Katniss stands in her home, looking into the fearsome eyes of President Snow. Katniss is used to seeing Snow on television, surrounded by flags. She wonders what he could be doing in District 12—usually, the President of the Capitol leaves Victory Tour business to other government officials.
Collins raises the suspense in this opening section by explicitly asking us—through Katniss's inner dialogue—what Snow is up to. His typical reliance on the media suggests that this must be an important occasion.
Katniss realizes why Snow must be here: he's angry with Katniss for disobeying the rules of the Games and allowing Peeta to win the Games along with her—usually, only one victor is allowed every year. This disobedience, she thinks, might be viewed as an act of rebellion. Katniss was allowed to be the co-winner with Peeta because the reporters liked their "story"—that they were "insanely" in love with each other. Standing before Snow now, Katniss is terrified: she knows full well that Snow is powerful enough to have her murdered with a wave of his hand.
Katniss survived the Hunger Games by manipulating the media, rather than dismantling either the media or the government: she can only save herself and Peeta by giving the reporters a "juicy story." Clearly, Snow has a problem with Katniss's actions, because they amount to a kind of rebellion, however harmless-seeming, against the usual state of affairs at the Games.
President Snow asks Katniss, point-blank, if she's going to be "difficult" during the Victory Tour. Katniss replies that she won't. Snow adds that Katniss shouldn't cause any problems, as she did during the Games, or else she'll endanger the lives of her family members, and of Gale. He invites Katniss to sit down, and she does so.
In contrast to the glossy appearance of power and authority that he projects on television, Snow is short and direct with Katniss face-to-face. It's important to note that he makes the stakes of Katniss's Victory Tour very clear: he threatens her family.
Snow continues explaining his position to Katniss. He's viewed Katniss as a "threat" to the Capitol, he explains, ever since Katniss and Peeta won the Games together. At the end of the Games, Katniss and Peeta threatened to kill themselves by eating poisonous berries if they weren't allowed to win the Games together. The designer of the Hunger Games, Seneca Crane, allowed them to win, Snow explains, because he was "sentimental." He calmly explains to Katniss that Seneca has been executed.
One question that isn't directly addressed in this section is why Snow cares about Katniss's performance in the Games at all. The answer is complicated. To people all over Panem, the Hunger Games are a symbol of the government's power. Thus, by breaking the rules of the Games, Katniss and Peeta made a statement challenging the government. This statement was only a challenge because of the equivalence between the Games and the government — an equivalence created by the government itself, because of the way it uses the Games to assert and symbolize its power.
President Snow goes on to explain that Katniss's threat of suicide has been dangerous for the Capitol's authority. Many of the other Districts saw Katniss's behavior as an act of rebellion. Snow fears that Katniss has inspired people in some Districts to start uprisings against the government's authority. Snow talks as if he's concerned for stability and peace, but Katniss knows that he doesn't care about peace at all—he only wants to stay in power.
Snow elaborates on the link between Katniss's behavior at the Games and growing rebellion. The irony, once again, is that Katniss's actions were only rebellious because the government itself has made such a point of directing people's attention to the Games, and of associating its own power with the Games. Katniss is a "Trojan Horse," entering the Games and then attacking the rules from the inside.
Katniss tells President Snow that she didn't intend to start uprisings, and Snow replies that he believes her. He notes that the clothes she wore during the Games, featuring fiery colors, were oddly appropriate—her example has provided a "spark" for other Districts, which may one day become a huge flame that destroys all of Panem (the "universe" of The Hunger Games.) Katniss asks Snow why he doesn't just kill her. Snow calmly replies that this would only create more outrage.
For all her maturity, Katniss is still unsure whether she wants to oppose the government or not. For the time being, at any rate, her priorities lie with her family, not with abstract causes of rebellion and uprising, as much as she hates the government and Snow.
Snow continues to prod Katniss for weaknesses. He asks her how Peeta, the "love of her life" has been, and notes that she's clearly indifferent to him. He calls Gale "handsome," noting that he could easily kill him off if he needed to do so. Katniss hates Snow for saying such things, and worries that he knows that she has feelings for Gale.
Snow is the embodiment of the government of Panem, in more ways than one. In Katniss's world, there is constant surveillance—everything people do is being recorded, and often even broadcasted on television. Snow, with his sixth sense for Katniss's actions, represents the surveillance state in all its glory and oppressive power.
Katniss thinks about a moment she spent with Gale in the woods shortly after winning the Games. Katniss has been surrounded with cameras and reporters for the last month, and seeing Gale in the woods was one of the first moments of privacy she'd had after her victory. Gale, she sensed, was angry with her for seeming to love Peeta. Katniss didn't know how to explain herself to Gale, but she strongly wanted to go back to being friends with him. Then, as they stood alone in the woods, Gale kissed her, saying that he had to kiss her "at least once." A week later, Katniss saw Gale, and Gale acted as if the kiss had never happened. As she thinks all this, Katniss looks at President Snow and wonders if he knows about her complicated relationship with Gale—it's possible that he's installed cameras in the woods.
Because there are cameras and reporters everywhere in her world, Katniss views privacy as a genuine luxury. She behaves completely differently around her loved ones when she thinks they're alone compared with when they're being filmed. Similarly, others, Gale included, are forced to change their behavior when they know that Katniss is being watched. It's also important to note Snow's gravitas and power of manipulation: even without saying that he saw Katniss with Gale, he makes Katniss worry that he did.
It's almost amusing that Snow is being so straightforward here: he began his visit by telling Katniss that he'd kill her family, and he ends it by telling her, in no uncertain terms, that he knows about Gale. There is little ambiguity to interpret here.
Arn, Jackson. "Catching Fire Chapter 2." LitCharts. LitCharts LLC, 4 Nov 2015. Web. 24 Apr 2019.
Arn, Jackson. "Catching Fire Chapter 2." LitCharts LLC, November 4, 2015. Retrieved April 24, 2019. https://www.litcharts.com/lit/catching-fire/chapter-2.


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The discount channel is set for growth, outpacing other store-based channels over the next five years. Progressive retailers in this channel continue to expand their physical store networks, with Aldi and Lidl at the forefront.
The fundamentals for the channel remain strong as shoppers favour more frequent shopping missions and an ever greater desire for value. Increasing levels of connectivity and a gradual movement of spend towards ecommerce will however pose a longer-term challenge.
Suppliers seeking incremental growth will find this channel attractive, requiring specific competencies and solutions to maximise opportunities.
PlanetRetail RNG's Global Discount Channel Report is a new report that provides unique and comprehensive coverage of this dynamic channel. Combining strategic perspectives, analysis, forecasts and guidance, this report will help you plan ahead in this 'decade of discount'.
A new report format for 2018, providing PlanetRetail RNG's most complete coverage of the global discount channel.
Specific actions on what it takes to win with discount retailers, covering four major CPG sectors* and supported with many supplier examples.
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Latest case studies and examples of discount initiatives across the winning strategies of Store of the Future, Ecommerce & Digital Ecosystem Management, Supply Chain & Fulfilment, Shopper Engagement & Retention development.
In-depth understanding of the factors influencing the channel, using the PlanetRetail RNG STEIP framework, with clear implications drawn out for the industry.
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*CPG sectors referenced include food & beverage, health, beauty and personal care, consumer electronics, household and pet care.


Governmental offices will be closed.
Friday's recycling and waste route will be combined with Thursday's route. Friday customers should roll containers to the curb Wednesday night for Thursday collection.
Wednesday and Thursday waste and recycling routes will be collected on Wednesday. Friday's route will be collected on Thursday.
Governmental offices, including the Sheriff's Office and the Davidson County Courthouse, will be closed.
The landfill and recycling drop-off sites will operate on a regular schedule.
Lake Thom-A-Lex and Boone's Cave Park will be open Friday.
North Davidson Garbage Service will collect regularly.
Governmental offices are not regularly open on Fridays. Offices will also be closed all of next week.
Trash and recycling will be collected regularly.
All DMV offices will be closed Friday and Saturday for Easter weekend.


