I recently saw a study that said speaking in front of a crowd is the number one fear of the average person. I found this very interesting and rather intriguing. And at this moment in time, I would be tempted to agree with this. Until I saw the number two fear. The number two fear for the average person is death. Death is number two. This means the average person would rather be in the casket than the given theology. After I seen this, I decided I no longer think that the fear of speaking should be number one. I don't think speaking publicly is quite that bad. However, this may suggest something about humanity. Something about ourselves. Don't take lightly the fear of man. The fear of man has all the potential to be very debilitating. We start our weekend with a theme called the local church. The title was faithful and flourishing. Now, when I looked at that title, I thought it's a bit lofty, right? But I would like to suggest to you that it's reachable, it's attainable, and it's possible. G.K. Chesterton had this to say. In political and social science, we often make an analogy. And it goes something like this. Our country is sick. Our nation is sick. Our people are sick. Our economy is sick. And we're in terrible shape. But if you put me in power, I can bring healing to all the sickness plaguing this nation. And I will make it better. Now, there's a very big difference between medical science and social and political science. When you go to the hospital because you're sick, the doctors may not agree on what is causing your sickness. But all the doctors will agree on what a healthy body should look like. The hospital may send you home by necessity with one leg less. But they will never send you home with one leg more. When you go to a hospital and you're sick, everyone agrees on what a healthy body should look like. They may disagree about the cause of the illness, but everyone will agree what is a healthy body. But when it comes to social science and political science, everyone seems more than happy to agree that we're all sick. But what they can agree on is what does a healthy society look like? Someone comes along and says, here's a solution for our sickness to another.  Chesterton says this is where we have radically gone wrong. The question we should be asking ourselves isn't what has gone wrong. But the question we should be asking ourselves is what is right? How do we do that which is right? How do we become a people that live right, act right, speak right, and walk right? The world wants you to believe that it's harder and harder to find that which is right. The world wants each and every one of you to truly believe in your heart that it is nigh until almost impossible, not only to find what is right, but surely impossible to do what is right. I suggest this evening that to find what is right is not far from where you sit. Out of this theme, faithful and flourishing, I was given an assignment for this evening. That assignment is God's blueprint for heaven on earth. I would again say it's a bit lofty. However, this thought is taken from our Lord's prayer in Matthew, Matthew 610, your kingdom come, your will be done on earth as it is in heaven. Your kingdom come, your will be done. Your kingdom and will be done on earth as it is in heaven. Wow! How can this be? Remember, Jesus does not present ideas to us without solutions, without showing us a path forward and giving us tools and examples of how to get there. He makes it reachable, attainable, possible, livable, and walkable. Something that we can all see, feel, and taste. There's a quote from Blaise Plascal that says this, I bring you the gift of these four words. I believe in you. You may say, what a strange quote to start a message off with. However, I hope that when I'm done, I can answer that for you. I hope to give you one spiritual tool tonight to take home with you to build on it. You can make it better. You can improve on it. And more importantly, you become a remarkable living example of what it means to live. I believe in you. Do you know what these four words represent? I believe in you. They represent one word, encouragement, the power of encouragement. Are you aware that there are many people who do not have the ability to give this gift of, I believe in you, or encouragement to others? Why are some people able to give this gift and others are not? Why do some people have the ability to communicate this gift of encouragement and others are not? So who is capable to give this gift? I'm going to try to communicate this thought by three short personal stories that happened in my life. As a young 18-year-old Christian, I started to attend Shippensburg Christian Fellowship. I was 51 yesterday. The pastor at the time was the late Brother Lynn Martin. And when you're 18, adults tend to respond to you in one of two ways. 18-year-olds are amazing people. They have big ideas, big visions. Their words tend to get ahead of their deeds and actions. They talk better than they live. Their ideas sound much better to them than it does to others. Because of this tendency in 18-year-olds, it's very easy for adults to become suspicious, cynical. They tend to ignore the 18-year-olds. They just don't have time for them. They appear not to care. And sometimes they even become critical towards you and judgmental. And sometimes they seem to an 18-year-old to spend most of their time correcting all the 18-year-olds. Brother Lynn did none of these to me. He did not respond in this way. Lynn presented me with a whole new idea, and it was this. He wanted me to succeed in my Christian life. He wanted me to win as a Christian. He wanted the best for me in everything that I did. He wanted the 18-year-old to become a mature Christian. He never said these exact words to me. I believe in you, Kevin. It is what he said by his deeds, actions and encouraging words that shouted to me from the house top. I believe in you. I encourage you. And that encouragement to me when I was 18 put a fire in my belly that I have never lost. Let me ask you a question. What kind of character does it take, and I wasn't sure how old he was at this time, for a 50-year-old to reach across the aisle and down the stairs to an upstart 18-year-old and communicate to the 18-year-old that he is on your side, that he believes in the 18-year-old and has what it takes to win in their Christian life. He believes the 18-year-old has what it takes to become a mature Christian. Imagine what that did for me. So I ask the question again. What kind of character does it take for a 50-year-old to reach across the aisle and down the stairs to the 18-year-old? Maturity. I would like to suggest to you that the Christians that can reach across the aisle and down the stairs to the 18-year-olds are mature in character. Remember something tonight. Maturity has the ability to give. Immaturity keeps and takes. Let me tell you another story. He's probably sitting in the room and I didn't ask for his permission and I didn't for a reason. I don't know how many years ago exactly it was. I'm kind of guessing eight, ten years ago. I found myself in a low spot and I was a bit discouraged. A couple years before, I had met a brother by the name of David Bursell. Most of you tonight probably heard of David Bursell. But most of you probably know David as a writer and a speaker. What most of you don't have is the privilege of knowing him on a personal level and working with him day after day. You may think that David's strength is in writing and speaking and that is what's having a large impact on the kingdom of God. All true. However, I will tell you that David has a