text:
Vincitore di un Latin Grammy Award su nove nomination, ha venduto in tutto il mondo 18 milioni di copie tra singoli e album, affermandosi come uno degli artisti di maggior successo ed influenza della musica latinoamericana urban.

È diventato famoso nel suo paese natale nel 2011 con i singoli Farandulera e Obsesión, raccolti nell'album di debutto, Magia (2012). Nello stesso anno Maluma è stato nominato per il Latin Grammy Award come "Best New Artist". Grazie poi ai brani La Temperatura e Carnaval (2014) ottiene successo nel resto dell'America Latina, confermato dal secondo album in studio, Pretty Boy, Dirty Boy, che è entrato al primo posto nella classifica di album latini di Billboard, e da cui sono stati estratti Borró Cassette, El Perdedor e Sin contrato.

Negli anni successivi collabora con Ricky Martin, Yandel e nelle hit Chantaje e Clandestino di Shakira, che permettono all'artista di entrare nelle classifiche internazionali. Nel 2018 esce il terzo album, F.A.M.E., anticipato dai singoli El préstamo, Corazón e Felices los 4, grazie al quale ottiene il Latin Grammy Award nella categoria "Best Contemporary Pop Vocal Album". L'anno seguente, oltre a prendere parte al singolo Medellín, estratto dal quattordicesimo album di Madonna Madame X, viene pubblicato il suo quarto album dal titolo 11:11, anticipato dal singolo HP, ricevendo una nomina ai Grammy Award 2020 come "Best Contemporary Pop Vocal Album".

Nel 2018 viene conferito a Maluma il "Extraordinary Evolution Award" ai Latin American Music Awards per il successo conseguito dall'inizio della sua carriera, e nel 2019 ASCAP gli assegna il premio "Songwriter of the Year" per i suoi contributi nella musica latino-americana.

Parallelamente alla musica, Maluma ha intrapreso una carriera televisiva come coach e giudice a The Voice in Messico e Colombia.

Biografia
Juan Luis Londoño Arias è nato il 28 gennaio 1994 nella città colombiana di Medellín, figlio di Marlli Arias e Luis Londoño; ha una sorella maggiore di nome Manuela che ha studiato psicologia e filosofia.

Nella sua prima infanzia, era un appassionato di calcio, uno sport che ha praticato per otto anni di seguito. Ha ricoperto le divisioni inferiori nelle squadre Atlético Nacional e Equidad Club Deportivo. Ha giocato anche a pallavolo, ping pong e scacchi.

All'età di 15 anni ha composto con un parente una canzone dal titolo No quiero e un anno dopo Juan Parra, suo zio, gli ha offerto come regalo di compleanno di registrare una canzone in uno studio. Inizialmente, non aveva alcuna intenzione di concentrarsi sulla musica.

Il suo nome artistico Maluma, è una combinazione delle prime due lettere del nome di sua madre Marlli, il padre Luis e sua sorella Manuela. Secondo l'artista stesso: "è un omaggio a loro, perché li amo e sono il motore della mia carriera". Suo padre, all'inizio si oppose all'idea del cantante, sostenendo che Maluma aveva ormai dedicato molto tempo al calcio e anche perché voleva che fosse un calciatore di successo e non un cantante di reggaeton; alla fine ha sostenuto la sua decisione. Nel 2010, all'età di 16 anni, Maluma ha iniziato a ricevere lezioni di canto, e nell'ultimo anno di scuola superiore, ha deciso di lanciarsi definitivamente nel mondo della musica.

2010-13: Inizi della carriera
Maluma ha iniziato a lavorare come cuoco nel 2010, ma dopo che Farandulera divenne un successo radiofonico locale, Sony Music e la sua consociata etichetta Sony Music Colombia decisero di firmare con il cantante per registrare il suo primo album in studio. L'etichetta ha quindi lanciato il suo prossimo singolo, Loco.

Nel 2012, Maluma ha pubblicato il suo primo album in studio, intitolato Magia. Fu anche nominato per lo Shock Award 2012 come "miglior nuovo artista". Gli ultimi due singoli del suo album di debutto furono Primer Amor e Miss Independent; quest'ultimo è stato accompagnato da un video musicale, registrato a Medellín, che ha 35 milioni di visualizzazioni su YouTube.

Nel 2013, La Temperatura, cantato con il cantante portoricano Eli Palacios, è stato pubblicato come il primo singolo del suo mixtape, PB.DB The Mixtape; la canzone ha raggiunto il 7º posto nei grafici National-Report. Divenne anche il suo primo singolo ad entrare nelle classifiche americane; ha raggiunto 25º posto nella classifica Tropical Songs, e l'8º posto nella classifica Latin Pop Airplay, il 24º posto nella classifica Billboard Top Latin Songs e il 4º posto nella classifica Latin Rhythm Airplay.

2014-2017: The Voice Kids e il secondo album in studio

Nel 2014 ha pubblicato i video musicali per i singoli La Curiosidad, Carnaval e Addicted. Ha anche collaborato con il cantante Elvis Crespo, su Olé Brazil registrato per la Coppa del Mondo FIFA 2014. Il 16 luglio 2014, Maluma è apparso alla Premios Juventud 2014, dove ha interpretato La Temperatura. Lo stesso mese, il cantante è stato confermato come giudice di The Voice Kids trasmesso su Caracol Television, al fianco di Fanny Lú e Andrés Cepeda. Ad agosto, ha esordito come presentatore ai premi inaugurali del Kids' Choice Awards Colombia del 2014.

All'inizio del 2015, Maluma ha pubblicato il mixtape PB.DB The Mixtape, una raccolta di vari singoli.

Il suo secondo album Pretty Boy, Dirty Boy è stato pubblicato alla fine del 2015. I singoli Borró Cassette, El Perdedor e Sin Contrato sono entrati nella top 10 di Hot Latin Songs.

Nel 2016 ha collaborato con artisti come Thalía, Ricky Martin e Shakira ed ha intrapreso il The Pretty Boy, Dirty Boy World Tour nel maggio 2016. Nel dicembre 2016, Maluma ha partecipato al remix di Me Llamas della band colombiana Piso 21.

Nel 2017, il suo singolo di successo Felices los 4 è diventato il suo quinto top 5 nella classifica Hot Latin Songs. Alla fine dello stesso anno è stato confermato come artista e giurato per il Festival di Viña del Mar.

2018-in corso: F.A.M.E., 11:11, Papi Juancho
Nel marzo 2018, Maluma è stato scelto per interpretare la versione spagnola della canzone Colors, l'inno per il Campionato mondiale di calcio 2018 presentata da Coca-Cola. La canzone in inglese è stata invece eseguita dall'artista americano Jason Derulo.

Nello stesso anno rilascia un nuovo album, dal titolo F.A.M.E. e l'anno seguente 11:11.

Nell'aprile 2019 viene annunciata la collaborazione tra Maluma e Madonna dal titolo Medellín, per l'album di quest'ultima Madame X. I due artisti si sono esibiti insieme sulle note del brano durante i Billboard Music Awards 2019. Successivamente viene annunciato l'imminente debutto di Maluma come attore nel film Marry Me, in cui reciterà al fianco di Jennifer Lopez.

Fra 2019 e 2020 Maluma pubblica molti singoli fra collaborazioni e brani da solista, tra cui: Que Pena con J Balvin, Que Chimba e Feel The Beat con i Black Eyed Peas.Il 21 agosto 2020 viene pubblicato il suo quinto album Papi Juancho. Il 24 settembre 2020 pubblica i singoli Pa' Ti e Lonely, entrambi in collaborazione con Jennifer Lopez. Il 5 novembre 2020 pubblica il remix del brano "Hawaii" in collaborazione con The Weeknd che per l'occasione canta in spagnolo.