Can Airbnb Experiences unlock a new side of Seattle?
Face it: You've probably already forgotten what the inside of a hotel looks like — as a budget-savvy traveler, you likely book, and maybe offer, a guest bedroom, bungalow, or even spare castle through Airbnb, a global marketplace for unique accommodations hosted by locals. Well, prepare to take the next step toward travel immersion, as Airbnb officially launches its Experiences program in Seattle this week, and Portland later this month.
Airbnb unveiled Experiences in a dozen cities last fall as part of Airbnb Trips, and entrepreneurial spirits already host more than 800 interactive activities worldwide.
Experiences are another way for hosts to share their view of Seattle while earning some extra income.
Airbnb takes 20 percent of a host's fee (except for social impact Experiences), and doesn't demand that Experiences be exclusive to its service. So the "Progressive Dinner," hosted by a founder of culinary tour company The Pretty Fork, for example, can also be found on The Pretty Fork's website, even for a lower price. But with millions of guests, Airbnb offers a platform unlike any other for attracting attention to your expertise.
Read on for an overview of the inaugural 9 Experiences in Seattle, then get involved at airbnb.com/experiences.
Seas the Day!: No, you don't get to pick a crab from a viewing-tank for your eggs benedict, but you do get to enjoy a before-hours breakfast in the Seattle Aquarium's underwater dome with Jim Wharton, Director of Conservation and Education. Chat with a diver then explore the popular attraction without any crowds blocking your shark selfie.
Sushi in Seattle: Host Fumiko, a Japanese chef who studied at Tokyo Sushi Academy, takes a-fish-ianados shopping at the local seafood market, then to her kitchen to teach you how to prepare sushi vinegar and rolls. You might not get a job offer at Sushi Kashiba at the end, but you'll surely be equipped to impress your next dinner guests.
Rowing Urban Waterways: Most of us don't get closer to Guinness World Record holders than reading a book, but host Jordan has been honored for rowing across the Atlantic Ocean, and will be happy to row with you or for you, if you'd rather sit back, sip and snack, and soak in an uncommon side of Seattle.
Progressive Dinner: Host Joey, Pretty Fork co-founder, guides you to 9 courses and 3 drink pairings spread across three restaurants in a single neighborhood, which won't be revealed until shortly before you embark. A beefed-up "trust the chef" tasting menu for diners with insatiable curiosity to match their appetites.
Meditative Mountain Hike: Zen-seekers leave the city for host Michelle's guided meditation, a 4-mile silent hike, and a picnic debrief amongst the evergreens of majestic Cougar Mountain Regional Park. Not for the workaholic who can't silence a cell phone.
Eat, Paint, and Play: Ride the ferry to host Pattie's Vashon Island hilltop sanctuary and art studio, fill your belly with goods sourced from the island's many artisan producers, and learn to let loose textures and colors on a postcard that will then be mailed to you. Tap into a new hobby and maybe you'll be the next Etsy superstar.
Sailing for Freedom: Host Derek will tell you about his unique mobile home — the sailboat you're taking for a ride — and even let you drive as he navigates Seattle's network of lakes and bridges. Go for a swim, drink at a lakeside bar, and decide if you, too, should leave it all behind and take to the open waters.
Restore a Seattle Park: Wander Cheasty Greenspace, a 43-acre wooded park in Rainier Valley, where you'll pull on gloves and pull up invasive weeds, plant native trees (seasonally), and prepare Seattle's first mountain-bike trails before tasting your way through Georgetown Brewing's line-up at its tasting room. It's fresh air and activism for the nature-loving do-gooder.
Bloody Mary Judge: Stick with host Shelley, founder of The Drunken Tomato, and you'll learn to officially judge this ubiquitous brunch cocktail at three different Capitol Hill bars. Next time you complain there's not enough bacon in your Bloody, your brunch buddies will have to respect your authority.


A woman in La Jolla, California suffered fatal injuries when a car hit her as she unloaded her vehicle on a busy street. The accident occurred midday when a driver backed out of a driveway across the street and struck the victim, pinning her between the two vehicles. Law enforcement will continue to investigate the incident, but believe that speed may have been involved. Police have not filed any charges related to the woman's death.
According to local residents, the area is known for heavy foot and auto traffic. In fact, near-misses are a common occurrence for vehicles and pedestrians, residents informed new sources. "This Silverado and Girard intersection is always busy with foot traffic and cars," said one resident, "You always have to watch what you are doing when you are walking across the street."
While most pedestrian accidents are causes by negligent drivers, pedestrians can take special care to avoid dangerous situations. Texting and walking, for example, can lead to accidents, injuries, and fatalities. In the same way that cellphones distract drivers from the road, electronic devices keep pedestrians from focusing on the sidewalk. Even a simple mistake, such as stepping off of the curb, can cause and accident.


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Kick Energy 2023 – starting the year with great Energy
Last January 13th, Grupel kicked off the new year with Kick Energy 2023, as traditionally.
This edition gathered employees from all departments of the company in person for a session of conferences and reflections, and later for a convivial dinner.
The Kick Energy 2023 was made, as in previous editions, to review 2022 with the whole team and discuss the goals achieved, the challenges overcome, the points for improvement, as well as to define the strategy for 2023, the goals set and what is expected from each department. In addition, we were able to celebrate Grupel's 47th anniversary together – something that has not happened since the beginning of the pandemic.
During the session, each department head, from administration to technical assistance and IT support, sales and marketing, operations, engineering, production, finance, human resources, supply chain, logistics and quality, had the opportunity to share their achievements in the year 2022, practical and operational aspects to be improved in the future, and concrete goals for the year 2023.
2022 was a historic year for Grupel, of great growth, not only in terms of optimization of processes and procedures, expansion and modernization of infrastructures and products, and the formalization of important partnerships. The financial results also show a remarkable growth, with Grupel reaching the significant goal of 30 million euros in invoicing and an increase in its turnover of about 40%.
The good results increase our responsibility and leave room for continuous improvement. Therefore, even more ambitious goals have been set for the year 2023, ranging from the expansion to new markets and the consolidation of current customers, to higher turnover and the design of new, more innovative and differentiated engineering projects.
After the afternoon of conferences, Grupelians gathered for a celebration dinner, where there was a lot of fun and socialization. Moments of sharing that strengthen close ties between those who work for the same goals, every day.
Kick Energy 2023 brought, besides inspiration, a reinforced commitment for the new year.
ПОВ'ЯЗАНІ НОВИНИ
Будьте в курсі новин про продукти та послуги компанії Grupel, а також про події в індустрії електрогенераторів.
Grupel in 2022
Grupel establishes agreement with worldwide producer, Cummins
Grupel's new corporate video: Energy that moves the world
Industry 4.0: the path to production process optimization
Interview with Raquel Martínez from the Engineering Department of Grupel
Grupel's sales team in France visits Vagos
Africa Energy Forum 2022: goals and expectations
Grupel at Africa Energy Forum 2022
Grupel travels to Egypt
You have in your COMPARATOR LIST. Access it now!
Grupel was born with a mission: to take Energy wherever it is needed with the help of products and services of the best quality. Find us in more than 70 countries.
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text:
\section{Introduction}\label{section:2}

In traditional machine learning approaches, neural network models are trained at a server or a data center. Thus, the centralized learning approaches typically require the raw data, e.g., photos and location information, collected by mobile devices to be centralized at the server \cite{jing2017crowdtracker}. The centralized learning approaches thus face big issues including privacy, long propagation delay, and backbone network burden \cite{mao2017survey}.

Recently, federated learning as a decentralized machine learning approach has been proposed  to address the above issues \cite{mcmahan2016communication, anh2019efficient}. In the federated learning, mobile devices, i.e., workers, are required to collaboratively train the neural network model of the model owner\footnote{In the rest of the paper, we use ``model owner" to refer to the server which creates the global model, and ``worker" is a mobile device which trains the global model.}. In particular, the model owner first transmits its global model to the workers. The workers then use their data to train the model locally and send the model updates to the model owner. The model owner aggregates the model updates from the workers to a new global model and transmits it back to the workers for training. The model owner and the workers periodically exchange and update the model until a target accuracy is achieved \cite{mcmahan2017federated}. By updating the models rather than the raw data, the federated learning alleviates many challenging problems, e.g, privacy issues and the backbone network burden issues, of the traditional machine learning \cite{lim2019federated}. However, the federated learning faces two limitations.

The first limitation is that the workers as mobile devices have energy constraints, and this can make the workers inactive. To address this limitation, a power beacon can be used to recharge energy to the workers. However, the model owner may need to pay an energy cost to the power beacon for the energy recharge. Thus, the model owner must decide appropriate amounts of energy recharged to the workers. The second limitation is that to achieve the target accuracy of the global model, a number of global model transmissions from the model owner to the workers may be required. This incurs a high network resource cost, i.e., the bandwidth cost, to the model owner. To reduce the network resource cost, the model owner uses the WiFi channel or Bluetooth channel, called \textit{default channel}, for the global model transmissions. As such, the model owner can transmit its global model to the workers with a free access cost. However, the default channel sometimes has low quality, and the global model transmissions may be unreliable \cite{xu2016enterprise}. Moreover, the coverage of the default channel is very short that may result in the communication interruptions between the model owner and the workers due to the mobility of the workers. The model owner can thus use so-called \textit{special channels} such as LTE channels or TV White Space channels for the global model transmissions \cite{afzal2018unlocking}. However, using such a special channel requires a high communication cost, i.e., channel cost.

As such, the problem of the model owner is (i) to decide amounts of energy recharged to the workers and (ii) to choose channels, i.e., the default channel or the special channels, for the global model transmissions to maximize the number of successful transmissions while minimizing the energy cost and the channel cost. This is challenging for the model owner since the mobile environment is stochastic in which the channel, energy, and mobility states are uncertain and unpredictable. In this paper, we thus propose to employ the Deep Q-Network (DQN) \cite{mnih2015human} that enables the model owner to find the optimal decisions on the energy and the channels with no existing network knowledge. In particular, we first formulate a stochastic optimization problem of the model owner that maximizes the number of global model transmissions while minimizing the energy cost and the channel cost. Then, the Deep Q-Learning (DQL) algorithm with Double Deep Q-Network (DDQN) \cite{van2016deep} is adopted to derive the optimal policy for the model owner. Simulation results show that the proposed DQL always achieves the better performance compared with baseline algorithms.

