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It’s Hard To Predict Who Will Be Suicidal. It’s Easier To Ensure People Can’t Shoot Themselves.
Some mental health experts favor suicide prevention steps that are applied broadly, with protections that aren’t tied to an individual’s specific risk.
What the weather can teach us about health
What if predicting your future health is more like forecasting the weather than we realize? New research suggests fundamental limits to how far ahead even perfect weather models can see. That idea raises a provocative question for precision medicine: Can more data and better models ever make an individual’s future truly predictable?
Michigan vs. Iowa Opening Point Spread Revealed
Michigan scored 52 against UTEP, but the opening line against Iowa predicts a low-scoring Big Ten battle at Michigan Stadium.
Predicting The Washington Wizards Depth Chart After Finalizing Their Roster
After re-signing veteran leader and fan favorite Anthony Gill to a one-year deal, the Washington Wizards' depth chart is finally finalized. With this revamped roster and a potential postseason push, the Wizards' lineup will no doubt look very different from last season. Ahead of one of the most exciting Wizards seasons since the John Wall-Bradley …
Final score predictions for Bengals vs. Texans in Week 2
Bengals vs. Texans final preview for Week 2.
Three Last-Minute Packers vs. Jets Predictions
The Green Bay Packers aim to stifle Garrett Wilson and ignite a struggling ground game against the New York Jets.
3 bold predictions for Broncos’ game vs. Jaguars
Here are our bold predictions for the Broncos' game against the Jaguars on Sunday.
Seahawks vs Cardinals live updates: Score, highlights, predictions, analysis
Seahawks play the Cardinals in a pivotal NFC West Showdown. Follow live score updates, highlights, injury news, key plays and instant analysis.
NFL Week 2 expert picks, predictions: Moneylines, against the spread
Our staff’s picks and predictions for all NFL Week 2 games of the 2026 season.
NFL Sunday Night Football best bets Week 2
Sunday Night Football brings the Indianapolis Colts to Arrowhead to face the Kansas City Chiefs, and there are several player props worth targeting. Here are four bets for the Week 2 NFL matchup. MORE: Bold predictions for Week 2 of…
A Cloudy Climate Mystery Over Antarctica Has Scientists Rethinking Their Models
Researchers compare observations with leading climate models to identify errors and help improve future climate predictions. Some simulations put too many low clouds over the Southern Ocean, yet still underestimate how much heat reaches the surface below. Observations from an Antarctic research voyage suggest that fixing the cloud count alone will not solve the problem.
Panthers vs Falcons: Round table predictions, odds, and Atlanta Bryce
This week should be different.
PSG XI vs Marseille – Predicted lineup and team news
PSG will be aiming to record a third consecutive victory in all competitions before the first international break of the season when they travel to face Marseille on Sunday.PSG head into Le Classique ...
AC Milan vs Lecce – Predicted lineup and team news
AC Milan will aim to keep their perfect, unbeaten run alive in Serie A this campaign as they host Lecce at the San Siro on Sunday, in what is their last outing before the international break arrives.T...
Defender Ng predicts big wins to come for Cardiff
Cardiff City defender Perry Ng says he expects the club to "batter some teams" moving forward as the Bluebirds finally put an end to their winless run.
Manchester City vs Sunderland – Predicted lineup and team news
Manchester City host Sunderland at the Etihad Stadium on Sunday looking to make it five wins from five in the Premier League. Enzo Maresca’s side have won every game across all competitions since he...
Liverpool XI vs Bournemouth – Predicted lineup and team news
Liverpool travel to the Vitality Stadium on Sunday for Andoni Iraola’s return to former club Bournemouth, where he took charge of 127 matches and guided the Cherries to their highest-ever league fin...
Chicago Bears vs. Minnesota Vikings Game Picks and Predictions
The WCG staff, including @GOOCH1701, @SamHouseholder, @iamcogs, and @wiltfongjr, makes their picks for the Bears vs. Vikings game!
Prediction For Buccaneers Week 2 Game Against Cleveland Browns
Tampa Bay is looking to bounce back in Week 2 against a Cleveland team that was dominated a week ago.
Steelers vs Patriots prediction: T.J. Watt, defense will flummox Drake Maye again
The Steelers forced five turnovers in beating the reigning AFC champions at Gillette Stadium almost exactly a year ago.
5 bold predictions for Bears vs. Vikings
Some big receiving performances for both teams and a signature rookie moment highlight our bold predictions for Bears vs. Vikings.
Duke vs. Stanford live updates: Score, highlights, predictions, analysis
Duke plays Stanford in a pivotal ACC showdown. Follow live score updates, highlights, injury news, key plays, and instant analysis.
Research suggests some voters using AI to help with midterms information
Researchers predict that more people will use AI to understand elections, and they find a mix of outcomes from voters using AI to understand elections.
Will unlikely starts continue in second week of NFL season?
Four NFL games feature 0-1 teams facing 1-0 opponents. History shows how rare and unpredictable these 2-0 starts are.
Fulham vs Manchester United predictions: Premier League tips and odds
Both Fulham and Manchester United are already under pressure this season ahead of their meeting at Craven Cottage on Sunday (kick-off, 4.30pm), in the final Premier League fixture before the international break.
How I underestimated how unpredictable mental health can be
The Lindsay Clancy trial is shocking, but the complexity of mental health and our inability to control everything should be humbling to us all.
