$ is $L_0$-Lipschitz continuous in $x$ for every $\xi_i \in \Xi_i$, for all $i=1,\ldots,N$.
\end{assumption}
Assumptions \ref{assump:J_convex} and \ref{assump:J_Lips} are common assumptions in the analysis of online learning in games \cite{bravo2018bandit}.

The goal of the risk-averse agents is to minimize the risk of incurring significantly high costs, for possibly different risk levels.
In this work, we utilize CVaR as the risk measure. For a given risk level $\alpha_i\in [0,1]$, CVaR is defined as the average of the $\alpha_i$ percent worst-case cost.
Specifically, we denote by $F_{i}^x(y)=\mathbb{P}\{J_i(x,\xi_i)\leq y \}$ as the cumulative distribution function (CDF) of the random cost $J_i(x,\xi_i)$ for agent $i$; and by  $J^{\alpha_i}$ the cost value at the $1-\alpha_i$ quantile of the distribution, also called the Value at Risk (VaR).
Then, for a given risk level $\alpha_i\in [0,1]$, CVaR of the cost function $J_i(x,\xi_i)$ of agent $i$ is defined as \begin{align*}
    C_i(x):& ={\rm{CVaR}}_{\alpha_i}[J_i(x,\xi_i)] \\
    :& = \mathbb{E}_F[J_i(x,\xi_i)|J_i(x,\xi_i)\geq J^{\alpha_i}].
\end{align*}
Notice that the CVaR value is determined by the distribution function $F_i^x(y)$ for a given $\alpha_i$, so we sometimes write CVaR as a function of the distribution function, i.e.,  ${\rm{CVaR}}_{\alpha_i}[F_i^x]:={\rm{CVaR}}_{\alpha_i}[J_i(x,\xi_i)] $.
In addition, we assume that the agents cannot observe other agents' actions, but the only information they can observe is the cost evaluations of the jointly selected actions at each time step.

Given Assumptions 1 and 2, the following lemma lists properties of CVaR, that will be important in the analysis that follows. The proof can be found in \cite{cardoso2019risk}.
\begin{lemma}\label{lemma:cvar_property}
Given Assumptions \ref{assump:J_convex} and \ref{assump:J_Lips}, we have that $C_i(x_i,x_{-i})$ is convex in $x_i$ and $L_0$-Lipschitz continuous in $x$, for all $i=1,\ldots,N$.
\end{lemma}

The following additional assumption on the variation of the CDFs is needed for the analysis that follows.
\begin{assumption}\label{assump_CDF_lips}
Let $F^w_i(y)= \mathbb{P}\{ J_i(w,\xi_i)\leq y\}$ and $F^v_i(y)= \mathbb{P}\{ J_i(v,\xi_i)\leq y\}$. There exist a constant $C_0>0$ such that
\begin{align*}
    \mathop{\rm{sup}}_{y} |F^w_i(y)-F^v_i(y)| \leq C_0  \left\|w -v \right\|.
\end{align*}
\end{assumption}
Assumption \ref{assump_CDF_lips} states that the difference between two CDFs can be bounded by the difference between the corresponding two action profiles. It is less restrictive than Assumption~3 in \cite{wang2022risk} since it does not depend on the algorithm but only on the cost functions.

A common measure of the ability of the agents to learn online optimal decisions that minimize their individual risk-averse objective functions $C_i(x)$ is the algorithm regret, which is defined as the difference between the actual rewards and best rewards that the agent could have achieved by playing the single best action in hindsight.
Suppose the action sequences of agent~$i$ and the other agents in the team are $\{\hat{x}_{i,t} \}_{t=1}^T$ and $\{\hat{x}_{-i,t} \}_{t=1}^T$, respectively. Then, we define the regret of agent~$i$ as
\begin{align*}
    {\rm{R}}_{C_i}(T) = \sum_{t=1}^{T} C_i(\hat{x}_{i,t},\hat{x}_{-i,t}) - \mathop{\rm{min}}_{\tilde{x}_i \in \mathcal{X}_i} \sum_{t=1}^{T} C_i(\tilde{x}_i,\hat{x}_{-i,t}).
\end{align*}
An algorithm is said to be no-regret if its regret grows sub-linearly with the number of episodes $T$.
In this paper, we propose a no-regret learning algorithm, so that $\lim_{T\rightarrow \infty}\frac{{\rm{R}}_{C_i}(T)}{T} = 0$, $i=1,\ldots,N$.

\section{A zeroth-order momentum method}\label{sec:method}

In this section, we present a new one-point zeroth-order momentum method that combines sample reuse as in Algorithm 2 in \cite{wang2022risk} and residual feedback as in Algorithm 3 in \cite{wang2022risk}, but unlike Algorithm 2 in \cite{wang2022risk} that reuses samples only from the last iteration, it uses all past samples to update the agents' actions. The new algorithm is given as Algorithm 1.

\begin{algorithm}[t]
\caption{Risk-averse learning with momentum} \label{alg:algorithm1}
\begin{algorithmic}[1]
    \REQUIRE Initial value $x_0$, step size $\eta$, parameters $a$, $b$, $\delta$, $T$, risk level $\alpha_i$, $i=1,\cdots,N$.
    \FOR{$ {\rm{episode}} \; t=1,\ldots,T$}
        \STATE Select $n_t=\lceil b U^2 (T-t+1)^a\rceil$
        \STATE Each agent samples $u_{i,t} \in \mathbb{S}^{d_i}$,  $i=1,\ldots, N$
        \STATE Each agent play $\hat{x}_{i,t}=x_{i,t}+\delta u_{i,t} $, $i=1,\ldots, N$
        \FOR{$j=1,\ldots,n_t$}
        \STATE Let all agents play $\hat{x}_{i,t}$
        \STATE Obtain $J_i(\hat{x}_{i,t},\hat{x}_{-i,t},\xi_{i}^j)$
        \ENDFOR
        \FOR{agent $ i=1,\ldots,N$}
        \STATE Build EDF $\bar{F}_{i,t}(y)$
        \STATE Estimate CVaR: $ {\rm{CVaR}}_{\alpha_i}[\bar{F}_{i,t}] $
        \STATE Construct gradient estimate\\ $\bar{g}_{i,t}=\frac{d_i}{\delta} \left( {\rm{CVaR}}_{\alpha_i} [\bar{F}_{i,t} ] -{\rm{CVaR}}_{\alpha_i} [\bar{F}_{i,t-1} ]\right) u_{i,t}$
        \STATE Update $x$: $x_{i,t+1} \leftarrow \mathcal{P}_{\mathcal{X}_i^{\delta}} ( x_{i,t} - \eta \bar{g}_{i,t})$
        \ENDFOR
    \ENDFOR
\end{algorithmic}
\end{algorithm}

Using the sampling strategy in \eqref{eq:sample_strategy}, each agent plays the perturbed action $\hat{x}_{i,t}$ for $n_t$ times and obtains $n_t$ samples at episode $t$.
For agent $i$, we denote the CDF of the random cost $J_i(\hat{x}_t,\xi_i)$ that is returned by the perturbed action $\hat{x}_t$ as $F_{i,t}^{\hat{x}_t}(y)= \mathbb{P}\{ J_i(\hat{x}_t,\xi_i) \leq y\}$. Since $\hat{x}_t$ depends on $t$, we write $F_{i,t}$ for $F_{i,t}^{\hat{x}_t}$ for ease of notation.
Using bandit feedback in the form of finitely many cost evaluations, the agents cannot obtain the accurate CDF $F_{i,t}$. Instead, they construct an empirical distribution function (EDF) $\hat{F}_{i,t}$ of the cost $J_i(\hat{x}_t,\xi_i)$ using $n_t$ cost evaluations by
\begin{align}\label{eq:edf_alg1}
    \hat{F}_{i,t}(y)= \frac{1}{n_t}\sum_{j=1}^{n_t}\mathbf{1}\{ J_i(\hat{x}_t,\xi_i^j) \leq y\}.
\end{align}
To improve estimate of the CDF, we utilize past samples for all episodes $t\geq2$, and construct a modified distribution estimate $\bar{F}_{i,t}$ by adding a momentum term:
\begin{align}\label{eq:edf_bar}
    \bar{F}_{i,t}(y)= \beta \bar{F}_{i,t-1}(y) + (1-\beta) \hat{F}_{i,t}(y),
\end{align}
where $\beta\in[0,1)$ is the momentum parameter.
For $t=1$, we set $\bar{F}_{i,t}(y)= \hat{F}_{i,t}(y)$.
The agents utilize the distribution estimate $\bar{F}_{i,t}$ to calculate the CVaR estimates and further construct the gradient estimate using residual feedback as
\begin{align}\label{eq:grad_alg1}
    \bar{g}_{i,t}=\frac{d_i}{\delta} \left( {\rm{CVaR}}_{\alpha_i} [\bar{F}_{i,t} ] -{\rm{CVaR}}_{\alpha_i} [\bar{F}_{i,t-1} ]\right) u_{i,t},
\end{align}
where $\delta$ is the size of the perturbation on the action $x_{i,t}$ defined above.
Depending on the size of the perturbation, the played action $\hat{x}_{i,t}$ may be infeasible, i.e., $\hat{x}_{i,t} \not\in \mathcal{X}_i$. To handle this issue, we define the projection set $\mathcal{X}_i^{\delta} =\{x_i \in \mathcal{X}_i | {\rm{dist}}(x_i,\partial \mathcal{X}_i) \geq \delta \}$ that we use in the projected gradient-descent update
\begin{align}\label{eq:projection}
    x_{i,t+1} = \mathcal{P}_{\mathcal{X}_i^{\delta}} ( x_{i,t} - \eta \bar{g}_{i,t}).
\end{align}