\section{System Model}
\label{section:3}

We consider a downlink model of a Federated Learning Network (FLNet) as shown in Fig.~\ref{fig:model}. The network consists of a model owner and a set ${\cal L}$ of $L$ workers, i.e., ${\cal L} \triangleq \{1,\dots, L\}$. The model owner generates a global model, i.e., weight parameters, and transmits the model to the workers for training. The training requires multiple iterations until the global accuracy is achieved \cite{tran2019federated}. Each worker is equipped with a capacity-limited battery that can store a maximum number of $e^{max}$ energy units. Note that as there is few energy in the battery, the worker may become inactive and cannot communicate with the server. The worker's battery can be recharged by a power beacon, and the model owner pays an energy cost to the power beacon for the energy recharging. To minimize the energy cost while reducing the inactivity probability of the workers, the model owner needs to determine appropriate amounts of energy recharged to the workers, e.g., through energy control links. Note that the workers may have different distances to the power beacon and consume different amounts of energy for uploading their local models. Let $\mu_l$ denote the weighted metric of recharging energy to worker $l$. Without loss of generality, assuming that the workers with higher index have longer distances to the power beacon and higher weighted metrics, i.e., $\mu_1 \leq \dots \leq \mu_L$. As such, using $\mu_l$ is to guarantee ``fairness" among the workers.

\begin{figure}[t!]
	\centerline{\includegraphics[width=0.5\textwidth]{Fig/model.pdf}}
	\caption{A downlink model of FLNet.}
	\label{fig:model}
\end{figure}

\begin{figure*}[t!]
	\centerline{\includegraphics[width=1\textwidth]{Fig/Energy_Markov.pdf}}
	\caption{Energy state transition model of worker $l$.}
	\label{fig:Emarkov}
\end{figure*}

As mobile devices, the workers have a possibility of movement that can result in the link disconnection and communication interruption as the worker is out of the coverage of the power beacon. Denote $q_l$ as the probability that worker $l$ is present in the coverage. Within the coverage, the model owner transmits its global model to the workers by using the WiFi channel or Bluetooth channel, i.e., default channel, with free-cost access. The default channel has an unstable connection due to the low quality. Let $p_{su}$ denote the probability that the model owner transmits its global model over the default channel successfully. This means that the transmission fails with a probability of $(1-p_{su})$. To mitigate the communication interruption between the model owner and the workers, special channels, e.g., the LTE channels and TV White Space channels, can be employed. Denote ${\cal N}$ as a set of $N-1$ special channels, i.e., ${\cal N} \triangleq \{2,\dots, N\}$. As the model owner decides to use special channel $n\in {\cal N}$, it is charged with an access cost $\lambda_n >0$ that is proportional to the channel quality. The special channels may have different quality, and we assume that $\lambda_2 < \dots < \lambda_N$. Denote $p_{su}^n$ as the probability that the global model transmission over special channel $n$ is successful.

The problem of the model owner is to decide which channel, i.e., the default channel or one of $N-1$ special channels, can be used to transmit its global model to each worker to maximize the number of successful transmissions and minimize the channel cost. In addition, the model owner needs to decide appropriate amounts of energy recharged to the workers to minimize the energy cost while mitigating the inactivity possibility of the workers.

\section{Problem Formulation}
\label{section:4}
Under the uncertainty of the states of the channels and the workers, the problem of the model owner can be formulated as a stochastic optimization problem that defined by a tuple $<{\cal S}, {\cal A}, {\cal P}, {\cal R}>$, where
\begin{itemize}
\item ${\cal S}$: The state space of the network.
\item ${\cal A}$: The action space of the model owner.
\item ${\cal P}$: The state transition probability function, where the current state $s \in {\cal S}$ transits to the next state $s' \in {\cal S}$ with probability $P_{s,s'}(a)$ when action $a \in {\cal A}$ is executed.
\item ${\cal R}$: The reward function of the model owner.
\end{itemize}

First, we consider the action space of the model owner. Given the problem mentioned in Section II, the action space of the model owner can be expressed as follows:
\begin{align}
\mathcal{A}= &\big{\{} \left (a_1^c, a_1^e, \dots, a_l^c, a_l^e, \dots, a_L^c, a_L^e \right ); \nonumber \\
&{\color{black}a_l^c \in \{0,1,2,\dots,N\}}, a_l^e \in \{0,1,\dots, e^{max}\}, l \in {\cal L} \big{\}}, \notag
\end{align}
where $a_l^c = 0$ means that the model owner does not transmit the global model to worker $l$, $a_l^c=1$ means that the default channel is used for transmitting the global model to worker $l$, $a_l^c=n$ means that special channel $n, n\in {\cal N}$ is used for transmitting the model to worker $l$, and $a_l^e$ refers to the amount of energy recharged to worker $l$.

Next, we consider the state space of the network. The state space of the network can be regarded as a combination of state spaces of $L$ workers, i.e., $\mathcal{S} = \prod_{l=1}^{L}\mathcal{S}_l$, where $\prod$ is the Cartesian product, and $\mathcal{S}_l$ is the state space of worker $l$ that is defined as follows:
\begin{align}
\mathcal{S}_l = &\big{\{} (w_l, e_l, X_l); \nonumber \\
&w_l \in  \{0,1\},  e_l \in \{ 0,1,\ldots,e^{max} \}, X_l \in  \{0,1\} \big{\}}, \notag
\end{align}
where {\color{black}$w_l$ is the state of channel that the model owner uses to transmit its global model to worker $l$, $w_l=1$ if the channel is good, and $w_l=0$ otherwise.} $e_l$ refers to the energy state of worker $l$ that is the current number of energy units in the battery. $ X_l$ is the mobility state of worker $l$, $ X_l = 1 $ means that worker $l$ is in the communication range of the model owner, and $X_l = 0$ otherwise.

Now, we consider the state transition of the model owner. For the energy transition, each worker consumes its energy for training the model required by the model owner and running its local applications. In general, the amount of consumed energy is unknown to the model owner. To model the transition of energy state for the worker, we use the Markov chain. We assume that in each training iteration, the worker consumes at least one energy unit and at most two energy units for training the model and running local application. The recharging energy to the workers happens after the training in each iteration. The energy state transition of worker $l$ is shown in Fig. \ref{fig:Emarkov}, where the energy is reduced by either one unit with a probability of $(1-p_l)$ or two units with a probability of $p_l$ under the condition of $e_l\geq2$. Note that at $e_l=1$, the energy state can directly transfer to state $e_l=0$ with the probability of $1$ due to the energy consumption of running local applications. For the channel and mobility, we model the channel state $w_l$ and the mobility state $X_l$ as a Bernoulli process, which takes value $1$ with probability $p_{su}$ or $p_{su}^n, \forall n \in {\cal N}$ and $q_l$, respectively.

Finally, we define the reward of the model owner. One of the objectives is to maximize the number of successful transmissions of the model owner. We assume that the model owner earns a positive utility $\Delta$ from the successful transmission to each worker. Let ${\cal I}_l$ denote the utility that the model owner receives for transmitting its global model to worker $l$. ${\cal I}_l$ can be expressed as follows:
\[
{\cal I}_l =\begin{cases}\begin{array}{ll}
\Delta & \mbox{if}\ \mbox{the transmission is successful}, \cr
0&\mbox{otherwise}.
\end{array}
\end{cases}
\]

As the model owner decides to use special channel $\lambda_n$ to transmit the global model to worker $l$, the model owner must pay channel access cost ${\cal C}_l^c=\lambda_n$, i.e., to a network provider. Otherwise, as the model owner chooses not to transmit or chooses the default channel to transmit the global model to worker $l$, ${\cal C}_l^c=0$. Thus, ${\cal C}_l^c$ is determined as follows:
\[
{\cal C}_l^c =\begin{cases}\begin{array}{ll}
\lambda_n & \mbox{if}\ a_l^c = n, n \in {\cal N}, \cr
0 & \mbox{if}\ a_l^c = 0 \; \mbox{or} \; a_l^c = 1.
\end{array}
\end{cases}
\]

When the model owner decides to transmit its global model to worker $l$, i.e., by using either the default channel or the special channel, the worker consumes energy for training and uploading the local model. The worker's battery can be recharged by the power beacon, and there is energy cost ${\cal C}_l^e$. ${\cal C}_l^e$ is the cost that the model owner pays the power beacon for recharging energy to worker $l$. ${\cal C}_l^e$ is a function of the weighted metric $\mu_l$ and the amount of recharging energy $a_l^e$. ${\cal C}_l^e$ is given by
\[
{\cal C}_l^e =\begin{cases}\begin{array}{ll}
\mu_l a_l^e & \mbox{if $X_l = 1$}, \cr
\mu_{out} a_l^e & \mbox{otherwise},
\end{array}
\end{cases}
\]
where $\mu_{out}$ is the weighted metric for recharging energy from outside the coverage. Note that the model owner pays a higher cost to recharge the energy if the worker is outside of the coverage, meaning that $\mu_{out} > \mu_l, \forall l \in {\cal L}$.

The reward of the model owner is defined as a function of state $s \in \mathcal{S}$ and action $a \in \mathcal{A}$ as follows:
\begin{equation}
\mathcal{R}\left ( s,a \right )= \displaystyle\sum_{l\in{\cal L}} \left( \alpha_{\cal I} \frac{{\cal I}_l}{{\cal I}^{max}} - \alpha_c \frac{{\cal C}_l^c}{{\cal C}^{c,max}} - \alpha_e \frac{ {\cal C}_l^e}{{\cal C}^{e,max}} \right),
\end{equation}
where $\alpha_{\cal I}, \alpha_c$, and $\alpha_e$ are the scale factors. ${\cal I}^{max} = \Delta L, {\cal C}^{c,max} = N L$ and ${\cal C}^{e,max} = \mu_{out} e^{max} L$ are the maximum values of the total utility, channel cost and energy cost, respectively.