Nel febbraio 2022 pubblica insieme a Jennifer Lopez il singolo Marry Me (Kat & Bastian Duet), che sarà la colonna sonora dell'omonimo film Marry Me.

Stile musicale e cultura di massa
Il suo stile musicale è stato influenzato dal cantante Justin Timberlake, che viene considerato da Maluma una delle sue grandi ispirazioni da giovane. È stato influenzato anche da Chris Brown, Jay Z, Daddy Yankee e Wisin & Yandel.

Maluma ha avuto una forte influenza sul pubblico latino: è l'artista latinoamericano con il maggior numero di seguaci su Instagram dopo Shakira.

Discografia

Album in studio
 2012 – Magia
 2015 – Pretty Boy, Dirty Boy
 2018 – F.A.M.E.
 2019 – 11:11
 2020 – Papi Juancho

Filmografia
 Marry Me - Sposami (Marry Me), regia di Kat Coiro (2022)
 Doppiatore: Camilo di Encanto
 Videoclip: Fuck You - Sam Smith

Televisione
 La voz Kids Colombia (2014) – giudice
 Kids' Choice Awards Colombia (2014) – presentatore
 La Voz Kids México (2017) – coach
 La Voz Mexico (2017) – coach
 X (The Film) (2017) – documentario
 Lo que era, lo que soy, lo que seré (2019) – documentario

Note

Altri progetti

Collegamenti esterni

Vincitori di MTV Europe Music Award
Vincitori di MTV Video Music Award


Featuring Engineered Wood with Scratch resistant powder coated steel frame. An extra 4th leg provides better stability, and a handy side panel provides extra work space. Featuring easy height adjustment (29" to 45.5") with pin spring system and large double-wheel non-marking casters with locking mechanism. Panels include a safety edge to prevent laptop accidents.


A rare compliment: C.W. informed me this morning that he had formed a deep appreciation for several of our accomplishments. He had found our universal crown of glory. You could have knocked me over with a limp metaphor.
"Your music, art, and literature are the best things about you," He announced from nowhere. I had come upon him sitting in one of his favorite spots—a grassy spot overlooking the Arkansas River. He had assumed the shape and dress of a country and western singer, complete with a coat adorned with musical notes and an oversized cowboy hat that would have made a real cowhand wet his chaps laughing.
People were stopping to stare from the path above where we were. I waved at them, pointed toward C.W., and shrugged as if to say we were rehearsing an act. They stared a few seconds more and moved on.
"So?" he looked at me as sincerely as any earthling could have.
He dropped his hat back down upon his forehead and appeared about ready to cry.
This didn't seem to cheer him much. I don't think he had found anything that I did well so far. I was damning him which faint empathy.
He slammed his hat on the ground and growled at me. "Partner, do you mean to tell me that the state won't train young people to do one of the few things you people excel in?


text:
Meddling Mom-In-Law.
Daily Prompts, Life, Parenting
Daily Prompts, Parents, Postaday, superstitions
Rupsa is a lovely, bubbly girl in her twenties. Just got married, her in-laws live with her in a palatial house. Husband Manish is a very handsome, well-educated man who loves Rupsa so much that he never forgets to ask her about any kind of inconvenience in the new household. For Rupsa, the house was new, the people were new as she did not know about Manish and her family earlier. It was like a dream marriage. The prince came, liked her so much that he could not wait for more than a week. The marriage took place all of a sudden. Rupsa and her parents were in dilemma to decide the venue, food, guests and before settling on anything, Manish's family did all the rituals and Rupsa was just robbed away from her parents. She was happy. Mother in law tried to teach her the new dishes, took care of her clothes, her makeup, her wardrobe, jewellery and what not. For everything she has to ask mom- in -law, wait for her approval. But she felt happy. It was like her own mom guiding her. Father- in- law was her team mate in any kind of rule breaking. Have a chess round in between the work or discussing political affairs of the country. Pa- in- law was just giving her company as like her dad. What else a girl can demand? Manish was loving, caring and fulfilling all her wishes. A small family in a lovely house, God was very kind.
Came the good news. The much awaited news of arrival of a grandchild made Rupsa's mom- in- law very happy. She was elated so much that she could not resist herself from declaring that from now on, she will take care of everything about Rupsa. Her food and timings of all activities, right from getting up from the bed to going to sleep in the night, will be monitored by the would be grandma. Initially, Rupsa could not understand what it was all about as it was first pregnancy. Slowly after two months, she started realising that all the rituals done by her mom in law are pure interference to block her freedom. Complete meddling!! She could not have the food she likes. All foods were too healthy, too bland. For her, every movement there came a cautionary word 'Take Care' or 'don't do'. Now she turned up to Manish. Asked him for help. Manish is a really good guy. Took Rupsa out of the house, convincing his mother. They savour food outside the house, watched movies. Life for Rupsa came back to normal, but her mom- in- law did not stop meddling. Rupsa was happy with Manish, so she tolerated and ignore her mom- in- law's activities.
Manish had to leave home one day as there was an important conference in a different city.He had to be there for three days. He was not willing to go, but the job rituals, one has to obey. So Manish left home by a flight and promised Rupsa that he will come back on time. Three days later, when all were watching TV and Rupsa was waiting in the terrace for Manish's arrival, there was a scream of her pa- in- law. Rupsa came running down the stairs, saw her pa- in- law crying and shouting. Before Rupsa could understand anything, her mom in law stated that Manish's plane crashed. It all happened because of the ill-fated child to be born and the inauspicious mother of the child. This was her statement " kill the child, the ill-fated, lost his father before even seeing the light". She started hitting Rupsa. Rupsa felt the pain in the abdomen and fainted.
When she revived her consciousness, she found Manish standing beside her. She was on the hospital bed. Tried to understand what might have happened. She saw her own parents standing in a corner weeping, sobbing continuously. Rupsa was told by her mom in law that the plane crashed was not Manish's. Her pa in law did that mistake of learning the correct flight number and the unfortunate incident happened which took a toll on Rupsa and her child's health. Rupsa listened quietly. Not uttered a word. Called her dad and called Manish. She declared that she would not stay with in-laws, rather she would love to stay in her parents' home. Too much meddling of her mom in law with her life was now suffocating her. She wants her child to be free of all kinds of superstitions, she wants her child to grow with humanitarian values, not with any kind of blind beliefs. She got up from the bed, moved towards her mom, and went out of the sight of Manish and his parents.
Manish tried to ask her to reconsider as his mother's behaviour was an emotional outburst, but Rupsa did not stay back, only assured Manish that she may come back once the child was born. Let Manish understand her trauma and wait till she returns.
Meddle

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Schedule Query Partition Table Hourly I have schedule query which runs hourly I want to partition the table hourly so in the destinaltion I have provided this mytable_{run_time|"%Y%m%d%H"}, but this is creating a new table for every run  in my BigQuery datasets , when I change the destination to mytable_{run_time|"%Y%m%d"}, it's partition the data correctly based on date
How to enable hourly partition in big query ?

