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Why we want a brand new measure of maternal well being: the “lifetime threat of maternal close to miss” – IJEblog

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Ursula Gazeley

In accordance with the latest knowledge from the World Well being Group, the lifetime threat of maternal demise for a lady in Chad is a staggering 1 in 15, in contrast with 1 in 43,000 in Norway. Because of this a woman in Chad has an nearly 3000 instances larger threat of dying from a maternal trigger throughout her reproductive lifetime than a woman in Norway. The lifetime threat of maternal demise is a helpful measure to assist us perceive this world inequality in maternal mortality.

Maternal demise is a tragic final result of being pregnant. Though it’s now uncommon in most elements of the world, progress is slowing. Moreover, many extra ladies expertise extreme being pregnant problems that carry them dangerously near demise — so shut that they’re very more likely to want emergency hospital care to save lots of their lives. Such occasions are referred to as “maternal close to misses” and are recognized primarily based on organ dysfunction (e.g. cardiovascular, respiratory, renal, haematological, hepatic or neurological) or complication-specific standards, comparable to eclampsia, septicaemia or the necessity for hysterectomy or blood transfusion following obstetric haemorrhage.

Maternal close to miss is a vital maternal well being final result that displays a well being care system’s means to offer emergency obstetric care and save a girl’s life when problems come up. Furthermore, experiencing such extreme problems can have long-term penalties for a lady’s bodily, psychological, sexual, social and financial wellbeing.

There have been many calls to enhance metrics on maternal morbidity, however comparatively little progress in reaching this. In our current examine, revealed within the IJE, we introduce a brand new measure referred to as the “lifetime threat of maternal close to miss” to estimate the burden of maternal close to miss morbidity throughout ladies’s reproductive lifetimes. This measure is analogous to the lifetime threat of maternal demise, utilized to life-threatening morbidity.

Present indicators of maternal close to miss prevalence — each the maternal close to miss ratio and maternal close to miss charge — solely account for the extent of obstetric threat related to a given being pregnant. Neither measure accounts for the dangers related to fertility ranges (ladies are prone to experiencing a close to miss throughout every being pregnant they’ve), nor ladies’s possibilities of surviving the reproductive ages of 15–49 years (to expertise a close to miss, a girl should not have died from a maternal trigger or the rest). The lifetime threat of maternal close to miss addresses these deficits and captures the dynamics related to obstetric threat, fertility ranges and ladies’s reproductive age survival.

In our examine, we demonstrated use of this measure in Namibia. Our estimates point out {that a} 15-year-old lady in Namibia faces a 1 in 38 lifetime threat of experiencing a maternal close to miss, in contrast with a 1 in 142 lifetime threat of maternal demise. When these dangers are mixed, the lady has a 1 in 30 probability of both dying from a maternal trigger or experiencing a near-miss complication throughout her reproductive years.

This mixed lifetime threat of maternal demise or close to miss is a vital instrument for advocacy — to focus on the influence of maternal well being on ladies’s lives and the necessity for the worldwide group to redouble its efforts to finish preventable maternal mortality and morbidity. Estimation is required throughout high- and low-income settings to attract consideration to world inequities in antagonistic being pregnant outcomes.

To measure a rustic’s lifetime threat of maternal close to miss, the perfect state of affairs is to make use of nationally consultant knowledge on the maternal close to miss ratio (the variety of maternal close to misses per 1000 reside births). The variety of maternal close to misses can solely come from well being care services. In international locations the place many ladies give beginning at dwelling, the variety of reside births ought to come from population-based estimates, in order that births at dwelling are additionally counted.

Throughout all world areas, births in well being care services are lowest in sub-Saharan Africa. We’ll subsequently overestimate the lifetime threat of maternal close to miss if we depend on (unadjusted) facility-based estimates of births in these settings. In our examine, we selected to use this indicator to Namibia as a result of, though it’s a high-burden setting, high-quality nationwide population-based maternal close to miss surveillance knowledge have been obtainable.

To start out measuring the lifetime threat of maternal close to miss globally, extra international locations have to routinely measure and report what number of maternal close to misses happen on the nationwide degree, as they do for maternal deaths. A number of high-income international locations already report this repeatedly (e.g. the Scottish Confidential Audit of Extreme Maternal Morbidity and the Irish Nationwide Audit of Extreme Maternal Morbidity). Except for the maternal close to miss ratio, all different knowledge required to estimate the lifetime threat of maternal close to miss can be found by way of open entry from World Inhabitants Prospects.

Learn extra:

Gazeley U, Polizzi A, Romero-Prieto JE, et al. Lifetime threat of maternal close to miss morbidity: a novel indicator of maternal well being. Int J Epidemiol 2023; 18 December. doi: 10.1093/ije/dyad169


Ursula Gazeley (@GazeleyUrsula) is a PhD scholar on the London Faculty of Hygiene and Tropical Medication. Her analysis pursuits give attention to measurement challenges of maternal mortality and morbidity, together with outcomes past the 42-day postpartum interval.



The Pitfalls Of OLS Regression In Panel Information Evaluation

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Distinction Between Panel Information and Cross-Part Information

Cross-sectional information and panel information are two distinct varieties of information buildings utilized in statistical and econometric analyses, every serving totally different analysis functions.