Given each state $s \in {\cal S}$, the model owner must determine the optimal action $a \in {\cal A}$ to maximize the accumulated reward. The output is the optimal policy, which is defined as $\pi^* : {\mathcal{S}} \rightarrow {\mathcal{A}}$. To obtain the optimal policy $\pi^*$, the conventional Q-Learning (QL) algorithm \cite{watkins1992q} can be utilized. The main idea of QL algorithm is to update $Q$-values, i.e., $Q(s,a)$, of state-action pairs for a $Q$-table by using Bellman's equation as follows \cite{watkins1992q}:
\begin{align}
Q(s,a) = \displaystyle\sum_{s'\in{\cal S}}  P_{\pi}(s,s') ({\cal R}(s,a) + \gamma V(s')).
\end{align}
Thus, the $Q$-value is updated as follows
\begin{align}
Q^{new}(s,a) = (1-\beta) Q(s,a) + \beta \big{(}{\cal R}(s,a) + \gamma \displaystyle\max_{a'\in {\cal A}} Q(s',a')\big{)},
\end{align}
where $\beta$ is the learning rate, and $\gamma$ is the discount factor, $0 \leq \gamma < 1$.

After updating the $Q(s,a)$ values, the model owner can rely on the $Q$-table to determine the optimal action from any state to maximize the accumulated reward. However, this QL algorithm is only feasible for networks with small state and action spaces. As the number of workers $L$ increases, the problem of the model owner is high dimensional due to the involvement of the large state and action spaces. Therefore, the Deep Q-Learning (DQL) algorithm \cite{mnih2015human}, which is a combination of deep neural network (DNN) and QL, is adopted to find the optimal policy for the model owner.

\section{Deep Q-Learning Algorithm}

\begin{algorithm}[!t]
	\centering
	\caption{DQL algorithm}\label{Alg1}
	\begin{algorithmic}
		\STATE \textbf{Initialization} $\gamma$, $\beta$, ${\boldsymbol{\theta}}, {\wei"}$, $\kappa=0$
		\REPEAT
		\STATE Initialize network state $s$ from $L$ workers, $i=0$
		\REPEAT
		\STATE Choose action $a$ according to $\epsilon$-greedy policy
		\STATE Execute action $a$ and obtain reward $r$ and next state $s'$
		\STATE Store tuple $<s,a,r,s'>$ in ${\cal M}$
		\STATE Sample ${\cal M}_b$ experiences $<s,a,r,s'>$ from ${\cal M}$
		\STATE Determine $a^{max} = \arg\max_{a'\in {\cal A}} Q(s',a';{\boldsymbol{\theta}})$
		\STATE Execute \eqref{ddqn} to obtain $y_{DDQN}$
		\STATE Perform gradient descent step on \eqref{loss} to update ${\boldsymbol{\theta}}$
		\STATE Reset ${\wei"}={\boldsymbol{\theta}}$ every ${\cal I}^{-}$ iterations
		\STATE Reset $i \leftarrow i + 1$
		\UNTIL $i$ is greater than maximum number of iterations in episode $\kappa$
		\STATE Reset $\kappa \leftarrow \kappa + 1$
		\UNTIL $\kappa$ is greater than maximum number of episodes
	\end{algorithmic}
\end{algorithm}

Different from the QL algorithm, the DQL algorithm uses a DNN to derive approximate $Q$-values, i.e., $Q^*(s,a)$, instead of the $Q$-table. The input of the DNN is one of states of the model owner, and the output includes $Q$-values $Q(s,a;{\boldsymbol{\theta}})$ of all possible actions, where ${\boldsymbol{\theta}}$ is the weights of the DNN. To obtain the approximate values $Q^*(s,a)$, the DNN needs to be trained by using experiences $<s,a,r,s'>$. In particular, the DQL algorithm updates weights ${\boldsymbol{\theta}}$ of the DQN to minimize the loss function defined as follows:
\begin{align} \label{loss}
L({\boldsymbol{\theta}}) = \EXs{}{(y_{DQN}(t) - Q(s,a;{\boldsymbol{\theta}}))^2},
\end{align}
where $y_{DQN}$ is the target value that is given by
\begin{align} \label{dqn}
y_{DQN} = r + \gamma \arg \max_{a'\in {\cal A}} Q(s',a';{\boldsymbol{\theta}}'),
\end{align}
where ${\boldsymbol{\theta}}'$ is the weights of the DNN from the previous iteration and $r$ is the current reward. Note that action $a$ is selected according to the $\epsilon$-greedy policy. From \eqref{dqn}, we observe that the max operator uses the same $Q$-value for both action selection and action evaluation. As a result, the derived policy may be inaccurate due to the over-optimistic estimation \cite{anh2018deep}.

To address the over-optimistic problem, the action selection should be decoupled from the action evaluation. For this, the Double Deep Q-network (DDQN) \cite{van2016deep} can be used. The main feature of DDQN is the use of two separate DNNs i.e., an online network with weights ${\boldsymbol{\theta}}$ and a target network with weights ${\wei"}$. The weights of the online network are updated at each iteration, while those of the target network are kept constant. For every ${\cal I}^{-}$ iterations, the target network's weights ${\wei"}$ are reset to ${\boldsymbol{\theta}}$. The target function of DDQN is defined by
\begin{align} \label{ddqn}
y_{DDQN} = r + \gamma Q(s',\arg \max_{a'\in {\cal A}} Q(s',a';{\boldsymbol{\theta}}); {\wei"}).
\end{align}

As seen from \eqref{ddqn}, the weights of the online network, i.e., ${\boldsymbol{\theta}}$, are used to select an action, while those of the target network, i.e., ${\wei"}$, are used to evaluate the action. Both the online network and the target network use the next state $s'$ to compute the optimal value $Q(s',a';{\boldsymbol{\theta}})$. Given $\gamma$ and $r$, the target value $y_{DDQN}$ is calculated based on \eqref{ddqn}. Then, a gradient descent step is performed to update the weights of online networks ${\boldsymbol{\theta}}$ based on the loss function $L({\boldsymbol{\theta}})$ in \eqref{loss}. To guarantee the stability of the learning, the DQL algorithm employs an experience replay memory ${\cal M}$, where a mini-batch of ${\cal M}_b$ experiences is taken at each iteration to train the DNNs. Algorithm \ref{Alg1} shows how to implement the DQL algorithm.

\section{Numerical Results}
In this section, we present experimental results to evaluate the
performance of the proposed DQL algorithm. For comparison, we use the QL~\cite{watkins1992q}, greedy, and random algorithms as baseline schemes. For the greedy algorithm, the model owner decides the maximum amount of energy charged to each worker and selects the special channel with the highest quality to transmit the global model to the worker. For the random algorithm, selecting the channel and deciding the amount of charging energy for each worker are random. The algorithms are implemented by using TensorFlow deep learning library \cite{abadi2016tensorflow}. In particular for the DQL, we employ two DNNs, and each DNN has a size of $32 \times 32 \times 32$. The Adam optimizer is used that allows to adjust the learning rate during the training phase. The learning rate $\beta$ is set to $0.001$ to avoid the loss of local minima. The DQL algorithm prefers the long-term reward, and thus the discount factor $\gamma$ is set to $0.9$. We use the $\epsilon$-greedy policy with $\epsilon=0.9$ that balances between the exploration and exploitation. During the training phase, $\epsilon$ is linearly reduced to zero that moves from the exploration to the exploitation. The probabilities of energy consumption and mobility of the workers are set as follows $p_l=p_{en}$ and $q_l=q_{mo}, \forall l \in {\cal L}$. Other parameters are shown in Table~\ref{table:parameters}.

\begin{table}[h!]
	\centering
	\caption{Parameter Settings}
	\begin{tabular}{p{4.5cm}||p{2.0cm}}
		\hline
		$L=N=e^{max}$ & $3$ 	\\
		\hline
		$\Delta$ & 5 \\
		\hline
		$\{\mu_1,\mu_2,\mu_3, \mu_{out}\}$ & $\{0.1,0.2,0.3,0.8\}$ \\
		\hline
		$\{\lambda_2, \lambda_3 \}$ & $\{2, 3\}$ \\
		\hline
		$\{p_{su}, p_{su}^2, p_{su}^3 \}$ & $\{0.5, 0.95, 0.98\}$ \\
		\hline
		$\{\alpha_{\cal I},\alpha_c,\alpha_e\} $ & $\{3,1,1\}$ \\
		\hline
		$\{p_{en}, q_{mo}\}$ & $\{0.5, 0.8\}$ \\
		\hline
	\end{tabular}
	\label{table:parameters}
\end{table}

\begin{figure}[t!]
	\centerline{\includegraphics[width=0.45\textwidth]{Fig/DQL_compare}}
	\caption{Reward and convergence speed comparison.}
	\label{fig:DQL_compare}
\end{figure}

In Fig. \ref{fig:DQL_compare}, we plot the comparison among algorithms in terms of convergence speed and reward. As seen, the convergence speed of the DQL algorithm is much faster than that of the QL algorithm. Specifically, the DQL algorithm converges to the stable value of the reward within $5000$ episodes, while the QL algorithm needs to take around $8000$ episodes for the convergence. Also, the reward obtained by the DQL is much higher than those obtained by the baseline algorithms. In particular, the rewards obtained by the DQL, QL, greedy, and random algorithms are $4300$, $2700$, $2550$, and $480$, respectively. These results show that the DQL algorithm enables the model owner to learn the optimal polity. In particular with the greedy algorithm, the model owner employs the special channel with the highest quality and decides the maximum amount of energy recharged to each worker. The greedy algorithm can enable the model owner to improve its utility. However, it incurs the high channel cost and energy cost that significantly reduces the reward. For the random algorithm, the model owner randomly selects channels and amounts of energy. This may reduce the number of successful transmissions, and the workers may often face low energy states. Therefore, the random algorithm obtains the worst performance.