A: What you are doing is aligned to table sharding, which you can do but it is not as performant and involves more management.  In theory it acts similarly to a partition but is not the same. What you are likely seeing when you use the format mytable_{run_time|"%Y%m%d"} is that you're inserting multiple hours into the same day, and depending on what your table definition is may be partitioned within a single day.
You will want to define the partition in the creation of the table see below:
https://cloud.google.com/bigquery/docs/creating-partitioned-tables#create_a_time-unit_column-partitioned_table

redpajama_set_name: RedPajamaStackExchange


text:
A Replacement for Displacement: How EcoBoost Technology Injects Muscle Car Performance and Fuel Economy into Ford's Expanding Model Range
Ford's award-winning EcoBoost engines are the company's most advanced ever, delivering outstanding performance and incredible fuel efficiency
The same powertrain technology that powers the incredible Ford F-150 Raptor pickup and the GT supercar is also available across the Ford range
Smaller, lighter and more compact engines boast the kind of on-demand power and torque you'd expect from much larger powerplants
Power and fuel efficiency once sat at opposite ends of the performance spectrum but thanks to Ford's EcoBoost range of engines, you can now spend more time on the road and less time at the fuel pump.
Ford's award-winning EcoBoost engines power a wide range of models in the current Ford line-up, including the nimble and sporty Focus and Focus ST, muscle cars like Ford Mustang, performance vehicles like the Ford F-150 Raptor, and even Le Mans-winning racing cars, like the new Ford GT.
And it's not just in Ford's extreme performance vehicles that you'll find EcoBoost technology. With up to 182-horsepower and 240 Nm torque available from its 1.5-litre EcoBoost engine, the Ford Focus delivers incredible performance from such a small engine. The 2.0-litre four-cylinder EcoBoost engine that powers the extremely popular Ford Taurus in Saudi Arabia not only produces more torque than the traditional, naturally-aspirated 3.5-litre V6, but manages to accomplish this while using less fuel. Adaptable and capable, the EcoBoost engine can be found in models across Ford's portfolio – from the EcoSport and Focus to the all-new Expedition and the mighty F-150 Raptor.
EcoBoost V6 engines are so capable that they're now used to replace larger displacement naturally aspirated V8 engines with more powerful, more responsive and more fuel efficient units without affecting performance at all. In fact, in most cases, EcoBoost engines outperform larger capacity naturally-aspirated engines by developing more power and torque, while also using less fuel. It's why the new Ford F-150 Raptor is powered by a 421-horsepower EcoBoost V6 engine – that also generates 678 Nm of torque – and why Ford is introducing the engine technology to more and more of its vehicles.
The secret to the success of all EcoBoost engines is the innovative combination of three key technologies; direct fuel injection, variable camshaft timing and turbo charging – and the control of these by unique Ford software that's designed to optimise the engine for any driving situation. Injecting precise amounts of fuel directly into the cylinders at high pressure provides ideal conditions for combustion, allowing engines to run more efficiently. Variable camshaft timing provides the perfect amount of fresh air for the combustion process, eliminating the risk of wasting fuel.
The turbocharger uses energy from the engine's exhaust to drive a turbine wheel, which in turn is mated to a compressor that sends cooled, pressurised air back into the engine. That cooled, pressurised air contains more oxygen which helps create a bigger "boost" of energy when reintroduced to the combustion process.
EcoBoost engines are also lighter and more compact than ever before. Ford uses strong materials such as compacted graphite iron (CGI) are used where needed, and lightweight materials like aluminium to reduce weight. That means the engines are not only robust and compact, but you don't waste fuel carrying around more weight than you have to. Ford's EcoBoost engine technology is so innovative that the company has secured 275 US patents and has 200 more pending – more than any other manufacturer when it comes to gasoline turbocharged direct-injection technology. EcoBoost's accolades include the 2017 International Engine of the Year "Best Engine Under 1.0-litre" – its sixth consecutive category win.
Whether you simply need to cruise long distances or want to tap the performance potential of your Ford, EcoBoost engines make it possible.
EcoBoost engines


Since our inception in 2014, we have been able to facilitate the adoptions of over 450 dogs. We are very happy to say that most of these dogs are still in their loving homes, and thriving.
Unfortunately, and for several different reasons, dogs do get returned. Although this makes us sad, it is often in the best interest of the dog.
Our goal is to place each dog with the perfect family, and find the perfect family for each dog. We do this by having you meet with our dog team, trainers, and sit down for an in-depth interview with a team member and/or the rescue. We ask lots of questions and check vet references as well as personal references. We send a great deal of material to you before the event so you may prepare yourselves for not only the event, but also post adoption. This information includes material on decompression, the two week shut down, crate training, and more.
In addition, we ask you to read an article and to write an essay on how you plan to introduce your dog to your new home and family. What steps are you willing to take to make this work? We've heard many times: "I'm a trainer", "I've always had dogs" and "I'd never return a dog", yet many times people truly don't know how different it can be to adopt a rescue/shelter dog. These dogs will not be their true selves until they've had time to decompress. They may have accidents, be shy or withdrawn, need leash training, and so much more. Please ask yourself, are you truly up to this task?
If you are a renter, we may not let you adopt a bully breed/pit mix dog.
If you have children that are acting inappropriately toward the dogs at our event, we may choose not to adopt a dog to you.
Please be honest about your lifestyle and what you are willing to do and not do for a dog, this will help us find a dog that is the best fit for you.
Please keep an open mind when attending one of our events. Many people fall in love with a dog they see promoted prior to an event, but go home with a completely different dog.
Are you hoping to adopt one of the many adorable puppies coming to our event? All adopters wanting to adopt a puppy are required to read and sign our puppy addendum form. There is a link below to download the form and review. Please initial, sign and email it back to us and we will attach it to your application. You will also be emailed this form if you have requested one of the puppies on your application. This DOES NOT guarantee you a puppy.
This is a new procedure our rescues are requiring to be sure our puppies are going to homes that are aware of what to expect when adopting a puppy.
Almost all of the articles say the same thing; TIME AND PATIENCE.
...Did we mention time? If you are serious about adopting a rescue dog, it is imperative to give them the time they need to adjust. These articles will help give you a better understanding.


Two weeks after his confirmation by the Senate, Mr. Adebayo Adekola Adelabu formally assumed his new role as the Deputy Governor of the Central Bank of Nigeria (CBN) on Wednesday, April 9, 2014.
Mr. Adebayo, who was accompanied to the Bank by his aides, was welcomed by the Deputy Governor, Corporate Services, Alhaji Suleiman Barau.
Introducing the trio of Yunusa Mohammed Sanusi, Ahmad Abdullahi and Ibitayo Olubenga Amu, Directors in charge of Corporate Secretariat, Governors' and Security Services Department, respectively, Alhaji Barau disclosed that Mr. Adelabu had been briefed and would be in charge of the Financial System Stability (FSS) Directorate. He therefore wished Adelabu well in his role, expressing confidence that his new colleague would settle down to work immediately.
Responding, Mr. Adelabu thanked Alhaji Barau and the Directors present for the warm reception accorded him. He said he was indeed eager to start work and contribute his quota to the success of the Bank.
Speaking further, Adelabu, who has over 22 years of cognate experience, said he was bringing on board initiatives that would move the Bank forward. While commending the Management and staff of the Bank for the successes recorded by the Bank in the execution of its mandate, he pledged to do his utmost in collaboration with his colleagues to ensure that the CBN takes its pride of place among Central Banks of developed countries around the globe.
Coming from the private sector and having been the Finance Team Leader of CBN's Corporate Renewal Project tagged: "Project Eagles" in 1999, Adelabu said he was not entirely new to the environment and will pursue a philosophy of productivity to eke out the desired result for the Bank.
"I don't believe so much in activities. It's more about productivity. I do more of the work than the talk. I don't believe that we should talk too much. If you have a work to do, do it well and everyone will see that it is a success," he declared.
Speaking on his youthful mien, the new Deputy Governor, Financial System Stability said he had come to the Bank at the right time as many key decision makers around the world were in their 40s and 50s, which he emphasized were the most productive years in the lives of executives. He therefore expressed confidence that the vibrancy and energy associated with that age bracket, coupled with the experience he had garnered in his professional life would manifest in the discharge of his duties.
Prior to his confirmation as CBN Deputy Governor, Mr. Adebayo Adekola Adelabu was an Executive Director and the Chief Financial Officer (CFO) at the First Bank of Nigeria Plc. He is married and has four children.


text:
\section{Introduction}
Anomaly detection is the task of identifying whether the target is normal or anomalous. In recent years, there has been a lot of research on how to achieve this task. It can be applied to various fields, such as detecting defective products in manufacturing industries, detecting abnormal behavior in human activities, and testing diseases in the medical field.

