#Encoding Methods Comparison

[uvaldecomputerrepairsstuff]

A Beginner's Guide to Streaming: Unveiling the Power of OBS Software and Hardware Encoding

Introduction In today’s digital age, streaming has become an increasingly popular way to connect, share content, and interact with a global audience. Whether you’re an aspiring gamer, a talented musician, or a knowledgeable creator, streaming allows you to showcase your talents and engage with viewers in real-time. To embark on this exciting journey, you’ll need the right tools and knowledge. In…

[gallifreyinstituteforlearning]

does gallifrey have vaccines? or some sort of equivalent?

Does Gallifrey have vaccines (or an equivalent)?

Yes, the Doctor's developed a few vaccines, noting that his people had long since mastered the science.

However, given the sheer breadth of Gallifreyan medical advancements, it's likely not the most cutting-edge method anymore. They probably view vaccines as quite basic immunological management.

🦠 How Does Gallifreyan Immunity Work? (A Theoretical Model)

Gallifreyan immune systems operate on the same fundamental principles as other adaptive immune systems. The immune system must first encounter a pathogen, recognise it as a threat, and develop a response. Once this happens, it will remember the pathogen forever and mount a rapid defence in future encounters.

Immunity can be developed in a few different ways:

⚔️ Natural Exposure – A Gallifreyan contracts a disease, fights it off, and retains the antibodies.

💉 Vaccination (or Equivalent Exposure) – Once used, but likely replaced with faster or more advanced immune-priming methods.

🧬 Passive Immunity from Biodata – Perhaps every new Gallifreyan arrives with a preloaded database of antibodies passed down from their mother (for womb-born) or directly encoded into their biodata (for loom-born).

🩸 Blood Transfusion Immunity – A blood transfer from a Gallifreyan can pass on antibodies, allowing the recipient's immune system to 'learn from' them—providing temporary or even long-term protection.

🏫 So ...

While vaccines (as humans know them) may not be widely used anymore, Gallifreyan immune systems are incredibly sophisticated, making disease management far more efficient than anything humans currently have.

Related:

🤔|🛡️🦠The Gallifreyan Immune System vs. Pathogen

💬|🛡️🦠Sickness Comparisons: Time Lords, Hybrids, and Humans: How diseases affect these species differently.

💬|🛡️✨Do Time Lords maintain immunity to disease in their subsequent forms?: If Time Lord immune systems have to ‘relearn’ certain diseases.

Hope that helped! 😃

Any orange text is educated guesswork or theoretical. More content ... →📫Got a question? | 📚Complete list of Q+A and factoids →📢Announcements |🩻Biology |🗨️Language |🕰️Throwbacks |🤓Facts → Features: ⭐Guest Posts | 🍜Chomp Chomp with Myishu →🫀Gallifreyan Anatomy and Physiology Guide (pending) →⚕️Gallifreyan Emergency Medicine Guides →📝Source list (WIP) →📜Masterpost If you're finding your happy place in this part of the internet, feel free to buy a coffee to help keep our exhausted human conscious. She works full-time in medicine and is so very tired 😴

[anyataylorjoys]

Hi! I was wondering how you make your gifs? Especially how do you sharpen them or make them such good quality? They look really pretty! <3

Hey! I pretty much summarized my gif-making process earlier this week here which includes a lengthy tutorial by user redbelles who uses the same process as me if you want all the details from start to finish. I also released a sharpen action pack here that will work with the method of dicom loading in screencaps with Photoshop.

Since someone else asked me in dms about this too, if you have a mac, it's easy to convert png/jpg files into dcm by selecting them all > right click > rename > convert [your img type] to dcm. If you have a pc, I don't know how to convert img files to dcm unfortunately, you'd have to use a batch-converting program possibly. But you can get the same results by going to file > script > load files into a stack. The biggest difference is files to stack loads frames in backwards, and dicom batch loads them in proper order, and faster. If you are using one of my actions and aren't batch loading dicoms, the actions will not reverse the frames so just a heads up on that. You can adjust my actions as you need to for your own processes.

If you want my personal tips on quality, I would say above all, obtaining HQ footage is top priority when it comes to getting the best quality gifs possible.

When choosing what to download, know that bluray remux rips provide full resolution, but imo bluray remux isn't the best choice in every single case. I suggest going for H. 265 (HEVC) compressed/re-encoded files when available over x264 (AVC) unless its remuxed (aka uncompressed). x265 offers higher quality compression by preserving more detail while x264 leaves more artifacts. Don't be confused though, AVC x264 is pretty standard for bluray discs so that codec will be preserved in the remux.