\begin{figure}[t!]
	\centerline{\includegraphics[width=0.45\textwidth]{Fig/Reward_qmo}}
	\caption{The reward versus mobility parameter.}
	\label{fig:reward_qmo}
\end{figure}

The performance improvement of the DQL algorithm compared with the baseline algorithms is maintained even if the mobility parameter $q_{mo}$ varies as shown in Fig. \ref{fig:reward_qmo}. Note that as the mobility parameter increases, the rewards obtained by all the algorithms generally increase. This is due to the fact as $q_{mo}$ approaches $1$, most workers are in the coverage and the default channel is used. This significantly reduces the channel and energy costs and increases the reward.

\begin{figure}[t!]
	\centerline{\includegraphics[width=0.45\textwidth]{Fig/noUser_utility}}
	\caption{The average utility with various $L$ values.}
	\label{fig:NoUser_incentive}
\end{figure}

Next, we evaluate the DQL algorithm as the number of workers $L$ varies.~Fig. \ref{fig:NoUser_incentive} shows the average utility obtained by the DQL algorithm under the different number of workers. As seen, the convergence speed of the DQL algorithm is slower as the number of workers increases. The reason is that as the number of workers increases, the action and state spaces increase that reduces the convergence speed of the algorithm. It is worth noting that the DQL algorithm converges to the same average utility regardless the number of workers. This is because of that the model owner already learns the optimal policy to obtain the maximum utility.

\begin{figure}[t!]
	\centerline{\includegraphics[width=0.45\textwidth]{Fig/3D_M05}}
	\caption{Channel policy for worker 1.}
	\label{fig:3D}
\end{figure}

Finally, it is interesting to consider how the model owner selects the channel for transmitting its global model to each worker given the worker's mobility state. Without loss of
generality, we consider the worker 1. As shown in Fig. \ref{fig:3D}, as the worker is outside of the coverage, $X_1=0$, the model owner selects one special channel. Specifically, special channel $2$, i.e., $n=2$, is selected since it has a lower cost than other special channels. As the worker is present in the coverage, both the channels, i.e., the special channels and default channel, can be used. However, the selection frequency of the default channel is higher than that of the special channels. The reason is that the default channel has quality enough, e.g., $p_{su}=0.8$, and using the default channel results in reducing the channel cost.

\section{Conclusions}

In this paper, we have presented the DQL algorithm for the resource allocation in the mobility-aware federated learning network. In particular, we first formulate the channel selection and energy decision of the model owner as a stochastic optimization problem. The optimization problem aims to maximize the number of successful transmissions of the model owner while minimizing the energy and channel costs. To solve the problem, we have developed the DQL algorithm with DDQN. The simulation results show that the reward obtained by the proposed DQL is significantly higher than those obtained by the conventional algorithms. This means that the proposed DQL algorithm enables the model owner to learn the optimal decisions under the stochastic and uncertainty of the network environment.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


For all out WooFDriver activities, the Jumbo Jet is used because it it's cargo carring capasity and comfort. Day to day use, I use the Dog Bus. I modified a Ford Transit Van be be an efficient WooF transport. Metal grate partition separate the dogs inside, similar to the Jumbo Jet. Spill proof waterdishes and fans have been installed for for the dogs comfort. After the dogs and the WooFDriver, there is only room in the front seat for a little gear or a passenger, so it can get crowded. But it gets much better milage and isn't quite as much of an "event" to drive it down the road, or park it in a parking lot. It's just a quick grab the dogs and go for a day's excersie of just me and the dogs.
Meet Us At the Dog Bus!


At Menor we are commited to giving you the best service, advice and quality. We love working with talented photographers and forming relationships with great schools, colleges and businesses of all kinds. Much of our printing and framing is bespoke. Our flexibility enables us to work with you to give a truly tailored approach to improve your workflow and your working life. Call us to see how we can work together.
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text:
News Space Sports Opinions Restaurants Obituaries E-Edition Legals
Guess what: Corey Feldman wowed at Wynfields in Satellite Beach
'80s actor sang Michael Jacksonesque music.
Jennifer Sangalang
Florida Today
Corey Feldman appeared in "Gremlins" and "Teenage Mutant Ninja Turtles"
Feldman briefly appeared on the A&E reality show "The Two Coreys"
Before his concert, Feldman spent some time on the beach at Satellite Beach
Though his music went viral in 2016, Feldman's been singing since the late '80s
SATELLITE BEACH — The "next" King of Pop made a stop at Wynfields Sports Bar. And the crowd went wild.
More than 250 people filed into the newly renovated neighborhood bar on beachside Tuesday night to hear Corey Feldman and the Angels perform. The '80s actor best known for his roles in "The Goonies," "Stand By Me" and "The Lost Boys" crossed over to music within recent years — to mixed reaction.
"He's a lot of fun. He's really original. He's a good guy," said Jay Johnson, head of entertainment at Wynfields.
[ COREY FELDMAN: 'The media called me weird' ]
In September 2016, Feldman went viral after performing songs from his album "Angelic 2 the Core" on NBC's "Today" show. For days after, his unique style dominated the headlines, leading to harsh criticism and online bullying. But despite that, Corey Feldman and the Angels played on.
Wynfields was the only Brevard stop in Feldman's summer tour.
Feldman's music emulates that of his late friend, the original King of Pop Michael Jackson. His costumes, routine and even his hair mimic a 1990s-era Jackson. (Check out his version of "Man in the Mirror" in our Facebook Live feed.)
According to promoter Mike Elko, the audience ranged in age from 21 to 60, with a few members of the young professional group 321 Millennials in attendance. Said Elko: "Turnout's great. (Regarding age), it's all across the board."
That news excited Desiree Holler, owner of Wynfields. She listed "Goonies" and "Dream a Little Dream" as her two favorite Feldman flicks.
[ WHAT?!?! We interviewed Chunk from 'The Goonies,' too ]
After the roughly three-hour concert, Holler and about 40 others waited around for a meet-and-greet with the "License to Drive" star. Many donned Corey Feldman T-shirts and carried posters from his '80s movies. Though nursing a cold, Feldman posed for pictures with fans and autographed memorabilia related to his acting career.
"We're going forward, looking for more shows like this," said Holler.
For more information on Feldman, visit coreyfeldman.net.
Sangalang is FLORIDA TODAY's entertainment reporter.
Contact Sangalang at 321-242-3630
or jsangalang@floridatoday.com.
Twitter: @byjensangalang
Instagram: @byjensangalang
Facebook: /jennifer.sangalang3
[ KATHY GRIFFIN: 'I expect a walkout at my show' ]