much greater strength than most of you will never know. When I was discouraged, he had the ability to come alongside of me and by his deeds and actions and words demonstrated to me, I believe in you. He walked me through it with gentleness, kindness, mercy and long-suffering. I was encouraged. He never said these exact words to me, I believe in you, Kevin. What kind of character does it take for a 65-year-old to come alongside a 40-year-old and bring encouragement to him? Let me add to that and tell you something else about David. David is an educated man. I am not. David is well-known. I am not. David is an excellent speaker. I am not. David is a great writer. I can barely speak the English language. What did he have to gain to come alongside and encourage someone like myself? Maturity of character. Maturity gives immaturity, keeps and takes. My final personal story. Most of you probably know, at least those who know me, looking at this crowd, there's probably a lot of people that don't know me, but I have a wife and her name is Jalee. She's a very lovely person. If you meet her, she's a much better person than I. Now, I know most of you men are probably not like me, but sometimes we have this weakness and this is my weakness.  I get really excited about my ideas, like feeding the poor, eradicating poverty, evangelizing the world, starting churches in unknown places, starting businesses that can feed the world, starting missionary schools to teach the world. I start all these enterprises from my soft arm chair. Jalee, who is my wife, sits and patiently listens and eagerly pretends to believe in my ideas, knowing full well that I will only be able to do in part only a few of these crazy, unreachable, unattainable, but good ideas. After all this, if you go ask her, she still claims that she believes in me. If you don't call that maturity of character, I don't know what is. Maturity gives immaturity keeps and takes. This is what the Bible says about a mature wife. Her husband is known in the gates when he sits among the elders of the land. She makes linen garments and sells them. She delivers sashes to the merchant. Strength and dignity are her clothing and she laughs at the time to come. She opens her mouth with wisdom and the teaching of kindness is in her tongue. She looks well to the ways of her household and does not eat the bread of idleness. Her children rise up and call her blessed, her husband also, and he praises her. All these stories having all these people share. What do they all have in common? As I suggested, I would like to bring you its maturity. It had nothing to do with me. It has everything to do with the quality of their character. All of them are living examples of maturity of character. Without maturity, none of them would have impacted my life. Remember, maturity gives immaturity keeps and takes. I believe the Christians that are mature of character will be the ones who impact this world and the people around them. Way more than any eloquent speaker and gifted writer and all the other ideas started on armchairs. I don't think it's possible for immature Christians to have the capacity to give this gift of I believe in you or encouragement to others. I want every person here to be among the few people of this world to have the maturity of character of giving this gift of I believe in you, encouraging others. Those of you that develop your character into maturity will single-handedly be the ones who leave a lasting impact to the person sitting beside you. Maturity gives immaturity keeps and takes. Remember, there are both Christians. Which one will you be? What is the single most important thing for every church to develop? Is it evangelism? Is it outreach? Is it praying all night? Is it church planting? Maybe it will help you if I ask another question. What are the things that divide us? Why do those things divide us? And there are many things that divide us. But the answer is not in fixing those things that divide us. The answer is not ignoring those things that divide us. I believe the answer comes down to pursuing one single thing. Where should your church invest most of their time? Where should Christians invest most of their energies? We teach what we know, but we reproduce who we are. There's a story told about Mahatma Gandhi in which a mother took her little boy to see the great leader. Gandhi, she said, please tell my little boy to stop eating sugar. The mother requested. Come back in three days, said Gandhi. In three days, the mother and the little boy returned. Gandhi said to the little boy, please stop eating sugar. Puzzled, the mother asked. But why was it necessary for us to return after three days? Couldn't you have told my boy to stop eating sugar when we visited, first visited? I could not tell him that then, replied Gandhi. Because three days ago, I was also eating sugar. We teach what we know, but we reproduce who we are. So what is the single most important thing for every church to develop? I would like to call your attention to maturity. What is maturity? Maturity is a rather fascinating quality. Do you know it's very easy to identify immaturity? But all of you can also identify maturity. This is what I find so fascinating about maturity, is watching how people respond to it. Each time you act mature, people are attracted to you. But every time you act immature, people avoid you. Why is maturity so essential to church life? Because I personally believe maturity is the gateway for trust in our church, in our church life and in our communities. Trust is the currency. Maturity is the gateway. When trust is lost or broken, it usually goes back to immaturity leadership, immaturity in fatherhood, immaturity in motherhood, immaturity among brethren, immaturity among sisters. Maturity is the gateway for quality of your character. To learn to respond correctly in each situation. When someone gives good advice but sets a bad example, you create confusion. We have an enemy that has sold us a cheap bill of goods. It's called modern Christianity. And I might add to that hybrid Anabaptist. Modern Christianity has sold us a gospel that tells us, just believe God and you're already mature. God has done it all for you. All you need to pray, all you need to do is believe. In fact, pray a little louder and a little longer. And just believe a little more. True prayer is one of our greatest assets. True faith is how we will inherit the kingdom of God. But have we cheapened our prayers and have we cheapened our faith? Modern Christianity now assumes and demands God to do everything for us. I now hear people assume and demand almost everything from God. They assume and demand that we be forgiven. They assume and demand that we become wise. They assume and demand that we're already obedient. They assume and demand that there's no need for obedience. They assume and demand that we're already spiritual. They assume and demand that we are strong. They assume in demand that we're already mature. This is a sign of immaturity. Maturity is not excelling in one single spiritual discipline. Maturity is excelling and learning to develop in many spiritual disciplines. Maturity is responding well in each and every situation that comes your way. Let's look at some scripture. Philippians 3.15. Let those of us who are mature think this way. And if in anything you think otherwise, God will reveal that also to you. King James uses these words for maturity, these words. Understand like a man. Be perfect. Be complete. The words perfect and complete, I find a bit intimidating. I find those words almost unreachable and unattainable. If I tell you