Ravens - Saints: How to Watch, Predictions, and More
Everything to know about the Baltimore Ravens' home opener against the New Orleans Saints.
49ers vs. Dolphins prediction: A vintage Christian McCaffrey performance
Are the Niners poised for another blowout?
Week 3 Game Predictions: Northwestern Wildcats vs. Colorado Buffaloes
The predictions are in for Northwestern vs. Colorado as the Wildcats return from their bye week for a highly anticipated Week 3 matchup.
The Big Holes in Wearable Heart Rate Variability And Readiness Scores
On September 9th, Apple announced it was revamping its Apple Watch Health Sensing System, rolling out a Readiness score (0 to 10), and increasing the frequency of heart rate variability (HRV) outputs 24-fold. This can be viewed as upping its competition with various other consumer wearable sensors. These “readiness scores” as a composite of multiple metrics, with heart rate variability (HRV) being front and center for most. The majority of Americans are now using wearable sensors, which equates to well over 100 million adults. HRV and Readiness scores are increasingly being marketed as a measurement of autonomic nervous system health, a digital marker for future disease, a clock for biological age, and a holistic metric to promote healthspan and even longevity. (Apple also introduced a new longevity tab and “Health Age”.) None of this has been proven. In this edition of Ground Truths I am going to review what we know about heart rate variability and readiness scores. Heart Rate Variability HRV is the variation in normal heart cycle timing. The variability of the heart rate, the barely perceptible millisecond changes in time between consecutive heart beats (see R-R intervals in the Figure below, left panel), is due to interplay between the sympathetic and parasympathetic (vagal nerve) inputs. Distinct from heart rate, individuals with the same heart rate can have very different HRVs. It is a rough reflection of the autonomic nervous system (ANS) activity, inadequate to say whether a person’s ANS function is abnormal. For more than three decades, heart rate variability (HRV) has been measured and several studies have found an association of low HRV and clinical outcomes, particularly a link with higher all-cause and cardiovascular mortality. There have also been less well established links of low HRV to risk of early cognitive impairment, dementia, mental illness, Type 2 diabetes and substance abuse. An important reminder is that HRV is a surrogate marker without any established cause-and-effect relationship. If you increase your HRV, that doesn’t mean it will improve health outcomes. In fact, there is no hard evidence for that. All that work linking to health outcomes was done with electrocardiogram (ECG) derived HRV. Now, in the era of consumer wearables, this is getting assessed differently, by optical pulse (yes, the lights you see) plethysmography (PPG) or what is called pulse rate variability (PRV). They are not the same, as shown below (right panel) and only concordant when the delay between the ECG and pulse is kept constant, which basically means at rest. I should mention there’s also what I will call MPV, a mattress mechanical movement sensor, a derived heart rate variability, that companies like Eight Sleep use, even further away from directly measuring HRV. HRV has not one uniform measurement but many different types of quantification, such as RMSSD, the magnitude of difference between successive R-R intervals of normal sinus beats (N-N) or SDNN, the standard deviation of NN intervals, both in milliseconds. SDNN is one of the so-called frequency domain HRVs (others are LF, HF, LF/HF). Different wearable sensors use different metics; Apple has relied on SDNN and nearly all of the others use RMSSD, which is generally considered the more accurate metric. There’s also the different length of time measured, such as for a matter of minutes, all day, or an overnight’s sleep. Short measurements are especially problematic since they don’t capture enough of respiratory modulation and other factors that influence HRV. Share Ground Truths How well does HRV correlate with PRV? There are very limited studies, especially independently done. One that is commonly cited was conducted by Air Force researchers in only 13 healthy adults assessing Oura ring 3 and 4, Whoop 4.0, and Garmin Fenix 6 and showed a correlation coefficient of 0.88 to 0.97 and a mean absolute percentage error from 6 to 10%. The correlation is not a perfect 1.0, but there’s at least a fairly high level of correlation. HRV is supposed to increase during the night due to takeover of the parasympathetic nervous system, and higher during deep sleep. A recent example of my 1 week, all day “HRV,” and one during sleep is shown below. As you can see, the N of 1 data are inconsistent for the same days from different sensors (Oura, AppleWatch, Fitbit Air, and Eight Sleep) by patterns, absolute numbers, and comparison with prior days and weeks. The largest study in over 8 million Fitbit users (the old version, not Google Fitbit Air, introduced in May 2026) gives you a sense of the effect of age, sex, and the 2 different main HRV (here PRV) metrics, with RMSSD on the left and SDRR (=SDNN) on the right below. That study, from data collected in 2018, is a major outlier, since all the more recent ones are tiny with respect to sample size. Many of the companies have not had independent evaluation of their HRV, such as Eight Sleep, but have published a low standard error on their website. There are some other published studies on the correlation between HRV and PRV, but they are all small and only in healthy adults. A scoping review emphasized the lack of study in underrepresented individuals, including the aged, people of color (which affects the PPG signal), and individuals who are underweight or obese. Add the typical adult age 60 plus with one or more chronic diseases. For example, one study in over 900 adults found poor correlation of HRV and PRV, non-uniformly underestimated across many chronic diseases (cardiovascular, endocrine, neurological, respiratory, and others), concluding PRV is “an invalid surrogate for HRV.” A recent systematic review of 43 studies comparing HRV and PRV found reasonable pooled absolute standardized error (HRV as gold standard) but only 10 of the studies provided quantitative synthesis in ideal conditions. Their main conclusion was similarly cautious: “PPG-derived HRV [PRV] should not be regarded as universally interchangeable with ECG-derived HRV across all devices, populations, and recording