Note that the agents are not able to obtain accurate values of $C_i(\hat{x}_t)$ using finite samples. In fact, if we use the distribution estimate $\bar{F}_{i,t}$ in \eqref{eq:edf_bar} to calculate the CVaR values, there will be a CVaR estimation error, which is defined as
\begin{align*}
    \bar{\varepsilon}_{i,t} :=
     {\rm{CVaR}}_{\alpha_i}[\bar{F}_{i,t} ]- {\rm{CVaR}}_{\alpha_i}[F_{i,t} ].
\end{align*}
As a result, the gradient estimate $\bar{g}_{i,t}$ in \eqref{eq:grad_alg1} is biased since $\mathbb{E}[\bar{g}_{i,t}]=\mathbb{E}\left[ \frac{d_i}{\delta}(C_i(\hat{x}_t)+ \bar{\varepsilon}_{i,t})u_{i,t} \right]= \nabla_i C_i^{\delta}(x_t)+ \mathbb{E}\left[\frac{d_i}{\delta}\bar{\varepsilon}_{i,t}u_{i,t} \right]$, with the bias captured by the last term.
In what follows, we present a lemma that bounds the sum of the CVaR estimation errors.
\begin{lemma}\label{lemma:sum_epsilon_bound}
Suppose that Assumption \ref{assump_CDF_lips} holds. Then,
the sum of the CVaR estimation errors satisfies
\begin{align}
    \sum_{t=1}^T \left\|\bar{\varepsilon}_{i,t}\right\|\leq \frac{U}{\alpha_i } \Big( \frac{\beta}{1-\beta} e_{i,1} + \frac{\beta\Sigma_1}{1-\beta}T  + \sum_{t=1}^T r_t \Big)
\end{align}
with probability at least $1-\gamma$, where $e_{i,t}:=\mathop{\rm{sup}}_{y} |\bar{F}_{i,t}(y)-F_{i,t}(y)|$, $r_t:=\sqrt{\frac{ {\rm{ln}}(2T/\gamma) }{2bU^2(T-t+1)^a}}$, and $\Sigma_1:=C_0 \eta \frac{d_i}{\delta}U\sqrt{N} + 2 C_0 \delta$.
\end{lemma}
\begin{proof}
To bound the CVaR estimation error, it suffices to bound the CDF differences using Lemma \ref{lemma:CVaR_bound}. Recall the definition of $\bar{F}_{i,t}$ in \eqref{eq:edf_bar}. Then, we have that
\begin{align}\label{eq_term7}
    &\mathop{\rm{sup}}_{y} |\bar{F}_{i,t}(y)-F_{i,t}(y)| \nonumber \\
    =&  \mathop{\rm{sup}}_{y} |\beta \bar{F}_{i,t-1}(y) + (1-\beta) \hat{F}_{i,t}(y)- F_{i,t}(y)|\nonumber \\
    =& \mathop{\rm{sup}}_{y} | \beta (\bar{F}_{i,t-1}(y)- {F}_{i,t-1}(y)) + \beta ({F}_{i,t-1}(y)- {F}_{i,t}(y)) \nonumber\\
    &+ (1-\beta) (\hat{F}_{i,t}(y)-{F}_{i,t}(y)) |\nonumber\\
    \leq & \beta \mathop{\rm{sup}}_{y} |\bar{F}_{i,t-1}(y)- {F}_{i,t-1}(y)| + \beta \mathop{\rm{sup}}_{y} |{F}_{i,t-1}(y)- {F}_{i,t}(y)| \nonumber\\
     &+ (1-\beta) \mathop{\rm{sup}}_{y} |\hat{F}_{i,t}(y)-{F}_{i,t}(y)|.
\end{align}
We now bound the latter two terms in the right-hand-side of \eqref{eq_term7} separately.
Using Assumption \ref{assump_CDF_lips} and Lemma \ref{lemma:cdelta_property}, we have that
\begin{align}\label{eq_term8}
    &\mathop{\rm{sup}}_{y} |F_{i,t}(y)-F_{i,t-1}(y)| \nonumber \\
    \leq & C_0 \left\|\hat{x}_t - \hat{x}_{t-1} \right\|
    \leq  C_0 \left\|x_t - x_{t-1} \right\| + 2 C_0 \delta \nonumber\\
    \leq & C_0 \eta \left\| \bar{g}_{t} \right\| + 2 C_0 \delta
    \leq C_0 \eta \frac{d_i}{\delta}U\sqrt{N} + 2 C_0 \delta:=\Sigma_1,
\end{align}
where the last inequality holds since $\left\|\bar{g}_{t}\right\| =\sqrt{ \sum_{i=1}^N\left\|\bar{g}_{i,t}\right\|^2} \leq \frac{\sqrt{N} d_i U}{\delta}$.
Then, applying the Dvoretzky–Kiefer–Wolfowitz (DKW) inequality, we obtain that
\begin{align}\label{event_At}
    \mathbb{P}\left\{ \mathop{\rm{sup}}_{y} |F_{i,t}(y)-\hat{F}_{i,t}(y)| \geq \sqrt{\frac{ {\rm{ln}}(2/\bar{\gamma}) }{2n_t}}\right\} \leq \bar{\gamma}.
\end{align}
Define the events in (\ref{event_At}) as $A_t$ and denote $\gamma=\bar{\gamma}T$. Then,  the following inequality holds
\begin{align}\label{eq_term10}
\mathop{\rm{sup}}_{y} |F_{i,t}(y)-\hat{F}_{i,t}(y)| \leq \sqrt{\frac{ {\rm{ln}}(2T/\gamma) }{2n_t}}, \forall t=1,\ldots,T,
\end{align}
with probability at least $1-\gamma$ since $1-\mathbb{P} \{ \bigcup_{t=1}^T A_t\} \geq 1- \sum_{t=1}^T\mathbb{P}\{A_t\}\geq 1-T \frac{\gamma}{T}\geq1-\gamma$.
Substituting the bounds in \eqref{eq_term8} and \eqref{eq_term10} into \eqref{eq_term7}, and using the definition of $e_{i,t}$, we get that
\begin{align}\label{eq:one_step_sum}
    e_{i,t}\leq \beta e_{i,t-1}+ \beta \Sigma_1 + (1-\beta)r_t.
\end{align}
Note that \eqref{eq:one_step_sum} holds for all $t\in\{2,\cdots,T\}$. By iteratively using this inequality, we have that
\begin{align}\label{eq:eit}
    e_{i,t}\leq \frac{\beta}{1-\beta}\Sigma_1 + (1-\beta)\sum_{k=0}^{k-2}\beta^{k}r_{t-k}+ \beta^{t-1} e_{1,i}.
\end{align}
Taking the sum over $t=1,\ldots,T$ of both sides of \eqref{eq:eit}, we have that
\begin{align}
    \sum_{t=1}^T e_{i,t}\leq \frac{\beta}{1-\beta} e_{i,1} + \frac{\beta\Sigma_1}{1-\beta}T  + \sum_{t=1}^T r_t,
\end{align}
with probability at least $1-\gamma$.
Finally, using Lemma \ref{lemma:CVaR_bound}, we can obtain the desired result.
\end{proof}