text:
A car in a rain swollen arroyo due to a storm in Hereford in 2016. Cochise County does not enforce the so-called "Stupid Motorist Law" which would allow up to $2,000 to be collected to cover reasonable costs of rescue.
MARK LEVY HERALD/REVIEW FILE
Your question answered: Is the 'stupid motorist law' enforced in Cochise County?
By Jamie Verwys jamie.verwys@myheraldreview.com
Jamie Verwys
Question: What is Arizona's "stupid motorist law" and is it enforced in the county?
During Arizona's monsoon season, flooded washes and roadways are common threats for drivers. Often times, when a motorist attempts to cross a flooded area they get stuck and need rescue.
This occurs so commonly that the state passed a law in 1995 to try and relive the financial burden upon taxpayers for the costly rescues.
The rescues also pose safety risks to the public safety and law enforcement agencies who perform them.
The law is commonly referred to as the "stupid motorist law."
Answer: The law, corresponding to section 28-910 of the Arizona Revised Statutes, states that any motorist who becomes stranded after driving around a barricade to drive through a flooded stretch of road may be charged for the cost of their rescue.
Cochise County Sheriff's Office Public Information Officer Carol Capas said that their office has not enforced this law thus far.
"We simply send people to rescue the people who have created the situation and we haven't charged," she said. "It has been problematic...including our personnel being placed in severe danger going into the water to rescue someone."
Capas said often times people attempt to cross flooded areas due to "overconfidence" in their vehicles.
"In that four-wheel drive that same one or two inches of water can get them in as much trouble as a small car, or they have overconfidence that they can traverse any type of roadway," she said.
Cochise County Sheriff Mark Dannels explained that they don't enforce the financial penalty for rescue because his focus is on public safety.
"One of the reasons is the fact that search and rescue is paid through tax dollars and people make mistakes, some are intentional but that's at a different level," he said. "It's just a mistake, and we aren't in business for making a profit — our job is to keep you safe."
He said that unfortunately, these types of rescues happen often in the county.
The cost of rescuing someone in this kind of situation is high. Capas said that multiple agencies typically respond to a call of this nature with people that must be paid. There is also a cost associated with putting up a barricade to prevent other motorists from doing the same thing.
Though the county doesn't enforce the stupid motorist law, Capas said it's important for people to remain mindful of flooded roadways and the dangers it brings for not only motorists but other people.
"It's important for people to understand that if they make ignorant choices it impacts other people including public safety officials," she said. "We want to make sure people obey any traffic device — that's why they are there."
"Don't try to go around it; it's not worth it and making such a poor choice doesn't just impact you."
The stupid motorist law caps the damages they can charge someone who was rescued from a flooded roadway at $2,000. Only "reasonable costs" can be charged including "the cost of providing police, fire fighting, rescue and emergency medical services at the scene of an incident and the salaries of the persons who respond to the incident," according to the statute.
A warning sign, such as one that reads "Do Not Enter When Flooded" is enough to trigger the stupid motorist law.
To view the law, visit www.azleg.gov/ars/28/00910.htm.
Motorist
Carol Capas
Mark Dannels
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text:
Radio City Music Hall Makes a "Spectacular" Upgrade with Meyer Sound's LEO Family
Posted on Wednesday, August 10, 2016
"The LYONs, with their extraordinary power-to-size ratio, were a perfect fit and significantly out-performed the previous solution."
- Andrew Keister
Partner, SCK Sound Design
A new permanent Meyer Sound LEO Family reinforcement system has been installed at New York City's Radio City Music Hall to support performances by the world-famous Radio City Rockettes, including this summer's New York Spectacular and the Christmas Spectacular slated for this upcoming holiday season.
"As the leading company in live entertainment, we are focused on delivering a theatrical experience at Radio City that is fresh, contemporary and unparalleled," says Larry Sedwick, senior vice president of production for the Madison Square Garden Company, which operates the iconic venue. "It's important to create a one-of-a-kind experience for our audiences no matter where they are seated in this 6,000-seat venue – the largest indoor theater in the world. Our latest sound system upgrades allow us to bring our productions to a whole new level and make our audiences feel even closer to the show."
The newly purchased Meyer Sound system, with main arrays of LYON loudspeakers concealed behind scrim in a false proscenium, is based on a nearly identical main system based on LYON arrays that was crafted by SCK Sound Design and first deployed for the 2014 Christmas Spectacular.
"The main PA had to go in a very specific location and be hidden from view," says Andrew Keister, a partner with Steve Canyon Kennedy in SCK Sound Design. "The spectaculars do projection mapping all over the front ceiling and false proscenium, which precludes use of large, flown arrays that would cast shadows. The LYONs, with their extraordinary power-to-size ratio, were a perfect fit and significantly out-performed the previous solution."
The main system used for the New York Spectacular, again with sound design by SCK, comprised left and right arrays of 16 each LYON line array loudspeakers, plus a center downfill array of eight LEOPARD line array loudspeakers. Ample bass support was supplied by 30 900-LFC low frequency control elements, deployed as 18 in a cardioid pattern on the side proscenium, six in a center cardioid and six on a rear truss. Eight additional 900-LFC elements were purchased for use with anticipated rock-style concerts.
The new system, supplied by PRG of New York with sale facilitated by Bob Rendon, also includes a Galileo Callisto loudspeaker management system with three Galileo Callisto 616 processors and six Galileo 616 processors. For immersive surround effects, this summer's New York Spectacular system also incorporated four arrays of eight each LEOPARD line array loudspeakers. "Most of these LEOPARDs were rentals," notes Keister, "as the requirements for surround systems will change from show to show."
The new system also incorporates a complete D-Mitri digital audio platform equipped with SpaceMap 3D panning and Wild Tracks multi-channel playback. "The power of the D-Mitri platform enabled us to program innovative, multi-dimensional effects that perfectly complement the video mapping," adds Keister. "The result is a truly immersive audio experience."
The Radio City Rockettes shows are mixed on a pair of DiGiCo SD7 digital consoles, with staff engineers Patrick Healey and Michael Jenkins handling the live orchestra, vocal, playback and supplementary sounds – including 80 channels of wireless dedicated to the special microphones built into the 80 Rockettes tap shoes.
A legendary tradition in American entertainment, the Radio City Rockettes have been performing at Radio City Music Hall since 1933. Located in Rockefeller Center and with art deco architecture designed by Edward Durrell Stone and Donald Deskey, Radio City Music Hall was added to the National Register of Historic Places in 1978.
12" Compact Linear Line Array Loudspeaker with Utmost Flexibility
Model: LYON
9" Compact Linear Line Array Loudspeaker
Model: LEOPARD
18" Compact Low-frequency Control Element
Model: 900-LFC


I started writing a little over a year ago, after finding "Technical Blogging" by Antonio Cangiano through Hacker News. Since then, a bit over 40,000 people have read articles I've written, not a huge number in the grand scheme of things, but enough to draw a few lessons. The more I write, the more good things happen on their own, but at random moments.
Before starting to write, I came upon ~650 followers on Google Plus through a mutual-follow program on Hacker News and was interested in the SEO potential. If you're signed into Google, people you follow are promoted in search results. Before this I built a site for a family member selling beehive plans and learned a bit about SEO. It turns out that making money by selling $10 print books in a narrow niche is difficult, especially when you can't do the writing yourself, so I've pivoted to pursue technical subjects.
Antonio recommends writing in a focused niche, but I am still developing direction. I started with vague ideas based on things that interest me – database tuning, machine learning, data science, statistics, and practical applications of these. In practice, I've written on wider subjects – anything within "full stack web development" is fair game, trying to focus on new, or popular tech – Scala, DevOps (Vagrant/Chef/Virtualization), Hadoop, R, and scraping. Typically I write with an audience in mind – e.g. a niche forum or subreddit. When this works well, it often ends up in the forum without my help, as in the case of writing about Flippa auction data, where I received links from the experienced-people forum and the Flippa corporate blog.
Choosing a strategy for generating material can be challenging. There are several models for success: just write what you're interested in; pick topics with a good ratio of searches to existing quality content; pick an area where you own a product; or document knowledge from your consulting services. The last approach is particularly valuable, as it's easy to establish business value. This has been used to build info-products with great success by Patrick Mckenzie, Brennan Dunn, and Nathan Barry.
I've followed the first two approaches – there are a few tools to help you pick topics, such as the very spammy looking, but useful Market Samurai. For a person with a generic IT blog, several types of articles naturally arise from this approach: proof of concepts, weird undocumented error messages, things that ought to be in product documentation, and things that are in product documentation, but can't be found through Google for whatever reason.
Another article generation technique that works well is "_Old Concept_ with _New Tool_". E.g. "Building a JSON Webservice in R", "Implementing K-Means in Scala" – basically trend-surfing. A key success of this is that someone actually applied to a job where I work because of a post called "Scala vs. Clojure." The appeal of this tactic for the author is the ease of writing; for the reader, it allows them to see a familiar and easily-digestable concept in a new language.
I have a long list of potential titles, which I update whenever I recognize something that might be of interest. I also have an editing checklist, which I refine over time. For programming blogs, how to format code is important – I use a WordPress plugin based on GeSHi. I found that code written in a REPL is often very dense, and I've had complaints from people on Reddit, so I try to format the code with more whitespace than I would normally use. I also push most of my code to github (if there is enough to justify it) – this lets me get feedback from people who are using it.
Recently, Google Webmaster Tools showed a massive spike (400%) in how often they showed my blog in search. This was because they put my wordpress /tag/scraping at the top of search results for a day. Few people clicked through – my WP tag pages were not setup to look appealing, and I've since begun to fix that. To that aim, I wrote a script to extract counts from Twitter, HN, and Reddit, to estimate popularity, so I could make each tag page a "Guide" to the subject in question, filtered to the most popular and useful articles I've written on each subject.
The other SEO lesson is that the "not provided" keyword in Google Analytics removes a lot of insight. When a user is logged into their Gmail account, keywords are blocked being sent to the recipient site by Google. A lot of traffic is also missing the name of the referring domain entirely, an frequent occurrence when one of my articles is posted on reddit. For me, "not provided" has risen from 50% of search traffic a year ago to 80% today. Since Google Analytics lets you see top articles by referring domain, having detailed URLs helps make up for the lack of keyword data.
I must confess, I've thought for a long time that Twitter was kind of stupid, but recently I've been disabused of this notion, although I may be the last person to figure it out. While attending Philly ETE I realized that during the keynotes people sit there checking their phones – looking around the room you see Twitter apps.Writing summaries of conference talks using the conference hashtag resulted in a bunch of local people reading my articles and following me. Software that follows twitter hashtags for you is essentially an niche search engine, of which it turns out there are many – hnsearch, and the WordPress plugin search.
I've written several articles which have been posted to Twitter by 20+ people. I only noticed in hindsight – t.co is the 6th most productive source of visits for me. Some of these posts are automated – e.g. from HN or DZone.One of the most popular posts of these posts was an entirely hypothetical commentary on using map-reduce techniques to farm database queries out to browsers. It's the only thing I've written that seems to have received significant attention on Google+ (19 events on G+, 24 on Twitter). Discovering how often this was shared prompted this post – I was surprised as it was something I'd whipped off in half an hour.
A few people have shared my articles on Reddit, which has introduced me to new subreddits, but most of these posts have fared poorly due to poor targeting. Posts I've made received more votes, even though they are self posts, because they are at least relevant. Unlike Reddit or Hacker News, DZone actively promotes it's writers. If your articles do well on the social media/voting side of their forum, they invite you to join their network, which I have found very helpful.
Most default WordPress themes render terribly in mobile devices, so it's worth testing, preferably on both Android and iOS. For me, about 10% of my visitors are on phones. The most common rendering issue is that they render the same view a desktop would see, but scaled down so small that you have to zoom and scroll both left-right and up-down. Installing a mobile plugin made my blog render usably, although not pretty, which has reduced the number of people who just give up.
For people who enter through mobile, most come from an app (Reddit, Hacker News, or Twitter), an email link, or an RSS subscription. An important consideration is whether it's actually possible for someone interested to follow you if they are on a phone. On a desktop you can easily copy a URL to an RSS reader, or type in an email address, but on a phone it's harder, which I suspect calls for tighter integration with twitter, etc, but I haven't proved this out yet.
The second technical issue worth paying attention to is page load time. Even when it's fast for you, it's not for others. I set this blog up on a Linode host, and have been slowly improving this (e.g. APC, Image sizes, more memory from Linode, Apache caching). The WPEngine blog is extremely helpful for finding useful tidbits – the wasted effort I've gone through tuning this site seems to be what they are trying to help businesses avoid.
If you run your own server, you eventually will get hacked, especially if you use popular software – I've recently started using Better WP Security to mitigate this risk.
It should come as no surprise that spam has become very sophisticated- for instance, there are people scraping comments from other sites to post on yours. I accepted one comment I never was able to figure out – a character named "Weng Fu", who goes around to tech blogs praising the glories of VB6 (!), presumably an exercise in trolling. I forgot about this comment until recently, when I found that Google Webmaster Tools shows I rank well for "Weng Fu VB6" – who knew.