contexts.” Factors Affecting HRV and PRV That gets me to the long list of factors that affect HRV (and PRV) besides the device, the type of measurement (RMSSD, SDNN or others), the person’s signal, the sensor site, the duration of data capture, if weighting by sleep stage is used, how artifact is processed and corrected. And this list is not complete!: Oura puts out data from their community of users (who input data) on what affects their overnight HRV. The factors currently provided are: no alcohol (increase 8%), melatonin (increase 2%, float tank (increase 2%), wine (decrease 4%), and party (decrease 14%) in overnight HRV. Must be some big parties! Subscribe What is a PRV measurement good for? It has been falsely characterized as an index of “autonomic balance” and a specific indicator of stress. A 2018 review of the studies available for HRV and its relationship to stress, not using any of the current wearables, found that stress can lower HRV. But so can many other factors. The non-specificity of the signal, indexed to the table above, is striking. Evidence from a UK Biobank study of over 46,000 participants with actual HRV looked at genetically predicted HRV, a genetic risk score, that failed to show the expected HRV-mortality link, indicating that _HRV is likely not causa_l, but rather a reflection of person’s physiologic state. A review of consumer wearable HRV data from 5 longitudinal studies showed that nighttime PRV was not associated with perceived stress, and surprisingly higher HRV, in the largest cohort (N=717 participants), was correlated with higher stress. An Oura ring cohort of 525 first-year college students found a link between overnight PRV and perceived stress, but that was also seen with resting heart rate, sleep, and respiratory rate. Several very small studies have examined the relationship of HRV and athletic injuries or guiding training with mixed, and predominantly negative results. HRV biofeedback training with paced breathing had no significant effect on reducing stress or raising HRV, as demonstrated with sham controlled trials. When HRV for multiple days showed a decline in conjunction with body temperature, the Oura ring published data for prediction of Covid. The WHOOP company sponsored an observational study, published in 2026, of 30,000 users for 72 weeks, without a control group, that reported reduced alcohol intake (5.8 % points) by self-report. That doesn’t tell us much, and particularly about the merits of HRV for behavioral change. If you use the same device and conditions as longitudinal trends for multiple (at least 2-3) weeks that may be the one way to get something useful from the measurements. Data for overnight sleep with minimal motion and using RMSSD is the best proxy for real HRV. The reason to look at trends rather than any given night is that it more likely represents something, even though you won’t know with certainty what the “it” is. Keep in mind there are no data, no peer-reviewed evidence, to show that HRV fluctuation in-person has any correlation with health outcomes. Share Readiness Scores These are proprietary scores that integrate different metrics for each of the wearables: no algorithms have been disclosed. They are unvalidated against health outcomes. In a review of 14 composite health scores of readiness and recovery, HRV contributed 86% to the scores, followed by reading heart rate (79%), physical activity and sleep duration (both at 71%). That review noted the substantial variability in measurement protools and lack of standardization. Sleep staging is notoriously inconsistent and inaccurate by these sensors, which adds further to the HRV uncertainties for what the scores, which use sleep stage data, mean. Only resting heart rate has been shown consistently across devices to be extremely accurate. I’ve made a Table to summarize what we know about which metrics are included, the scores, any peer-reviewed studies that compared the readiness score with health outcomes, and the corresponding (if any, NA-not available) citation. You will note that some companies do not use the term “readiness," such as WHOOP for recovery, and Garmin, which has 2 different scores, one of which is Body Battery. Eight Sleep uses the term “Fitness Score.” They all include HRV; Apple includes a new metric they call “Recovery HRV” which among other components uses 7-days of sleep, but it is unclear what this means or how it is differentiated from other scores (there are clearly no data for outcomes). We have no knowledge of how the different components are weighted or whether any of these scores are better than resting heart rate, HRV alone, physical activity, or any other single metric. Since none of these are standardized, they are not interchangeable, so if you get a 90 for Oura that has no relationship to a 90 on a Google Fitbit Air. Notably, the company can update its algorithm for readiness score at any point without notification to device users. Without any useful evidence of actionability for these scores or established relationship with health outcomes, it is hard to make a case for their value. At the Apple recent announcement they showed their Readiness score (0-10) on the watch (Figure below) but there are no published data on this score, not even on their website. It’s available only on their new Watch Series 12 or Ultra 4 [of course, ;-)]. That exemplifies the problems with these scores, lack of data and evidence for being meaningful to promote health. Perhaps the best study (which isn’t saying much) is the WHOOP Recovery for golfer performance, because it did correlate with an objective outcome, even though there was no control group and the authors were all from the company. Among the 389 pro golfers, an absolute 10-per cent point increase in Recovery score was associated with about 0.5 fewer strokes per round. But that’s hardly a health outcome! WHOOP is also conducting a study in over 2,700 runners to see if their recovery score will be linked to less injuries and improved performance, but that is not yet published and has no control group or randomization. Putting This in Context For two decades I’ve been enthusiastic about the potential for digital health and particularly wearable biosensors. Over the years, we’ve seen some great progress for their ability to promote physical activity and accurately detect atrial fibrillation (the first FDA cleared deep learning AI for consumers). That work was the subject of rigorous research. But there are holes in the data and evidence for other metrics. One notable one is the “VO2 max” story that I wrote about earlier