Fullmoonfever
Top 20
I thought it was a selfieHey how do you know I'm in Adelaide...
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
gilti
Regular
I call bullshit on the current short numbers
On 22/11 105,910,818 outstanding
0n 1/12 26,243,516 outstanding
ie a reduction of 79,677, 387 when the total volume of transactions was 150,986,590
this represents 53% of all trades and only increased the price 10% bullshit
on 23/11 total sales 9,528, 733 of which 15,141,526 were short covers bullshit
thats a 5,612,793 shortfall
on 27/11 total sales 5,888,356 of which 11,221,511 were short covers bullshit
thats a 5,333.155 shortfall
on 1/12 total sales 13,732,839 of which 35,990,106 were short covers bullshit
thats a 22,257,267 shortfall.
Some of the 1/12 shortfall may be explained by late trade on the 30/11 but the rest???
On 22/11 105,910,818 outstanding
0n 1/12 26,243,516 outstanding
ie a reduction of 79,677, 387 when the total volume of transactions was 150,986,590
this represents 53% of all trades and only increased the price 10% bullshit
on 23/11 total sales 9,528, 733 of which 15,141,526 were short covers bullshit
thats a 5,612,793 shortfall
on 27/11 total sales 5,888,356 of which 11,221,511 were short covers bullshit
thats a 5,333.155 shortfall
on 1/12 total sales 13,732,839 of which 35,990,106 were short covers bullshit
thats a 22,257,267 shortfall.
Some of the 1/12 shortfall may be explained by late trade on the 30/11 but the rest???
There's a tracker in your shorts.Hey how do you know I'm in Adelaide
HopalongPetrovski
I'm Spartacus!
I thought he was just glad to see us.
Which part of "Top Secret" does the DG of ONI not understand?
Seems like BRN NDAs are more secure ... not that there's any dots there ...
https://www.msn.com/en-au/news/aust...E&cvid=abe905e658cf42af923d00a8bcb2ac9a&ei=33
Australia is developing a "top secret" intelligence community cloud to help national security agencies better detect threats through shared data, while also being interoperable with US and UK spy networks.
Director-General of the Office of National Intelligence (ONI), Andrew Shearer, has revealed some details of the project which would facilitate the sharing of "vast amounts of data", during an address to the Centre for Strategic and International Studies in the United States.
"In Australia we are working very hard on a 'top secret' cloud initiative, where we're hoping that we will be in a position to take that initiative forward," Mr Shearer told a CSIS audience in Washington DC this week.
"What that will do is obviously transform how we do our work as agencies but also it will open up a shared collaborative space that will really reinforce this sense of working together as a genuine community and bringing all those different capabilities to bear on problems."
Mr Shearer said the "experiences" of the US and UK in building out their respective intelligence clouds has also shaped Australia's program, helping confront "problems" and avoid "some of the pitfalls".
"I think that's a really powerful demonstration of how we can learn from each other … As we move into the world of artificial intelligence, there's also a massive amount that we can learn and share," he added.
"The ability to share vast amounts of data and to work on it together will be a massive change for us as a community, but it will also make us interoperable with the US IC (intelligence community), and also with our British colleagues who are moving in that direction as well."
Precise details of the cloud, including its expected cost and operational launch date, are not known, but ONI has confirmed it began the project ahead of the AUKUS partnership being formed between Australia, the United Kingdom and United States in September 2021.
"ONI led an open Request for an Expression of Interest (REOI) process in 2021 to determine possible partners for a TS Cloud to ensure better connectivity and collaboration across the National Intelligence Community (NIC)," a spokesperson told ABC News.
The spokesperson declined to name which companies were involved or how the new cloud would manage material classified 'Australian Eyes Only', saying "for reasons of national security we don't provide further detail regarding capabilities or technologies".
A tender document published in late 2020 states "ONI intends to identify potential commercial partners with the capabilities, qualities, interest, and commitment to partner with the NIC to provide value for money TOP SECRET, scalable, private, community cloud services".
During his CSIS address Mr Shearer confirmed a new Australian National Intelligence Community strategy would include a "set of principles" that will make sharing of new technologies across various agencies mandatory.