Cross-Sectional Information:

  • Definition: Information collected by observing many topics (equivalent to people, corporations, nations, or areas) at a single level or interval in time.
  • Traits:
    • Offers a snapshot of a inhabitants at a particular second.
    • Helpful for analyzing variations amongst topics with out contemplating temporal adjustments.
  • Instance: Surveying 1,000 people in 2025 to evaluate their present well being standing, with none details about their well being historical past.

Panel Information:

  • Definition: Multi-dimensional information involving measurements over time, the place observations are made on the identical topics at a number of time factors.
  • Traits:
    • Combines each cross-sectional and time-series information, permitting for the evaluation of dynamics over time.
    • Permits researchers to check adjustments inside topics and management for individual-specific variables that don’t differ over time.
  • Instance: Monitoring the annual earnings and employment standing of the identical 500 people over a decade to research earnings mobility.

Key Variations:

  • Temporal Dimension:
    • Cross-Sectional Information: No time dimension; captures information at one cut-off date.
    • Panel Information: Incorporates a time dimension; tracks adjustments over a number of durations.
  • Evaluation Capabilities:
    • Cross-Sectional Information: Appropriate for figuring out correlations and variations amongst topics at a particular time.
    • Panel Information: Permits for inspecting causal relationships, particular person dynamics, and temporal results by observing the identical topics over time.
  • Pattern Dimension and Construction:
    • Cross-Sectional Information: Sometimes entails a bigger pattern dimension, offering a broad overview of a inhabitants at a particular time.
    • Panel Information: Might have a smaller pattern dimension as a result of requirement of repeated observations over time, however gives richer insights into temporal adjustments.

Understanding these variations is essential for choosing the suitable information construction primarily based on the analysis targets and the character of the evaluation.

What occurs if we use similar regression methodology (OLS) for each Panel and Cross-Part information?

Making use of the identical regression strategies to each panel information and cross-sectional information can result in suboptimal or deceptive outcomes as a result of inherent variations between these information buildings.

Cross-Sectional Information:

  • Nature: Observations are collected at a single cut-off date throughout a number of topics.
  • Evaluation: Customary regression methods, equivalent to Peculiar Least Squares (OLS), are applicable, assuming that explanatory variables are uncorrelated with the error time period.

Panel Information:

  • Nature: Observations are collected over a number of time durations for a similar topics, capturing each cross-sectional and temporal dimensions.
  • Evaluation: Specialised strategies account for individual-specific results and temporal dynamics.
    • Fastened Results Mannequin: Controls for time-invariant particular person traits by differencing out these results, specializing in within-individual variations over time.
    • Random Results Mannequin: Assumes that individual-specific results are uncorrelated with explanatory variables, permitting for each inside and between-individual variations to tell estimates.
    • First Distinction Estimator: Examines adjustments between consecutive time durations to remove individual-specific results, appropriate when information spans solely two time durations.

Utilizing customary OLS regression on panel information with out contemplating its construction can result in biased estimates because of unaccounted individual-specific results and potential endogeneity points. Due to this fact, it’s important to use regression strategies tailor-made to the information construction to acquire legitimate and dependable outcomes.



Sequential linear() Animation With N Components

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Let’s suppose you may have N parts with the identical animation that ought to animate sequentially. The primary one, then the second, and so forth till we attain the final one, then we loop again to the start. I’m certain you understand what I’m speaking about, and also you additionally know that it’s difficult to get such an impact. You have to outline advanced keyframes, calculate delays, make it work for a selected variety of objects, and many others.

Inform you what: with fashionable CSS, we are able to simply obtain this utilizing a number of strains of code, and it really works for any variety of objects!

The next demo is at the moment restricted to Chrome and Edge, however will work in different browsers because the sibling-index() and sibling-count() capabilities achieve broader assist. You’ll be able to observe Firefox assist in Ticket #1953973 and WebKit’s place in Situation #471.

Within the above demo, the weather are animated sequentially and the keyframes are so simple as a single to body altering a component’s background coloration and scale:

@keyframes x {
  to {
    background: #F8CA00;
    scale: .8;
  }
}

You’ll be able to add or take away as many objects as you need and all the things will hold operating easily. Cool, proper? That impact is made potential with this unusual and complex-looking code:

.container > * {
  --_s: calc(100%*(sibling-index() - 1)/sibling-count());
  --_e: calc(100%*(sibling-index())/sibling-count());

  animation: 
    x calc(var(--d)*sibling-count()) infinite 
    linear(0, 0 var(--_s), 1, 0 var(--_e), 0);
}

It’s a bit scary and unreadable, however I’ll dissect it with you to grasp the logic behind it.

The CSS linear() operate

When working with animations, we are able to outline timing capabilities (additionally referred to as easing capabilities). We will use predefined key phrase values — corresponding to linear, ease, ease-in, and many others. — or steps() to outline discrete animations. There’s additionally cubic-bezier().

However we have now a more moderen, extra highly effective operate we are able to add to that listing: linear().

From the specification:

A linear easing operate is an easing operate that interpolates linearly between its management factors. Every management level is a pair of numbers, associating an enter progress worth to an output progress worth.

animation-timing-function: linear creates a linear interpolation between two factors — the begin and finish of the animation — whereas the linear() operate permits us to outline as many factors as we would like and have a “linear” interpolation between two consecutive factors.