this year. At that time many subscribers asked me to cover heart rate variability, which I finally got to here. When I dived into the research and publication for HRV and readiness scores, I expected to find at least some that were of high quality and demonstrated their utility by linkage to health outcomes. To my surprise, I found none. The wearable sensor measurements for HRV (PRV) are, for the most part, accurate, but that validation work has only been done in small studies of healthy adults and does not take into account the long list of factors, from the device, software side, and the user side, that affect HRV measurements. Moreover, this metric chiefly relies on optical sensing and, as we have learned for heart rate PPG sensing, may be less accurate in people of color. Keep in mind that all of the health outcome association evidence comes from ECG-derived HRV; none are from wearable sensor data. I will repeat the key point: there's no peer-reviewed evidence to show that in-person HRV fluctuation—or efforts to raise your HRV— has any correlation with health outcomes. For those of you who look at your HRV on awakening, or even 2+ week trends of it being low, I hope this context helps to relieve any anxiety. Yes, low HRV (not PRV) has been shown to increase risk of some diseases as summarized above. But efforts to raise your HRV—a surrogate metric— has not been established for improving any health outcomes. HRV does not have any evidence of causality (the genetic evidence actually goes against this possibility). In this summary, I have not included data for other wearable sensors such as Polar, Samsung, Withings, Suunto, Amazfit, Coros, Ultrahuman, or additional mattress sensors. These are beyond my first-hand experience, and as far as I know from my in-depth review none have any peer-reviewed published data that differ from the 6 sensors I’ve reviewed here. From the points I’ve gone over above, we’re not ready for readiness scores. Besides being proprietary, they are predominantly based on metrics that have their own issues. It’s compounding the problem, like building a house without a solid foundation that has never undergone a rigorous inspection, and then selling it. Like I mentioned for PRV, you can look at trends over weeks rather than any single day, to get a handle, but even that may not be helpful. There’s simply no evidence that these scores meaningfully relate to health outcomes. I’d emphasize they might, but that requires doing prospective or randomized studies to prove it. None exist. There’s great promise for HRV/PRV utility_. For example**,**_ Prof Maiken Nedergaard, who discovered the brain glymphatics that are essential in eliminating metabolic waste products from the brain during sleep, has posited that HRV could be a non-invasive marker for neuromodulator oscillations, brain-body regulatory circuits, and brain clearance. That would be extremely useful, but like everything else on HRV and readiness scores it requires solid research and validation. The lay media isn’t helping much to get the story straight. Earlier this year The Economist published a piece entitled “The most useful indicator of your overall health” which ordained HRV as an “accumulated stress score.” That’s akin to the false assertion about VO2max: “V02 max is the singular most powerful marker for longevity.” As I’ve summarized here, that is not established. The fact is that so many things can lower HRV, including physical exercise (especially an intense workout), reduced sleep quality, stress, the list above, no less the device, signal, and software. Whatever fluctuations observed have not been correlated with any health outcome. Sadly, “datamaxxers” are widely using HRV and readiness scores that have never been validated to mean anything. We already know that for some people using the sensors for sleep metrics, it can induce “orthosomnia,” an obsession to get high sleep quality, with associated high levels of anxiety. In an experiment done by a company to promote sleep quality for its employees, “For those employees who did use the trackers, many reported feeling perfectly rested until their tracker told them they had had a terrible night. Others were told that they had slept like a baby when they had actually been lying awake worrying about the quality of their sleep. “ The same problem can result from preoccupation with HRV or readiness scores, with anxiety that would lead to further reduction in both. It you are using a wearable like >100 million American adults, it’s OK to look at these data, but contextualized with the major caveats reviewed here. If you are one to require evidence that HRV or readiness scores are linked to health outcomes, you may not even want to look. The companies make it hard to turn them off! Let me end with the companies that make and sell wearables. Apple’s doubling down on HRV (24-fold more reporting and heart rate very 5 seconds) and introduction of a Readiness score tells us that consumers have bought into these metrics and they are joining the club. However, all of this is occurring with a backdrop of tens millions of users, claims about the data that are not backed up by adequate evidence, marketing way out in front of whatever limited data exists, and not being transparent about their readiness score algorithms. The companies can well afford to do the research that is needed to connect these metrics with health outcomes show, once and for all, that increasing HRV or using readiness scores promotes our health. If they believed and invested in the products they are selling, we’d not be in this position of not knowing. That’s essentially where we are with HRV and readiness scores. Perhaps someday this will change and we’ll have good reason to embrace them. NB: I wrote this post. No AI. I have no conflicts of interest with any of its content. Loading... Ground Truths has 215,000 subscribers from every US state and 214 countries. There are over 300,000 followers of Ground Truths so more than 90,000 folks who can easily convert to be free subscribers. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. If you’re not a subscriber, please join! If you found this interesting PLEASE share it! Share Ground Truths The proceeds from all voluntary paid subscriptions go to support our summer internship program. It enabled us to accept and support a record number of 62 summer interns that joined us in 2026! These are high school, college and medical students selected from thousands of applicants. We couldn’t do this expanded program without the funds coming in through Ground Truths. Thank you!
