If you want to score this a little differently, you can award points for every game guessed correctly in addition to the rank points that are awarded. You also have the option to customize the sheets by editing the title and by adding rules and prize. If you need help deciding on how many places to pay, check out our Pick'em Payout Structures. Below you will find our Week 5 schedule/pick sheet. The pick sheet is fillable, or simply print as a blank football pick. The person with the highest point total wins. You can access the full NFL schedule or college football schedule for each week. The first is to 'View with Standard Title' the second is to 'View with Fillable Title'. When you find the bracket you need you will see two options. We have recently added the ability to customize the title of your bracket. This will be used as a tie breaker if two or more participants end up with the same total.Īfter the final game of the week is over, add the points(points = rank) of only the games that were picked correctly. Edit the Title of the Bracket As you scroll down the page you will find a list of all of our Fillable Tournament Brackets. For a slight variation you can try our Week 10 Confidence Pool where participants rank their picks based on their confidence of the winning. You also have the option to customize the sheets by editing the title and by adding rules and prize information. They should also write down what they think will be the total number of points scored in the Monday Night Football game. Below you will find our Week 10 schedule/pick sheet. The team they are most confident will win, should get the highest number, the game they are least confident in should get a 1. The numbers the participants are writing down represent how confident they are in their pick. Each participant should circle the team they think will win each game, and also write down a number from 1 to 16 in the box beside each game(the 16 will change to 13,14 or 15 based on how many games there are that week). Pass out a copy of the current week's schedule to family, friends, and co-workers.
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Unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates ( UN 2019). Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using Unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates ( UN 2019) Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using Unconstrained top-down methods for all countries of the World for each year 2000-2020. Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs.ĭatasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively): With either the unconstrained or constrained top-down disaggregation method used. The remaining datasets in the links below are produced using the " top-down" method, They can also be visualised and explored through the woprVision App. These are 100m resolution gridded population estimates using customized methods (" bottom-up" and/or " top-down") developed for the latest data available from each country. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.īespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below. WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Ok, now we can start to paint the template or put in the logos , On the top you see now 4 open foulders: (like in the screenshot abouf)Ĭlick on the A318, and you must have the A318 template again. To 50-100, i use here 100 like you see here:ĭelete all parts around the tail that it looks like that:ĭone ? Then save it as: Air France tail ready.PNG (again only as PNG format) Ok, now we need the eraser tool (the magic wand tool not works here somethimes)īut we need to change the brush width (on the left up corner in the top) (normal past (and keep canvas size if you would ask)) (the new, is as big as the selected area) click ok. (under edit, with copy)Īnd then open a new foulder in (under file) then it looks like that: Now we use the rectangle select tool like that: , so open the Air France Picture with that it looks like that : but we still need the tail of the air france (Importent only use the PNG format, or it not works) Then save it as: Air France titels ready.PNG (Like you see here if you scoll in the picture with +-) Do the same again with each white in the titels ! Ok, but now we have the white in the titels open. (if it is transparent it looks like a chess backround) So use the magic wand tool click on the white around the logo ,Īnd click the delete button at your keyboard. Ok done ? then open the air France logo with Then you see it like that: The problem is that we need always a transparent backround. The same we do with a sharp Air France A318 picture (from the side, where you can see the tail good). So search in the Internet the logo (a small one) and save it in your SA templates foulder or in your pictures foulder. Se we need the Air France Logo, and the tail colors. We only need everthing from the windows of the plane and up. If you want to creat a airline, you need a picture ,from the side where you can see all details. you can change the 100% with that + – on the right down corner. On the left side you have the tools, left down the colors, on the right side down you see the layers and on the right down corner you can scroll into the picture/template up to 6400%, now we have 100%, we need that so see the pixels better, so the template is better. There you change the Mode from Normal to Mutiply(then you can see what is under your color)Īlso you can change the Opacity (trie that out ,but then let it at 255). ĭoubleclick on the layer 2, then a new window opens like that: Then go to the right down, there is a white window, with backround and Layer 2. OK now, very Importent, add a new layer (5th button from the left) (If you have it at your desktop, go to desktop, there to your foulder, there to the A318 and doubleclick on it, then it will be opened in ) It should look like that: Open, and there at files open your A318 at your “Sim Airport templates) Next is Step 2 : Step 2: Coloring of the template part 1 Now we know always the Aircraft name and the ID, that we need later. “Sim Airport templates” foulder, and rename it to : A318 – SHOGT65441. Now close notepad++ now we see again this : WE have now the ID, but we need also the template, copy the template and put it in your new The ID here is SHOGT765441 This ID write down please, we need that later. Do on the config and click with the right mouse, then it opens a window like that:Īnd click on edit with notepad++ then a new window opens like that: here you see the Name of the plane, and also the Reference ID. With that ID the game knows what aircraft we use. In that foulder you see the config of the aircraft and the template.