It’s a bit complicated at first look, however as soon as we begin working with it, issues turns into clearer. Let’s begin with the primary worth, which is nothing however an equal of the linear worth.

linear(0 0%, 1 100%)

Now we have two factors, and every level is outlined with two values (the “output” progress and “enter” progress). The “output” progress is the animation (i.e., what’s outlined throughout the keyframes) and the “enter” progress is the time.

Let’s contemplate the next code:

.field {
  animation: transfer 2s linear(0 0%, 1 100%);
}

@keyframes transfer {
  0%   {translate: 0px }
  100% {translate: 80px}
}

On this case, we would like 0 of the animation (translate: 0px) at t=0% (in different phrases, 0% of 2s, so 0s) and 1 of the animation (translate: 80px) at t=100% (which is 100% of 2s, so 2s). Between these factors, we do a linear interpolation.

As an alternative of percentages, we are able to use numbers, which implies that the next can be legitimate:

linear(0 0, 1 1)

However I like to recommend you stick with the share notation to keep away from getting confused with the primary worth which is a quantity as properly. The 0% and 100% are implicit, so we are able to take away them and easily use the next:

linear(0, 1)

Let’s add a 3rd level:

linear(0, 1, 0)

As you’ll be able to see, I’m not defining any “enter” progress (the share values that characterize the time) as a result of they don’t seem to be necessary; nevertheless, introducing them is the very first thing to do to grasp what the operate is doing.

The primary worth is at all times at 0% and the final worth is at all times at 100%.

linear(0 0%, 1, 0 100%)

The worth can be 50% for the center level. When a management level is lacking its “enter” progress, we take the mid-value between two adjoining factors. In case you are accustomed to gradients, you’ll discover the identical logic applies to paint stops.

linear(0 0%, 1 50%, 0 100%)

Simpler to learn, proper? Are you able to clarify what it does? Take a couple of minutes to consider it earlier than persevering with.

Obtained it? I’m certain you probably did!

It breaks down like this:

  1. We begin with translate: 0px at t=0s (0% of 2s).
  2. Then we transfer to translate: 80px at t=1s (50% of 2s).
  3. Then we get again to translate: 0px at t=2s (100% of 2s).

Many of the timing capabilities enable us to solely transfer ahead, however with linear() we are able to transfer in each instructions as many occasions as we would like. That’s what makes this operate so highly effective. With a “easy” keyframes you’ll be able to have a “advanced” animation.

I might have used the next keyframes to do the identical factor:

@keyframes transfer {
  0%, 100% { translate: 0px }
  50% { translate: 80px }
}

Nonetheless, I gained’t be capable of replace the share values on the fly if I desire a totally different animation. There isn’t a strategy to management keyframes utilizing CSS so I must outline new keyframes every time I want a brand new animation. However with linear(), I solely want one keyframes.

Within the demo under, all the weather are utilizing the identical keyframes and but have fully totally different animations!

Add a delay with linear()

Now that we all know extra about linear(), let’s transfer to the primary trick of our impact. Don’t overlook that the concept is to create a sequential animation with a sure quantity (N) of parts. Every factor must animate, then “wait” till all of the others are carried out with their animation to begin once more. That ready time might be seen as a delay.

The intuitive means to do that is the next:

@keyframes transfer {
  0%, 50% { translate: 0px }
  100% { translate: 80px }
}

We specify the identical worth at 0% and 50%; therefore nothing will occur between 0% and 50%. Now we have our delay, however as I mentioned beforehand, we gained’t be capable of management these percentages utilizing CSS. As an alternative, we are able to categorical the identical factor utilizing linear():

linear(0 0%, 0 50%, 1 100%)

The primary two management factors have the identical “output” progress. The primary one is at 0% of the time, and the second at 50% of the time, so nothing will “visually” occur within the first half of the animation. We created a delay with out having to replace the keyframes!

@keyframes transfer {
  0% { translate: 0px }
  100% { translate: 80px }
}

Let’s add one other level to get again to the preliminary state:

linear(0 0%, 0 50%, 1 75%, 0 100%)

Or just:

linear(0, 0 50%, 1, 0)

Cool, proper? We’re capable of create a posh animation with a easy set of keyframes. Not solely that, however we are able to simply regulate the configuration by tweaking the linear() operate. That is what we are going to do for every factor to get our sequential animation!

The complete animation

Let’s get again to our first animation and use the earlier linear() worth we did earlier than. We’ll begin with two parts.

Nothing stunning but. Each parts have the very same animation, in order that they animate the identical means on the similar time. Now, let’s replace the linear() operate for the primary factor to have the other impact: an animation within the first half, then a delay within the second half.

linear(0, 1, 0 50%, 0)

This actually inverts the earlier worth:

Tada! Now we have established a sequential animation with two parts! Are you beginning to see the concept? The purpose is to do the identical with any quantity (N) of parts. After all, we aren’t going to assign a special linear() worth for every factor — we are going to do it programmatically.

First, let’s draw a determine to grasp what we did for 2 parts.

Two square graphs fside by side showing the lines of the first two items. It's the same upward pointing spike, only shifting along the x-axis as you compare the graphs.

When one factor is ready, the opposite one is animating. We will establish two ranges. Let’s think about the identical with three parts.

Three square graphs from right to left showing the lines of the first three items. It's the same upward pointing spike, only shifting along the x-axis as you compare the graphs.