Broncos vs. Jaguars score predictions for Week 2
Do you think the Broncos will bounce-back against the Jaguars in Week 2? Here’s our score predictions.
Browns Predicted to Shelve Deshaun Watson Mid-Game Against Buccaneers
The Cleveland Browns are expected to essentially end their relationship with quarterback Deshaun Watson during Week 2. The post Browns Predicted to Shelve Deshaun Watson Mid-Game Against Buccaneers appeared first on HEAVY.
Predicting the Broncos' 16 remaining games and final record
Will the Broncos win enough games to make the NFL playoffs this season? Here's our prediction.
Bold predictions in Fantasy Football Week 2
Fantasy Football bold predictions for Week 2.
The Athletic predicts upset in Florida's SEC opener against Auburn
Florida enters Saturday as a road favorite, but The Athletic predicts Auburn will hand the Gators their first loss of the season.
Sutton's predictions: Brighton v Arsenal
There might be a bit of an edge to this one after Brighton boss Fabian Hurzeler had a pop at Arsenal over their time-wasting when they beat the Seagulls near the end of last season. It doesn't seem to matter who they are missing from their team, Brighton are just bold as brass with the way they press and get men forward. Luka Vuskovic, who they got from Spurs for £46m in the summer, is only 19 but he strolled through the game against United.
Expert score prediction: Nebraska vs. North Dakota
Nebraska’s final non-conference opponent has shaped up to be a unique challenger. North Dakota is 3-0 and has scored 40-plus points in each contest.
Predicted Newcastle United team v Hull City with four changes
Looking at this Newcastle United team v Hull City. Matthias Jaissle and his players looking to bounce back from Monday’s defeat at Leeds.
Juventus v Atalanta match preview and prediction
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Brighton v Arsenal Team News, Predicted Line-up, Score Prediction
Arsenal face a significant test at the Amex on Saturday as they attempt to maintain their perfect start to the season against a free-scoring Brighton side.Having looked at the wider challenge posed by...
AS Roma vs Inter Milan – Predicted lineup and team news
AS Roma and Inter Milan, the two leading teams in the current Serie A standings, will meet in a major top-of-the-table contest at the Stadio Olimpico in Rome on Saturday. The match will be the final f...
Barcelona XI vs Sevilla – Predicted lineup and team news
Barcelona will aim to extend their winning run and maintain their impressive goalscoring form ahead of the international break as they travel to face Sevilla on Saturday in their upcoming La Liga fixt...
Titans vs. Eagles predictions: Pain
It is going to be another tough Sunday at Nissan Stadium.
Bears vs. Vikings Predictions: Spread, Total, and 1 Bold Pick!
Our guy @wiltfongjr makes his Bears-Vikings pick vs. the spread, the total, and more right here.
Sunderland vs Manchester City Premier League Prediction, Odds, Pick
Read Sunderland vs Manchester City predictions, picks and odds in our Premier League betting preview for Sunday, September 20.
Bilodeau’s Bakery Returns for Palmetto Sports Daily’s Locks of the Week
WATCH: Palmetto Sports Daily crew predicts their “Locks of the Week” for college football games including a cupcake selection and splitting on LSU-Ole Miss.
Social Inequality and Structural Vulnerability as Predictors of Health-Related Quality of Life: A Prospective Cohort Study
Background Social inequality and structural vulnerability are increasingly recognized as important social determinants of health; however, longitudinal evidence regarding their impact on heal...
Memphis ranked No. 94 in EvanMiya’s preseason ranking
Memphis basketball will begin its season in a little over a month, but as we get closer to the start of the basketball season, more preseason predictions are being released. On Friday, Evan Miya released his preseason rankings for all 365 Division I teams, and he had the Tigers ranked No. 94.
Steelers Afternoon Drive: Patriots Preview & Predictions
Welcome to the Steelers Afternoon Drive, our new daily podcast featuring all the latest Steelers news and analysis. Alan Saunders […]
Chargers vs. Raiders I preview, prediction and how to watch
Las Vegas Raiders (1-0) AT LA Chargers (0-1)When: Sunday 1:05 pmWhere: SoFi StadiumTV: CBS-2 (Los Angeles), CBS-8 (San Diego), Paramount/NFL+ Radio: Los Angeles: KYSR-FM (98.
Iowa Football: Betting Prediction For Northern Iowa Game
HawkeyeRoundtable Publisher Brad Schultz shares his betting prediction for Iowa's non-conference finale, including spread, total and more.
Vikings-Bears Week 2 score predictions from the Bring Me The Sports staff
Four of our five pickers didn't drink enough purple Kool-Aid this week. Sad, but true.
Florida's Jadan Baugh Earns Strong NFL Draft Prediction
Florida Gators running back Jadan Baugh has received a bold prediction for the 2027 NFL Draft.
Florida vs Auburn Prediction: Which Rebuild Takes the Next Step?
Things have gone well so far for Florida and Auburn under their new coaching staffs. Which team takes the next big step? Here's the CFN preview and prediction.