actions to enforce contracts directly arising out of and connected with interstate commerce equally with actions having no relation to such commerce, and, after so construing the statute, the court held it to be a reasonable exercise of the police power of the state and in no wise repugnant to the commerce clause of the Constitution of the United States. In two earlier cases, the court had taken a different view of the statute ( Rex Buggy Co. v. Dinneen, 23 S.D. 474; Sioux Remedy Co. v. Lindgren, 27 S.D. 123), but, in the opinion rendered in this case, they were disapproved.
Through a long series of decisions dealing with the scope and effect of the commerce clause, it has come to be well settled that a state, while possessing power to adopt reasonable measures to promote and protect the health, safety, morals, and welfare of its people, even though interstate commerce be incidentally or indirectly affected, has no power to exclude from its limits foreign corporations or others engaged in interstate commerce, or, by the imposition of conditions, to fetter their right to carry on such commerce, or to subject them in respect to their transactions therein to requirements which are unreasonable or pass beyond the bounds of suitable local protection. Crutcher v. Kentucky, 141 U. S. 47 ; Minnesota Rate Cases, 230 U. S. 352 , 230 U. S. 401 -402, 230 U. S. 410 , and cases cited; Barrett v. New York, 232 U. S. 14 , 232 U. S. 31 , and cases cited. And so the solution of the question here presented lies within narrow lines.
were transactions in interstate commerce, and entirely legitimate notwithstanding the plaintiff's noncompliance with the state statute. International Textbook Co. v. Pigg, 217 U. S. 91 ; Buck Stove Co. v. Vickers, 226 U. S. 205 ; Flint & Walling Mfg. Co. v. McDonald, 21 S.D. 526. After delivery of the merchandise according to the contract, the plaintiff was lawfully entitled to the purchase price. The defendants were likewise obligated to pay it. And, by reason of their refusal, the plaintiff had a right of action on the contract. Thus much was recognized by the supreme court of the state and is now conceded by counsel for the defendants. But it was held by that court, and is here contended, that, while the state could not make noncompliance with the statute a ground for forbidding or invalidating sales in interstate commerce, it could make such noncompliance a ground for preventing the maintenance of any action in the courts of the state based upon such a sale -- in other words, that the state, although unable to condition the right to make the sale or its validity upon a compliance with the statute, could so condition the right to sue for the purchase price in the courts of the state.
purposes of such commerce." Western Union Telegraph Co. v. Kansas, 216 U. S. 1 , 216 U. S. 27 . We think that, when a corporation goes into a state other than that of its origin to collect, according to the usual or prevailing methods, the purchase price of merchandise which it has lawfully sold therein in interstate commerce, it is there for a legitimate purpose of such commerce, and that the state cannot, consistently with the limitation arising from the commerce clause, obstruct or hamper the attainment of that purpose. If it were otherwise, the purpose of the Constitution to secure and maintain the freedom of commerce by whomsoever conducted could be largely thwarted by the states, and the commerce itself seriously crippled.
* At the time of the allowance of the present writ of error, the record had been sent to the Circuit Court of Turner County, and so the writ was directed to that court. See Gelston v. Hoyt, 3 Wheat. 246, 16 U. S. 304 ; Atherton v. Fowler, 91 U. S. 143 , 91 U. S. 146 -149; Polleys v. Black River Co., 113 U. S. 81 ; Lee v. Johnson, 116 U. S. 48 .


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Hot 100 Q&A: Event review
By Dean
Brockholes nature reserve played host to the latest get-together in the Lancashire Business View events calendar, with 70 delegates attending the Hot 100 Q&A.
A panel discussion saw our group of business leaders discuss some hot topics and take questions from the floor.
Four Hot 100 companies were represented on the panel. Stephen Thorpe, chief executive of CME Medical, Fiona Lugiano, deputy chief executive of Ascentis, Carmel Marie Satterthwaite, sales and marketing director of Panaz and Richard Greathead, business development director of Nutrition Group PLC offered guidance to all the delegates and reflected on their experiences.
The trail-blazers of the county's economy shed light on how they have become the most profitable SMEs in Lancashire.
Richard Slater, publishing editor of Lancashire Business View, said: "It was teriffic to see such a diverse group of business leaders come to the event and take the opportunity to grill the biggest business brains in the county on how they have transferred their burning passion into commercial success."
The Hot 100 is sponsored by Haworths Chartered Accountants and Accrington and Rossendale College. Rosebud Business Solutions sponsored the event.
For a full event review, including photography and highlighted advice from our panel, see our September/October edition. You can subscribe here.
[cta link="http://www.lancashirebusinessview.co.uk/wp-content/uploads/2013/08/LBV-51-Hot-100.pdf"]DOWNLOAD THE HOT 100[/cta]
Eric Wright acquires £2.5m Salford site
Eric Wright Group has acquired the 37,311sq ft industrial unit at Wardley Industrial Estate
Barnoldswick jobs saved as Rolls-Royce turns attentions to green technology
Rolls-Royce and Unite have struck an agreement which will see a minimum of 350 workers employed at its Barno...
Blackpool Airport secures government support
Blackpool Council has secured specialist government support to help it develop and market Blackpool Airport....
YourZooki plans international expansion following successful 2020
YourZooki is to add to its product range and expand its presence in the USA after trebling revenues through...
By Piranha Advertising and Marketing Solutions - Jan 18 2021
Alison Handling is proud to announce the launch of a new product
Record new business sales of more than £1.4m in December
By Kingsley Asset Finance - Jan 08 2021
We finished 2020 on a great high, with December converting over £1.4m of asset finance for a wide variety of...


Micah Chrenek, BS, Lab Manager at the confocal microscope in the Functional Genomics and Proteomics (FGP) Core lab.
The Atlanta Vision Research Community (AVRC) provides support and scientific expertise for individual, collaborative, and pilot research in the areas of Structural Biology and Imaging, Functional Genomics and Proteomics, and Bioinformatics and Biostatistics, as they relate to basic and translational eye research.
6. Encouraging non-vision scientists to study vision by offering Core support.
The resource/service cores enhance collaboration, support pilot projects leading to new R01 proposals, and complement the annual support for research by Emory Eye Center, the Emory School of Medicine Department of Ophthalmology and other departments within Emory University, the Atlanta VA Medical Center, Georgia Institute of Technology, Georgia State University, and Morehouse School of Medicine.
This core supports a robust and highly competitive program of training and core scientific resource support for pre- and postdoctoral fellows supported through an NEI Training Grant, K-, and F-series awards.


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Jagdish N. Sheth
Dr. Jagdish N. Sheth is a world-recognized authority on global competition, strategic thinking, and customer relationship management. Dr. Sheth is Charles H. Kellstadt Chair of Marketing Strategy in the Goizueta Business School at Emory University. He has served as a distinguished faculty member at the University of Southern California, the University of Illinois, Columbia University, and the Massachusetts Institute of Technology. Dr. Sheth has published more than two dozen books and hundreds of research papers in different areas of marketing and business strategy; many are considered classics in their fields. His books include The Rule of Three: Surviving and Thriving in Competitive Markets and Tectonic Shift: The Geoeconomic Realignment of Globalizing Markets (both co-authored with Rajendra S. Sisodia). With Banwari Mittal, he co-authored ValueSpace: Winning the Battle for Market Leadership. He also co-authored Clients for Life with Andrew Sobel. His latest book, Firms of Endearment: How World-Class Companies Profit from Passion and Purpose (Wharton School Publishing), was co-authored with Rajendra Sisodia and David Wolfe.
Breakout Strategies for Emerging Markets: Business and Marketing Tactics for Achieving Growth
By Jagdish N. Sheth, Mona Sinha, Reshma Shah
Firms of Endearment: How World-Class Companies Profit from Passion and Purpose, 2nd Edition
By Rajendra Sisodia, Jagdish N. Sheth, David Wolfe
Give Your Business a Heart (Collection)
By Dev Patnaik, Jagdish N. Sheth, Rajendra Sisodia, David Wolfe
Converting Nonusers to Users
Building Business on Love and Care
Firms of Endearment: How World-Class Companies Profit from Passion and Purpose
Why Do Good Companies Go Bad?
What Makes a Firm of Endearment?