Seems like BRN NDAs are more secure ... not that there's any dots there ...
https://www.msn.com/en-au/news/aust...E&cvid=abe905e658cf42af923d00a8bcb2ac9a&ei=33
Australian spy chief reveals 'top secret' data cloud project that's interoperable with US, UK
Story by By defence correspondent Andrew Greene • 14hAustralia is developing a "top secret" intelligence community cloud to help national security agencies better detect threats through shared data, while also being interoperable with US and UK spy networks.
Director-General of the Office of National Intelligence (ONI), Andrew Shearer, has revealed some details of the project which would facilitate the sharing of "vast amounts of data", during an address to the Centre for Strategic and International Studies in the United States.
"In Australia we are working very hard on a 'top secret' cloud initiative, where we're hoping that we will be in a position to take that initiative forward," Mr Shearer told a CSIS audience in Washington DC this week.
"What that will do is obviously transform how we do our work as agencies but also it will open up a shared collaborative space that will really reinforce this sense of working together as a genuine community and bringing all those different capabilities to bear on problems."
Mr Shearer said the "experiences" of the US and UK in building out their respective intelligence clouds has also shaped Australia's program, helping confront "problems" and avoid "some of the pitfalls".
"I think that's a really powerful demonstration of how we can learn from each other … As we move into the world of artificial intelligence, there's also a massive amount that we can learn and share," he added.
"The ability to share vast amounts of data and to work on it together will be a massive change for us as a community, but it will also make us interoperable with the US IC (intelligence community), and also with our British colleagues who are moving in that direction as well."
Precise details of the cloud, including its expected cost and operational launch date, are not known, but ONI has confirmed it began the project ahead of the AUKUS partnership being formed between Australia, the United Kingdom and United States in September 2021.
"ONI led an open Request for an Expression of Interest (REOI) process in 2021 to determine possible partners for a TS Cloud to ensure better connectivity and collaboration across the National Intelligence Community (NIC)," a spokesperson told ABC News.
The spokesperson declined to name which companies were involved or how the new cloud would manage material classified 'Australian Eyes Only', saying "for reasons of national security we don't provide further detail regarding capabilities or technologies".
A tender document published in late 2020 states "ONI intends to identify potential commercial partners with the capabilities, qualities, interest, and commitment to partner with the NIC to provide value for money TOP SECRET, scalable, private, community cloud services".
During his CSIS address Mr Shearer confirmed a new Australian National Intelligence Community strategy would include a "set of principles" that will make sharing of new technologies across various agencies mandatory.
Tothemoon24
Top 20
Neuromorphic acoustic sensing using an adaptive microelectromechanical cochlea with integrated feedback
Neuromorphic acoustic sensing using an adaptive microelectromechanical cochlea with integrated feedback - Nature Electronics
A microelectromechanical cochlea, which consists of a bio-inspired acoustic sensor with a thermo-mechanical feedback mechanism, exhibits active auditory sensing, allowing the sensor to adapt its properties to different acoustic environments.
Looking at the screen listing all the companies in the AI Solutions Ecosystem reminds me of the pantomime scene where the audience yells out to the blissfully ignorant heroine "BRN's behind you!" ... (but in fact it is way ahead).
AMD MI300X launch -
https://twitter.com/search?q=#AdvancingAI&cn=ZmxleGlibGVfcmVjcw==&refsrc=email
... but at least they are ahead of Nvidia
Bob O'Donnell
@bobodtech
Diving straight into competitive benchmarks, @amd notes that their new MI300X can match the H100 for training purposes but more impressively (and more importantly for the long term) can outdo @nvidia’s offering for inference. #advancingai
AMD MI300X launch -
https://twitter.com/search?q=#AdvancingAI&cn=ZmxleGlibGVfcmVjcw==&refsrc=email
... but at least they are ahead of Nvidia
Bob O'Donnell
@bobodtech
Diving straight into competitive benchmarks, @amd notes that their new MI300X can match the H100 for training purposes but more impressively (and more importantly for the long term) can outdo @nvidia’s offering for inference. #advancingai
Kachoo
Regular
There is alot of misunderstanding in regards to the short cover.
The shorter covered the shares buy closing out the contract with the institution. They could have covered the short a month earlier but held the shares to keep pressure on the stock. They did not have to buy back that day and actually if they buy that day there is a t+2 settlement. Before they even own the share to give back to the institutions.
There is no magic in the daily volume vs day closed two different things. There was plenty if days that total shorted exceeded the volume traded vise versa.