Everton vs Ipswich Town: Predicted Line-Up
Toffees host Ipswich looking to get back to winning ways
Kentucky at Texas A&M: Final Thoughts and Prediction
Kentucky faces a tough matchup as they travel to Kyle Field to take on #9 Texas A&M. Here are my final thoughts and prediction of the game.
Success Indicators for UW Against Eastern Washington; Preview & Prediction
Eastern Washington has yet to win a game on the road in two tries this season. It enters Husky Stadium with a 1-2 record to play Washington in what should be a definitive opportunity for the Huskies to put their early-season issues behind them. On paper, the FCS opponent provides a mismatch in favor of …
Falcons Roundtable: Can Atlanta Overcome Its Quarterback Crisis Against Panthers?
The Atlanta Falcons face the Carolina Panthers in Week 2 with major quarterback concerns after their loss to the Steelers. Our Falcons Roundtable breaks down Cooper Rush, Tua Tagovailoa's injury, Kevin Stefanski's challenges and score predictions for the NFC South matchup.
KCTV Sports Huddle: Preview, predictions for Chiefs-Colts primetime matchup
The KCTV sports team gathers to answer pressing questions and predict what's to come for Chiefs-Colts this week on 'Sunday Night Football.'
On Your Side: Missouri attorney general orders prediction markets to stop unlicensed sports wagering
Missouri Attorney General Catherine Hanaway announced that her office has issued cease-and-desist letters to multiple prediction market operators offering sports “event contracts” to Missourians.
Everton vs Ipswich: Preview, Team News, and Predicted XI
Everton host Ipswich Town in the Premier League at the Hill Dickinson Stadium on Saturday afternoon.
Engrams Embedding Entendre: Codesign for Efficient DRAM/SSD Offloading
Engram extends standard token embeddings with learned multi-token lookups. Recurring local patterns retrieve vectors directly, reducing the need to reconstruct them through attention and feed-forward layers. With Engram model architecture optimization, it allows for lower HBM capacity to be needed for models at the same quality. This does not mean there won’t be an insane demand for HBM but it just means that model architecture will continue to innovate around constraints. This model architecture design is naturally codesigned for parameter offloading: each token accesses a few embedding rows whose addresses depend on token IDs, not hidden states. The runtime can prefetch those rows from host DRAM while earlier layers compute, keeping the table outside HBM without transferring entire weight matrices. Our Memory model contains our latest estimates of quarter by quarter HBM, DRAM, & NAND supply and demand. Offloading frees HBM for model weights and KV cache, potentially supporting larger batches or more concurrent sessions. When DRAM becomes the next constraint, NVMe offers another tier. Recommendation systems already cache frequently or recently accessed embedding rows in faster memory while backing colder rows with SSDs. After NVIDIA roadmap had to change due to massively despec’ing Rubin Ultra from 1024GB to now ~200GB of HBM per chip, model architecture optimizations like emgram maybe helpful. Our DeepSeek-V4.1-Flash configuration uses roughly 189 GiB of memory for Engram. We replace it with a memory-mapped (mmap) file and measure serving performance with offload to SSD. Later on in our report, we will show our Engram offloading experiments along with the official InferenceX agentic inference serving results on Engram models like DeepSeekv4.1 Flash across all 6 NVIDIA GPU SKUs along with MI355X. Unsurprisingly, the CUDA Moat is still mogging MI355X on the ultra popular DeepSeekV4.1 Flash model. We also show how even on high capacity HBM SKUs, offloading emgrams to DRAM could result in even better performance for most of the pareto than keeping the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX is the only inference benchmark in the world to have TPUv7, Jalapeño, Nvidia Rubin NVL72, AMD, and soon, SambaNova and Trainium. Due to how realistic AgentX scenario is to real world agentic inference workloads, AMD has committed to collaborating on MI455X UALoE72 too. DeepSeek did not release the original paper’s two trained Engram models. We replicated its setup on fineweb-edu using the released code and training hyperparameters, at an estimated 6E18 FLOPs per run. We observed the same U-shape scaling: Engram improved performance over pure MoE baselines. We also reproduced DeepSeek’s results, where earlier-layer representations with Engram resembled those of later layers. Like the original Engram paper, it is possible to probe Engram’s gate scores to see what n-grams DeepSeek-V4.1-Flash makes the most use of. Our gate scan finds names, code fragments, relational phrasing, and boilerplate. These examples prioritize interesting-ness over gate strength. One unexpected result was Wright : Ace Attorney. These examples suggest learned memory optimizes the training objective, not a judgment of which facts deserve storage. Licenses, bibliography fragments, API scaffolding, and website furniture can provide prediction shortcuts, so the value of additional Engram capacity may depend on what survives data preparation. This does not show that table capacity is “wasted”: the evaluation-corpus scan establishes neither training exposure nor the capacity occupied by each category. For offloading, strong gates do not identify cache-hot rows. Low gates do not automatically save reads either: computing the gate requires the retrieved key and undoes the performance gain of a fused kernel. Skipping reads would require a separate usefulness predictor before retrieval. In the original paper’s inference-time ablation, factual-knowledge benchmarks retained just 29–44% of their original performance, while reading comprehension retained 81–93%. This is due to the training–inference mismatch. The resulting degradation therefore measures this trained model’s dependence on Engram, not the performance difference between models trained with and without it. In our ablations, suppressing Engram worsens token likelihood across all evaluated domains, especially encyclopedia text and several code corpora. Surprisingly, GSM8K accuracy stays within measured run-to-run variation and removing Engram has no effect. Engram is not a detachable dictionary beside an unchanged MoE. Removing it changes downstream features and expert selection. We tested whether rerouting