This time, we want three ranges. Every factor animates in a single vary and waits in two ranges. Do you see the sample? For N parts, we want N ranges, and the linear() operate can have the next syntax:

linear(0, 0 S, 1, 0 E, 0)

The begin and the finish are equal to 0, which is the preliminary state of the animation, then we have now an animation between S and E. A component will wait from 0% to S, animate from S to E, then wait once more from E to 100%. The animation time equals to 100%/N, which suggests E - S = 100%/N.

The primary factor begins its animation on the first vary (0 * 100%/N), the second factor on the second vary (1 * 100%/N), the third factor on the third vary (2 * 100%/N), and so forth. S is the same as:

S = (i - 1) * 100%/N

…the place i is the index of the factor.

Now, you could ask, how will we get the worth of N and i? The reply is so simple as utilizing the sibling-count()and sibling-index() capabilities! Once more, these are at the moment supported in Chromium browsers, however we are able to count on them to roll out in different browsers down the highway.

S = calc(100%*(sibling-index() - 1)/sibling-count())

And:

E = S + 100%/N
E = calc(100%*sibling-index()/sibling-count())

We write all this with some good CSS and we’re carried out!

.field {
  --d: .5s; /* animation period */
  --_s: calc(100%*(sibling-index() - 1)/sibling-count());
  --_e: calc(100%*(sibling-index())/sibling-count());

  animation: x calc(var(--d)*sibling-count()) infinite linear(0, 0 var(--_s), 1, 0 var(--_e), 0);
}
@keyframes x {
  to {
    background: #F8CA00;
    scale: .8;
  }
}

I used a variable (--d) to manage the period, nevertheless it’s not necessary. I needed to have the ability to management the period of time every factor takes to animate. That’s why I multiply it later by N.

Now all that’s left is to outline your animation. Add as many parts as you need, and watch the outcome. No extra advanced keyframes and magic values!

Observe: For unknown causes (in all probability a bug) you could register the variables with @property.

Extra variations

We will prolong the essential thought to create extra variations. For instance, as a substitute of getting to attend for a component to fully finish its animation, the subsequent one can already begin its personal.

This time, I’m defining N + 1 ranges, and every factor animates in two ranges. The primary factor will animate within the first and second vary, whereas the second factor will animate within the second and third vary; therefore an overlap of each animations within the second vary, and many others.

I can’t spend an excessive amount of time explaining this case as a result of it’s one instance amongst many we create, so I allow you to dissect the code as a small train. And right here is one other one so that you can examine as properly.

Conclusion

The linear() operate was primarily launched to create advanced easing corresponding to bounce and elastic, however mixed with different fashionable options, it unlocks a whole lot of prospects. By this text, we received a small overview of its potential. I mentioned “small” as a result of we are able to go additional and create much more advanced animations, so keep tuned for extra articles to come back!

Reinvent Buyer Engagement with Dynamics 365: Flip Insights into Motion

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Sponsored Content material

 

 
Reinvent Buyer Engagement with Dynamics 365: Flip Insights into Motion
 

Do you know that within the US, regardless of loving the model or product, about 59% of shoppers will stroll away after just a few unhealthy experiences, in keeping with a PwC report? The identical report additionally mentions that about 17% will go away after only one disagreeable expertise.

The chances and numbers could differ by geography and demographics, however these are nonetheless alarming statistics. As a result of for a enterprise, each client counts.

Subsequently, it could not be fallacious to say that whereas a wonderful product and pricing technique are important to standing out, it’s the buyer expertise that leaves an impression. However no enterprise deliberately desires to depart a poor impression on its finish customers. Or deal with them indifferently. But this may occur, as client wants have grown exponentially over the previous couple of years.

They count on manufacturers to be agile and empathetic whereas being exact of their supply. However assembly prospects at their most well-liked time, as per their comfort 24/7, is tougher than ever.

As enterprise grows, personalization and real-time responsiveness could appear robust. Additionally, many organizations wrestle to show buyer knowledge mining into significant motion. So, how can organizations improve buyer engagement? That is the place Microsoft Dynamics 365 adjustments the sport.

 

What’s the Want for Microsoft Dynamics 365?

 

Prospects can work together with a enterprise throughout a number of touchpoints. Web site. Fb or Instagram. Or straight by means of buyer assist. At every touchpoint, new knowledge is generated for the enterprise. A lot of this knowledge is siloed or disconnected. The advertising and marketing staff derives insights from the web site and social media platforms. Whereas the gross sales staff makes use of a various set of information, and so forth. This fragmented view doesn’t assist perceive or predict buyer habits. Additionally, this doesn’t assist the service groups as nicely.

For instance, a buyer who has inquired about your newest product on social media chat would count on the agent to concentrate on their particulars upon interplay on a telephone name. However normally, the brokers wouldn’t have any visibility on this communication. Dynamics 365 addresses this by unifying knowledge throughout departments, enabling groups to work with real-time insights.

In easy phrases, Dynamics 365 or D365 provides AI-powered ERP and CRM purposes to assist companies higher handle gross sales, advertising and marketing, and provide chain operations. This permits manufacturers to construct significant connections as a result of they now have a transparent view of the operations. A few of benefits are:

 
D365D365
 

These features collectively assist energy a unified buyer view, turning insights into motion.

 

Actionable Intelligence: Turning Insights into Affect

 

https://www.salesforce.com/ca/hub/service/famous-customer-service-quotes/, founder and CEO of Amazon.com, says,

“We see our prospects as company to a celebration, and we’re the hosts. It is our job day by day to make each necessary facet of the shopper expertise a bit bit higher.”