Comparisons can be made below:

Is the quality better with bluray remux? Well technically yeah, the details are more defined thus most would say it's clearer and the colors are slightly more accurate. But it's also very grainy while the 720p is smoother, and the gif size result is a huge difference for 40 frames. Many people (including me) love grain but sometimes it makes coloring and sharpening a challenge and can limit you, plus 6GB for a single episode of a tv show can take up a looot of storage space so it's preference but note that you CAN get high quality results with 720p, you just have to know which files to choose.

Also when coloring, don't push colors farther than they can go. You will know what this means when your colorings start to bring out visible pixels in a gif. Sometimes minimal coloring adjustments and maintaining a natural level of contrast is the better sacrifice to make for higher quality results.

Anyway I hope all this helps at least a little, I don't know your skill level but the tutorial I linked is a great source for beginners, and probably could even help experienced gif makers too.

[spacetimewithstuartgary]

Field-level inference: Unlocking the full potential of galaxy maps to explore new physics

Galaxies are not islands in the cosmos. While globally the universe expands—driven by the mysterious "dark energy"—locally, galaxies cluster through gravitational interactions, forming the cosmic web held together by dark matter's gravity. For cosmologists, galaxies are test particles to study gravity, dark matter and dark energy.

For the first time, MPA researchers and alumni have now used a novel method that fully exploits all information in galaxy maps and applied it to simulated but realistic datasets. Their study demonstrates that this new method will provide a much more stringent test of the cosmological standard model, and has the potential to shed new light on gravity and the dark universe.

From tiny fluctuations in the primordial universe, the vast cosmic web emerged: galaxies and galaxy clusters form at the peaks of (over)dense regions, connected by cosmic filaments with empty voids in between. Today, millions of galaxies sit across the cosmic web. Large galaxy surveys map those galaxies to trace the underlying spatial matter distribution and track their growth or temporal evolution.

Observing and analyzing millions of galaxies turns out to be a daunting task. Hence, standard analyses first compress the three-dimensional galaxy distribution into measurements of the spatial correlation between pairs and triplets of galaxies, technically known as the two- and three-point correlation functions (see TOP IMAGE).

These restricted statistics, however, potentially leave out a lot of information in galaxy maps, especially information encoded on smaller spatial scales. In addition, they do not tell us where in the maps to look further, should some surprising result turn up in these statistics. How much more information can be extracted?

A recent study published in Physical Review Letters by MPA researchers and alumni, led by Dr. Minh Nguyen, provides compelling evidence for significant information beyond the reach of two- and three-point functions.

For the study, the team have developed and validated a rigorous probabilistic framework, LEFTfield, to model the clustering of galaxies. How the LEFTfield framework leverages the Effective Field Theory of Large-Scale Structure (EFTofLSS) to produce robust and accurate predictions of the observed galaxy field with high efficiency was the topic of another MPA research highlight.

LEFTfield foward–models the evolution of primordial fluctuations into large-scale structure and galaxy clustering, preserving the entire information in the three-dimensional distribution of galaxies. Further, the LEFTfield forward model is differentiable, allowing for field-level inference (FLI) of both parameters in the cosmological model and the primordial fluctuations from which all structure in the universe emerged.

In the study, the team set up an apples-to-apples comparison between FLI and the standard two-point plus three-point ("2+3-pt") inference. Both inference pipelines adopt the same LEFTfield forward model, and use the observed maps on strictly the same scales, as illustrated by the centre image.

Analyzing the same catalogs of dark-matter halos from the same set of N-body simulations, the team found that FLI improves constraints on the amplitude of structure growth by a factor of 3–5, even with conservative scale cuts in both analyses.

The improvement implies that even without aggressively pushing down to very small scales—where we expect EFTofLSS or even N-body simulations to fail—much more information can still be extracted from galaxy clustering simply by opening up another dimension: getting rid of the compression of the input data.

The lower image compares the constraints on the amplitude of structure growth from the FLI and "2+3-pt" analyses. The parameter σ8 quantifies the typical amplitude of structure in the initial ("linear") density field on a certain scale.

Essentially, galaxy clustering constraints on σ8 probe the growth of structure from the early universe (where we have precise measurements thanks to the cosmic microwave background) to late times. For this reason, this is a parameter that is generally modified in non-standard cosmological models, for example, if gravity is not correctly described by General Relativity, or if dark matter is not cold.

A factor of 5 improvement in parameter constraints effectively "increases" the survey volume by more than an order of magnitude, which is a huge improvement given the time-consuming and expensive process of mapping out the galaxy distribution over a large volume. Moreover, FLI in principle guarantees optimal extraction of cosmological information: there is no data compression, hence no information loss.

While this study used dark matter halos in simulations, the conclusions also hold for significantly more realistic simulated galaxies, which were the subject of a parallel study by the Beyond-2pt Collaboration that includes two researchers from the MPA team, the FLI approach based on the LEFTfield framework again returns unbiased and improved constraint on growth of structure.