hurts or compensates by holding tokens fixed in a teacher-forced experiment on CRUXEval, a code-reasoning benchmark of small Python functions where the model predicts a function’s output from its code and an input, and scoring the reference answer. Removing Engram raised answer loss from 0.2848 to 0.3093 bits/token. Forcing the ablated model to use the original Engram-on expert choices made it worse still, at 0.3375 bits/token. Rerouting partially compensates for the missing memory. Memory features and expert selection work together, rather than following a clean “memory stores facts; experts reason” division. On the same CRUXeval, removing Engram during either phase reduced accuracy and increased generated tokens; removing it throughout produced the largest changes. Keeping Engram for prefill leads to more correct answers than keeping for decode likely due to semantically richer KV cache transferred to decode workers, allowing it to mitigate some of the performance loss. Engram’s table is large, but each lookup is small. DeepSeek-V4.1-Flash requests 24 rows at each of two Engram layers, about 12.4 KiB per processed token position across the model, or 3.1 KiB per GPU when split across four GPUs. Currently as of Day 7 since Model Release, MI355X is still 2-4x worse performance per dollar compared to B200 even when normalized by Mi355X’s lower TCO. Our full total cost of ownership breakdown comes from our AI Cloud TCO Model along with monthly market surveys of over 100+ gpu clouds & gpu cloud customers. On the Day 0 release of DeepSeekv4.1 Flash, NVIDIA vLLM works out of the box with zero issues across all 6 SKUs: H100, H200, B200, B300, GB200, GB300! This was thanks to the amazing work by the NVIDIA & Interact teams! In comparison, AMD vLLM did not work on day 0 for DeepSeekv4.1 Flash.Source: SemiAnalysis InferenceX AMD’s vLLM documentation points to using vllm/vllm-openai-rocm:deepseekv41-flash-0909, but from hour 0 of the model release to hour 23, AMD has not publicly released the image. AMD claims, “SPEED IS THE MOAT,” yet it has still not released it by the 23rd hour. We wish that, going forward, the AMD team has a better process for hour 0 model releases. Eventually when they did publicly release for “day 0” image support, performance-wise, it is currently up to 14.8x worse perf per dollar than H200 and up to 42x worse perf per dollar than B200/B300. The power of the CUDA MOAT is NVIDIA’s collaboration with its massive 6 million-developer community ecosystem including most of the vLLM & SGLang & Tokenspeed maintainers which means that CUDA is optimized on day 0. Overall, AMD did make significant improvements but the performance per dollar is still currently 2-4x worse than B200. AgentX Engram DRAM Offloading Improving Performance The HBM and DRAM offload use the same GPU kernel to select and dequantize rows. With HBM, it reads GPU memory; with Unified Virtual Addressing (UVA), it reads pinned host memory directly. Both support full decode graphs. Moving the table into HBM accelerates only the sparse lookup, leaving decoder computation and communication unchanged, resulting in little overall benefit while consuming memory otherwise available to KV cache. Another benefit of Engram offloaded to DRAM which means you can reduce the communication overhead by using less HBM GPUs per replica. For example, when enabling Engram offloading on B300, we are able to switch from TP4 to now TP2 which improves the pareto curve by up to 1.6x. When iso-model quality, less HBM is required as the engrams could be offloaded to host DRAM. Thus HBM bandwidth matters way more than HBM capacity. For inference workloads where memory bandwidth matters the most, 4-hi HBM provides the best $/bandwidth and therefore lowest cost per token. If China continues to make more and more revolutionary model architecture innovations, soon it could potentially 0Hi HBM stacks. [ ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) [ Long Live the Short King: Why 4-hi HBM Wins ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Myron Xie, Bryan Shan, and 3 others · Sep 13 [ Read full story ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Moreover, on B300 and week-0 stack, moving the Engram table back to HBM did not improve results and stay within run-to-run variance. This is a result of the work optimizing DRAM offload, such as async, and overlap. SSD Offloading On B200, we created a unoptimized vLLM fork and stored the Engram tables in memory-mapped files on local SSDs. File backing lets the OS reclaim table pages when other applications need RAM. Pages already cached in memory can be served without reading the SSD again. Furthermore, note that we were unable to turn on GDS The unoptimized SSD implementation changes how rows reach the GPU. It copies row IDs to the CPU, deduplicates them, gathers the requested rows into pinned buffers, copies those rows back to the GPU and dequantizes them. This work runs between segments of the GPU execution graph. Native UVA performs row selection and dequantization directly on the GPU, avoiding the CPU round trip. A warm filesystem cache removes physical SSD reads, but leaves the coordination, row gathering and transfers. This is why a file already cached in RAM can still perform worse than a pinned DRAM table. The comparison measures the whole serving path; it does not separate the time spent on each of these operations. B200 DRAM dominates both measured SSD serving curves in total tokens per dollar and P90 interactivity. Near 125 tokens/s/user, DRAM delivers 121 million total tokens per dollar versus 52 million for SSD. For production serving, SSD offloading is likely not worth the tradeoff. On the B200 configurations we measured, SSD offloading loses on both measures: every observed SSD point has a DRAM alternative that delivers higher P90 interactivity and more total tokens per dollar. The only points where Cheaper storage does not automatically produce a cheaper inference service. Moving Engram to SSD leaves the same four expensive GPUs and the rest of the server in place. Reclaiming RAM only creates an economic benefit if it enables a cheaper server configuration or additional useful capacity. The current unoptimized path provides neither benefit, and the filesystem cache still consumes RAM when table pages are resident. Next we will look at the mechanisms and specific implementation of ngrams from DeepSeekv4.1 Flash, LongCat, Qwen3.8 Flash Next.