Approaches to anomaly detection can be classified into supervised and unsupervised methods. In supervised methods, both normal images without defects and abnormal images with defects are used for training. In supervised methods, both normal images with no defects and anomaly images with defects are used for learning. And it is common to learn to classify them into normal and anomalous classes. However, in the actual application of anomaly detection, it is often not possible to collect enough anomaly images for training.However, it is impossible to collect enough images of abnormalities for learning in the field where anomaly detection is introduced in many cases. Also, there are cases where there are countless anomaly patterns or where the state of the anomaly is unpredictable, making it difficult to learn the features of the anomaly properly. For these reasons, supervised methods are employed only in a limited number of cases. For these reasons, supervised methods are applied only in limited cases. On the other hand, in unsupervised methods, only normal images are used for training, and the model learns their distributions. Then, anomaly detection is achieved using the measure of how far the target sample is from the distribution. Since it does not require anomalous images for training, there has been much research on unsupervised methods in recent years.

In this paper, we focus on anomaly detection based on Autoencoders \cite{pimentel2014review}, which is a popular unsupervised method. In this method, the normal image set is used as input, and the model is trained to be an identity mapping. The network includes the structure of the bottleneck. The bottleneck forces the model to learn a compressed representation of the training data expected to regularize the reconstruction towards the normal class. Anomaly images are not used for training the model. Thus, when an anomaly image is an input to the trained model, a normal image close to the input is reconstructed. Based on this, many methods formulate the anomaly score based on the reconstruction error \cite{bergmann2018improving, pol2019anomaly}. On the other hand, when using the Mean Squared Error (MSE) to train an Autoencoder, outputs tend to produce blurry output images that high-frequency components are lost. As a result, fine textures and edges of normal images are detected as defects, which reduces the accuracy of anomaly detection.

Various methods have been proposed for this problem. The first is the Generative Adversarial Networks (GANs). GANs are capable of reconstructing high-quality images \cite{radford2015unsupervised}, and many variations of GANs have been applied to anomaly detection \cite{sabokrou2018adversarially, schlegl2017unsupervised, schlegl2019f,akccay2019skip}. While GANs are able to generate high-quality images, they have many problems, such as learning instability and the possibility of mode collapse \cite{goodfellow2014generative}. Another Autoencoder-based approach is to use Structural SIMilarity (SSIM)\cite{wang2004image} instead of Mean Square Error for the loss function\cite{bergmann2018improving}. SSIM is known to be a measure of image similarity that is relatively close to human perception, and adopting it as a loss function has yielded better results than methods that use mean square error. However, the formulation of SSIM is parametric, and its hyperparameters need to be determined by the validation data set. On the other hand, due to the characteristics of the anomaly detection problem, it is rare that sufficient verification data will be available, so it is necessary to improve the quality of the reconstruction through non-parametric formulations.

In view of the above, we formulate a new non-parametric loss function in the frequency domain for anomaly detection based on Autoencoders. This improves the quality of the reconstruction, especially the high frequency components.

We use the Discrete Fourier Transform (DFT) to formulate the loss function in the frequency domain. The input image and the reconstructed image are transformed into the frequency domain by DFT, respectively. This is divided into an amplitude spectrum and a phase spectrum, so we consider the distance based on both. It is a known property that the L1 distance does not overestimate large values such as outliers compared to the L2 distance\cite{zhao2016loss}. By applying this method, the low-frequency components that are often included in images are not overly taken into account in the learning process. Our main contributions are summarized as follows:

\begin{enumerate}
    \item We have proposed a new Autoencoder-based method that considers the frequency domain in the problem of anomaly detection.
    \item The proposed method improves the reconstruction quality of high-frequency components by setting a weight matrix for the loss function.
    \item Compared to the existing Autoencoder-based methods, the proposed method achieved the best accuracy in anomaly detection on average.
\end{enumerate}

\section{Related Works}
\subsection{Anomaly detection based on generative models}
Various methods have been proposed for the problem of anomaly detection. Method based on Autoencoder is one of the popular methods which provide information that can be used to determine whether a pixel is a normal class or not to solve this problem.

Conventional Autoencoder methods make anomaly detection based on the difference between the input and reconstructed images when using the MSE. Since the model learns normal features from the training images, it is assumed that the missing regions will be reconstructed as normal with high quality when the quality of the reconstruction is especially low. Autoencoder methods usually create a model with a bottleneck structure and train it to be a constant function based on MSE:

\begin{equation}
    \MSE(\bm{f}, \bm{\hat{f}}) = \sum^{M-1}_{x=0}\sum^{N-1}_{y=0} (\bm{f}(x,y) - \bm{\hat{f}}(x, y))^2
\end{equation}
where $\bm{f}(x, y)$ is the intensity value of image $\bm{f}$ at the pixel $(x, y)$, $\bm{\hat{f}}(x, y)$ is the intensity value of reconstructed image $\bm{\hat{f}}$ at the pixel $(x, y)$.

This loss has the disadvantage of contributing to blurry reconstructions with lack of high-frequency components, and has the problem of increasing residuals, even though the image is normal. Therefore, it was not possible to distinguish the reconstruction error in the normal region from that in the abnormal region, and the accuracy of the abnormality determination could not be sufficiently obtained. To improve this problem, a method that incorporates SSIM into the reconstruction error has been proposed so far \cite{bergmann2018improving}. However, since the accuracy of Autoencoders by SSIM depends on hyperparameters, it is not a practical model when sufficient validation data is not available.

It is also believed that GANs can acquire the distribution of normal images and produce sharper images compared to Autoencoders \cite{radford2015unsupervised, sabokrou2018adversarially, schlegl2017unsupervised, schlegl2019f, akccay2019skip}.  However, GANs have many problems, for example, mode collapse problems, unstable learning, etc. In addition, some GAN-based methods require the search of latent space vectors (to generate the image closest to the input image) during inference, which is computationally expensive.

\subsection{Neural networks in the frequency domain}
In recent years, frequency-domain models have been proposed in the field of neural networks. The typical examples are works on super-resolution tasks \cite{fritsche2019frequency, sims2020frequency}. In \cite{fritsche2019frequency}, accurate super-resolution is achieved by applying different processing (neural networks) to the image's low and high frequency components. The approach in \cite{sims2020frequency} is similar, and efficient super-resolution has been achieved by defining a loss function in the frequency domain and varying the weights for each frequency.

\section{Method}
\label{sec3}
 Our proposed method is anomaly detection based on Autoencoder reconstruction. Our main focus is on the reconstruction accuracy of high frequency components. And the proposed method reconstructs the sharpness images compared to the existing Autoencoders. This makes it easier to detect fine texture anomalies and anomalies near edges, thereby improving the accuracy of anomaly detection.