We are at the end of the short campaign they clearly dropped the price bellow what many thought. From this stage on there will be less liquidity on sales tighter floats. The LDA shares will likely get absorbed by the institution that shorted the stock to cover maybe the rest of the shares and maybe take some long positions imo.
There really is a limit to what the price can drop for BRN im sure if BRNbwalked to Nvidia today and said take over my company for 500 million USD they would likely do it hands down IMO that's 42 cents AUD would you all be happy with that out come if it came to it? Likely not so despite what fear mongers are saying about solvency and value lack of sales you need to realise there is value in Akida more then we likely can accurately measure.
2 IP sales raised 5.5 million dollars they did not spend the money just for kicks and Imnsure Megachips had plenty of time to play before they even committed to the product.
The lack of buying is really 2 things.
1. Investor confidence no new money coming in to invest.
2. Fear driven tactics and poor communication and value identification.
The SP will likely be we north of these prices before we see revenue on ink. The parties that drove the price down will drive it up. But again some are very cautious and want to see revenue before they buy shares or invest more thats fair and that comes at a premium.
Your basicly buying a bucket of dirt here that may or may not have gems in it. If you knew there was a Gem in it after the fist 5 % was removed the price of the rest of the bucket goes up and you are out that gem. It is a risk the biggest risk was the technology and its adaptability.
There has been plenty of positive feed back from many Tata included so in my mind it's only a matter of time before you see some ink.
The shorter covered the shares buy closing out the contract with the institution. They could have covered the short a month earlier but held the shares to keep pressure on the stock. They did not have to buy back that day and actually if they buy that day there is a t+2 settlement. Before they even own the share to give back to the institutions.
There is no magic in the daily volume vs day closed two different things. There was plenty if days that total shorted exceeded the volume traded vise versa.
We are at the end of the short campaign they clearly dropped the price bellow what many thought. From this stage on there will be less liquidity on sales tighter floats. The LDA shares will likely get absorbed by the institution that shorted the stock to cover maybe the rest of the shares and maybe take some long positions imo.
There really is a limit to what the price can drop for BRN im sure if BRNbwalked to Nvidia today and said take over my company for 500 million USD they would likely do it hands down IMO that's 42 cents AUD would you all be happy with that out come if it came to it? Likely not so despite what fear mongers are saying about solvency and value lack of sales you need to realise there is value in Akida more then we likely can accurately measure.
2 IP sales raised 5.5 million dollars they did not spend the money just for kicks and Imnsure Megachips had plenty of time to play before they even committed to the product.
The lack of buying is really 2 things.
1. Investor confidence no new money coming in to invest.
2. Fear driven tactics and poor communication and value identification.
The SP will likely be we north of these prices before we see revenue on ink. The parties that drove the price down will drive it up. But again some are very cautious and want to see revenue before they buy shares or invest more thats fair and that comes at a premium.
Your basicly buying a bucket of dirt here that may or may not have gems in it. If you knew there was a Gem in it after the fist 5 % was removed the price of the rest of the bucket goes up and you are out that gem. It is a risk the biggest risk was the technology and its adaptability.
There has been plenty of positive feed back from many Tata included so in my mind it's only a matter of time before you see some ink.
Stable Genius
Regular
This looks interesting:
webinar.intel.com
Join us for Intel's 'AI Everywhere' event one week from today! You don't want to miss this. Register now and tune in on Dec.14 @ 15:45 CET: https://intel.ly/3NefZZO
I wonder what AI Intel are marketing?
AI Everywhere EMEA – Livestream event
AI Everywhere EMEA public virtual event on 14 December 2023.
webinar.intel.com
Join us for Intel's 'AI Everywhere' event one week from today! You don't want to miss this. Register now and tune in on Dec.14 @ 15:45 CET: https://intel.ly/3NefZZO
I wonder what AI Intel are marketing?
Stable Genius
Regular
This looks interesting:
AI Everywhere EMEA – Livestream event
AI Everywhere EMEA public virtual event on 14 December 2023.
Join us for Intel's 'AI Everywhere' event one week from today! You don't want to miss this. Register now and tune in on Dec.14 @ 15:45 CET: https://intel.ly/3NefZZO
I wonder what AI Intel are marketing?