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Canada is looking for deeper research and education links with the EU, according to its prime minister, Mark Carney. His comments followed hard on the heels of an offer from European Commission President Ursula von der Leyen to give his nation associate membership of the EU, a status that currently has no legal status or definition. Both sides have yet to detail how this newfound political enthusiasm will translate into concrete action.Addressing the European Parliament on September 17, Carney cited both the Horizon Europe research and innovation programme and the Erasmus+ student mobility scheme as areas for future collaboration.Association with the next iteration of Horizon Europe “would allow us to pool resources, cooperate on frontier technology, leverage our world-class research institutes and universities,” he told MEPs. Canada joined the present programme in 2024, but only takes part in Pillar 2, which supports collaborative research projects. Carney also said that young people should be allowed to work, study and live where they want, on either side of the Atlantic. “Canadian membership of Erasmus+ would give your students and ours more opportunity to learn at some of the world’s top universities, to broaden their horizon and to build those lasting ties that will sustain our alliance,” he said.Limited exchanges with Canada are already possible, for example in the Erasmus Mundus strand of the scheme, which supports joint master’s programmes. This would be considerably increased if the country became a full associate member, a club currently limited to nations neighbouring the EU.Most favoured nation?As for the offer of associate membership of the EU, the aim would be to build “an alliance for the future to create a common prosperity and economic security base,” von der Leyen said during her state of the European Union address, also in the European Parliament in Strasbourg, on September 16.“We will work on intelligent manufacturing,” she went on. “We will create a tech alliance. We will integrate defence industrial bases. We will make the Arctic a flagship joint project. We will work on energy-critical minerals and batteries, on [artificial intelligence], quantum, cyber and economic security.”Mona Nemer, Canada’s chief science advisor, told Science|Business that the political speeches by Carney and Von der Leyen will open the door to even more scientific cooperation between Canada and the EU. She even attributed the broader political partnership to the scientific diplomacy of Horizon Europe association. “The science and research collaborations have predated this political rapprochement,” she said.While she is not part of the government decision-making process, Nemer thinks Carney’s speech in the European Parliament is more than “political posturing” and the Canadian government is likely to come up with more practical ideas for how to boost collaboration in the near future. “The prime minister mentioned Horizon [Europe] and Erasmus+, maybe there is something more concrete around this,” she said.She went on to suggest that Canada and Europe could also work more closely together on setting international standards for emerging technologies, AI safety, data sovereignty and joint research infrastructures, such as particle accelerators and marine research fleets.Nemer confirmed that science cooperation will be on the agenda at an upcoming EU-Canada summit in October. She met recently with EU ambassadors to Canada to "see where the possibilities are, to signal that there is interest."Room for expansionLike other countries outside Europe that are associated to Horizon Europe, such as South Korea, Australia or Japan, Canada is not allowed to host European Research Council (ERC) grantees. However, Canadian universities hint they would like re-open that discussion. “Canada’s participation in Horizon Europe, the world's largest research funding programme, has been a big success,” said Robert Asselin, the chief executive of U15 Canada, an umbrella body for some of the country’s biggest research universities, in a statement. “Ensuring swift re-association to the next research programme, will be vital to maintain this momentum. We would welcome efforts to explore further opportunities to collaborate with European partners, such as through the European Research Council,” he said. Related articlesHow a new multilateral defence bank could boost innovationEurope’s independence hinges on innovation, von der Leyen saysBut so far, there are no details from the Commission on how von der Leyen’s proposal would boost Canadian access to the ERC or other sensitive research calls in Horizon Europe. And it is unclear how other non-European associated countries, such as New Zealand or Australia, would react if Canada was given better access than them. The same questions may arise closer to home. “How would a new category of associate member of the EU differ from Norway’s participation in EU programmes through the European Economic Area?” asked Jan Palmowski, secretary general of the Guild of European Research-Intensive Universities. “And how does this proposal for Canada change discussion for the UK, Norway and Switzerland for the next Multiannual Financial Framework?”Another area for discussion is how far Canadian organisations can participate in sensitive projects. “I would expect some of the most crucial issues at hand to be whether Canada can participate more closely and predictably in calls that are deemed sensitive on research security or EU preference grounds, on critical technologies, for instance,” Palmowski said. Canada’s participation in other parts of Horizon Europe, as well as the European Competitiveness Fund, might also be discussed, he added.Whatever the details, closer cooperation between the EU and Canada represents a “substantial” opportunity, according to Mattias Björnmalm, secretary general of the Cesaer university group. “Scientific excellence depends on talented people being able to work together, exchange knowledge and access complementary expertise and infrastructure,” he said. “A closer EU-Canada partnership would strengthen these conditions and provide greater certainty for long-term collaboration.”
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