 In this chapter, we first define the frequency representation of an image using the Discrete Fourier Transform. Second, in order to improve the accuracy of the recovery of high frequency components, we define new weights in the frequency domain and define a loss function using these weights (WFDL). Finally, we define the anomaly score, which is the criterion for determining the anomaly.
\subsection{Image processing in the frequency domain}
 In this section, we define the Discrete Fourier Transform to deal with images in the frequency domain. And confirm its property. We used the dataset provided by MVTec \cite{bergmann2019mvtec} for our confirmation.

The main component in WFDL is the Discrete Fourier Transform (DFT). It is represented by equation (\ref{eq2}).

\begin{equation}
    \label{eq2}
    F(u, v) = \sum^{M-1}_{x=0} \sum^{N-1}_{y=0} f(x, y) e^{-j2\pi(\frac{ux}{M} + \frac{vy}{N})}
\end{equation}
where the image size is $M \times N$, $(x, y)$ denotes the coordinate of an image pixel in the spatial domain, $\bm{f}(x,y)$ is the intensity value of image $\bm{f}$ at the pixel $(x, y)$, $\bm{F} (u, v)$ is the frequency representation of the image $\bm{f}$, $(u, v)$ is the coordinate of the spatial frequency on the spectrum.

In addition, the exponential part of this equation can be rewritten by Euler's formula:
\begin{equation}
    e^{j\theta} = cos\theta + j sin\theta
\end{equation}
as follows.

\begin{equation}
    \label{eq4}
    e^{-i2\pi (\frac{ux}{M} + \frac{uv}{N})} = cos2\pi (\frac{ux}{M} + \frac{uy}{N}) - j sin2\pi (\frac{ux}{M} + \frac{uy}{N})
\end{equation}

As can be seen from equation (\ref{eq4}), the Fourier transform is a method of decomposing a function into a sum of a sine wave function and a cosine wave function with frequencies multiplying the fundamental frequency. The angle of the frequency is determined by the spectral position (u, v).

\begin{figure*}[h]
    \centering
    \begin{minipage}[]{0.3\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig1_original_image.pdf}
        \includegraphics[scale=0.3]{fig1_original_image_fre.pdf}
        \subcaption{the original image}
        \label{fig:fig1a}
    \end{minipage}
    \begin{minipage}[]{0.3\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig1_low_image.pdf}
        \includegraphics[scale=0.3]{fig1_low_image_fre.pdf}
        \subcaption{the filtered image}
        \label{fig:fig1b}
    \end{minipage}
    \begin{minipage}[]{0.3\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig1_high_image.pdf}
        \includegraphics[scale=0.3]{fig1_high_image_fre.pdf}
        \subcaption{the filtered image}
        \label{fig:fig1c}
    \end{minipage}
    \caption{The result of filtering the frequency components for MVTec's bottle} \label{fig:fig1}
\end{figure*}

Fourier transform is a useful technique for frequency filtering. For a visual understanding, we show Fig.\ref{fig:fig1}. This is the result of filtering the frequency components of the MVTec bottle. In Fig. \ref{fig:fig1}, \subref{fig:fig1a} is the original image and its Fourier representation. In the Fourier representation, frequencies are lower closer to the center and higher farther away. It can be seen from \subref{fig:fig1a} that the bottle contains more low-frequency components and less high-frequency components. \subref{fig:fig1b} is the result of passing the bottle through a low-pass filter. This shows that high-frequency components such as edges and fine textures are lost. \subref{fig:fig1c} is the result of passing the bottle through a high-pass filter. This shows that low-frequency components have been lost, and only edges and fine textures remain.

Now, we proceed to discuss the reconstruction images by the Autoencoder in the frequency domain. Fig. \ref{fig:fig2} shows the result of the reconstruction of the bottle by the Autoencoder based on the minimization of the L2 norm of the error and the corresponding Fourier representation. Compared to Fig. \ref{fig:fig1}\subref{fig:fig1a}, the low-frequency component is well reconstructed, but the high-frequency component is insufficient. This is because the loss function of the Autoencoder is defined as the L2 norm of the error.

\begin{figure}[]
    \centering
    \begin{minipage}[b]{0.45\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig2_recon.pdf}
        \subcaption{the reconstructed image}
        \label{fig:fig2a}
    \end{minipage}
    \begin{minipage}[b]{0.45\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig2_recon_fre.pdf}
        \subcaption{the fourier repl.}
        \label{fig:fig2b}
    \end{minipage}
    \caption{The result of the reconstruction of the bottle and corresponding Fourier representation}
    \label{fig:fig2}
\end{figure}

In the next section, we define the loss function in the frequency domain. In addition, we propose a weighted loss function (WFDL) depending on the frequency value.

\subsection{Weighted frequency error}
To consider the loss function in the frequency domain, we define the distance between the before and after reconstructed images in the frequency domain. The image transformed into the frequency domain by the Fourier transform shown in equations (\ref{eq2}) and (\ref{eq4}) is mapped onto the complex space. Therefore, it is necessary to define a distance that considers the real and imaginary parts. Therefore, we calculate the difference in the complex plane between the images before and after the reconstruction. Then, the absolute value of the difference considering the complex numbers is calculated, and the average, equation (\ref{eq5}), is defined as the distance between images in the frequency domain.
\begin{equation}
    \label{eq5}
    d(\bm{F}, \bm{\hat{F}}) = \frac{1}{MN}\sum^{M-1}_{u=0} \sum^{N-1}_{v=0} | \bm{F}(u, v) - \bm{\hat{F}}(u, v) |
\end{equation}
\begin{equation}
    \label{eq6}
    |\bm{F}(u, v)| = \sqrt{\mathrm{Re}(F(u, v))^2 + \mathrm{Im}(F(u, v))^2}
\end{equation}
where $\mathrm{Re}()$ is a function to get the real part and $\mathrm{Im}()$ is a function to get the imaginary part.

 Furthermore, we consider weighting equation (\ref{eq5}) according to the frequency value to improve the accuracy of the reconstruction of high-frequency components. The weights should be set so that the higher the frequency, the higher values are taken. This can be expected to increase the gradient on the Autoencoder's reconstruction error of high-frequency components. When each frequency component corresponding to a two-dimensional Fourier representation is denoted by $F(u, v)$, the corresponding weight $w(u, v)$ is defined as follows:
 \begin{equation}
    \label{eq7}
    w(u, v) = \sqrt{u^2 + v^2}
 \end{equation}

Based on equations (\ref{eq5}) and (\ref{eq7}), the loss function WFDL in this paper is defined as follows:
\begin{equation}
    \label{eq8}
    L_{\mathrm{WFDL}}(\bm{F}, \hat{\bm{F}}) =  \frac{1}{MN}\sum^{M-1}_{u=0} \sum^{N-1}_{v=0} w(u, v) | \bm{F}(u, v) - \bm{\hat{F}}(u, v) |
\end{equation}

To train a model using this loss function, we first obtain a Fourier representation from the original image by 2D Fourier transform. Then, the original image is used as input to the Autoencoder to obtain the reconstructed image. Moreover, the Fourier representation of the reconstructed image is obtained by a 2-D Fourier transform as well. The Autoencoder in this paper is composed of a convolutional network. We show the architecture in Fig.\ref{fig:fig3}.

\begin{figure*}
    \centering
    \includegraphics[scale=0.3]{autoencoder.pdf}
    \caption{The architecture of the Autoencoder}
    \label{fig:fig3}
\end{figure*}

\subsection{Calculating the degree of abnormality}
Finally, to detect anomalies using the Autoencoders trained by WFDL, we define an anomaly score. Autoencoders trained by WFDL are expected to be capable of sufficiently reconstructing high-frequency components. Therefore, by calculating the anomaly score based on the difference between the input image and the reconstructed image, only the anomalous regions can be considered anomalous while edges and fine textures are not. Therefore, the anomaly score $A()$ is calculated as in equation (\ref{eq9}) based on the difference in real space, following the conventional method of Autoencoder-based anomaly detection.

\begin{equation}
    \label{eq9}
    A(\bm{f}) = \sum^{M-1}_{x=0}\sum^{N-1}_{y=0} (\bm{f}(x,y) - \bm{\hat{f}}(x, y))^2
\end{equation}

\section{Experiments}
\subsection{Experimental setup}
In this chapter, we describe the experiments to demonstrate the effectiveness of WFDL. We conducted the experiments using images from the MVTec AD dataset \cite{bergmann2019mvtec}, which contains 15 different item types. All images were resized to 256x256[px].