Lemma \ref{lemma:sum_epsilon_bound} bounds the CVaR estimation error caused by using past samples.
The next lemma quantifies the error due to residual feedback in the gradient estimate \eqref{eq:grad_alg1}. Specifically, it bounds the second moment of the gradient estimate.
\begin{lemma}\label{lemma:sum_gt}
Let Assumptions \ref{assump:J_convex}, \ref{assump:J_Lips} and \ref{assump_CDF_lips} hold. Suppose that the action is updated as in \eqref{eq:projection}. Then, the second moment of the gradient estimate satisfies
\begin{align}\label{eq:gt}
    \sum_{t=1}^T \left\|\bar{g}_{t}\right\|^2
    \leq \frac{1}{1-\sigma} \left\| \bar{g}_{1}\right\|^2 + \frac{1}{1-\sigma}\Sigma_2 T,
\end{align}
where $\bar{g}_t:=(\bar{g}_{i,t}, \bar{g}_{-i,t})$ is the concatenated vector of all agents' gradient estimates,  $\sigma=\frac{4d_i^2L_0^2N \eta^2}{\delta^2}$, $\Sigma_2=16d_i^2N^2L_0^2 + \frac{48C_0^2 d_i^4 U^4 N \eta^2 \beta^2}{\delta^4 (1-\beta)^2} (\sum_i \frac{1}{\alpha_i^2}) + \frac{192 C_0^2 d_i^2 U^2 \beta^2}{ (1-\beta)^2}(\sum_i \frac{1}{\alpha_i^2}) + \frac{12 \ln(2T/\gamma) d_i^2}{b\delta^2}(\sum_i \frac{1}{\alpha_i^2}) + \frac{24 d_i^2 U^2 \beta^2 e_m^2}{\delta^2}(\sum_i \frac{1}{\alpha_i^2}) $, and
$e_m:= \mathop{\max}_i \{e_{i,1}\}$.
\end{lemma}

\begin{proof}
Using the fact that $r_t\leq \sqrt{\frac{ {\rm{ln}}(2T/\gamma) }{2bU^2}}:=r$ and  \eqref{eq:eit}, we have that
\begin{align}\label{eq:eit_ineq}
    \left\| e_{i,t} \right\|^2 & \leq \left\| \frac{\beta}{1-\beta}\Sigma_1 + r+ \beta e_{m} \right\|^2  \nonumber\\
    & \leq 3\left\| \frac{\beta}{1-\beta}\Sigma_1 \right\|^2 + 3\left\| r\right\|^2  + 3\left\| \beta e_{m}\right\|^2,
\end{align}
where the last inequality holds since $\left\| a+b+c\right\|^2 \leq 3 \left\|a\right\|^2+3 \left\|b\right\|^2+3 \left\|c\right\|^2$. Then, applying Lemma \ref{lemma:CVaR_bound} to \eqref{eq:eit_ineq}, we obtain that
\begin{align}\label{eq:epsibar_bound}
    \left\|\bar{\varepsilon}_{i,t}\right\|^2 \leq \frac{U^2}{\alpha_i^2}\left( \frac{3\beta^2  \Sigma_1^2}{(1-\beta)^2} + 3 \left\| r\right\|^2+ 3\beta^2 \left\| e_m\right\|^2\right).
\end{align}
By the definition of $\bar{g}_{i,t}$ in \eqref{eq:grad_alg1}, we have that
\begin{align}    \label{eq_bar_git}
    &\left\|\bar{g}_{i,t}\right\|^2
    =\frac{d_i^2}{\delta^2} \left( ({\rm{CVaR}}_{\alpha_i} [\bar{F}_{i,t} ]- {\rm{CVaR}}_{\alpha_i} [\bar{F}_{i,t-1} ] ) u_{i,t}\right)^2 \nonumber \\
    &\leq \frac{d_i^2}{\delta^2} \left({\rm{CVaR}}_{\alpha_i} [\bar{F}_{i,t} ]- {\rm{CVaR}}_{\alpha_i} [\bar{F}_{i,t-1} ] \right)^2 \left\| u_{i,t}\right\|^2 \nonumber \\
    &\leq \frac{d_i^2}{\delta^2} \Big( 2 ({\rm{CVaR}}_{\alpha_i} [F_{i,t} ]- {\rm{CVaR}}_{\alpha_i} [F_{i,t-1} ])^2  \nonumber \\
    &\quad + 2 (\bar{\varepsilon}_{i,t}-\bar{\varepsilon}_{i,t-1})^2 \Big)  \left\| u_{i,t}\right\|^2 \nonumber \\
    & \leq \frac{d_i^2}{\delta^2} \Big( 2 L_0^2 \left\|\hat{x}_t-\hat{x}_{t-1} \right\|^2 + 4|\bar{\varepsilon}_{i,t} |^2 + 4|\bar{\varepsilon}_{i,t-1} |^2 \Big),
\end{align}
where the last inequality is due to the fact that the CVaR function is Lipschitz continuous as shown in Lemma \ref{lemma:cvar_property}. We now bound the term $\left\|\hat{x}_t-\hat{x}_{t-1} \right\|^2$ in \eqref{eq_bar_git}. Recalling the update rule $x_{i,t}= \mathcal{P}_{\mathcal{X}_i^{\delta}} ( x_{i,t-1} - \eta_{i} \bar{g}_{i,t-1})$, we have that
\begin{align*}
    &\left\|\hat{x}_t-\hat{x}_{t-1} \right\|^2
    =  \left\| x_t-x_{t-1} +\delta u_t-\delta u_{t-1} \right\|^2  \nonumber \\
    \leq& 2 \left\| x_t - x_{t-1}\right\|^2 +2\delta^2(2\left\|u_t  \right\|^2 + 2\left\|u_{t-1} \right\|^2 )  \nonumber \\
    \leq& 2  \left\| x_t - x_{t-1}\right\|^2 + 8 N\delta^2
    \leq 2 \eta^2 \left\|\bar{g}_{t-1} \right\|^2 + 8 N\delta^2. \nonumber
\end{align*}
Substituting the above inequality and the inequality in \eqref{eq:epsibar_bound} into the right hand side of \eqref{eq_bar_git} and summing both sides over all agents $i=1,\ldots,N$, we have that
\begin{align}\label{eq_sum_of_g}
    &\left\|\bar{g}_{t}\right\|^2 =\sum_{i=1}^N \left\|\bar{g}_{i,t}\right\|^2 \nonumber\\
    &\leq \frac{d_i^2 N }{\delta^2} \Big( 4L_0^2 \eta^2 \left\|\bar{g}_{t-1} \right\|^2 + 16 L_0^2N\delta^2 \Big) \nonumber \\
    &\quad + \frac{8 d_i^2 }{\delta^2} \sum_{i=1}^N \frac{U^2}{\alpha_i^2}\left( \frac{3\beta^2  \Sigma_1^2}{(1-\beta)^2} + 3 \left\| r\right\|^2+ 3\beta^2 \left\| e_m\right\|^2\right) \nonumber \\
    &\leq \sigma \left\| \bar{g}_{t-1}\right\|^2 + 16d_i^2N^2L_0^2+ \frac{12 \ln(2T/\gamma) d_i^2}{b\delta^2} (\sum_i \frac{1}{\alpha_i^2}) \nonumber \\
    & \quad + \frac{24d_i^2 U^2\beta^2  }{\delta^2 (1-\beta)^2} (C_0 \eta \frac{d_i}{\delta}U\sqrt{N} + 2 C_0 \delta)^2 (\sum_i \frac{1}{\alpha_i^2}) \nonumber \\
    &\quad +\frac{24 d_i^2 U^2 \beta^2 e_m^2}{\delta^2}(\sum_i \frac{1}{\alpha_i^2}) \nonumber \\
    & \leq \sigma \left\| \bar{g}_{t-1}\right\|^2 + \Sigma_2,
\end{align}
where the last inequality is due to the fact that $(C_0 \eta \frac{d_i}{\delta}U\sqrt{N} + 2 C_0 \delta)^2\leq 2C_0^2\eta^2\frac{d_i^2}{\delta^2}U^2 N + 8C_0^2\delta^2$. Summing \eqref{eq_sum_of_g} over all episode $t=1,\ldots,T$ completes the proof.
\end{proof}