Further to my previous post after 5.50 mins in this video there is talk of an NPU being part of Intels new offerings. I’d have to look at their partners page to see who is offering Intel an NPU
And given the timing of the new hire in our Sales, i would not be surprised if a relation with Intel is annouced. Imagine the possibilitiesThis looks interesting:
AI Everywhere EMEA – Livestream event
AI Everywhere EMEA public virtual event on 14 December 2023.
Join us for Intel's 'AI Everywhere' event one week from today! You don't want to miss this. Register now and tune in on Dec.14 @ 15:45 CET: https://intel.ly/3NefZZO
I wonder what AI Intel are marketing?
And the shorts being aggressively covered now, what do they know?
DingoBorat
Slim
We've had an official relationship with Intel (Intel Foundry Services) for almost, smack on a year now (December 11, 2022)..
BrainChip Joins Intel Foundry Services IP Alliance to Advance Neuromorphic AI at the Edge
BrainChip joins with Intel Foundry Services to advance edge-based neuromorphic AI, revolutionizing the field of artificial intelligence.
www.google.com
"A new generation of devices that demand independent learning and inference capabilities, faster response times and limited power consumption has created opportunities for new products with smarter sensors, devices, and systems. Integrating AI into the SoC delivers efficient compute and the unique learning and performance requirements of Edge AI. BrainChip’s AkidaTM, enables low-latency and ultra-low power AI inference and on-chip learning"
So indeed, imagine the possibilities
Australia developing 'top secret' intelligence cloud computing system
The program is expected to work with US and UK spy networks to help national security agencies better detect threats.
Tothemoon24
Top 20
A new next-generation sensor, the VALEO SCALA 3 LiDAR, which will equip the autonomous car of tomorrow": Valeo's LiDAR in the spotlight on French television BFMTV.
"This technology exceeds human capabilities" stresses journalist David Bouteiller in this report made on our Milovice test track, a few kilometers from Prague in the Czech Republic.
François Marion, Valeo's EVP Corporate Communications and Investor Relations, points out: "It has such high resolution that it is able to see a black tire on a road in the middle of the night at 150m, which neither the human eye nor cameras can see. What's more, it never tires or gets distracted."
Valeo has already booked more than a billion euros worth of orders for this technology, from Stellantis, an Asian automaker and an American robotaxi company.
There is a news report & footage attached to the link spoken in French
,Happy Friday Chippers
"A new next-generation sensor, the Valeo SCALA 3 LiDAR, which will equip the autonomous car of tomorrow": Valeo's LiDAR in the spotlight on French television BFMTV. | Valeo
"A new next-generation sensor, the Valeo SCALA 3 LiDAR, which will equip the autonomous car of tomorrow": Valeo's LiDAR in the spotlight on French television BFMTV. "This technology exceeds human capabilities" stresses journalist David Bouteiller in this report made on our Milovice test track...
Last edited:
Australia developing 'top secret' intelligence cloud computing system
The program is expected to work with US and UK spy networks to help national security agencies better detect threats.www.abc.net.au
Any relevance?
BrainChip and Quantum to Demonstrate Interoperability of BrainChip Studio with StorNext File System
Interoperability of BrainChip Studio with StorNext File System Demonstrated by BrainChip and Quantum Mar 26, 2018
brainchip.com
Tothemoon24
Top 20
December 7, 2023
Credit: Unsplash/CC0 Public Domain
Researchers have developed an adaptable algorithm that could improve road safety by predicting when drivers are able to safely interact with in-vehicle systems or receive messages, such as traffic alerts, incoming calls or driving directions.
The researchers, from the University of Cambridge, working in partnership with Jaguar Land Rover (JLR) used a combination of on-road experiments and machine learning as well as Bayesian filtering techniques to reliably and continuously measure driver "workload." Driving in an unfamiliar area may translate to a high workload, while a daily commute may mean a lower workload.
The resulting algorithm is highly adaptable and can respond in near real-time to changes in the driver's behavior and status, road conditions, road type, or driver characteristics.
This information could then be incorporated into in-vehicle systems such as infotainment and navigation, displays, advanced driver assistance systems (ADAS) and others.
Any driver-vehicle interaction can be then customized to prioritize safety and enhance the user experience, delivering adaptive human-machine interactions. For example, drivers are only alerted at times of low workload, so that the driver can keep their full concentration on the road in more stressful driving scenarios. The results are reported in the journal IEEE Transactions on Intelligent Vehicles.