For all images, we trained the Autoencoders using the loss function WFDL described in Chapter \ref{sec3}. We use RAdam optimizer\cite{liu2019variance} (with an initial learning rate of 0.001, beta1 set to 0.9, beta2 set to 0.999, a weight decay set to 0.0001, and a batch size set to 64). The training ran for 2000 epochs. The architecture of the Encoder is shown in Table \ref{tab:table1}.

\begin{table}[]
    \centering
    \begin{tabular}{cc} \hline
         Block & Output Size \\ \hline
         Input & $256 \times 256 \times 3$ \\
         Residual block1 & $128 \times 128 \times 32$ \\
         Residual block2 & $64 \times 64 \times 32$ \\
         Residual block3 & $32 \times 32 \times 64$ \\
         Residual block4 & $16 \times 16 \times 64$ \\
         Residual block5 & $8 \times 8 \times 128$ \\
         Residual block6 & $4 \times 4 \times 128$ \\
         Residual block7 & $2 \times 2 \times 256$ \\
         Residual block8 & $2 \times 2 \times 256$ \\ \hline
    \end{tabular}
    \caption{The architecture of the Encoder. We use the Residual block \cite{He2016resnet}.}
    \label{tab:table1}
\end{table}

For evaluation, we use the area under the receiver operating characteristic curve (AUROC). We use the Autoencoder with L2 norm loss and the Autoencoder with SSIM loss \cite{wang2004image} for baselines.

\subsection{Results}
First, we evaluated the performance of each method quantitatively by AUROC. Table \ref{tab:table2} shows the results using the MVTec dataset\cite{bergmann2019mvtec}. This dataset has ten types of objects and five types of textures. AUROC by WFDL is the best value when averaged over these types, indicating the effectiveness of this method.

\begin{table}[]
    \centering
    \begin{tabular}{|c|c|c|c|c|}
    \hline
         \multicolumn{2}{|c|}{Category} & AE(SSIM) & AE(L2) & WFDL \\ \hline
         Texture & Carpet & $\bm{0.72}$ & $0.50$ & $0.54$ \\ \cline{2-5}
          & Grid & $0.63$ & $\bm{0.78}$ & $0.77$ \\ \cline{2-5}
          & Leather & $0.26$ & $0.44$ & $\bm{0.45}$ \\ \cline{2-5}
          & Tile & $0.33$ & $\bm{0.73}$ & $\bm{0.73}$ \\ \cline{2-5}
          & Wood & $0.86$ & $0.74$ & $\bm{0.89}$ \\ \hline
         Objects & Bottle & $0.86$ & $0.80$ & $\bm{0.94}$ \\ \cline{2-5}
          & Cable & $0.72$ & $0.56$ & $\bm{0.73}$ \\ \cline{2-5}
          & Capsule & $0.68$ & $0.62$ & $\bm{0.71}$ \\ \cline{2-5}
          & Hazelnut & $0.78$ & $0.78$ & $\bm{0.83}$ \\ \cline{2-5}
          & Metal nut & $0.62$ & $\bm{0.73}$ & $0.65$ \\ \cline{2-5}
          & Pill & $0.51$ & $0.62$ & $\bm{0.81}$ \\ \cline{2-5}
          & Screw & $0.51$ & $0.69$ & $\bm{0.82}$ \\ \cline{2-5}
          & Toothbrush & $0.92$ & $\bm{0.98}$ & $0.94$ \\ \cline{2-5}
          & Transistor & $0.78$ & $0.71$ & $\bm{0.83}$ \\ \cline{2-5}
          & Zipper & $0.57$ & $\bm{0.80}$ & $0.71$ \\ \hline
    \end{tabular}
    \caption{AUROC for anomaly detection on MVTecAD.}
    \label{tab:table2}
\end{table}

Fig.\ref{fig:fig4} and Fig.\ref{fig:fig5} shows a qualitative comparison of the performance for the data types bottle and leather. In Fig.\ref{fig:fig4} and \ref{fig:fig5}, (a) is the original image, (b) is the Fourier representation of the original image, (c) is the reconstructed image, (d) is the Fourier representation of the reconstructed image, and (e) is the residual image.

\begin{figure*}[h]
    \centering
    \begin{minipage}[]{0.18\linewidth}
        \centering
        \includegraphics[scale=0.1]{fig4_ori.pdf}
        \subcaption{original}
        \label{fig:fig4a}
    \end{minipage}
    \begin{minipage}[]{0.18\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig4_ori_fre.pdf}
        \subcaption{fourier repl.}
        \label{fig:fig4b}
    \end{minipage}
    \begin{minipage}[]{0.18\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig4_ssim.pdf}
        \includegraphics[scale=0.3]{fig4_wfdl.pdf}
        \subcaption{reconstructed}
        \label{fig:fig4c}
    \end{minipage}
    \begin{minipage}[]{0.18\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig4_ssim_fre.pdf}
        \includegraphics[scale=0.3]{fig4_wfdl_fre.pdf}
        \subcaption{fourier repl. of the reconstructed}
        \label{fig:fig4d}
    \end{minipage}
    \begin{minipage}[]{0.18\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig4_ssim_diff.pdf}
        \includegraphics[scale=0.3]{fig4_wfdl_diff.pdf}
        \subcaption{residual}
        \label{fig:fig4e}
    \end{minipage}

    \caption{The qualitative comparison of the performance, top: SSIM, donw: WFDL(bottle)}
    \label{fig:fig4}
\end{figure*}

\begin{figure*}[h]
    \centering
    \begin{minipage}[]{0.18\linewidth}
        \centering
        \includegraphics[scale=0.1]{fig5_ori.pdf}
        \subcaption{original}
        \label{fig:fig4a}
    \end{minipage}
    \begin{minipage}[]{0.18\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig5_ori_fre.pdf}
        \subcaption{fourier repl.}
        \label{fig:fig4b}
    \end{minipage}
    \begin{minipage}[]{0.18\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig5_ssim.pdf}
        \includegraphics[scale=0.3]{fig5_wfdl.pdf}
        \subcaption{reconstructed}
        \label{fig:fig4c}
    \end{minipage}
    \begin{minipage}[]{0.18\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig5_ssim_fre.pdf}
        \includegraphics[scale=0.3]{fig5_wfdl_fre.pdf}
        \subcaption{fourier repl. of the reconstructed}
        \label{fig:fig4d}
    \end{minipage}
    \begin{minipage}[]{0.18\linewidth}
        \centering
        \includegraphics[scale=0.3]{fig5_ssim_diff.pdf}
        \includegraphics[scale=0.3]{fig5_wfdl_diff.pdf}
        \subcaption{residual}
        \label{fig:fig4e}
    \end{minipage}

    \caption{The qualitative comparison of the performance(leather)}
    \label{fig:fig5}
\end{figure*}

In the quantitative evaluation, WFDL performed relatively better than the other methods in the bottle type. In contrast, it performed rather worse in the leather type, so we will discuss these two types by checking them.