brother so-and-so is perfect, or sister so-and-so, she is complete. How do you relate to that? Like is there any room for growth and improvement? But if I tell you someone is mature, that we can wrap our heads around and understand. We can understand one another if I tell you someone is immature. If someone is immature, I'm okay with that. All we need to do is help them grow from immaturity to maturity. God asks each of us to become mature. The beautiful thing about maturity is you can grow and continue to become mature. And it doesn't stop. It's a very beautiful, beautiful concept. Hebrews 6.1. Therefore, let us leave the elementary doctrine of Christ and go on to maturity, not laying again a foundation of repentance from dead works and a faith toward God. The call is for maturity. The warning against is immaturity is when you continue to lay down foundations and elementary doctrines over and over modern gospel. Listen to someone's gospel message. Do they tell you to rebuild your foundation or build on your foundation, I'm sorry. Do they tell you to rebuild your foundation over and over? Or do they tell you to build on the foundation? What's the difference between maturity or immaturity? What is the single most important thing that you should invest in as a Christian? Immaturity is when you become stuck on an event and try to repeat it and reproduce it over and over. Maturity is to build on that event. Hebrews 6.7, it says, for land that has drunk the rain that often falls on it and produces a crop useful to those whose sake it is cultivated, receives a blessing from God. I hope you got that verse. For everyone who lives on milk is unskilled in the Word of righteousness since he is a child. But solid food is for the mature. Now listen to these words. For those who have their powers of discernment trained by constant practice to distinguish good from evil, you see all that's happening in that verse? You're developing your maturity. The mature are the ones who are able to receive solid food. Those who have the powers of discernment trained by constant practice doing it over and over and getting better and improving, leaving immaturity, going to maturity. And it's up to you to develop yourself in the maturity. Nobody else can do this. Colossians 1-8, him we proclaim, warning everyone and teaching everyone with all wisdom that we may present everyone mature in Christ. Look at that. The goal, the passion, the daily exercise of every church is to present everyone mature. Maturity is revealed in two ways, in the way you think and in the way you respond. Let's look at how maturity is developed. Ephesians 4-13 says, until we all attain to the unity of the faith and of the knowledge of the Son of God, to mature manhood, mature womanhood, to the measure of the statue of the fullness of Christ, why? So that we no longer be children, tossed to and fro by the waves and carried about by every wind of doctrine, by human cunning, by craftiness and deceitful schemes. Maturity will keep you from that, rather maturity speaking the truth in love. We are to grow up in every way unto him who is the head unto Christ as our example, from whom the whole body join and held together by every joint with which it is equipped. When each part is working properly, it makes the body grow so that it builds itself up in love. It's called maturity. Now to give you a picture of how maturity is developed, I want you to think about this. I call this the pattern of maturity. I think maturity actually follows a very reliable process. Maturity doesn't just happen. I like to think about it in a triangle. Maturity is developed under two issues that will be compared and balanced against one another. What you teach, what you teach and speak actually matters. How you teach matters, what you say matters. Are the things that you teach accurate and true? Is it the faith once delivered that you teach? Is the faith that you teach real? This isn't exactly easy to do. It's difficult to teach and to speak well. The second thing in developing maturity is your living example. How do you live what you teach? Do you act the way you teach? Do you teach a high standard but when difficult situations are handed to you, you respond very immaturally. How do you respond in church life? What do your children say about your living example? How do you act in a men's meeting when you think it isn't going your way? Do you yell and pound your fists at men's meeting? Do you act disdainfully? Are you condescending in the way you speak and walk? Where we're told by modern Christianity that we're to assume that we're mature. But do we recognize that what we teach and how we live is our only test? If you give good advice but set a bad example, you create confusion and it looks like this. What you teach and have a bad example, you create confusion around you. This goes individually and collectively as churches. When you see confusion and almost always goes back to this pattern, when what you teach and you live a bad example, your life return is confusion. However, there's a more attractive way and that is this. If you teach well and live a good example, your life return is credibility and this goes personally and collectively as a church. I'm 51 now and something I started to notice as I get older. I watch how people age spiritually. It is very interesting for me to watch. We age spiritually in one of two ways, either well or poorly. Poorly is as we age spiritually, we become cynical and bitter, grumpy and complaining. Complicated old men and annoying and self-centered. Everybody wants to run from the grumpy old father and mother.  You're gracious to the 18-year-olds. Everyone wants to be around the wise old father and mother because they bring encouragement to everyone they meet. Don't assume you are mature. Maturity will determine how well you age spiritually, what your return in life will be. You hold the key. Will your life return be confusion or credibility? Remember, maturity is the gateway that allows churches to duplicate, grow and expand. And this is done through the currency of trust and credibility. I don't think there's any shortcuts. You cannot give what you do not have. And I'm going to close with the Lord's Prayer. Very then like this, our Father in heaven, hallowed be your name. Your kingdom come, your will be done, on earth as it is in heaven. Give us this day our daily bread and forgive us our debts as we also forgive our debtors. And lead us not into temptation, but deliver us from evil. For if you forgive others their trespasses, your heavenly Father will also forgive you. Don't assume you're mature. But if you do not forgive others their trespasses, neither will your heavenly Father forgive yours. Thank you.


I want to challenge you to begin to put something on the altar, put Netflix on the altar, put Instagram on the altar, put that relationship that you know you're not supposed to be in on the altar. Take that unforgiveness, put it on the altar, take that pride, put it on the altar, take that addiction, put it on the altar. The fire's not burning, give it something to consume.


The new compound, OddMapk, has been developed as a potential antimicrobial agent. It has shown strong binding affinity to the protein BSA, indicating its potential use in drug delivery systems. Furthermore, it has been found to have antibacterial properties against two common food poisoning bacteria, E, Coli and S. Aureus. This suggests that OddMapk could be used as a safe and effective alternative to current antibiotics. This article was authored by Vishwas D. Suryavanshi, Laxman S. Whale Car, Anil H. Gore and others.