"More and more data is made available to drivers all the time. However, with increasing levels of driver demand, this can be a major risk factor for road safety," said co-first author Dr. Bashar Ahmad from Cambridge's Department of Engineering. "There is a lot of information that a vehicle can make available to the driver, but it's not safe or practical to do so unless you know the status of the driver."
A driver's status—or workload—can change frequently. Driving in a new area, in heavy traffic or poor road conditions, for example, is usually more demanding than a daily commute.
"If you're in a demanding driving situation, that would be a bad time for a message to pop up on a screen or a heads-up display," said Ahmad. "The issue for car manufacturers is how to measure how occupied the driver is, and instigate interactions or issue messages or prompts only when the driver is happy to receive them."
There are algorithms for measuring the levels of driver demand using eye gaze trackers and biometric data from heart rate monitors, but the Cambridge researchers wanted to develop an approach that could do the same thing using information that's available in any car, specifically driving performance signals such as steering, acceleration and braking data. It should also be able to consume and fuse different unsynchronized data streams that have different update rates, including from biometric sensors if available.
To measure driver workload, the researchers first developed a modified version of the Peripheral Detection Task to collect, in an automated way, subjective workload information during driving. For the experiment, a phone showing a route on a navigation app was mounted to the car's central air vent, next to a small LED ring light that would blink at regular intervals.
Participants all followed the same route through a mix of rural, urban and main roads. They were asked to push a finger-worn button whenever the LED light lit up in red and the driver perceived they were in a low workload scenario.
Video analysis of the experiment, paired with the data from the buttons, allowed the researchers to identify high workload situations, such as busy junctions or a vehicle in front or behind the driver behaving unusually.
The on-road data was then used to develop and validate a supervised machine learning framework to profile drivers based on the average workload they experience, and an adaptable Bayesian filtering approach for sequentially estimating, in real-time, the driver's instantaneous workload, using several driving performance signals including steering and braking. The framework combines macro and micro measures of workload where the former is the driver's average workload profile and the latter is the instantaneous one.
"For most machine learning applications like this, you would have to train it on a particular driver, but we've been able to adapt the models on the go using simple Bayesian filtering techniques," said Ahmad. "It can easily adapt to different road types and conditions, or different drivers using the same car."
The research was conducted in collaboration with JLR who did the experimental design and the data collection. It was part of a project sponsored by JLR under the CAPE agreement with the University of Cambridge.
"This research is vital in understanding the impact of our design from a user perspective, so that we can continually improve safety and curate exceptional driving experiences for our clients," said JLR's Senior Technical Specialist of Human Machine Interface Dr. Lee Skrypchuk.
"These findings will help define how we use intelligent scheduling within our vehicles to ensure drivers receive the right notifications at the most appropriate time, allowing for seamless and effortless journeys."
Using machine learning to monitor driver 'workload' could help improve road safety
by Sarah Collins
Credit: Unsplash/CC0 Public DomainResearchers have developed an adaptable algorithm that could improve road safety by predicting when drivers are able to safely interact with in-vehicle systems or receive messages, such as traffic alerts, incoming calls or driving directions.
The researchers, from the University of Cambridge, working in partnership with Jaguar Land Rover (JLR) used a combination of on-road experiments and machine learning as well as Bayesian filtering techniques to reliably and continuously measure driver "workload." Driving in an unfamiliar area may translate to a high workload, while a daily commute may mean a lower workload.
The resulting algorithm is highly adaptable and can respond in near real-time to changes in the driver's behavior and status, road conditions, road type, or driver characteristics.
This information could then be incorporated into in-vehicle systems such as infotainment and navigation, displays, advanced driver assistance systems (ADAS) and others.
Any driver-vehicle interaction can be then customized to prioritize safety and enhance the user experience, delivering adaptive human-machine interactions. For example, drivers are only alerted at times of low workload, so that the driver can keep their full concentration on the road in more stressful driving scenarios. The results are reported in the journal IEEE Transactions on Intelligent Vehicles.