Īt first we must know the Reference ID of the Aircraft. We take now the A318-100, search it in the list (A-Z) it looks like that :Ĭlick on the “Open Foulder” button, then you should see this: Not not use that Aircrafts with following ID’s A318 + A319 + A320 + A321 + A359 + A388 + ATR72 +E190, all other you can use. Now we have a list of aircrafts you can use all, but in the moment some aircrafts not work correct. Open Sim Airport, then go to mods, and there to Custom Aircraft. So at first we need a template of an aircraft, we find that in the game Sim Airport. Creat a foulder at your desktop “Sim Airport templates”If you have that, we can start :)PS: Sorry for my english, it is not the best) Step 1: Finding a template & SA Mods Editor (a grafic program) (You can also use Gimp or other grafic programs) (windows paint NOT works) Link: (use DOTPND)4. notepad ++ (a editor for the config) Link. from 01/2019 You need a few programs at first 1. Here you learn how you can create a custom airline (a airline mod). Mountain Standard Time (MST) in the United States is seven hours behind Greenwich. Next time change is highlighted.ĭata for the years before 1970 is not available for Centennial, however, we have earlier time zone history for Denver available. In the United States of America, Colorado is in the Mountain Time Zone. 5:53:19 AM GMT +00:00 Current time now in Time Zone: America Denver (USA Mountain Time) Parker, Travel. Parker, CO Town of Parker, Colorado, USA. Time Changes in Centennial Over the Yearsĭaylight Saving Time (DST) changes do not necessarily occur on the same date every year. Time Zones Sun and Moon Timepieces Articles. Current time now in Time Zone: America Denver (USA Mountain Time). The graph above illustrates clock changes in Centennial during 2024. Check official timezones, exact actual time and daylight savings time conversion dates in 2024 for Fort Collins, CO, United States of America - fall time change 2024 - DST to Mountain Standard Time. and done MST stands for Mountain Standard Time. Simply mouse over the colored hour-tiles and glance at the hours selected by the column. Business Date to Date (exclude holidays) Current local time in Fort Collins, Larimer County, Colorado, USA, Mountain Time Zone. 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It brings to the forefront the importance of carefully differentiating whether a patient’s stress reactions pertaining to their cancer experience are acute, yet interfering with functioning, indicative of AD, or more severe psychopathology such as PTSD.Īlthough ADs have been documented to be highly prevalent in cancer patients ranging up to 35% ( 7, 8), they have tended to be overlooked in studies specifically investigating the prevalence and characteristics of ca-PTSD. This heuristic framework also has potential utility for delineating psychological disturbances arising from cancer-related stress. Friedman and his colleagues ( 6) assert that there is heuristic value in grouping this set of disorders in a specific stress-related category as it enables clinicians to differentiate between normal (non-pathological) distress, from acute, diffuse clinically elevated stress reactions indicative of AD, to more severe and chronic psychopathology (including PTSD). In DSM-5, disorders which are precipitated by specific stressful and potentially traumatic events are included in a new diagnostic category, “Trauma and Stress-Related Disorders,” which includes both Adjustment Disorders (ADs) and PTSD ( 5). DSM-5: Trauma and Stress-Related Disorders The purpose of this paper is to evaluate the changes to the PTSD criteria in DSM 5, and the repercussions this has for screening, assessment, and treatment practices for cancer-related stress problems. With the Fifth Edition of DSM, there are some notable changes to screening for stress-related disorders and which have important implications for screening cancer patients and survivors. In fact, the prevalence rate for ca-PTSD has been documented to be considerably lower when utilizing the latter approach, ranging from 0 to 22% ( 3). The majority of research has been based on self-report questionnaires which has tended to inflate the rates of ca-PTSD (with prevalence rates as high as 55%) compared to studies that have used the gold-standard assessment method of structured, clinical diagnostic interviews ( 4). Since 1994, there has been a proliferation of studies investigating the prevalence and characteristics of cancer-related PTSD (ca-PTSD). Therefore, in the Fourth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), being diagnosed with a life-threatening illness such as cancer was included for the first time as a potential traumatic event that could induce posttraumatic stress disorder (PTSD) ( 2). Indeed, cancer-related distress is common at pivotal periods in the prototypical trajectory of a cancer patients’ experience (including the diagnostic, treatment, recovery, and recurrence phases) and ranges on a continuum from normal, acute responses which may comprise initial fear post-diagnosis, to more severe, potentially chronic stress reactions that adversely impact