セミナリティクリサンプリングの仕事です。トモユキオブチとタカハシの組み合わせです。バックランドのモチベーションを始めましょう。ラッフリースピーキングはマシンラインプローブレムがフォローされています。データセットで、インプルとアウトペアの数のインプルとアンペアのタスクレーズをパラメータに移動し、データセットでオプテマリーに適用されています。このセットで2つのディスクラッションの問題です。1.アルゴリズムのデベルトメントです。2.アルゴリズムのデベルトメントです。3.アルゴリズムのデベルトメントです。このデータで、バックプロバゲーションやフォローラティクリサンプリングのコンベクスリアクションのデータのリアクションのコンベクスリアクションにアトロンマシンラインプペアのコンベクスリアクションのコンベクスリアクションのコンベクスリアクションにフォローラティクリサンプリングをBG Debatteを呼ばせね。このデータのコンベクスリアクションは彼らがとてもインプルプロバゲーションをこのデータでコンビデアと多いなと思います。同時に、2回目は、このエヴァリュエーションには多くのプロポーゼルがあります。まずはクロスバリデーション、インフォメーションクライテリアン、エンピカルベース、リサンプリング、これをここにフォーカスしています。フォーマーを比べると、去年のディケイドでは、実はこれが重要ですが、最初のディケイドでは、私はそれを読めることができしませんが、すぐに読むことができますし、私が信じることはできません。なので、ディサンプリング、これが hour bootstrapのこのディケイドです。1981年符読は、赤いネメリカルのメモを求めたリランムーヌードのリアルプラメタの自動注文。そのため、 single datasetでエラバーを定義できることはできる外覧のブトスラップのリサーンプレイはとても簡単ですデッセットのDMを見ると、このギリギリで、エンヴィリカルディスタビューションを作ることができます just history.and but Rach's graph just repeat this process. So evaluate parameter distribution by repeating the following stuff many times, just repeat many times. So first one is the generation of the bootstrap sample. So from the given dataset, we resample the data of the set, mostly the same size.And for the generated data we land the parameter.Just it.これは多くの準備をしていますこのクラムを見ることができるのでこの理解を持っていますこのバッグポイントの理解を順番に必要だと思いますそのため良いポイントはスターブにある問題など良いポイント自体は原宿性化簡単なポイントですねそのため、データセットやランクアルゴリズムを使用することは、アイデアサンプションが受け入れます。そうすると、それを行うことができます。アルゴリズムの簡単な技術は、リサンプリングとラーニングを再現することを再現します。とても簡単です。でも、当然、バットポイントがあります。まずは、コンピュータションのバットポイントです。そのため、リサンプリングが多くのラーニングを再現することは、コンピュータションが多くのラーニングやインファレンスがユメリカリを再現することを再現します。次に、サポートの適切な技術の適切な技術を再現します。なので、サポートの適切な技術によって、全実的に知れなめらかにご確認しました。ただ、全実的に知らない技術の適切な技術を再現しています。今回はリサンプルトラッシュ時に、COITの勉強技術をを再現しています。でも、確実な技術と実際の交換技術の実際、一般的な技術の少しは不満点な技術を重ねます。そのため、この技術の適切な技術を再現するのはこの問題は常に大きな問題があると思うしかしここで最初の問題を探していますコンピューティションを減らせることはできませんこのデータを減らせるためにこのデータを減らせるために大きなバイアスを減らせますこの話の目標は一部のモデルです一部のモデルを減らせるためにアプロクシュメートアルゴリズムをスタッティスティックを減らせるために実際にリサンプリングを減らせるためにレプリカメソドとメッセージパッシングタイプアルゴリズムを減らせるためにレプリカメソドを減らせるためにマルザーン&オッパー2003のリサンプリングを最初にマルザーン&オッパー2003のリサンプリングを熊田裁券を切るために2階のリサンプリングを減らせるために工事波の数を減らせるために本規定の2階のリサンプリングを使用するために2階のリサンプリングを減らせるためにオッパー裁券を減らせるためにオッパーロンハーチ ラソンについてマルザーン&オッパー2003のリサンプリングを減らせるためにこのような状況は、YとXのデータセットに関して数字の数字はMと数字の数字の数字の数字はNとYは何かの目的な数字ですこのような状況は、DとPとXとYに関してDに関してPとPとPとPとPとPとPとPとPとPとPとPとPと全ての自己でだと要するにやっぱり違いはプラクティカルなデータの目的な数字が異常な分析のどのような概要についてでたらなければならない現在は、料理について約25年の前がティブシーラーニーのプロフェーサーのティブシーラーニーはこの問題により良いプロクレイメーションを提供していますL1ペナルティを使用しています次のコストファンションは基本的にはL1ペナルティとL1ペナルティを使用していますこのコンビックスプロフェーサーはとても簡単ですそしてこのコンビックスプロフェーサーをプロフェーサーを使用していますその場合はコンビックスプロフェーサーを使用していますそしてこのコンビックスプロフェーサーの少しL1ペナルティの意味ですそしてここではラストに適切な パラメータの区別の他様ですそしてその場合はラストはこれはつまり、彼らのコリレーションは非常に少ないです。ラソウは実際にバリアブルを選択することができます。しかし、このコリレーションは、エクスプラネートリバリアブルは無いのです。このコリレーションは、このコリレーションのプロファイルについて、バーサスの力のレグラリーションのパラメートについて、このコリレーションのパースです。このコリレーションのパースは、不穏な behaviorとして、バリアブルを選択することができます。このコリレーションは、マイシャンセンやブルーマンは10年前です。このコリレーションのプロファイルについて、リボフラビンデータのプロダクションのリボフラビンデータです。このコリレーションは、データの数は100です。エクスプラネートリバリアブルは4000です。このコリレーションは6バリアブルの レイトです。コリレーションのパラメートにより強く コリレーションについて、このテーマのモノトニック 하고は、このクリームのパラメートについて、それから、それからそのバリアブルを選択することができます。このコリレーションのパラメートについて、ラソはそのままの素晴らしいものではありませんそうですこの方法は、バラブスの選択を使用するために良いメソードを持っていますリサンプリングを使用するためにこのレースはションレイドですラソのリサンプリングを使用するためにこのレースはシューレスレースを使用するためにラソのリサンプリングを使用するためにマスケマティカルのディスクリスションを使用しますこのディスクリスションのリプレゼントをお聞かせしますこのディスクリスションのリプレゼントをお聞かせしますボツスローフ・サンプルでのディズニアデータを生成させるのです鑑賞のデータはありませんそのデータとの取り組みをしていますこのデータを改正するために株式に作られたデータでこのディスクリスションの場合作られたデータでのデータに作られたデータでのデータを作られますx2 y2 y2 y2 y2 y2 y2 y2 y2 y2 y2 y2 y2 y2 y2 this one is a sample twice, okay?x3 y3 this pair is sampled once, x4 y4 sampled once, but this is not sampled, okay?In this case we can characterize or specify this sample by the number of being sampled, okay?In this case this and this vector, counting vector, 0211 is equivalent, okay?So we use this notation for representing the resampled data, okay?Then we can show that the distribution of the resampled data can be represented by theマルチノミアル distribution, like this form, right?This is first shown by the Marzano opa, the 2003, and for large, when the mb or the m, okay, the number of the site of the data is large, this can be well approximated by the independent person distribution, right?So the distribution of the resampled data is easy to handle, actually.It is just a product of the independent person distribution, okay?Then we can formally link the boost of average and replica method by this formula, okay?So we want to, okay, for the given resampled data c, this c special resampled, each resampled data, then the error function for the lasso is given by this form, okay?Then we can introduce the partition function here, right?And in the beta infinity limit we can get the lasso solution, okay?So we want to take an average with respect to the resampling of the outside, from the outside.For this, we use replica formula like this.For fixed d, okay, this is given data, we valid the boost of average of the free energy using the replica method in conjunction with, I mean, field approximation, okay?So this is just a replica formula, okay?So if we can do this computation for fixed d, we can evaluate the boost of average.But of course, for given d, computing the partition function is difficult.So resolving this difficulty, we use certain kind of refilled approximation, okay?This is the idea.So for the first one is to use belief propagation, so cavity method, okay?So first idea is to resolve the difficulty of computing the partition function by the cavity method.For this, let, okay, this is distribution, conditioned by the boost resampled data, a single resampled data, okay?Then we can draw this kind of bipartite graph. This is the beginning of the belief propagation, okay?So then for given this graph, we can perform a belief propagation.But without doing this, we do the energy replicate system and take the boost of average in this stage, right?But the replication does not change the structure of the graph.So the graph is combated to this form, okay?And the factor here is easily computed by, or analytically computed by the independent force on distribution, right, in this form.Then we can construct the belief propagation algorithm for the replicated graph, okay?So this is computing the cavity bias and computing the cavity distribution and after completing this and having the convergent solution, we can get the belief, marginals, okay, for finite n.But we have to take n0 limit.So how can we do that?For this, we introduce a replication metric and that's, okay?This, in this case, we can state as follows, parameterize the message in the following form employing the replication metric and that's, or the exchangeability of the system and central remix theory.Then we can limit the functional form