"More and more data is made available to drivers all the time. However, with increasing levels of driver demand, this can be a major risk factor for road safety," said co-first author Dr. Bashar Ahmad from Cambridge's Department of Engineering. "There is a lot of information that a vehicle can make available to the driver, but it's not safe or practical to do so unless you know the status of the driver."
A driver's status—or workload—can change frequently. Driving in a new area, in heavy traffic or poor road conditions, for example, is usually more demanding than a daily commute.
"If you're in a demanding driving situation, that would be a bad time for a message to pop up on a screen or a heads-up display," said Ahmad. "The issue for car manufacturers is how to measure how occupied the driver is, and instigate interactions or issue messages or prompts only when the driver is happy to receive them."
There are algorithms for measuring the levels of driver demand using eye gaze trackers and biometric data from heart rate monitors, but the Cambridge researchers wanted to develop an approach that could do the same thing using information that's available in any car, specifically driving performance signals such as steering, acceleration and braking data. It should also be able to consume and fuse different unsynchronized data streams that have different update rates, including from biometric sensors if available.
To measure driver workload, the researchers first developed a modified version of the Peripheral Detection Task to collect, in an automated way, subjective workload information during driving. For the experiment, a phone showing a route on a navigation app was mounted to the car's central air vent, next to a small LED ring light that would blink at regular intervals.
Participants all followed the same route through a mix of rural, urban and main roads. They were asked to push a finger-worn button whenever the LED light lit up in red and the driver perceived they were in a low workload scenario.
Video analysis of the experiment, paired with the data from the buttons, allowed the researchers to identify high workload situations, such as busy junctions or a vehicle in front or behind the driver behaving unusually.
The on-road data was then used to develop and validate a supervised machine learning framework to profile drivers based on the average workload they experience, and an adaptable Bayesian filtering approach for sequentially estimating, in real-time, the driver's instantaneous workload, using several driving performance signals including steering and braking. The framework combines macro and micro measures of workload where the former is the driver's average workload profile and the latter is the instantaneous one.
"For most machine learning applications like this, you would have to train it on a particular driver, but we've been able to adapt the models on the go using simple Bayesian filtering techniques," said Ahmad. "It can easily adapt to different road types and conditions, or different drivers using the same car."
The research was conducted in collaboration with JLR who did the experimental design and the data collection. It was part of a project sponsored by JLR under the CAPE agreement with the University of Cambridge.
"This research is vital in understanding the impact of our design from a user perspective, so that we can continually improve safety and curate exceptional driving experiences for our clients," said JLR's Senior Technical Specialist of Human Machine Interface Dr. Lee Skrypchuk.
"These findings will help define how we use intelligent scheduling within our vehicles to ensure drivers receive the right notifications at the most appropriate time, allowing for seamless and effortless journeys."
Tothemoon24
Top 20
#CES2024 is just around the corner! If you’re making the annual journey to Vegas and want to see the event-based future of IoT, AR/VR/XR, next-generation mobile photography and much more - make sure to book a meeting with us.
Take your consumer applications to the next level with extreme energy efficiency, high dynamic range, and processing performance.We'll have our CES Award-Winning GenX320 Metavision® sensor on display, showcasing use cases in low-power Edge IoT such as eye tracking, gesture tracking, fall detection, and driver monitoring systems.
Plus, experience the future of mobile photography through a live demo of our event-based Metavision® sensor and AI running on the latest Qualcomm Snapdragon 8 Gen 3.
Stay ahead with Prophesee – book a meeting at our suite 29-312 in the Venetian now 
https://lnkd.in/dfe3jhZy
Humble Genius
Regular
He looks like he's had a few burgers since he left Brainchip, eh.......? Probably explains why he is a growth executive..... and given him so much energy to continually post on Linkedin. At least he looks happy now.
"....he didn't sell any chips because he ate all the chips......"
"....he didn't sell any chips because he ate all the chips......"
Last edited:
Labsy
Regular
God I hope so but I get less that positive vibes from O'l Pattie boy at Intel... not holding my breath on this one even though I'm ever the optimist. I always said they should just adopt akida under licence and just call it loihi 3 or loihi edge or loihi science fiction or whatever..
But he doesn't seem the type given their obsession with X86 architecture and his recent comments on arm and how they stack up against that... like he's in fairly land.
But he doesn't seem the type given their obsession with X86 architecture and his recent comments on arm and how they stack up against that... like he's in fairly land.
Similar threads