Before proving the main theorem, we present a regret decomposition lemma that links the regret to the errors bounds on the CVaR estimates and gradient estimates as provided in Lemmas \ref{lemma:sum_epsilon_bound} and \ref{lemma:sum_gt}.
\begin{lemma}\label{lemma:regret_decomp}
Let Assumptions \ref{assump:J_convex}, \ref{assump:J_Lips} and \ref{assump_CDF_lips}  hold. Then, the regret of Algorithm \ref{alg:algorithm1} satisfies
\begin{align}\label{eq:regret_decomp}
    {\rm{R}}_{C_i}(T) \leq &\frac{D_x^2}{2\eta}  +\frac{ \eta}{2}\mathbb{E} \big[ \sum_{t=1}^{T} \left\|\bar{g}_{i,t}\right\|^2 \big]   + (4 \sqrt{N}+\Omega)L_0\delta T \nonumber \\
    &+ \frac{d_i D_x }{\delta}\sum_{t=1}^T|\bar{\varepsilon}_{i,t}|,
\end{align}
where $\Omega>0$ is a constant that represents the error from projection $\mathcal{P}$.
\end{lemma}
\begin{proof}
The proof can be adapted from Lemma 5 in \cite{wang2022risk} and is omitted due to the space limitations.
\end{proof}
\begin{theorem}\label{thm:1}
Let Assumptions \ref{assump:J_convex}, \ref{assump:J_Lips} and \ref{assump_CDF_lips}  hold and select $\eta=\frac{D_x  }{d_i L_0 N}T^{-\frac{3a}{4}}$,  $\delta=\frac{D_x }{ N^{\frac{1}{6}}}T^{-\frac{a}{4}}$, $\beta=\frac{1}{ U^2  T^{\frac{a}{4}} } $.
Suppose that $n_t$ is chosen as in \eqref{eq:sample_strategy} with $a\in(0,1)$, and the EDF and the gradient estimate are defined as in \eqref{eq:edf_alg1} and (\ref{eq:grad_alg1}), respectively. Then, when $T\geq (8N^{\frac{2}{3}} )^{\frac{1}{a}}$, Algorithm \ref{alg:algorithm1} achieves regret
$$
{\rm{R}}_{C_i}(T)= \mathcal{O}( D_x d_i L_0 NS(\alpha) \ln(T/\gamma) T^{1-\frac{a}{4}} ),
$$
with probability at least $1-\gamma$ and where $S(\alpha):=\sum_{i=1}^N\frac{1}{\alpha_i^2}$.
\end{theorem}
\begin{proof}
Substituting the bounds in Lemmas \ref{lemma:sum_epsilon_bound} and \ref{lemma:sum_gt} into Lemma \ref{lemma:regret_decomp}, we have that
\begin{align}
    {\rm{R}}_{C_i}(T) \leq &\frac{D_x^2}{2\eta}  +\frac{ \eta}{2} \left( \frac{1}{1-\sigma} \left\| \bar{g}_{1}\right\|^2 + \frac{1}{1-\sigma}\Sigma_2 T \right)
      \nonumber \\
    &+ \frac{d_i D_x U}{\delta \alpha_i} \Big( \frac{\beta}{1-\beta} e_{i,1} + \frac{\beta\Sigma_1}{1-\beta}T  + \sum_{t=1}^T r_t \Big) \nonumber \\
    & + (4 \sqrt{N}+\Omega)L_0\delta T.
\end{align}
Substituting $\delta$, $\eta$ and $\beta$ into the above inequality, we can obtain the desired result. The detailed proof is straightforward and is omitted due to the space limitations.
\end{proof}
Note that Algorithm \ref{alg:algorithm1} in fact achieves the regret
${\rm{R}}_{C_i}(T) =\tilde{\mathcal{O}} (T^{1-\frac{a}{4}})$, in which we use the notation $\tilde{\mathcal{O}}$ to hide constant factors and poly-logarithmic factors of $T$. This result matches the best result in \cite{wang2022risk}. Compared to \cite{wang2022risk}, the analysis of Algorithm 1 that combines historical information and residual feedback is nontrivial and requires an appropriate selection of the momentum parameter to show convergence. Note that Algorithm 3 in \cite{wang2022risk} is a special case of Algorithm 1 for the momentum parameter $\beta=0$.

\section{Numerical Experiments}\label{sec:simulation}
In this section, we illustrate the proposed algorithm on a Cournot game. Specifically, we consider two risk-averse agents $i=1,2$ that compete with each other. Each agent determines the production level $x_i$ and receives an individual cost feedback which is given by $J_i=1-(2-\sum_jx_j)x_i+ 0.2x_i+\xi_i x_i$, where $\xi_i\sim U(0,1)$ is a uniform random variable. Here we utilize the term $\xi_i x_i$ to represent the uncertainty occurred in the market, which is proportional to the production level $x_i$. The agents have their own risk levels $\alpha_i$ and they aim to minimize the risk of incurring high costs, i.e., the CVaR of their cost functions.

Note that the regret for agent $i$ depends on the sequence of the other agent's actions $\{x_{-i,t}\}_{t=1}^T$, and this sequence depends on the algorithm. Therefore, it is not appropriate to compare different algorithms in terms of the regret.
Instead, we use the empirical performance to evaluate the algorithms. Specifically, we compute the CVaR values at each episode, and observe how fast the agents can minimize the CVaR values during the learning process.

To compute the distribution function $\bar{F}_{i,t}$, we divide the interval $[0,U]$ into $n$ bins of equal width, and we approximate the expectation by the sum of finite terms.
We compare our zeroth-order momentum method with the two algorithms in \cite{wang2022risk}, which we term as the algorithm with sample reuse and the algorithm with residual feedback, respectively.
The number of samples $n_t$ at each episode for Algorithm~1 is shown in Figure~\ref{fig_samples}. We set the momentum parameter $\beta=0.5$.
Each algorithm is run for 20 trials and the parameters of these algorithms are separately optimally tuned. The CVaR values of these algorithms are presented in Figure \ref{fig_momen}. We observe that all the algorithms finally converge to the same CVaR values, but the zeroth-order momentum method proposed here converges faster. This is because our method uses all past samples to get a better estimate of the CVaR values and thus allows for a larger step size. Moreover, we also observe that the standard deviation of both our proposed algorithm and the algorithm with residual feedback is sufficiently small, which verifies the variance reduction effect of using residual feedback.

\begin{figure}[t]
\begin{center}
\centerline{\includegraphics[width=0.9\columnwidth]{figures/number_of_samples.pdf}}
\caption{The number of samples of Algorithm \ref{alg:algorithm1}.}
\label{fig_samples}
\end{center}
\vskip -0.2in
\end{figure}

\begin{figure}[t]
\begin{center}
\centerline{\includegraphics[width=0.9\columnwidth]{figures/alg4_sample.pdf}}
\caption{Comparative results among our proposed zeroth-order momentum method, and the three algorithms in \cite{wang2022risk}. Shaded areas represent $\pm$ one standard deviation over 20 runs.}
\label{fig_momen}
\end{center}
\vskip -0.2in
\end{figure}

\begin{figure}[t]
\begin{center}
\centerline{\includegraphics[width=0.9\columnwidth]{figures/beta.pdf}}
\caption{CVaR values achieved by Algorithm \ref{alg:algorithm1} with different $\beta$ values. Shaded areas represent $\pm$ one standard deviation over 20 runs.}
\label{fig_vari_beta}
\end{center}
\vskip -0.2in
\end{figure}

Moreover, we explore the effect of different momentum parameters $\beta$. In Figure \ref{fig_vari_beta}, we present the achieved CVaR values of our method for various $\beta$ values and all other parameters unchanged. Recalling the definition of the distribution estimate in \eqref{eq:edf_bar}, large values of $\beta$ place more weight on outdated cost values and thus reduce the convergence speed.

\section{Conclusion}\label{sec:conclusion}
In this work, we proposed a zeroth-order momentum method for online convex games with risk-averse agents.
The use of momentum that employs past samples allowed to improve the ability of the algorithm to estimate the CVaR values. Zeroth-order estimation of the CVaR gradients using residual feedback allowed us to reduce the variance of the CVaR gradient estimates.
We showed that the proposed algorithm theoretically achieves no-regret learning with high probability, matching the best known result in literature.
Moreover, we provided numerical simulations that demonstrated the superior performance of our method in practice. While sample reuse and residual feedback had been both separately shown to improve the performance of online learning, here we showed that their combination yields yet better performance.

\section{INTRODUCTION}

redpajama_set_name: RedPajamaArXiv


text:
VOI 012
Esker – Destruction Time Again
Forgoing traditional melody and song-writing, Esker's distinctive sound focuses on the interplay between noise, texture and rhythm. VOITAX is happy to present her stunning debut EP, which was assembled from various recording sessions over the past two years.
Formerly a drummer and guitarist in various bands in Melbourne's noise scene, the influence of her past is evident in this record – a combination of processed guitar, mangled samples and bizarre, ghostly synthesis form a deep and complex sonic space.
The effect of almost a decade in Berlin is just as clearly heard in the EP's thumping techno pulse. Wielded with a drummer's feel for timing and meter, phasing rhythms, crunching lo-fi percussion and thundering kicks provide a solid backbone. Having become absorbed in techno culture, this is her contribution.
Releases August 27th 2018
Photograph by Nina Röder, taken from the series »A Little Deeper Than You Thought«, used with permission of Nina Röder and Galerie Burster.


I saw this bloke chatting up a cheetah, I thought 'he's trying to pull a fast one'.
I said 'I've been on telly but I'm no Dean Martin'.
So I said to the Gym instructor 'Can you teach me to do the splits?'.
He said 'How flexible are you?' I said 'I can't make Tuesdays'.
So I was having dinner with Garry Kasparov and there was a check tablecloth.
It took him two hours to pass me the salt. He said 'You remind me of a pepper-pot', I said 'I'll take that as a condiment'.
trying to pack myself in a small suitcase. I can hardly contain myself.
So I met this gangster who pulls up the back of people's pants, it was Weggie Kray.
So I said 'Do you want a game of Darts?', he said 'OK then', I said 'Nearest to bull starts'. He said 'Baa', I said 'Moo', he said 'You're closest'.
The other day I sent my girlfriend a huge pile of snow. I rang her up, I said 'Do you get my drift?'.
So I went down the local supermarket, I said 'I want to make a complaint, this vinegar's got lumps in it', he said 'Those are pickled onions'.
So I went to the Chinese restaurant and this duck came up to me with a red rose and says 'Your eyes sparkle like diamonds'. I said 'Waiter, I asked for a-ROMATIC duck'.
But I'm in great mood tonight because the other day I entered a competition and I won a years supply of Marmite……… one jar.
So this bloke says to me, 'Can I come in your house and talk about your carpets?' I thought 'That's all I need, a Je-hoover's witness'.
You see my next door neighbour worships exhaust pipes, he's a catholic converter.
Now did you know all male tennis players are witches, for example, Goran, even he's a witch.
So I was in Tesco's and I saw this man and woman wrapped in a barcode.