of the message to this form.We're using three parameters, A, B, C, right?Then this has a form that's functionalized with replica.So we can derive, we can analytically continue the expression to the real n and take n0 limit, okay?This is the afterline of the algorithm follows, okay?Then taking n0 limit and beta infinity, we can get algorithm to compute the three parameter A, B, C for each component, AI, AI for each entries, okay?Okay.This is the first result of our talk, right?Okay, validation by the sensitive data.So to justify the obtained algorithm, we checked the result between our derived algorithm and direct simulation of the resumption.This is for the first moment, this is for the second moment and this is done by the n equal1,000 and the data size is 5,500 and the number of non-zeros in true signals is 200, okay?K noise and lambda here.And you can see that both cases, for both first and second moments, we can get very accurateアプリクシメイト for the bootstrap statistics, okay?Okay.Then for what purpose do we use this technique, okay?The second part is the application of the developed algorithm.So this is known to the stability selection.So we examine the practical usefulness of the developed algorithm by the application to a new, recently proposed variable selection method for LASSO known as stability selection.This is proposed about 10 years ago by Einstein and Buhlman.So what is a stability selection?Okay.This is a robust variable selection method based on LASSO and resampling.Basic ideas are also simple, okay?We combine the bootstrap and randomization of the L1 strings, lambda.And this provides distribution of estimators.Then for each variable, we can evaluate the probability of this is active, the component is non-zero or not, okay?By doing many resampling, we can evaluate the histogram of the zero or non-zero for each component.And if this pii, so active probability is sufficiently large, we judge this component is non-zero.So select it as a relevant parameter, and otherwise we discard it, okay?There are several parameters, three parameters for this method.First one is that how to randomize the lambda.So we introduce a dependence of lambda for each component, right?So normally in the proposal, this is this form.So this is original strength lambda, okay?And by probability p, we change it lambda over a, and often a equal to one-half is used.The other parameter is that, okay, the size of the bootstrap sample.It is also, this parameter one-half of the original size is used, okay?Then this is from their papers, and this is the synthetic experiment, right?Actually, there are 200 explanatory variables, and blocks are irrelevant ones.So not has an influence for the output, the X observed variable, and these are all, okay, this.And blue also irrelevant, so not has an influence to the output, but has some correlation for the relevant variable, red one, okay?So in the case of that, we do not introduce the randomization of lambda. This has some peculiar behavior in the solution passes, stability passes. So this is plotted by the active probability, right?So although this is irrelevant variable, the blue one, so it has a very high probability around here.So it is difficult to distinguish this is irrelevant or irrelevant, but introducing the randomization of lambda, the curve clearly separated from the red one. So we can clearly distinguish relevant one and irrelevant one.So this makes the lasso estimation more robust than the naive one.So good, but computationally demanding to this method.Actually evaluating accurately the active probability needs a lot of resampling, usually more than 1000.So the naive doing this requires 1000 times of that of naive lasso.So we want to reduce this computational cost by using our approximation of algorithm.So we, yes, based on our algorithm, we developed the resam, semi-analytic resampling methodfor the stability selection like this.And check its usefulness by the new mega experiment.The first one is the synthetic data. We assume that this model, so just linear model with this, right?And we set the stability section parameter like the proposals and compared semi-analytic resampling with our method and direct resampling.Our method is coded by the MATLAB and not optimized.Numerical direct simulation is GLMNet, this is an optimized one, and the stability path, we use the 1000 time resampling for the direct simulation.Then we can get it.The first one is the comparison of the comparison with the active probability between the approximate algorithm and the resampling.Actually there are two data approaches here, but almost overlapped.We cannot distinguish the two.So this means that the approximate algorithm is actually very accurate estimate for this synthetic data at least.And you can show that the two curves, this is relevant entries, this is four or irrelevant data set.And you can see that these two groups are clearly separated.So we can accurately select the relevant variables from this type of plot.And this panel shows the actual computational time.And this, yes, as the list shows that four, the direct simulation.And the other is semi-analytic one, and this straight line is slope two.Actually the Lasso method requires all the probabilities in square.But using the model, they have some trick.Still, it has lower computational cost, but probably if n is very large, this provides this slope too.But then still we have a lot of gain in the computational cost, we can save.The cost of computation by the approximate algorithm.The second validation is by the real data.We took the wine quality data set from this repository.This is composed by the objective value of 10 grades evaluation for white wine states.This is given by the blind check by a professional sommelier.And the size is quite large, about 5,000 only white wine.And the explanatory variables are chemical constituents such as density, acid, sugar, something.Right?This is the result, the active probability of the 11 entries.And some could denote our approximate algorithm and as a result of the sampling.You can see that this same color shows the same component.You can see that all the cases.We can classify the entries into two classes by a certain criterion.But both cases, you can show that very good agreement with this.And some of it has discrepancies.But totally fairly good agreement with between the approximate algorithm and directory sampling.So, we can say that the developed approximate algorithm exhibits fairly good approximation accuracy when correlations between the explanatory variables are not so strong.But unfortunately, still it can fail in providing accurate estimates of bootstrap averages when correlations of explanatory