functionality and general well-being. To this end, in 2009, the International Psycho-Oncology Society endorsed distress as the sixth vital sign in cancer care ( 1). It is well documented that being diagnosed and treated for cancer is understandably, a challenging experience associated with heightened distress. That’s why, over the last few years, a few companies have experimented with making crossbows more relevant for normal folks looking for something in between a toy and Hollywood. Which means most normal folks don’t really engage with crossbows, leaving it to Hollywood badasses like Daryl from The Walking Dead or Katniss from The Hunger Games. Traditional crossbows can be mechanically complicated, physically hard to cock, are heavy and bulky, expensive, don’t fire enough rounds quickly enough to help in a ‘tactical’ situation, etc. But, as discussed in TP’s roundup of the best survival bows, crossbows in particular usually aren’t a good choice. If you’re relying on a knife, pepper spray, or even a hunting trap, that requires getting very close to the target, which is harder or riskier.Īrrows are usually the best consistent way to put non-firearm projectiles down range. But self/home defense and hunting become much more difficult without guns because you can’t hit things far away from you. It’s okay if you can’t or don’t want to have firearms. Although I don’t have a lot of crossbow experience, I find them mechanically interesting and have wanted better answers when people ask about firearm alternatives. The build quality is roughly the same as the cheapest AR-15s, for example. seems fine, although I’d like to see the overall cost (including the bolts you need) closer to $300-325. The bolts do not always fly consistently, hit the target straight on, etc. The limiting factor is accuracy, since you still have enough ‘power’ at 50+ feet but it becomes impractical to hit what you’re aiming at.The effective range is about 50 feet (15 m) in a self defense situation where you’re aiming at someone’s torso and about 25-30 feet (8 m) when hunting small game like rabbits.Besides being recreationally fun, the Stinger mostly makes sense if you can’t or don’t want to have a firearm, yet still want to be able to hit targets on the other side of the room/house/yard instead of being limited to something like a baseball bat.I’m glad this product exists and hope to see more focus put into the “crossbows for normal people’s prepping” category. Therefore, from the model quality perspective, temporal model degradation introduces a completely new challenge, which we would like to refer to as “ AI aging”. While much research has been done on various types and markers of temporal data drifts, there is no comprehensive study of how the models themselves can respond to these drifts. Known as “concept drift”, this evolution of data inevitably affects the quality of the models, to the point where the model may no longer correspond to its new reality 17, 18, 19. However, data-producing environments 14, 15, 16, often change with time, and their statistical properties change alongside them. Model dependency on time, on the contrary, has been virtually ignored in practical AI implementations: it is commonly assumed that once a model has been trained to achieve the required quality, it is ready to be deployed and used without further updates or retraining. As a result, it is becoming more and more common to apply these tools to newly trained models, to make sure they can correctly scale to their diverse environments 11, 12, 13. AI bias tests help identify and stop the propagation of data bias and “shortcuts” into ML models 9, 10. Transfer learning gives additional training to the model when the model is moved to a new environment 8. For example, federated learning allows models to be trained, validated, and shared between different sites with varying data patterns 7. Model dependency on the training data location has been studied to a great extent, and several approaches have already been proposed to minimize its impact on model quality. We can loosely divide this model-data dependency into two principal types: location-specific and time-specific. Trained from and driven by data, these models become inherently dependent on the data as it was at the time of training. The principal challenge in maintaining AI model quality stems from the very nature of the current ML models. As these practical uses multiply, the applied aspects of sustainable AI model quality become increasingly important, calling for more efforts to make AI implementations dependable and robust. Similar content being viewed by othersĪrtificial Intelligence (AI), and machine learning (ML) models in particular, are becoming increasingly present in many real-life applications, from finance 1, 2 and manufacturing 3 to agriculture 4 and healthcare 5, 6. Finally, we indicate potential causes of temporal degradation, and suggest approaches to detecting aging and reducing its impact. We also demonstrate the principal differences between temporal model degradation and related concepts that have been explored previously, such as data concept drift and continuous learning. Using datasets from four different industries (healthcare operations, transportation, finance, and weather) and four standard machine learning models, we identify and describe the main temporal degradation patterns. In this study, we present the first analysis of AI “aging”: the complex, multifaceted phenomenon of AI model quality degradation as more time passes since the last model training cycle. The principal challenge in this task stems from the very nature of current machine learning models, dependent on the data as it was at the time of training. As AI models continue to advance into many real-life applications, their ability to maintain reliable quality over time becomes increasingly important. |
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