The operation of 400 Internet companies and entities was disrupted by the actions of Russia's telecom watchdog, the Federal Service for Supervision of Communications, Information Technology and Mass Media, which tried to block the Telegram instant messaging service, the watchdog told Vedomosti.
Roskomnadzor said that the agency is now working together with the Regional Public Center for Internet Technologies (ROCIT) and the companies affected by the ban.
According to the newspaper, the Russian watchdog said it believes that this was because Telegram and foreign hosting providers are evading the court ruling, and the watchdog makes local decisions that would allow the Internet resources of these companies to continue working.
The federal service began blocking Telegram on April 16, but the messaging service began using the resources of cloud hosting providers Amazon, Google, and Microsoft. The Russian media watchdog responded by blocking entire subnets of IP-addresses belonging to Google and Amazon, among others. Innocent services that also used these addresses were blocked.
Numerous reports of failures in the operation of Internet services forced the watchdog to open a hotline on April 20.
According to Vedomosti, the federal service is not revealing the exact number of complaints but reports that only 1,300 calls indicated specific Internet resources or IP-addresses that were blocked. The remaining several tens of thousands were either outright spam or letters without any specifics, the service affirmed. Verification of addresses showed that the operations of 400 resources had been affected by the efforts to block Telegram.
On Tuesday, Telegram representatives filed a complaint with the Appeal Board of the Russian Supreme Court requesting a repeal of the Court's earlier decision. The Supreme Court on March 20 rejected a claim by Telegram and ruled that a Russian Security Service order to Telegram for the transfer of user encryption keys was lawful.


Tiffany & Co. is a brand that is known of spectacular quality and craftsmanship. The company has been around since 1837, and has produced countless unique and gorgeous pieces. Tiffany & Co. jewelry is coveted and sought after by almost everyone that I know. Unfortunately, due to the brand's popularity and status, Tiffany & Co. has been a target of counterfeits. Like many of you, I personally love and own many Tiffany pieces. I like to be as knowledgeable as possible with every piece that I purchase. Because of this, I have decided to write and publish a series of Tiffany & Co "Real vs. Fake" picture guides with the goal of helping you, my customers and readers, as you contemplate on your next piece of treasure from Tiffany & Co. Thank you so much for browsing our site and please let me know of any questions.
Tiffany & Co.'s Teardrop Hoops are from designer Elsa Peretti's line. With this in mind, you should look for not only the Tiffany & Co logo but also Peretti's signature on the piece itself.
Take a look at the two pairs of Teardrop hoops on the picture – which one do you think is real?
Answer: The pair on the right is the REAL piece.
It is not very easy to tell at first if you are looking from afar. However, upon closer inspection, you will notice that the real piece has the Elsa Peretti signature very neatly and clearly spelled out. The quality of an authentic Tiffany piece can be clearly seen. On the other hand, the fake piece only has the words "Tiffany & Co / 925" and it is not even cleanly etched on the piece.
The hoop on the left is a fake. The right one is real.
A clearer picture of what the real piece looks like. As you can see, the writing on it is very meticulously printed.
Another telltale sign on the fake piece is its shape. Again, it is a bit difficult to tell if you are looking at the earrings on its own. However, once it is placed side by side with the authentic pair of Teardrop hoops, you can easily see that the shape of the real pair if more of an oval round, while the counterfeit one is less oval and a slightly more rounded, as seen in the pictures below.
Note how uneven and unclear the markings are on the hoops below.
With more research, time and experience, you will be able to immediately distinguish between an authentic Tiffany piece from a fake piece. I hope this quick picture guide proved helpful to you. Please have a look around our store for only 100% authentic Tiffany & Co. and other designer pieces. And of course, please send us an email at Sales@Madison880.com for any further questions or if we can help with anything else at all.
Back to Tips & Guides page.