variables are not negligible.So, we actually applied our methods, algorithm for the data of the riboflamma, of the original paper.Then we found that accuracy is not so good or bad.This is the first moment, second moment, and active probability.So, if our algorithm accurately evaluates the directory sampling, we should have the straight line here.But it has a very large outliers and also it has outliers and scattered, so not so good.So, we extend our methods to more accurate algorithm.So, for handling the correlated cases, we extends the replica-based algorithm to expectation propagation, or the vector-approximated message-passing bump.And this is because these are empirically known to offer more accurate estimates than BP or MPand the correlations are rather strong.In the rest of the time, we introduced its outline.So, what is the expectation propagation, or bump, vector-approximated message-passing?This is originally proposed by the MINCA, 2001.Lovely speaking, this is a combination of the belief propagation and the approximation by the exponential family.And the exponential in most of the case, Gaussian.And the empirically is accurate inference, even when the couplings are statistically correlated.Let's explain by a simple example of the Ising spin.Normally, if you have this model, you draw this type of bipartite graph.But we have another choice, this choice.In this expression, we hand the bunch of the factors, we denote the bunch of the factors by a single node.Here, the correction of these are in this figure, just represented by a single node.And also, the bunch of these factors, factorized factors are denoted by a single node.And the correction of the variables here are represented by the single node.So, we can use another expression of the graph.So, in the expectation propagation, we use this graph without, instead of this one.The good point of this graph is that if we employed BP, this graph is exact result.It is trivial because this graph represents exact calculation.So, it is trivial that it is exact result.But unfortunately, this is computational heart since we have the interaction.And the non-trivial distribution, non-linear distribution, even if this is factorized.But the computational difficulty can be resolved by a factorized Gaussian approximation.This is the idea.So, the computational difficulty is resolved by the Gaussian approximation.So, to do this, we introduce three types of approximation.This is the original form.And here, we replace this factor by the factorized approximation.And the second approximation, we replace the coupling factors by the factorized Gaussians.A third one is combining these two.Okay?Then what we should do is how to determine the parameters lambda and gamma for each approximation.For this, we impose moment matching requirement.So, three approximations, we can get expression of the first and second moments and there should be equal.So, we import this requirement for this.And this provides the condition to determine these parameters for the approximate message.Then we can get the expression propagation for the simplified system like this.So, just repeat one, two, four, many times until convergence.Right?Then, after having the convergence solution, we can get the first moment like this.Basically, we apply the algorithm for the generalized rhythm model, but there is a remaining problem.Okay?For employing expectation propagation, the target distribution should be of the formof exponential of some quadratic form times factorized distribution.The target distribution should be of the form of this.But unfortunately, original GLM is not of this form.Okay?This is, okay, this is like this.So, not exponential of quadratic, but we can convert the expression for this form by introducing the delta function and expression of the Fourier by the Fourier transform.Okay?Then we finally get the form of the exponential quadratic or this is bilinear form, but the bilinear form special case of the quadratic form.So, no problem.That's factorized.It's firstly introduced the technique by Oppa and Wiener 2001.Then we can introduce the EP or vector approximate message passing for this GLM.So, we first draw this bar-by-tatograph under replication plus average with respect to the sum, pring, and randomization of lambda.So, and take the limit n0 and beta infinity, finally we get this complicated algorithm.Okay?But, principally like this.Okay?Check.So, to validate the utility of our algorithm, we apply our method for the synseq data and real data.The synseq data is a random selection of the discrete cosine transform.Okay?It is a problem of the complex sensing.And the other one is the Devoflaming data from the Mannstein-Bruhman.Then you can see that both cases,Bruhman, the synseq data by changing the number of theデーサイズ.And the red one is the Devoflaming for the real data.So, both cases, you can see the exponential decay.So, very fast convergence in the iteration.Okay?The second one is that this panel shows the comparison between the result of the approximate algorithm and directly sampling.And top is for the synseq data and bottom, the Devoflaming data, real data.So, you can see that for the first moment, second moment, active probability, all the cases for both of the synseq data and the real data, we have a very good, considerably good approximation accuracy.Yes.So, I think, so we think that this is promising approach for the real data, not only forthe synseq data, artificial data.So, summary.So, in this talk, we develop semi-analytic resampling algorithm for generalizing the real model based on the message patch, two types of message-passing algorithm.First one is the belief propagation, and second one is the expectation propagation.And both are combined with the replica method.And the good point, bad point.The good point of the first one is the low computational cost.It is work by order mn, the size, time, the number of explanatory variables per update.So, first.But not necessarily calculate when the explanatory values are strongly correlated.So, these of these drawbacks, we extend the method for the EP.So, a good point is that empirically, a considerably good approximation accuracy, even for the real data.But also, we have a bad point, drawback, high computational cost.It requires, in general, order n-cube of updates.So, maybe depending on the situation, we should select either of them.Okay.And we showed a usefulness of our algorithm for the application to the stability selection method.And this provides a stable value of selection method based on resampling.And we have a future study that application to various real data analysis.Okay.Thank you for listening.This is the reference of our work.