For the bottle type, the detection of defects is stable because the residuals of defective areas are large for all methods. Areas with detailed textures and areas near edges tend to have residuals even though they are not defects. However, even in these regions, the WFDL has the lowest residuals. Also, the Fourier transform of the reconstructed image shows that WFDL can reconstruct high-frequency components relatively well. In contrast, the other methods can only reconstruct low-frequency components. These observations indicate that WFDL has improved the reconstruction of high-frequency components.

On the other hand, for the leather type, the results are poor for all methods. Residuals are occurred in both defective and normal regions, making it difficult to distinguish between them. We believe that this is because the leather type contains many high-frequency components. In the WFDL reconstruction for the leather type, the reconstruction did not go well because the weight for the high-frequency component was too strong. Therefore, we believe that improvements such as dynamically changing the weight equation (\ref{eq7}) according to the distribution of the frequency components of the input image will improve the accuracy.

\section{Conclusion}
 This paper has improved the accuracy of anomaly detection based on Autoencoders by defining a new loss function (WFDL) in the frequency domain. WFDL is specifically designed to improve the quality of reconstruction of high-frequency components.  This improved the accuracy of edge and fine texture reconstruction compared to existing Autoencoder-based methods, contributing to anomaly detection accuracy. The accuracy comparison using the MVTec dataset shows that the new method gives better results than the existing Autoencoder-based methods. On the other hand, there are still issues with detecting anomalies in images that contain many high-frequency components such as leathers. However, we expect that it can improve this by adaptively modifying the weights in equation (\ref{eq7}) to the frequency components contained in the image.

\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


A great introduction to non-alcoholic red wines. Spend an evening with friends and family trying a fantastic selection of wines from California, France, Italy, and Spain. Enjoy the complex flavors of wine without the negative health consequences of alcohol.
* Ariel Cabernet Sauvignon: Aromas of black currants, cherry, blueberries and chocolate, with soft tannins and a dry finish. Produced in Napa Valley, California.
* Domaines Pierre Chavin Grande Reserve Rouge: Generous notes of ripened fruits. Bodied, structured red fruits and spices with vanilla aromas. Nice complexity and subtle elegance. Produced in France as a classic Bordeaux.
* Princess Rosso Dry: Displays an intense, deep red color. Exhibits aromas of ripe fruits with a pleasing and elaborate taste. Produced in Italy.
* Élivo Cardio Zero Red: Ripened red fruit aroma, spiced and minerals. Presents an intense aromatic complexity marked by a plum base. Produced in Spain.
* Senorio de la Tautila Tinto: Ripe cherry-colored wine of mild intensity. Voluminous input in mouth, persistent structure with personality. Produced in Spain.


Bobby and Debbie Kopetsky marveled over the happenings of that night for hours. It was only the third time their neighbors came over to talk about God, and faith was already stirring inside everyone in the group.
Fifteen years earlier, the Kopetskys moved into their redbrick home on Amen Corner Road in Pflugerville, Texas. They smiled and kindly waved at their neighbors on their frequent walks, and Bobby chatted with the men at the neighborhood golf course. The interactions were brief––surface level at best. "You could recognize one another in the grocery store, but you wouldn't know anything about their life," Debbie explains.
In August of 2013, Bobby and Debbie heard about "Explore God," a 7-week study series for churches, small groups, and anyone who had questions about faith and God.
The Kopetskys looked at each other, their faces saying it all: We have to do this in our neighborhood. "It was the Holy Spirit moving in us. God gave us the idea. He gets all the credit," Bobby says.
A few weeks later, Bobby and Debbie laced up their shoes and went door-to-door on Amen Corner Road. They invited their neighbors over for dinner to explore those hard questions about God––something that neither of them, in over 30 years of being Christians, had ever done before. "We thought to ourselves, If we don't make disciples now, when will we do it?" Debbie says.
"Our invitation was simple," Debbie remembers. "I just asked them if they wanted to come over for fajitas and have a conversation about God." Some neighbors didn't make eye contact, some said they weren't interested in faith conversations, and some didn't open their doors. But some were excited. Some were intrigued. Some were searching.
A total of nine neighbors showed up on the first night. "We were excited about what God was going to do," says Debbie. As they looked around the circle of neighbors, Bobby and Debbie sensed that everyone was eager to learn more about God. But when the conversations started, it was clear that not everyone believed God is real, that He is good, or that Jesus is their hope.
In fact, some of their neighbors had never opened a Bible before. One neighbor lost his parents at an early age and blamed God. Another neighbor was raised to hate Christianity. "There were a few who had some biblical background, but they didn't really follow through. It was like they believed Jesus, but was Jesus their Savior? They didn't know that. They couldn't tell you that," Debbie says.
For the next seven weeks, the group continued their discussions, despite the discomfort and doubt. As leaders, Bobby and Debbie didn't always have all the answers, but God kept bringing the neighbors back each week. "It's just about being obedient to what God is calling you to do. You don't have to have all the answers," Debbie explains.
On the last night of the study, the group aimed to answer the question "Why does God allow pain and suffering?" One by one, everyone shared their experience with suffering. A father had passed when one neighbor was only eight years old. Another neighbor's mother gave her up for adoption after two weeks. It was a circle of brokenness and pain.
But there was a problem—an unspoken assumption: Bobby and Debbie seemed to have it all together. It appeared as though the Kopetskys never experienced real suffering.
From there, Debbie and Bobby told the story of their marriage. They met over 25 years ago, when the boys were still in elementary school. In that moment, the group saw that Debbie and Bobby's faith wasn't built on a life of smooth sailing. By God's design, their lives stood on a solid rock—on something bigger. Someone stronger. Everyone was pointed to Jesus that night.
So, they did just that.
Over time, Debbie became familiar with the eyes-wide, hands-covering-her-mouth routine. She was in awe. God was stirring faith in her neighbors.
Now, two years after their first meeting, the group continues to gather regularly for dinner and time in the Word. What started out as a group of nine neighbors has grown to 15 today. "They say Jesus is their Savior," Debbie shares.
One woman in the group confessed that the Word of God has restored her relationship with her daughter. Another neighbor said the group is like a family to them. "Now, they pray. They all pray," Bobby adds, beaming.
Chris, who once said he only believed about 80% of the Bible, says he now believes in it 100% after seeing God's goodness in the Kopetskys' loss of Tyler.