text:
GFM Spotlight Interview: L. Young Talks New Album, His Harmony Videos on Facebook and Coming Correct As A Male R&B Vocalist
|In Indie Soul, Interviews, New Artists, R&B
|By Kimberly Kennedy Charles
Grown Folks Music caught up with R&B vocalist and Top Notch Entertainment artist L. Young. L. chopped it up about his super tight, five-part harmony videos he posts on his Facebook page, his re-packaged album that is picking up steam, ReVerb, his musical influences (the usual suspects and one you may not ever have guessed) and singing with passion and emotion as an R&B male vocalist.
GFM: Before we talk about the album… you have been killin' us with those five-part harmony videos on your Facebook page. They are phenomenal and I noticed you take suggestions. How did those come about?
LY: I saw a couple of artists kinda doing little snippets of it. I was wondering what they were doing… what app they were doing. I wanted to figure it out. Nobody would tell me, so because they wouldn't tell me it just kinda fueled my fire to kinda figure out what was going on. I had this notion to do one one day and I did one… like a 15-second one for Instagram. It was okay. It was cool, I didn't get that many likes or whatever and then I was like, 'you know what? I wanna make these longer. I wanna do a full song.' I was riding down the street one day and the song "Lean On Me" was on the radio and it reminded me of the movie Lean On Me, and then I thought about the group Riff's song in that movie. The Eastside High School Song. I was like, 'You know what? I'm going to do that as one of the harmony videos. That's a cool song. It's very catchy. I think a lot of people would like that. It's something that reminds us of the '90s and being kids and stuff and that movie is just nostalgic.' I did that… not putting too much effort into it… not too much time and then I wake up the next morning and it's got like a thousand shares. I'm like, 'Okay… wow… okay… alright… something's going on here.' Then I go to sleep, and the next day I wake up and it's like ten-thousand shares. It's like, 'Wow… okay.' (laughing) So it just kinda took off from there. I just kept doing more and more and more.
GFM: Well they're awesome. We enjoy them. That's how I got to you… through some of the five-part harmony videos and then (I) came to find your original material. Many of the Grown Folks don't know who you are, so I'd like to just let you tell your story about your musical journey and introduce yourself to the Grown Folks audience. Tell us your musical story.
LY: I was born and raised in Louisville, KY. I grew up in the church. (I've) been singing and playing music ever since before I could talk. Music runs in my family. Everybody in my family is either a musician or a singer or a preacher. So we were in church all day– honing the musical skills and all that. Later on in life… probably about 11 years old… I started performing professionally in professional theater– regional theater around the city of Louisville and Indiana. I eventually went to the performing arts high school in Louisville called the Youth Performing Arts School, which is one of the top… well it was at that time… top performing arts schools in the nation. From there, I caught that bug. I was like, 'This is what I'm gonna do for the rest of my life definitely.' I always knew that music was what I wanted to do, but it was solidified once I started traveling outside of the state and then outside of the country with the high school performing in other countries. I went to Wright State University– studied acting for a little while, but soon got a record deal so I left college to pursue music that way. That record deal didn't turn out too good, but I ended up moving to LA a couple years after that and (I've) just been running full steam ahead ever since. I've worked with R. Kelly. I've worked with B2K when they were hot and together (laughs). I've worked with KeKe Wyatt. I've worked with a number of artists and producers out here in LA as a writer and producer and as a performer myself and here we are today on my third album, ReVerb.
GFM: Let's talk about the third album– ReVerb. Let's talk about the writing and the production. We have a few singles. Am I correct that ReVerb came out in 2013? Has it been repackaged this year? It seems like it's picking up steam this year. Tell us about the album ReVerb.
LY: I wrote and produced the whole record. It's funny… technically this could be called my fourth record because I had a record in 2011/2012 called Love Is A Verb. Most of the records that are on ReVerb are those records, but because I didn't have any push behind it– I didn't have any promotion or anything– most of those songs went unheard. "Love Is A Verb" got a little bit of airplay in the DC area on WHUR. Shout out to WHUR for being the first to play it, but other than that it didn't go very far. So when I hooked up with my current management, Top Notch, my manager Marvyn was like, "Man we gotta put this album back out. People haven't heard it." But at that time, I was already recording a new record. I was about four songs into a new record and my mind was on to the new music. I was like, 'I don't wanna do that… that's old.' But you know, he made the valid point that it was only old to me,'cause you know no one else has heard it. So, what we did was we took a couple of the joints off of the Love Is A Verb record and I replaced them with some of the new ones I was working on and we repackaged it and I called it ReVerb— like a reissue of Love is A Verb— and we put that out. It had a little traction– especially in the UK and Japan and other places. But, every since the videos on Facebook have taken off it's starting to get a lot of momentum now. We're starting to see the sales really pick up and people are starting to connect the dots with who I am as an artist.
GFM: Is "Fairytales" one of those songs you were gonna leave behind?
LY: (Laughter) Yes. I was doing a whole 'nother album… I was in a whole 'nother place you know?
GFM: Well shout out to Marv Mack, Top Notch, for having the vision to repackage the album so that we could hear "Love Is A Verb" and so that we could hear "Fairytales". I don't think life would be complete without us having heard that material, so shout out to Marv and Top Notch.
GFM: Who are some of your vocal influences?
LY: Stevie Wonder and Michael Jackson are probably the two most obvious. Also, I listened a lot… a lot, a lot… to Hall and Oates when I was little. Daryl Hall really influenced me a lot in my writing style and my approach to the energy I put behind singing a lyric– with a lot of emotion– believe or not. I still hear him come out sometimes. When I'm singing I can hear it all day. I'm like, 'Daryl Hall would've done that.' Those are my three major vocal influences. I had a bunch of influences musically– music wise growing up in the Midwest. Of course Zapp and Roger were very big. I really loved his stuff growing up. (I) loved Earth Wind and Fire of course, Lionel Richie, and we even had some country thrown up in there. I even listened to George Straight and stuff like that.
GFM: Interesting. Perhaps we can get Daryl Hall this interview and you can get on Live From Daryl's House. That would be awesome. We're gonna put that out right now in the atmosphere.
GFM: I was watching one of your live performances on YouTube. During that performance… I think you were singing "Fairytales"… I heard you say you were trying to bring the old school back. I've heard other artists make similar statements like that. What does that mean to you… bringing the old school back? What is so significant about old school music and why is it so important for you and other artists who make the same claim to be on that mission?
LY: Music… well I should say art in general… has the ability to kinda shape and not only reflect, but actually shape and drive ideas and revolutions and change the direction of a society. So it's important that we get back to some of the things that we used to do as a society. Not actually as a whole in America, but particularly in the black community, where we've lost that sense of what love really is. You know I used to complain a lot… or I used to hear my friends complain a lot.. about songs today. (They'd say) "They're all about sex. They're all about going to the club and picking up this girl and that girl and what kind of whip you got.' I had to examine it from a deeper issue, and I'm like, 'Well it's not that they're not singing about love, it's just that's what they equate love to.' A lot of them have not grown up in two-parent households or seen a real, real representation of a loving relationship between a man a woman and a loving family. We had love songs come out in our days in the '70s, '80s and the early '90s because that's what we were used to. But now, the whole dynamic has changed and the music is reflecting in our kids in the younger generation. So we're trying to get back to singing those songs about what it means to actually find real love, or what it means to get with a girl a first time but it's not all about taking her back to the hotel, but trying to build a future with her. Or, how you talk to a woman metaphorically about saying what you wanna do, but not actually coming right out and being foul and vulgar with it. It's songs that you can use in times of sorrow… in times of hurt… when you wanna escape… in times of joy… times of just having fun and partying. All of that. It needs to be well rounded. We need to get back to that balance… is what I like to call it… in urban music.
GFM: Along those lines, it seems that the R&B male vocalist has become a bit of an endangered species. I'll say the R&B male vocalist who has been put out in front. There are a lot of strong vocalists, but a lot of them are independent artists like yourself… people who have not been put in front. We don't hear them on the radio or see them on the television. We do have some veterans who have persevered like Joe, Brian McKnight and Eric Benet, but my question to you as a younger artist is what does it mean to carry the mantle of the R&B male vocalist?
LY: I mean a woman… a woman… loves to hear a man sing to her. I mean sing like cry his heart out… put his foot into it… put his whole body into what he's trying to say to her. There's nothing like those kinds of records. I don't know what the generation up under me… (what) they've been listening to… who they've been listening to, but they definitely have not gone back and listened to the greats like David Ruffin and Eddie Kendricks from The Temptations. Or, James Brown, or Jackie Wilson… anybody that knows how to really sing a song and to put that umph in it (like) Bobby Womack. I don't know what they've grown up listening to. It seems they've grown up listening to each other. Everybody sounds like Chris Brown. It may sound good. That's what the industry is into to now, they're into selling sounds. 'Cause long as it sounds a certain way, they'll put auto tune on something and don't care about it because it sounds good. But they're not making music that feels good. There's no emotion behind it. I was trying to be PC here, but the guys are singing like women. They're singing like women now and you can't approach a woman like that. You've gotta be a man when you come to a woman. It's very important for me… it's kinda like in a way a big brother teaching his little brother how to be a man… how to sing to a woman. You've gotta sing from a man's place, not from this feminine place where you're approaching her. I don't know man, I just gotta do my small part in bringing back what I call those real emotions on a record and I'm glad Eric Benet and all those cats that are still doing it. We don't know any other way,'cause that's how we grew up. We grew up listening to people like Stevie Wonder and Donny Hathaway. That's how we learned to sing. It's natural for us.
And one other point, not to be long-winded on this, but when you go back and you listen to those records of Marvin Gaye and The Temptations and Stevie and (what) all those cats were doing, looking back we tend to think that because they're older now that they were older then. But when they made these records… when David Ruffin was like, (singing) "I know you wanna leave me…" when he was in all that passion he was in his twenties. That wasn't no old man singing that. But, you could tell that he'd been in love… that he had been in some kind of pain. He was putting it all out on record. Heck, even little bitty Michael Jackson… when he was eleven… was singing like that. So, it's not just about the age. It's just about who you're trying to emulate, who you're (listening to) growing up, who you're studying and then actually going through something in life and putting it on record as emotion. We've gotta bring that back.
GFM: What's next for L. Young? I also saw in my research and you were mentioning (it) also earlier in the interview that you just don't sing, but you perform… so you're an actor as well. Is that something that you also will be pursuing in the future?
LY: Yes definitely, but you know I have a couple more albums that I think that are in me that I want to put out so I'm concentrating on the music right now, but that is definitely something for the long term that I plan to get back into. I studied it for a year. I'm probably really rusty right now, but that's something that you can always pick back up and get into. There's always a role for a 60-year-old, 70-year-old actor, but you don't see a 60-year-old R&B star out on the stage… not too often… if they didn't already have hits back from when they were in their twenties and they just happen to be touring still. Trying to get the music to resonate and connect is really something I'm working on now while I'm young enough to still do that. We're shooting another video at the end of this month (August) for the next single off the record, "Facetime". We're gonna work on getting another full album out probably early next year. I'm also releasing probably in about a week another free EP I'm just gonna give away to the people. I'm doing a remake album of all those people that we talked about earlier that influenced me… remaking some of their songs, but I'm putting a twist on it and not just like straight remakes. It's like totally different. (I) flip the songs around. We're doing that as well and just trying to stay as busy and as in your face as much as possible. That's it.
ReVerb is available NOW on iTunes
Check out the video for "Love Is A Verb"
Connect with L. Young
Kimberly Kennedy Charles
I ask questions of your some of your favorite artists.
Daryl Hall Facetime Fairytales L. Young Love Is A Verb L Young and Da Youngstaz R&B R&B Male Vocalists ReVerb Top Notch Entertainment
Val B. King: So Many Ways
Grown Folks MusicR&BNeo-SoulBlack History MonthIndie SoulSoul
Leela James & Avery Sunshine LIVE [Photos]
Grown Folks MusicLive PerformancesR&BLive ShowsSoul
A Night of R&B Featuring Tank & Lyfe Jennings
GFM Spotlight Interview: 50 Years of Sounds Of Blackness