All right, great. Thanks, everybody. I'm Leo Leung. I'm the VP of Marketing for a company called Scality. What we provide is software-based scale out storage for petabyte scale types of applications. So today I'm going to walk through a little bit of our open stack approach, what we're seeing in the storage environment, as well as some use cases and case studies of deployments of Scality. So as much as I think we'd like to believe that we're in a world now where we have pure flexibility, where we have the cloud-like aspects that we're looking for, what we're seeing in the market is sort of falling into the trap that we've been in for decades now, which is, well, maybe I need a different type of storage for every different type of use case or every different type of requirement that the applications have. So while I think there are some vendors here that are talking about one system that is good for every single application, what we see in practice is people starting to split into silos of different types of storage. So while we have increasing requirements of open stack, where you're talking about thousands of VMs, as well as tons and tons of application data, again, I think we're starting to fall into the trap of building many, many different storage silos, which leads to all the problems that people have had for decades, where they have to manage all those silos. In addition, while you're starting to see more and more adoption of software defined and storage architectures that are based on software, you still have lots of technologies that are very, very tied to hardware and tied to proprietary stacks. So I'm going to talk about that a little bit as well, versus an approach that gives you the economics that you're really looking for. So again, in some ways, open stack is trying to support different requirements, but also adding some complexity in terms of the different APIs, the different ways you want to manage your storage environment. So you're familiar with a lot of these different projects, different services that are available, to be able to handle both your ephemeral storage as well as your persistence from the boot all the way to your block storage or your volume into your image repository as well as now the file storage service called Manila. So lots of different types of services, lots of different things to think about as you're building your storage environment. When we look at the requirements though, another way to look at why you would even think about needing all those things or needing different types of storage, really the drivers are the applications and the data types. So in talking to customers, when you're thinking about images, it's really smaller scale in the megabytes. When you start to get into storing whole catalogs, that's when you start to get into the gigabytes. But what we're really seeing is an explosion of the application data. So not only you're talking about retention of more and more data from these applications, but you're also talking about larger and larger data sets. So a classic example is media or video, where you're talking now about 4k frames that are each 50 megabytes each going to the terabytes for an hour of content. So large, large content starting from the documents into your video, your IoT types of data, lots of types of stuff that drive the requirements around the storage. And when you look at the difference that block diagram I showed earlier, there have been various approaches to try to address these different types of data, whether you're talking about, again, dealing with the actual boot data, the ephemeral data, all the way into being able to store the application data, looking at object storage, looking at different types of storage underneath, and now, again, moving into the file environment. It's interesting that file was not a big discussion point until very recently. But again, there's some use cases that require it. So one approach is to go and layer lots of different storage technologies underneath. All these different types of data, all these different types of protocols and requirements. And again, this is what we're seeing, falling into that trap of building many, many different silos to try to service the different applications and the different requirements. We fundamentally believe that it doesn't have to be that way. You have technologies now that are able to encompass multiple use cases, multiple different types of data sets, and reduce the complexity as opposed to increasing the complexity. What we see is very much a world moving away from, again, purpose-built types of storage for every different use case, every different application. So in many of the customers we talked to today, it's very typical to see three, four different types of storage, tiers of storage. And in some cases, many, many more, if you're counting all the different locations and facilities. That's your classic SAN, NAS, multiple tiers of NAS, some object, and even tape in these environments. What we're seeing is a movement away from that into essentially a two-tier kind of world, where you're always going to have your very, very latency sensitive types of applications, where that's really moving away from the SAN and into your, for example, all flash array types of products. So certainly there's going to be a need for that, but it's usually a smaller amount of data relative to the overall data set. And sometimes you need custom hardware to deal with it. Absolutely a need for that. For a lot of the rest of the types of data that I just talked about, whether you're talking about documents or images or backups or large media types, you could really deal with it with what we would call capacity-driven types of storage. So being able to encompass all those different types of files, all those different types of use cases into the exabyte scale is what's available now. Five years ago, maybe not so much, but today you can absolutely do it with various technologies that are available. So I sort of laid out some of the challenges earlier. You have this massive capacity growth. So typically people refer to growth at a 50% type of growth. It actually varies greatly in the customers we deal with. In classic enterprises, we actually see it's more like a 10% to 15% growth every year, whereas certain industries, whether you're talking about some of the genomics research, for example, media, oil and gas, that's more like a triple digit type of growth in terms of data. So a big extreme in terms of growth, but certainly a challenge for people. And the desire is to be able to scale out a solution without a lot of work, being able to continuously grow that environment without new administrators, without taking the system down. Second is this silo problem that still exists. As much as we'd like to say that it does not exist, go into any enterprise today and you will see many, many tiers of storage, many, many islands that don't talk to each other that require separate maintenance. We believe it's possible to consolidate a lot of those things, again, into something that can handle all the capacity requirements that you need without adding overhead. Actually making the environment simpler. Third is the requirement for always on. So there's the classic three nines or five nines types of availability, which typically doesn't count maintenance. It's no longer adequate. There's not a single one of you here that's going to tolerate a service being down for even a few minutes if you're trying to use it in your day to day work life or even your personal life. So the infrastructure to support it has to change. It cannot be a three nines or five nines types of infrastructure any longer. And then finally, the economics. So happy to talk about that in depth, but what we see is when you talk about proprietary types of technologies, proprietary