Equally, many distinguished personalities globally reiterate the significance of fine customer support. That’s the reason context-rich insights, similar to prospects’ previous interactions and buy historical past, open service points, and buyer engagement ranges, are invaluable. That mentioned, regardless of these insights, there isn’t any assure of success until you act upon them. However with Microsoft Copilot, an AI assistant, that’s a part of this suite, you will get insights and motion gadgets from conferences, chats, and different sources to drive higher outcomes.

Right here is the way it works in motion throughout departments:

 

Gross sales:

 

Gross sales-qualified leads (SQL) or marketing-qualified leads (MQL) can come from-

  • Main enterprise web site
  • Social media channels
  • Efficiency advertising and marketing campaigns

Collating, segregating, and fine-tuning these manually could be time-consuming and susceptible to errors. However with AI-based lead scoring, prioritized outreach enabled by AI-powered co-assistance, and Microsoft Copilot, gross sales groups can shut extra offers sooner.

 

Advertising and marketing:

 

Buyer journeys are normally ready by entrepreneurs in Excel information and PowerPoint displays that usually get misplaced in emails or within the cloud. With out placing this knowledge into motion, groups are leaving cash on the desk. With D365, advertising and marketing groups are empowered to take it to the subsequent stage. They will phase audiences dynamically. Set off customized campaigns and optimize marketing campaign efficiency in a single window.

 

Service Groups:

 

Brokers can achieve predictive insights from customers’ distinctive points. This helps them supply extra immediate, proactive assist. Prospects really feel heard and cared for.

 

Seamless Integration with the Microsoft World

 

A lot of the company world and enterprises at present function on Microsoft 365 software program suites that embrace Outlook, Groups, and Energy BI, amongst others. Since Dynamics 365 is a part of the identical “Microsoft ecosystem”, it seamlessly integrates with these options, making the general journey smoother. So, there isn’t any downtime. That’s the reason it has additionally been known as a single supply of reality.

 

Conclusion: Creating Higher Buyer Experiences

 

The shopper is king. And the king is flooded with a number of selections at present. To appease the king, companies have to supply immediate gratification, customized expertise 24/7. And as operations scale and competitors intensifies, corporations should discover new, modern methods to interact repeatedly. However how lengthy will legacy instruments be supported, particularly when the information is big? That is why transitioning to Microsoft Dynamics 365 turns into important.

It unifies knowledge, empowers groups, and prompts real-time insights at scale. D365 helps you construct significant, lasting relationships. In order that your advertising and marketing, gross sales, and repair brokers can have interaction smarter.

Selective retraining helps AI study new expertise with out forgetting, research finds

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To check whether or not this downside holds for as we speak’s giant multimodal fashions, the workforce carried out a managed analysis. They skilled the chosen fashions on 5 goal duties, together with fine-grained fowl classification, counting, medical visible query answering, OCR studying, and time studying. They then measured how a lot efficiency dropped throughout eight customary benchmarks that weren’t a part of the fine-tuning set.

These experiments led to 2 key discoveries, in response to the paper. Tuning solely the self-attention projection layers (SA Proj), the a part of the mannequin that helps it determine which enter parts to give attention to, allowed the fashions to study new duties with little or no measurable forgetting. Additionally, what initially appeared as forgotten information usually resurfaced when the mannequin was later skilled on one other specialised process.

“We thus hypothesize that maybe what seems like forgetting or interference after fine-tuning on a slim goal process is definitely bias within the output distribution as a result of process distribution shift,” the researchers added. “By in-depth evaluation when tuning the counting process, we affirm this speculation: tuning the MLP will increase goal accuracy but in addition will increase the chance of outputting numeric tokens and a extremely correlated drop in held-out process accuracy, whereas tuning the self-attention achieves the goal studying with out a lot bias towards numeric tokens and with out dropping held-out accuracy.”

Be A part of the AI Revolution on the Chatbot Convention Tomorrow! | by Cassandra C.


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Tomorrow, September 24, 2024, San Francisco will host one of many largest international AI occasions of the yr: the Chatbot Convention! Whether or not you’re keen about synthetic intelligence, interested by chatbots, or just keen to attach with business leaders, this convention is for you.

Why You Ought to Attend

That is greater than only a convention; it’s your alternative to discover how AI is remodeling industries all over the world. Right here’s what you’ll be able to sit up for:

  • Inspiring Talks: Hear from AI innovators main the way in which in expertise and enterprise.
  • Interactive Workshops: Roll up your sleeves and create AI options which can be prepared for the true world.
  • Networking Alternatives: Meet like-minded professionals, tech fans, and thought leaders who’re driving the AI dialog.
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What’s on the Agenda?

The occasion will function every thing from cutting-edge chatbot demos to hands-on AI improvement workshops. Uncover how AI brokers are evolving, and study greatest practices from seasoned professionals. You’ll stroll away with actionable insights and new connections.

Discover the complete agenda on the Chatbot Convention.

Act Now, Don’t Miss Out!

That is your probability to participate in an occasion that may outline the way forward for AI and chatbot expertise.

See you there! Collectively, let’s study, collaborate, and be impressed.

Capita to pay £14 million for knowledge breach impacting 6.6 million individuals

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The Data Commissioner’s Workplace (ICO) within the UK has fined Capita, a supplier of data-driven enterprise course of providers, £14 million ($18.7 million) for a knowledge breach incident in 2023 that uncovered the private info of 6.6 million individuals.