text:
Apartments / Multi-Family Units
Chemical Distribution
Video Surveillance System Vs Security Guards
by Blue Eye Defense | Aug 25, 2020 | security | 0 comments
In today's business environment, security is paramount. Internally, cyber attacks are constant and growing, both in frequency and sophistication. In addition to threats from outside, safety and security is essential for employees and for an organization's physical and intellectual assets.
For businesses of any type – retailers, manufacturers, and office suites – facilities security and safety is essential, as well.
Typically, this security is provided with either video surveillance or security guards.
Some Pros and Cons of Video Surveillance Services and Security Guards
Most businesses, if they provide any kind of security service at all, opt for one or the other of these two traditional approaches.
Here's a brief overview of the "pros" and "cons" of both options:
Advantages of a Security Guard Service
Having a human security presence on-site certainly has tangible benefits. For example, security personnel can physically intervene in situations where the threat of bodily injury or crime is imminent. Simply being on-site can serve as a real deterrent in many situations.
In addition, a security guard service offers these advantages:
The ability to patrol all areas of a facility.
Quick response for intervention or reporting to authorities.
Security checking for personnel and belongings.
On-site response to utility issues and weather damage.
Disadvantages of a Security Guards
A security guard service does have limitations, however. The most obvious is the need to hire security personnel, or outsource these services, and the costs this entails. And these costs are even greater to obtain a 24/7 security presence.
There are other downsides to on-site security services:
Poor training can lead to deficient performance and create possibility liability issues.
Obtaining a security guard service requires the need for additional insurance coverage.
Issues from lackluster hiring practices, high-turnover, and the threat of bodily harm.
Management for security patrolling can be a high-stress function and expensive.
Advantages of a Traditional Video Surveillance System
Traditional video surveillance systems make use of closed-circuit television, or CCTV, cameras. The earliest CCTV surveillance equipment was bulky and limited by the video technology of the time. Today, digital video technology has enabled most video surveillance systems to be less obtrusive and visible, and provides high definition imagery.
Video surveillance can be a valuable resource for documenting criminal activity and even for identifying perpetrators.
Quality, state-of-the-art video surveillance system benefits include:
Low maintenance 24-7 coverage
Consistent long-term cost
Easy access to secured archived video data for analysis
Passive deterrent
Disadvantages of Traditional Video Surveillance
On the other hand, there are also some real concerns with video surveillance as a stand-alone security system. For example, while it can act as a deterrent in most situations, a determined or disguised intruder may simply disregard the passive video surveillance.
Among the other disadvantages:
Surveillance cameras cannot take any actions to stop or deter threats.
The visibility and range of the cameras can be limited.
Cameras can be vandalized or disabled.
Video surveillance can record an incident but not prevent it.
Combining Video Surveillance and Security Guard Services
For those businesses and enterprises with the resources to do so, combining on-site guard services with a network of video surveillance cameras can provide comprehensive security.
This option can potentially leverage the best of both approaches with each approach negating the downsides inherent in both.
Incorporating surveillance cameras can vastly improve the coverage that on-foot or mobile patrols can provide, while minimizing the risk of threats to on-site security guards. The presence of cameras can help deter intruders and mitigate any violent actions that might otherwise occur.
However, this is also an expensive endeavor and many, if not most, businesses would be hard pressed to afford such an elaborate security and surveillance program.
A Fourth Option to an Old Security Problem
The choice, however, does not have to come down to either on-site security guards or a video surveillance system.
In fact, there is another option that is an optimal solution and that provides the deterrence and intervention that can replace live guards at a lower price, but still provides the full site protection that video technology provides.
An effective technology solution is preferable to a human presence solution in most security surveillance situations. Cameras don't have to take breaks, don't fall asleep, get sick, or quit for a better job.
But existing CCTV technology still falls short of an optimal solution, which is why on-site personnel are still added many times.
So, what would the optimal solution look like?
A system with the capability for processing analytics to trigger a response to a security protocol breach, for example.
An optimal technology security solution would have the capacity to detect the dwell time and direction of travel of individuals on camera using advanced AI technology. And it would possess barrier detection and advanced, on-board speaker technology accessible by real-time surveillance technicians.
This means the benefits of issuing audio warnings, which can reduce the risk of false alarms. In addition, AI technology can reduce the time needed for surveillance.
In other words, an optimal solution would be a security system that gives you the best of both worlds.
Blue Eye can provide you with a solution that provides just that. And at a surprisingly affordable price.
Our solution is an AI-empowered managed service platform that requires no up-front capital costs. Give us a call at 855.258.3662 or email us at inquiries@blueeyedefense.com. Let us design an effective solution for your business!
apartment complex security
courtesy patrol
storage facility security
3 Steps For Improving Your Cannabis Security
Security For Cannabis Dispensaries Doesn't Have To Be Complicated
Cannabis Security 101
How Live Remote Video Monitoring Can Improve Your Cannabis Dispensary Security
Cannabis Security: Protect Your Product With Proactive Theft Prevention
Copyright © 2022 Blue Eye. All Rights Reserved.
JESSE FIRTH
Jesse has over 17 years of experience in the sales engineering, systems design, and systems integration verticals. Starting in the electrical trade, he became an electrical foreman as an early apprentice and lead a service division designing fiber optic networks, data networks, UPS systems, and genset applications. Jesse joined with Blue Eye in 2011, bringing his sales, systems and dynamic integration experience to the team. Jesse's primary responsibility is to develop strategic sales, and vendor relationships, while ensuring the delivery of service from Blue Eye's network operations center to our clients' properties nationwide.
Ryan Hughes
VST Manager
Ryan has 15 years of experience working in the media industry and team management. Since joining our team, Ryan now manages the staffing, recruitment, company policies, and security protocol.
He enjoys cooking, traveling, and karaoke after his workday is over.
Lilith Anderson
Lilith has been with Blue Eye since 2019 in multiple positions, including Client Services Manager. After completing her bachelor's degree in communications, she began developing the marketing department for Blue Eye.
Lilith enjoys gaming and knitting outside of work.
Luis Hernandez-Budd
National Account Manager
Luis joined the Blue Eye team in 2015 as one of our VSTs, watching camera streams to defend sites from trespassers. Luis has been promoted several times within the company since then. First to be Integrations Technician and then to his current position as a National Account Manager where he can use all the knowledge and experience of performing our service from multiple levels to better assist our clients in making the best decisions for their security needs.
Matthew Brantley
Linux SME
Matt has worked been working in tech for quite some time wearing multiple hats in the process. He joined the Blue Eye team initially as an Integration Technician and has moved on to working with more involved challenges in IoT.
Jack Robbins
Jack started with Blue Eye in 2018 as a VST, moving into new roles as needed. His hard work and dedication to our team led to his promotion to being of our two National Account Manager. He's responsible for overseeing the onboarding process of a new location after the paperwork has been completed.
Outside of work, Jack loves spending his time with his husband and dog.
Mary Ruble
Mary is a highly experienced client services manager who has worked for companies such as Disney, Microsoft, and Netflix. She has developed a strong eye for detail that gives her the ability to provide our clients with world-class client care.
Chris Fish
Integration Technician
Chris has been with the Blue Eye team since (year). Currently, he works to help our clients set up and maintain their security solution. Before that he worked as a VST Lead, helping our VSTs to provide security to our clients.
Outside of work, Chris is studying Computer Engineering at the University of Utah.
Amanda Alvarez
Amanda has been with Blue Eye for 1.5 years and is one of our Client Services Managers. Her passion for service has led her to work as an Administrative Assistant and Leasing Professional. She then joined the Blue Eye team shortly after she moved to Utah as a Video Surveillance Technician. She was promoted recently to our Client Services team and now works closely with our clients to meet their needs.
Ian has had a few roles in the Blue Eye tech department over the years and is currently one of our Integration Technicians responsible for setting up security service for our clients. Ian also provides technical support to field technicians and clients when issues occur.
When not in the office, he is most likely on a mountain or in a garage fixing race cars.
Pou Pole
VST Supervisor
Pou is an emerging security professional, quickly promoted to lead position after six months in her first position with Blue Eye. She served as a VST lead then trainer and has now been our Virtual Security Technician Supervisor for a total of two years. Pou is responsible for building and motivating dynamic teams. She has been cultivated a company culture in which staff members feel comfortable voicing questions and concerns as well as contributing new ideas that drive company growth.
Walker Goodwin
Workforce Administrator
Walker has over 13 years of experience in the security industry including military service with the Idaho National Guard. In 2011 while deployed to Baghdad he was awarded an ARCOM for redesigning the security camera layout around the base he was on to increase coverage and reduce blind spots in a combat zone. He has been with Blue Eye since 2015, working in several different positions, including VST Manager, before becoming our Workforce Administrator.
Doug Bolter
Doug's diverse background began after earning his bachelor's degree in Finance / Business Administration from the University of Wisconsin, Milwaukee in the financial markets as a registered investment advisor to business owners and high net worth individuals for 23 years. He then set out to explore the telecommunications industry at Nextiva and later, high-tech sales at OpenTech Alliance where he honed his expertise with security controls and automation serving the self-storage industry.
Consulting with successful, experienced business owners for over 30 years in various fields has empowered Doug to use that business acumen with his clients. Strategizing to mitigate risks through high technology solutions, Doug takes a consultative approach to uncover and present optimal – tactical solutions to protect a client's property and limit liability.
Doug enjoys traveling, mountain biking, off-roading with his family and friends in the scenic desert-scape of Phoenix Arizona, and aspires to grow his pack of rescue dogs.
Brent Grangier
Brent has been in the electronic security industry for 15 years as a sales engineer. Before that, he spent 10 years as a national distribution manager in the electronic interconnect industry. Brent graduated from Indiana University Southeast in 1992 with a BS in Business Marketing.
Brent enjoys watching college basketball, coaching basketball, and spending time with his wife, 5 kids, 2 dogs, and 7 cats.
JACK DANKS
NATIONAL ACCOUNT EXECUTIVE
Jack joined Blue Eye in 2017 where he has been responsible for leading the fixed sales team, developing sales strategies and marketing material, and helping to drive Blue Eye's vision. He has added several national top-tier companies to Blue Eye's client list.
Jack graduated from Indiana University where he served as a Chairman and Sub-Committee Director of the nation's second-largest student-run philanthropy and was admitted to the School of Public and Environmental Affairs.
Jack is the favorite uncle to his many nephews and nieces and he like puppies.
ANDREW ABRAHAM
DIRECTOR OF PRODUCT DEVELOPMENT
Andrew Abraham has over 13 years of experience in information technologies and software management.
Starting in the IT field, he quickly became proficient in various computer and network technologies while working closely with an array of clients to deliver a service based approach to implementing IT in different homes and businesses.
Andrew joined the Blue Eye team in 2016 to bring his management, engineering and customer oriented approach to problem solving to the team.
Andrew's primary responsibility is to cultivate and grow the engineering department, while collaborating closely with various technology partners, customers and sales to ensure that Blue Eye is always on cutting edge to best serve our clients and security team.
JERMAIN LEE
Jermain is responsible for creating, developing and implementing managed strategies while developing long term positive and profitable relationships. He collaborates with sales, operations, and marketing to help grow the list of potential clients as well as developing our dedicated teams to be flexible and efficient.
Jermain served 8 years in the military in which he helped develop team members, managed inventory, created and presented large scale tactical and operational plans. He also spent 14 years in the financial industry spanning from banking, managing 401k accounts, stock and options trading, along with advising and managing $340 million dollars across 460.
JOHNNY SENGMANICHANH
Johnny has been in upper management and in leadership roles the for 15+ years. Previously, he was in a practice director role for Intermountain Healthcare overseeing operations of three clinics with a staff of over 300 employees, 60 physicians. He was able to use his expertise in developing culture, process/change management, as well as using LEAN methodology to make each clinic profitable. He was also in the role as a regional sales director for a merchant services company in the Seattle, Washington area for two years.
In addition to his role at Blue Eye, he is also employed as an "Educator" at lululemon Athletica, as well as volunteering a few hours a week for the Make-A-Wish Foundation as a wish granter.
DAVIS LIM
Davis has 17 years in Operations & Business Development. 10 year of International trade. Managing member of Garuda Group Cambodia (agriculture, real estate, logistics).
DAMON KIRCHMEIER
Damon is our CEO and has been guiding the ship since 2015 where he joined from Epic Ventures after investing in Blue Eye.
He is the past Managing Director for InnoVentures Capital Partners, a Salt Lake based venture fund. In 2001, he and his two partners founded InnoVentures and went on to raise and invest two funds into more than 100 companies including Entrata, Jive Communications and CarbonX.
Damon began his career in 1988 at Westinghouse Electric Corporation in the Bettis Atomic Power Laboratory. Mr. Kirchmeier served as a nuclear plant engineer and later managed a crew of Naval personnel and civilian engineers at a dual reactor plant.
Damon holds silver and bronze medals from the Masters World Championships of mountain biking and is the past Honorary Commander of the 421st fighter squadron – the Black Widows, call sign "Speed."
Damon holds a degree in Mechanical Engineering from Oregon State University and an MBA from the University of Utah. He is also a graduate of the Naval Nuclear Power School in Orlando, Florida.