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by Douglas Greenwood
21 October 2020, 10:16am
What you need to know about Netflix's 'lesbian Twilight' TV show
When it's coming out, who it's made by and the vampire book it's based on.
Still from Paradise Hills
Netflix, our kings of commissioning excellent TV shows and then fobbing them off two seasons later, have just announced another exciting next venture, and it's a Twilight-esque take on a lesbian love story! Titled First Kill, the show -- which was announced this month as one of Netflix's key projects for 2021 -- is based on a short story by the author V.E. Schwab, and was originally part of a wider vampire anthology called Vampires Never Get Old.
This story is set in a small American town, and focuses on a teenage vampire called Juliette who's expected to prove herself to her family and secure her first kill soon. Her main focus becomes the new girl in town, called Calliope, but when she comes to seduce her, all along with the intention of sinking her teeth into her neck, Juliette finds that Calliope is, in fact, a vampire hunter. And so, a complicated game of romantic cat and mouse comes to the fore.
The series is headed up by someone who has their own experience with campy, queer-adjacent horror: American Horror Story star Emma Roberts. The actor and producer, known best for her turn in Ryan Murphy's other over-the-top scary series Scream Queens, apparently beat out 18 other production companies to secure the rights to the book through her own venture, Belletrist Productions. As for the scripts? Well, V.E. Schwab will oversee those too, ensuring the adaptation stays faithful to her original source material.
It's thought First Kill will start filming in the next few months, and will eventually comprise of eight hour-long episodes that will hit Netflix next year. As for the casting decisions, it's unknown if Emma Roberts will appear in the series herself, or if she has other plans in place for new cast members. Whatever happens, expect whoever plays Juliette and Calliope on this show to be the next big talk of Hollywood. Bella Swan and… Bella Swan? Eat your heart out!


Chris Slocum is a Research Scientist/Scholar I in the Regional and Mesoscale Meteorology Branch (RAMMB) Tropical Cyclone Group. His research interests focus on how processes in the inner-core of tropical cyclones impact storm intensity and structure. To explore these topics, he uses simple dynamical models and machine learning. He joined CIRA in 2017 to develop tropical cyclone rapid intensification and genesis products using the GOES-R series of satellites.
Basic concepts involved in tropical cyclone boundary layer shocks .


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← Splendor in New Zealand as Nicky Wu and Liu Shi Shi Release Additional Wedding Pictures
K-actress Kim Ha Neul and Businessman Fiancee Get Married in Seoul →
Jang Geun Seok Looks Calm and Collected in New Star1 Pictorial
Posted on March 19, 2016 by ockoala
Jang Geun Seok with not as much excitement as he used to elicit but certainly still equipped with the acting talent to merit checking out. The Royal Gambler (Daebak or Jackpot) premieres in less than two weeks and is marketed less Jang Geun Seok centric (as it should be) and more geared for the sageuk aficionado. I think he's been looking very energized recently, not just with filming the drama but also doing the MC gig on hit reality music show Produce 101. There's a new profile on him for Star1 looking very relaxed and quietly confident, which I take as continued good signs that he's close to fully resuming his entertainment career in Korea and getting it back on track with the domestic audience.
Posted in K-dramas	Tagged Jang Geun Seok, The Royal Gambler	permalink
Jang Geun Seok Looks Calm and Collected in New Star1 Pictorial — 16 Comments
amy on March 19, 2016 at 5:56 AM said:
my gaydar is strong looking at those pix
kalel on March 19, 2016 at 7:49 AM said:
If your gayradar is on while checking out news on international entertainment business , then you won't be able to relax even for a second, dear.
Entertainment has the highest percentage of gay people all over the world -and that's a fact as old as Shakespeare. We don't need to explain the reasons, do we?
PS Nothing to do with this particular post though. The man is gorgeous and has a very intense face too.
yun on March 20, 2016 at 12:57 AM said:
but he does look like some character from a yaoi manga.
garrett on March 20, 2016 at 1:09 AM said:
Amy, who is JKS to you? I am his fan, that's y I read all articles about him. Even though I have been an eel for 6 years, I never comment in those article, and that doesn't lessen my admiration towards him. Then I notice that in all of those articles about him, you will appear and gave negative comments. In this article, you even be the first to comment. That's amazing. I know it's your right to comment in anywhere, but I am just curious that you dislike JKS so much but you care to follow all his news and even waste your precious time to cooment in it. An eel like me is not as committed as you to constantly comment in his news. LOL
royana on March 20, 2016 at 1:25 AM said:
Sorry, you should try it on another place.
Here you have someone who is a public figure from many years and who has chameleon visuals.
Check in Google and then speak.
bulenda on March 19, 2016 at 6:45 AM said:
She's really pretty.
whatever on March 19, 2016 at 6:53 AM said:
Jealous? Lol
yamapisukii on March 19, 2016 at 7:06 AM said:
That second picture though! Straight out of a manga!♡
i was thinking the same thing.
anna on March 20, 2016 at 6:05 PM said:
Thinking the same thing too. Love the second picture. <3
Hmmm on March 19, 2016 at 7:39 AM said:
He suddenly looks so young! Some vulnerable and others fierce. I love it!! Hopefully he will get his groove back as well the Knetz and ahjumma's heart. He has the talent that's for sure.
purpleplum on March 19, 2016 at 9:23 AM said:
Jang geun seuk's girly features has always been his strongest asset….that's why i think he's so popular in Japan…as well as in Korea…so i think this pictorial is trying to capitalize on that again…I just hope that his superb acting abilities will be showcased as well when this drama is released….it would be a shame and a waste if it doesnt. Sigh….
CC on March 19, 2016 at 5:41 PM said:
I know he could be a great actor, the problem is that he needs to focus. Hopefully this drama, the actors with him and the Knetizen disliking him would make him work hard and focus. It sucks to see such great potential going to waste.
Gaby on March 19, 2016 at 6:11 PM said:
Don't worry. Song Hye Kyo and Lee Byung Hun is the real example as long as the story is good and knetz like it, they'll get success again and forgetting their scandal. I don't even think what he did is that bad.
Jenga on March 19, 2016 at 6:41 PM said:
If Lee Byung Hun can make people "forget" about what he did, what JGS did is nearly nothing in my opinion.
jordane on April 4, 2016 at 10:10 AM said:
I saw JGS first on Hong Gil Dong, and you all know how mighty fine he was in that – a prince with a pretty face and a very masculine voice, wow! Then You're Beautiful, Marry me Mary, Love Rain, he capitalized on that pretty face and the long hair but his music, the movie My Pet just got a little weird for me like it was confusing coz maybe I it was all too much for me then "Pretty Man" just didn't cut it. 'guess we are tired of the same I'm Pretty route so it was good that he went on a hiatus for awhile then his comeback would hv been on three meals with PD Na but that tax brouhaha surfaced which I think was something concocted by someone who hates him, that would hv been a really great show.
But the eels and the rest of us who believes in his talent no matter what coz I think he is a good person just based on interviews I've seen of him, he speaks what's on his mind, unlike the others who are very reserved.
So I am just happy that he chose a sageuk this time sort of an reintroduction of JGS version 2.0. Let's hope it is a good story to match the topnotch acting he will bring here coz JGS is still one of the best from his generation.
Spate of High Profile Star Couple Breakups Hit the Chinese Entertainment Industry
MBC and KBS May Not Hold Annual Year End Drama Awards for 2017 Due to Strike Fallout
Ji Sung Sends Sweet Drink Support Truck to the Set of Wife Lee Bo Young's Hit tvN Weekend Drama Mine
Yunho Signs on as Leading Man in New Drama Melo Holic from PD of Another Oh Hae Young
Supermarket Showdowns and Library Meet Cute Coming Up From New Stills for Episode 3 of Goblin
MBC Mon-Tues Romance Drama Dinner Mate Premieres to 4.8%, 6.1% Ratings with All the Hallmarks of Classic Boy Meets Girl Hijinks and Cuteness
Kim Min Jae Confirmed for Rom-com Drama Dal Li and Gamja Tang with Park Kyu Young
Spy Myung Wol Episode 8 Recap


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Court Sides with Apple in Apple v. Epic Case
Ruling affirms ability of marketplace owners to set marketplace rules
On Friday, U.S. District Judge Yvonne Gonzalez-Rogers announced a decision in the antitrust lawsuit between Epic Games and Apple Inc., ruling that Apple's 30 percent commission cap and app developer restrictions are not a violation of antitrust law. The judge's decision reaffirmed the principle that marketplace owners are allowed to set their own marketplace terms, while directing Apple to end its "anti-steering" restrictions.
"Antitrust law should focus on protecting consumers, but this case has always been about Epic Games trying to save a few million dollars. The judge's rejection of most of Epic's arguments should give policymakers pause in legislating to help some companies' bottom line," said Chamber of Progress CEO Adam Kovacevich.
Kovacevich also noted that the ruling affirmed marketplaces' ability to set their own rules. "So many tech policy and legal disputes revolve around whether a platform can set their own rules to serve consumers, even when that upsets suppliers. The judge clearly backed platforms, and that will impact the other big pending lawsuits against major tech platforms."
In her full decision, Judge Gonzalez-Rogers determined that Apple does not have a monopoly in the market of digital mobile gaming transactions, saying that "the court cannot ultimately conclude that Apple is a monopolist under either federal or state antitrust laws," and that, "ultimately, Epic Games overreached."