Capita is a serious UK-based outsourcing {and professional} providers firm that gives consulting, digital, and software program providers to native councils, the NHS, the Ministry of Protection, and organizations within the banking, utilities, and telecommunications sectors.

With round 34,000 workers and an annual income of £3 billion, Capita’s purchasers are principally within the UK and Europe.

A whole bunch of retirement plan suppliers impacted

The ICO had initially set the advantageous to a a lot bigger £45 million, however the company determined to scale back the penalty after the corporate accepted legal responsibility, carried out essential safety enhancements, and provided knowledge safety providers to uncovered people.

The info safety authority fined Capita plc £8 million and Capita Pension Options Restricted obtained a penalty of £6 million.

The ICO’s investigation has now confirmed that the stolen knowledge impacts 6.6 million individuals, and lots of of Capita purchasers, together with 325 pension scheme suppliers within the UK.

In April 2023, the corporate introduced that it had been focused by hackers who tried entry to its inside Microsoft 365 atmosphere, forcing some programs offline as a part of its response.

An replace three weeks later confirmed that hackers had accessed 4% of Capita’s inside IT infrastructure, and exfiltrated personal recordsdata hosted on the breached programs.

The Black Basta ransomware gang claimed the assault and threatened to leak all stolen recordsdata except the corporate paid a ransom.

Hackers had entry for 58 hours

The cyberattack occurred on March 22, 2023, when a Capita worker downloaded a malicious file that gave hackers entry to the corporate’s inside community.

The ICO feedback that, though the breach was detected inside 10 minutes, Capita didn’t isolate the contaminated system for an additional 58 hours, giving the attackers ample time to maneuver laterally, unfold on the community, and entry delicate databases.

“This file enabled the deployment of malicious software program onto the Capita community, permitting the hacker to remain within the system, acquire administrator permissions and entry different areas of the community,” Data Commissioner’s Workplace

“Between 29 and 30 March 2023, practically one terabyte of information was exfiltrated. On 31 March 2023, ransomware was deployed onto Capita programs and the hacker reset all consumer passwords, stopping Capita workers from accessing their programs and community,” states UK’s knowledge safety authority.

Capita is now fined for poor entry controls (absence of tiered admin account mannequin), delayed response to safety alerts, working an understaffed Safety Ops Heart, and failing to carry out common penetration testing and danger administration workout routines.

Capita’s CEO Adolfo Hernandez introduced the settlement with ICO, underlining the hassle and funding that has gone into strengthening the agency’s cybersecurity stance for the reason that incident.

The chief additionally famous that they don’t count on the fee of the advantageous to have an effect on beforehand revealed investor steerage.

Be part of the Breach and Assault Simulation Summit and expertise the way forward for safety validation. Hear from prime specialists and see how AI-powered BAS is reworking breach and assault simulation.

Do not miss the occasion that can form the way forward for your safety technique

A novel development mannequin made potential by Bayesian software program

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One motive I like Bayesian software program (comparable to JAGS, and so on.) is for its capability to precise novel fashions that are not prepackaged in canned stats packages. In some latest analysis, I had the chance to create a novel development mannequin and estimate its parameters in JAGS. 

We had knowledge as graphed in Fig. 1, and I had to consider a mannequin to explain the traits

Fig.1. Information to be modeled.

The variable on the vertical axis is a ranking of emotion (comparable to disappointment, happiness, and so on.) briefly tales. The horizontal axis is the retelling of the story, such that 0 is the unique story, 1 is a retelling of the unique, 2 is a retelling of the first retelling, and three is a retelling of the 2nd retelling. Retellings are inclined to lose plenty of info however however retain some data too. Do they preserve feelings? Every curve within the graph corresponds to a special unique story. I assumed the traits within the knowledge appeared just like the completely different unique tales have been converging towards (or diverging from) a typical backbone, as in Fig. 2:

Fig. 2. Mannequin predictions.

So, I invented a easy development mannequin to precise that concept, and I programmed it in JAGS. As a result of the rankings have been on an ordinal scale, I used an ordered-probit response distribution on a latent scale that adopted an underlying linear backbone with exponential convergence, as steered in Fig. 3:

Fig. 3. The mannequin, with latent scale in left panel and ranking scale in proper panel.

You’ll be able to learn all the small print of the mannequin within the HTML doc at https://osf.io/nbuxg/ (obtain the HTML doc after which view it in a browser). The revealed article describing the analysis is titled Serial copy of narratives preserves emotional value determinations by Fritz Breithaupt, Binyan Li, and John Okay. Kruschke. It may be obtained from https://doi.org/10.1080/02699931.2022.2031906 and the ultimate pre-publication manuscript is at https://osf.io/hwvza/.

Such a meat supercharges muscle progress after exercises

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A brand new examine examined how muscle tissues reply to weight coaching when individuals eat various kinds of pork afterward. Researchers in contrast high-fat and lean floor pork burgers that contained the identical quantity of protein to see how every affected short-term muscle progress. The outcomes stunned the group and added to rising proof that the physique’s muscle-building course of after train relies upon not simply on how a lot protein is consumed, however on the kind of protein as properly.

The analysis was printed within the American Journal of Scientific Vitamin.

“What we’re discovering is that not all high-quality animal protein meals are created equal,” stated Nicholas Burd, a professor of well being and kinesiology on the College of Illinois Urbana-Champaign, who led the analysis with graduate scholar Žan Zupančič.