hardware, they use commodity components. There's usually a very big margin that's built into that versus being able to buy software and run it on standard X86 servers. So when you look at a large storage vendor, typically you're talking about margins in the 50% or 60% range versus a server vendor where the margins are only in the 10% range. So there's a huge difference in terms of cost when you're talking about proprietary storage hardware versus even standard X86, even name brand X86. So I represent a company called Scality. What we do is, again, we provide a software-based storage solution that addresses some of the things I just talked about. First is, again, this ability to run standard commodity X86 hardware and standard Linux to be able to build a massive type of scale environment. One of the interesting things you'll see as you start to roll out these solutions is you're going to have a very heterogeneous hardware environment, even if it running commodity. So having experience with those types of scenarios is what's going to give you the reliability and environment you want. So for example, we have customers that are running five different generations of hardware, completely different form factors under one system in production. So just a common scenario we see in the environment, particularly over time. Second is the ability to create a very large pool of storage. And that's not just capacity, but also the amount of objects that are in the system. So for example, we have a customer that has over 60 billion objects in a single system. So not just capacity, but many, many, possibly many small objects. Reliability, so reliability not just from a data protection perspective, but also from an availability perspective, from a fault tolerance, whether you're talking about software or hardware failures, very, very key to this type of storage. Performance, so you can certainly get performance in many different ways. We believe that the architecture itself has to be a very high performance, has to be able to serve many applications in parallel. When you're talking about consumer clouds or you're talking about video streaming, for example, very, very high performance requirements out of the storage. And we can do that natively. And then finally, being able to support not just archive types of use cases, but also more tier one, higher performance types of use cases. So again, the way we have worked with customers is, absolutely there's a low latency type of requirement, and then a high capacity, high bandwidth type of requirement, we fall into that second category. Okay, so we've taken this ring architecture, the Scality Ring product, and we've applied it to OpenStack, okay? So we've been working with some of the OpenStack services for close to three years now, starting with Cinder, then building up Swift support. Okay, so we fully support the Swift API, including all the storage policies. Announced Glance earlier in the year, and we now have a technology preview of a Manila driver as well, all right? So all the APIs and protocols you're familiar with, you can leverage them on top of the storage platform that I just described. So talking a little bit about a few use cases, and I'll talk about some more generally in terms of what Scality has done. So recently we've been working with a large service provider around a very, very big file environment, okay? It's a data as a service type of environment, over 12 petabytes, multiple sites, okay? They started with a different solution, wanted to go into production, moved into us, again, seamless ability to take advantage of the OpenStack APIs, but change the backend. And now they're going into production with us, again, multi-site, they're using ArrayShr coding across those sites, and being able to support many, many different clients hitting that same environment. So again, what we've seen is you need the ability to scale, but you also need the ability to support performance scale, right? The ability to handle multiple clients hitting the environment, reads and writes in a large scale. Second customer, numergy, public cloud, IaaS provider, currently using Swift, looking at different implementations using OpenStack as well. So going into a broader set of use cases, so where we came from, where Scality came from is from the service provider space. So we have over 30 customers using us in production for email storage, right? Very tough workload, small files, large files, many, many objects, again, customers in the tens of billions, tens of billions of objects, as well as oftentimes multi-site, okay? So going left to right, you're talking about large archives, again, oftentimes is very high bandwidth requirements. So for example, video production, right? Again, you're talking about having to deal with not just larger content, but if you're handling video on demand, you're having to support binge watching, right? Producing 13 episodes or 22 episodes at a time. Massive scale in terms of needing storage to support those environments. Going to the second category, you have tons and tons of video on demand. So every geo that we're working in, you have really the end users saying, I don't wanna watch broadcast anymore, right? I wanna be able to watch content whenever I want it, which means storage, right? And you're not just storing the titles that are available today, potentially you're recording for network DVR, right? You're recording personal programs that people wanna watch later. All that requires storage, all that requires a huge amount of bandwidth capability out of storage in addition to capacity. So deluxe on demand is a customer like that. As I said, lots of web and cloud services providers, many of them in the email space, but more and more into other areas. So recently we've signed a document provider. Again, billions of documents that they have to store for their customers, most of which are law firms, right? Another customer building a big radiology cloud, serving many, many private practices and hospitals with radiological images, whereas in the past, literally still shipping things around. Sometimes through FedEx, sometimes through very, very basic file stores. Okay, and then the last category, I think is the most interesting is the notion of being able to leverage a unified platform across many different use cases. Oftentimes that first use case is a backup type of use case, archive, sync and share type of thing, but once you build the economics, once you build a platform that can handle that much scale, you start to get some great efficiencies of scale. So a little plug about the company. We've been around again since 2009, so not a brand new entity, have had many customers in production for years. Our oldest customer is actually from 2010, has been actually 100% available that entire time, okay? They've grown 20 times in capacity to over 10, two petabytes. They've had many, many generations of hardware live at the same time and retired hardware all available all the time, right? And I think that's a testament to what we're able to do. Over 170 employees and just recently got some additional funding. So check us out if you're interested in working for a company like this. We're over at booth T60, right down the hall here. Happy to talk more about your challenges, your use cases, your applications, try to figure things out. We're not a fit for everything, unlike what some other people might say here, but happy to talk through some of the use cases you might have. I'll point you out to scality.com slash trial. Completely free, you can actually just sign up and actually run the software in a completely hosted environment. So you don't have to stand up VMs, don't have to stand up hardware. You can try the technology yourself. Absolutely free, scality.com slash trial. Thanks very much and I'll take any questions on the side.