The Salvation Army will accept unrestricted gifts, as well as gifts restricted for specific programs and purposes, provided that such gifts are not inconsistent with its stated mission, values, purposes, and priorities. Gifts may be restricted to specific Salvation Army programs/purposes or communities throughout Canada and around the world where the Army operates.
While the Army will accept gifts that contain restrictions that they be used to provide programs or services that benefit a particular type of client (e.g. children, families, individuals with developmental challenges, etc.), no gift can be accepted that stipulates a specific individual who is to benefit.
Before a gift is accepted, the Army reserves the right to decline any gift that cannot be used to support its mission or that is too restrictive to allow it to be administered or used effectively. The final decision to decline any gift shall be made by the Territorial Finance Council.
After a gift has been accepted, if circumstances should at any time make it, in the view of the Territorial Finance Council acting reasonably, impractical to apply the gift to the designated purpose, it may redesignate the purpose of the gift using its best efforts to adhere as closely as possible to the original intent of the gift. Where possible, the Territorial Finance Council will seek input from the donor before the redesignation is made.


text:
A lot of talk about modern art and cabaret
Based on these two photoshoots by photographer Alexi Lubomirski, one might argue that the January 2008 issue of German Vogue is an art-focused issue. Burlesque "frou frou" costumes coexist with avant-garde styles. Fashion is a representation of a dream and an aesthetic expression of the body in modern art. Giles, Valentino, Zac Posen, Salvatore Ferragamo, Akris, Missoni, and Burberry Prorsum showcase avant-garde creations.
By velvet cushion on Wed, 10/06/2021 - 12:11
Nature becomes the subject of a fashion photographer's lens
Drew Doggett began his career as a photographer in New York City. As an...
Similar Bookmarks
Jennifer Lopez launches a green beauty line
Jennifer Lopez announced the launch of her beauty line with natural products...
Choose the correct earrings for your face
Earrings are one of the most loved accessories by women. Choosing the...
Nicole Scherzinger in the Mexican desert
The 42-year-old Pussycat Dolls star delighted her fans who praised her for her...
Finding the perfect white summer dress?
Are you on the hunt for the perfect white summer dress? Join the club. We've scoured...
Vanessa Huppenkothen is fine and healthy
Vanessa Huppenkothen worried her fans after she published that she had to be...
MADELEINE is quickly expanding in the USA
MADELEINE, an online fashion store owned by the TriStyle Group, greatly...
Fred Einaudi witnessed a disturbing truth
Fred Einaudi's gloomy paintings include a cheerful lady carrying a diabolical...
Connected Bookmarks
Now Don't Feel Embarrassed Due to Aging Skin
Everyone would like to have always-glowing skin on their body, especially on...
Elegant, Subtle, or Exquisite Nail Designs
Do you need new nail art ideas? Here at Nail Ideas, we have a collection of elegant, subtle, and...
Natural Skin Care Lotion Online
Nourish Mantra provide the best body lotion and skin care lotion online at...


Consider the following situation. Imagine that India is preparing for the outbreak of a disease, which is expected to kill 600 people. Two alternative treatments to combat the disease have been proposed.
If Treatment A is adopted, 200 people will be saved.
If Treatment B is adopted, there is a 1/3rd probability that 600 people will be saved and a 2/3rd probability that no people will be saved.
Most likely you prefer the certain option – Treatment A over the gamble – Treatment B.
If Treatment A' is adopted, 400 people will die.
If Treatment B' is adopted, there is a 1/3rd probability that nobody will die and a 2/3rd probability that 600 people will die.
Most likely you prefer the gamble – Treatment B over the certain option – Treatment A.
Now look closely and compare the two versions: the consequences of Treatment A and A' are identical and so are the consequences of Treatment B and B'. However, your options most probably differed. Did you choose to save 200 lives for sure in the first version and chose to gamble rather than accept 400 deaths in the other?
Embarrassed? So were we. Even when this test was shared with public health professionals in the US, they were swayed by this framing effect!
This shows how a small manipulation can have drastic impact on decision-making. And if you still believe that we humans behave rationally, think again.


Posted on July 6, 2017 by thelostlighthouse	in Comics, Literature and tagged art, Brain Shoodles, comic book review, comic books, Emily B. Owen, indie comics, Literature, mental health, TLL.
This isn't a comic I knew much about beforehand, and as with most indie comics I review I wasn't sure what to expect when I first opened it. For a comic with a very simple approach and style it was actually a very intense read. It's clear from the beginning that this has been a very personal project for Owen which she has put a lot of herself into. Owen says in the introduction that this was never intended to be a comic, beginning as a way for her to process some of the thoughts and reactions she has had in dealing with mental health issues, particularly as a reaction to common advice that she and many others have been given for how to deal with it.
Owen's format is a very simple one, each chapter begins with one of these common statements and follows a simple visualisation of both a response to it or how it can feel when following that advice when faced with mental health challenges.
As for the art – in one of the first panels Owen says she is no artist, this hasn't at all prevented her from producing art. Owen has a very simplistic art style, and I don't think any other would have been appropriate for a comic like this one. People are portrayed as simple faces on a variety of shapes which look a little like pieces of toast. In the same way in which emoji's are designed to perfectly capture a single emotion which anyone can interpreted this simple style provides no barrier or ambiguity to what it's like to be in the situations that are being portrayed. Rob Jones provided the clear and simple lettering, matching the doodle style of the comic.
This will be one of those comics that stays with me. I'd recommend this to anyone, if you deal with mental health issues yourself it may be something you can relate to, and if you don't it can give you an idea of what it's like to face these challenges.