Burd’s earlier work revealed comparable patterns. One earlier experiment confirmed that consuming entire eggs after weight coaching led to larger muscle-protein synthesis than consuming the identical quantity of protein from egg whites alone. One other examine discovered that consuming salmon produced a stronger muscle-building response than consuming a processed mix with the identical vitamins in equivalent proportions.

Collectively, these findings recommend that entire meals stimulate muscle progress extra successfully after train than processed variations do. Burd famous that in some instances, the pure fats content material of entire meals would possibly really improve muscle-building exercise.

On this newest examine, the analysis group used superior methods to measure and monitor muscle-protein synthesis in 16 younger, bodily lively adults. To organize the check meals, they collaborated with the College of Illinois Meat Science Laboratory to create pork patties with exactly outlined fats ranges.

“That took us a yr as a result of it was so laborious to get these fats ratios appropriate,” Burd stated. The entire meat got here from a single pig, and the patties had been despatched to an exterior lab for detailed nutrient evaluation. As soon as the fat-to-lean ratios and different dietary values had been verified, the burgers had been frozen till used throughout the experiment’s feeding section.

Earlier than individuals started the exercise and meal trials, they acquired an infusion of isotope-labeled amino acids. This method allowed the scientists to observe how shortly these amino acids had been built-in into muscle tissue. Blood samples had been additionally collected all through the method to measure circulating amino acid ranges.

Muscle biopsies had been taken each earlier than and after the two-hour infusion to ascertain a baseline for muscle-protein synthesis.

“After which we took them to the gymnasium,” Burd stated. “And so they had been wheeling that infusion pump and all the pieces else with them.”

On the gymnasium, individuals carried out leg presses and leg extensions, then returned to the lab to eat one in all three check meals: a high-fat pork burger, a lean pork burger, or a carbohydrate drink. 5 hours later, the researchers took one other muscle biopsy to measure how the meal and train collectively affected protein synthesis.

After a number of days of restoration, 14 of the 16 volunteers repeated the method however switched meal sorts to make sure that particular person variations didn’t skew the outcomes.

As anticipated, amino acid ranges within the blood rose sharply after consuming pork in contrast with the carbohydrate drink. Nevertheless, those that consumed the lean pork burger confirmed the most important will increase in amino acids — each whole and important.

“If you see an elevated focus of amino acids within the blood after you eat, you get a fairly good concept that that’s coming from the meals that you simply simply ate,” Burd stated.

Those that consumed the lean pork burger after a bout of weight coaching additionally had a larger charge of muscle-protein synthesis than those that ate the high-fat pork burger. This was a shock to Burd, as “the earlier research utilizing fattier meals, akin to entire eggs or salmon, usually confirmed enhanced post-exercise muscle-protein synthesis in contrast with decrease fats meals akin to egg whites or dietary dietary supplements,” he stated.

Though weight coaching boosted muscle-protein synthesis within the teams consuming pork, the protein within the high-fat burger appeared to don’t have any additional benefit within the hours after individuals consumed it, whereas the protein within the lean pork gave muscle-protein synthesis a lift.

“For some purpose, the high-fat pork really blunted the response,” Burd stated. “In reality, the individuals who ate the high-fat pork solely had barely higher muscle-building potential than those that drank a carbohydrate sports activities beverage after train.”

Decoding the outcomes of this examine for individuals who need to optimize muscle beneficial properties from weight-training is hard, Burd stated. It might be that processing the bottom pork patties, which concerned grinding the meat and including the fattier meat to the lean, affected the kinetics of digestion.

“There was a bit of bigger rise within the amino acids obtainable from consuming lean pork, so it might have been an even bigger set off for muscle-protein synthesis,” Burd stated. “However that appears to be particular to the bottom pork. Should you’re consuming different meals, like eggs or salmon, the entire meals look like higher regardless of not eliciting a big rise in blood amino acids.”

Burd stresses that train is the strongest stimulus for muscle-protein synthesis.

“A lot of the muscle response is to weight-training, and we use vitamin to attempt to squeeze out the remaining potential,” he stated. “In relation to consuming after weight-training, what we’re discovering is that some meals, notably entire, unprocessed meals appear to be a greater stimulus.”

Burd is also a professor of dietary sciences and is affiliated with the Carl R. Woese Institute for Genomic Biology on the U. of I.

The Nationwide Pork Board’s Pork Checkoff program supported this analysis. The funder had no involvement in examine design, information assortment or evaluation.

From Indignant Telephone Calls to Future

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At any time when somebody asks my spouse and I how we met, we battle. We battle as a result of we’ve got three completely different origin tales of our relationship, and one official one. The 4 tales got here collectively not as a result of we wished to deceive. My spouse and I are usually reserved about our non-public lives, regardless of what you see and browse on social media. For every story we let you know, there are 100 extra we don’t. So, when folks ask us about that first date or that first time we noticed one another, we sort of simply combine issues up slightly.

State of affairs #1: An Indignant Telephone Name to the Lab

On this situation, it’s late within the night, and I’m working on the lab. The telephone rings, and I reply it. On the opposite finish is my spouse… Earlier than she married me, after all. She is a doctor affiliate (PA) at a household observe, and he or she is in search of outcomes for one in all her sufferers. She is offended as a result of she ordered the checks to be accomplished stat, which means the outcomes have to be reported inside 60 minutes of the pattern being collected. Since she has not heard from us, she assumes we’re messing round and never doing our jobs.