Botulinum toxin A (BoNT-A) umožňuje cílenou terapii spasticity a stal se běžným prostředkem léčby u pacientů s dětskou mozkovou obrnou (DMO). Nicméně, informace o efektu opakovaného podávání BoNT-A a dlouhodobé výsledky zatím chybí. Naší hypotézou bylo, že děti léčené víceetážovou opakovanou aplikací vysokodávkovaného BoNT-A nezhorší svůj chůzový stereotyp po dobu minimálně čtyř let.
Osmnáct pacientů s diplegickou, spastickou formou DMO, Gross Motor Function Classification System I–III a průměrným věkem 6 let a 9 měsíců bylo léčeno pomocí konceptu integrované léčby BoNT-A po minimální dobu 4 let. Jejich chůzový stereotyp byl opakovaně měřen pomocí kinematické a kinetické analýzy chůze a jako hlavní výsledný parametr byl zvolen Gait Deviation Index (GDI).
Průměrná doba sledování činila 5 let a 9 měsíců (SD 1 rok a 5 měsíců). Každému z dětí byl aplikován BoNT-A průměrně 1,02krát (SD 0,37) ročně. Celkový GDI zůstal nezměněn po celou dobu sledování, nicméně některé kinematické a kinetické parametry poukazují na změnu chůzového stereotypu během léčby. Nejlepších výsledků bylo dosaženo v oblasti hlezna, kdežto hybnost kyčelního kloubu se i přes léčbu zhoršovala.
Studie prokazuje, že pomocí integrovaného přístupu k aplikaci BoNT-A můžeme dlouhodobě zabránit deterioraci chůzového stereotypu u dětí se spastickou formou DMO. Integrovaný přístup má však své limity při léčbě flexorů kyčelního kloubu a hamstringů.
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text:
The Sea Pines Resort Announces Trio of Premier Golf Packages
(HILTON HEAD, S.C.) – The Sea Pines Resort – Hilton Head's most celebrated golf destination – announces a trio of premier golf packages featuring its acclaimed Pete Dye-designed courses, Harbour Town Golf Links and Heron Point. The Villa Golf Package is ideally suited for small groups to enjoy Harbour Town and Heron Point while staying in one of the resort's 300 well-appointed private villas (one to four bedrooms). All units feature a fully-equipped kitchen, dining room and living room for relaxation après golf. Perfect for couples, The Inn & Club at Harbour Town Package is highlighted by a stay at resort's boutique hotel with daily breakfast for each player. Overlooking Harbour Town, the much-admired inn is the only Forbes Four-Star rated property on Hilton Head Island. Play on both Harbour Town and Heron Point is available. For golf groups of 12 or more, the Pete Dye Signature Group Golf Package features three rounds of golf (including Harbour Town), three nights' deluxe villa accommodations, complimentary resort gift card, and personal on-site coordinator. Additionally, the group organizer plays free. All three packages include complimentary afternoon replays on Heron Point for cart fee only (based on availability), free warm-up range balls, plus complimentary wireless Internet and use of The Sea Pines Fitness Center. "Our golf packages are perfect to enjoy the essence of what has made us a premier destination," says Cary Corbitt, Vice President of Sports & Operations. "With our wide array of deluxe lodging options plus all the world-class amenities we offer, few resorts anywhere can provide a golf experience on such a high level." Harbour Town – the seminal Dye / Jack Nicklaus design and home of the PGA TOUR's RBC Heritage – reopens in late Sept. following agronomic and playability enhancements. The entire layout was re-grassed and a new state-or-the-art irrigation system was installed. Recently named "Golf Course of the Year" by the South Carolina Golf Course Owners Association, Heron Point underwent a series of subtle modifications in 2014 under Dye's close supervision to make the layout more "player friendly." Together, the courses provide golfers with two stylistically different, yet complementary, experiences. Visitors to The Sea Pines Resort will discover the new Harbour Town Clubhouse which opened in March as well as resort's recently built Plantation Golf Club, winner of Golf Inc.'s 2015 "New Clubhouse of the Year." The Plantation Golf Club is also home to the resort's topnotch Golf Instruction Center led by Tim Cooke. For more information about the resort and its wide-variety of golf packages: www.seapinesgolf.com, 866.561.8802.
About The Sea Pines Resort Situated on the southernmost tip of Hilton Head Island, the legendary Lowcountry destination features five miles of unspoiled beaches, 21 clay tennis courts, 14 miles of bike and walking trails, horseback riding, Eco-Adventures, water sports, and the 605-acre Sea Pines Forest Preserve filled with wildflowers, wetlands and more than 130 species of birds. As the first Eco-planned destination in the U.S., The Sea Pines Resort has become the blueprint for numerous beach developments around the country. One of the crown jewels in the world of golf, The Sea Pines Resort is home to Pete Dye's classic Harbour Town Golf Links (home of the PGA TOUR's RBC Heritage) Heron Point by Pete Dye, and the Ocean Course. The property has recently gone through extensive redevelopment with its Sea Pines Beach Club and Plantation Golf Club both opening in 2014. The new 55,000-square-foot Harbour Town Golf Links Clubhouse opened in April. Guests may choose from an array of accommodations, including 300 villas, 100 rental homes and the luxurious 60-room Inn at Harbour Town, a Forbes Four-Star boutique hotel and Preferred Hotel Group member. The resort's best-in-class collection of golf courses, amenities, meeting facilities and accommodations makes Sea Pines one of the most sought-after leisure and group destinations in America.
#GolfNews #PressReleases #BBIG #SeaPines #StayandPlay


text:
Q: Update table in rows which are partial duplicates My dataset consists of daily (actually business days) timeseries for different companies from different industries and I work with PostgreSQL. I have an indicator variable in my dataset taking values 1, -1 and most of the times 0. For better readability of the question I refer to the case where the indicator variable is unequal to zero for a given company as indicator event.
If there is more than one indicator event for a given industry on a given day, the indicator variables of the respecting companies shall be updated to 0.
We can think of the following example dataset:
day              company     indicator     industry
2012-01-12       A           1             financial
2012-01-12       B           1             consumer
2012-01-12       C           0             consumer
2012-01-13       A           0             financial
2012-01-13       B           1             consumer
2012-01-13       C           0             consumer
2012-01-16       A           1             financial
2012-01-16       B           -1            consumer
2012-01-16       C           1             consumer

So the indicator values that shall be updated to zero are on 2012-01-16 the entries for companies B and C because they both come from the same industry and experienced the indicator event on the same day.
My idea was to work with the exists operator:
    update mytable t1 set indicator = 0
    where exists (
              select 1
              from mytable t2
              where t2.day = t1.day
              and t2.industry = t1.industry
              and t2.indicator <> 0
              and t1.indicator <> 0)

But somehow that updated all indicator values to 0, and I cannot figure out why.
Do you have any ideas how to fix this, or how to solve my problem with another approach?

A: You might want to add a condition to not join a row to itself (which will always be true), e.g.
update mytable t1 set indicator = 0
where exists (
          select 1
          from mytable t2
          where t2.day = t1.day
          and t1.company <> t2.company
          and t2.industry = t1.industry
          and t2.indicator <> 0
          and t1.indicator <> 0)


This beautiful tray with three milk jug-inspired planters delivers a stunning farmhouse look for your indoor or outdoor space. Galvanized, shiny silver metal pots sit inside of a tray and decorate in style all on their own. Add some plants, get creative with crafts or use with artificial floral for beautiful arrangements without the upkeep.


Rupert Murdoch and Jerry Hall have announced their engagement.
The media mogul and actress-model made the announcement in the births, marriages and deaths section of the Times newspaper, which is owned by Rupert Murdoch's News Corporation company.
It will be the fourth marriage for Rupert Murdoch, 84, and the first for Jerry Hall, 59, although she lived with singer Mick Jagger for many years.
He added they were "thrilled to be getting married and excited about their future".
Australian-born Rupert Murdoch, who now has US nationality, split up with his third wife Wendi Deng in 2013.
Jerry Hall's 23-year relationship with Rolling Stones frontman Mick Jagger, with whom she had four children, ended in 1999.
Rupert Murdoch and Jerry Hall were first spotted together in public in October 2015 at the Rugby World Cup final in London between Australia and New Zealand.
The Times reports Rupert Murdoch and Jerry Hall were introduced to each other in Australia by one of the mogul's sisters and his niece.
It says the couple got engaged while in Los Angeles at the weekend, where they attended the Golden Globe awards ceremony.