After I clarify to her that the affected person was solely in for the blood and urine assortment 20 minutes in the past, she nonetheless desires the outcomes shortly. She ends the decision, and I get her the outcomes quarter-hour later. Effectively, not her, however the workplace supervisor.

Just a few weeks later, De involves the lab to drop off a specimen. It’s late within the night, and my colleagues are all grabbing dinner on the snack bar whereas I man the lab. I ask De if she was the one who known as and was offended at me. She confirms it. I inform her I like her spunk, and he or she rolls her eyes. Then I ask her if she’s free that weekend. She is.

We go to a bookstore with a espresso store inside, drink some espresso and chai, discuss books, and the remaining is historical past.

State of affairs #2: The Physician Performs Matchmaker

On this situation, it’s the midnight, and De’s boss has come into the hospital to see a affected person. Whereas she’s sitting within the emergency division, writing her notes on the case, she sees me stroll by and asks if I’m courting anybody. I inform her I’m flattered, however I do know she’s married. She laughs and tells me that her observe simply employed a brand new PA. The brand new PA is from Pittsburgh, and he or she doesn’t know anybody on the town. Her boss thinks we’re a great match as a result of we’re across the identical age, and I’m one of many few out there younger males with a school diploma. I inform her boss that I’m not courting anybody on the time. I would like a while to myself after the final relationship (three years earlier than) threw me for a loop.

Days later, her boss is within the hospital once more, and he or she fingers me a slip of paper with De’s telephone quantity on it. She tells me three years is sufficient to not date, and orders me to name De. She jogs my memory that I’m about to complete my grasp’s diploma in public well being, and that it might be good to have somebody to have fun it with. You see, she is aware of I’m in that little city on their own, with my closest relations being 1000’s of miles away. (I used to be the one one in my prolonged household to be on this aspect of the Mississippi up till two years in the past.)

One Wednesday afternoon, I see the slip of paper and name De. We have now a great dialog on the telephone. She tells me she’s finding out for her certification examination, so she doesn’t have time to hang around. I inform her I’m touring to see my mom, so I’ll give her a name on the best way again. We conform to strive for a date then. On the best way to Nebraska and again, we’ve got fast conversations by way of textual content. As soon as I get again, we discover time to go to the bookstore and drink espresso and chai, discuss books, and the remaining is historical past.

State of affairs #3: A Match Made in Our on-line world

On this situation, I’ve been single for 3 years. Whereas I’m prepared to start out courting once more, it’s been onerous to rebuild a social life. On the one hand, I’m busy with work and the grasp of public well being program. I work full time after which go to Washington, DC, two or 3 times per week (relying on my class schedule) to take lessons. There isn’t per week I’m not exhausted. Whereas buddies invite me to go to a espresso home and hearken to some music, I simply don’t have it in me to rise up and go more often than not.

Then somebody suggests signing up for on-line courting, and I do it. I find yourself happening a number of dates, however nothing critical comes from it. There’s the one lady with the “hypoallergenic” cat that just about kills me from an allergic bronchial asthma assault. There’s additionally the one lady who finds me by my weblog and desires to attach as a result of she can also be considering public well being. This latter younger woman lives in New Jersey, so we find yourself assembly in Philadelphia at a museum. We stroll by one other museum known as the Mütter Museum, and he or she tells me that museum is within the constructing that homes the School of Physicians of Philadelphia.

That is what I name foreshadowing.

Although we agree to satisfy once more for one more date, we don’t. The following time I see her is at an journey race in Maryland, and that’s that. My legs nonetheless ache at teh thought, and my brother and I snicker at how silly it was of me to try to try this complete race simply to impress her. Then once more, I ran the Baltimore marathon the earlier fall and completed it with little preparation. I’m know to do silly issues with out considering a lot concerning the penalties.

That can also be foreshadowing, pricey readers.

Lastly, late one evening, whereas crawling by the profiles on the courting website, I run into one from a younger girl who claims to have held the Ebola virus in her hand. My curiosity is piqued, and I modify my profile to say that I’m finding out epidemiology, and that sure epidemiologists who’ve additionally labored with Ebola are my private {and professional} heroes. Then I click on on “LIKE” for her profile. Hours later, she does the identical for mine.

After exchanging some emails, we name one another, agree to satisfy for espresso at a bookstore, have an superior dialog about completely different books and about ourselves whereas consuming espresso and chai, purchase a DVD of Eddie Izzard’s newest comedy present, then head again to her condominium to look at mentioned DVD. I find yourself leaving previous midnight after watching the comedy present and chatting about all of our desires and fears. There’s a parking ticket on my Jeep, however I don’t care. And the remaining is historical past.

Which Is True? We’ll By no means Know

In fact, there are a few different origin tales about us on the market. You’d know the true story in case you learn this weblog circa August 2006, however I don’t know if anybody that learn me then remains to be studying this at the moment. (Perhaps one or two of you?)

To know the true story, you’ll have needed to been studying then, or know us know. And that’s okay. You don’t must know completely all the pieces about us.

I imply, I’m flattered that you simply need to. Nevertheless it’s not likely needed. Simply know we’re pleased and going sturdy 19 years after that first date… And that I’ll make a much bigger and far more significant weblog put up about it subsequent 12 months.