Beyond improved parameter constraints, FLI also offers numerous ways to find out where evidence for physics beyond the standard model of cosmology might come from, should such evidence appear.

Since we have samples of universes that are compatible with the data, we can look for those regions most strongly deviant from the standard model, and investigate what is unusual about them. We can also employ independent datasets, for example, by correlating the inferred matter density with gravitational lensing maps, which are an entirely different probe of structure.

The team now set their eyes on applying the novel FLI approach and LEFTfield framework to real data from galaxy surveys. To connect FLI to observations, a better understanding, hence more studies, of how observational systematics impact the model predictions at the field level will be required. A flexible-yet-efficient forward-modeling framework like LEFTfield will be the key for such studies, and for unlocking the full potential of FLI from galaxy maps.

TOP IMAGE: Summary statistics like the two- and three-point correlation functions compress the galaxy field into spatial correlations between pairs and triplets of galaxies (left panel). Field-level statistics bypass the compression step to access the entire information in the galaxy field. Credit: MPA

CENTRE IMAGE: The comparison between FLI and 2+3-point inference adopts the same forward model, LEFTfield, for both inference schemes. The key difference is FLI analyzes the entire galaxy field while 2+3-point inference analyzes only the 2+3-point summaries of the (same) galaxy field. Credit: MPA

LOWER IMAGE: Constraints on the amplitude of growth of structure σ8 are improved by up to a factor of 5 when analyzing the whole galaxy field compared to just the 2- and 3-point correlation functions. Credit: MPA

[homokeiju]

i love ppl who rip n encode n upload movies online especially when they do so well n see to feel pride in their craft look at this shit bout this rebellion torrent i found:

2.0 and 5.1 audio included. There is an alternate honorifics track in this release. Set your media player to play “enm” language tracks by default to automatically play honorifics tracks. It turns out that Aniplex, a major anime publisher, has been intentionally sabotaging the quality of many of its Blu-Rays for more than a decade. Though it doesn’t seem like the Monogatari series has been affected, high-profile shows like Kill la Kill, Lycoris Recoil, Promare, Haruhi, Kimetsu no Yaiba, Fullmetal Alchemist, Fullmetal Alchemist: Brotherhood, Fate/Zero, Fate Stay/Night: UBW, Fate literally/everything/else, the Shirobako movie, Ascendance of a Bookworm, Soul Eater, and Steins;Gate contain scenes where the quality has been reduced for no good reason, particularly the first three anime in the list. The only way to fix this is to use an alternate source like Crunchyroll (which doesn’t always work that well because the scenes with this damage often have a high bitrate) or a non-US, non-JP Blu-Ray. This movie, too, is affected by Aniplex’s shenanigans, so the German Blu-Ray was used as an alternate source. (The Italian Blu-Ray was a copy of the JPBD.) This was principally used to replace the chroma in many scenes, with MTBB having better colors than the JPBD in comps 03-06 below, for example. Comp 28 is about as bad as it gets. About 20% of the movie is affected. But the German Blu-Ray has all kinds of artifacting in high-motion scenes, so it normally was not worth it to use it for luma. The chroma replacement was handled automatically, with scenes alternating between the JP and GER BDs depending on which source had stronger edgemasks for chroma. You can see that the chroma is more blurry in the bad JPBD scenes, so I figured an edgemask would be correspondingly weaker, and that method seems to have worked pretty reliably. Another aspect of this encode is the fractional rescale. This movie is a great demonstration of the power of a proper descale --> waifu2x cunet double. Some of the lineart repair going on here is downright miraculous, like in comp 27 below, even in comparison to Kawaiika. The Monogatari series basically has the same sort of lineart as Madoka, so it would also benefit from this process—though it wouldn’t be that much of an improvement over Kawaiika-Raws/OZR. If you want to encode it, the descale is 719.78 fractional for the res and probably (0, 0.5) bicubic for the kernel, though I think that changes at some point in the series. Bakemonogatari in particular would benefit from better lineart filtering than the best current encode (Beatrice-Raws). Anyway, here’s some comparisons. Kawaiika was included because it is by far the best JPBD-based encode.

[govindhtech]

Analyzing C2C Communications In Financial Malware

As Ursa, the remote overlay financial malware Mispadu targets banks in Spanish- and Portuguese-speaking countries like Mexico, Colombia, Argentina, Chile, Portugal, Spain, and others. Remote overlay malware is a malicious program that controls a victim's keyboard and mouse while the fraudster observes their live screen.

Control and command When Mispadu reopened, C2C communications encoding was changed.

Preparing C2C communications

Goal of C2C communications

The assault relies on the malware's remote overlay communication with its operator. This connection is used by the fraudster to deliver operational orders to the victim's infected application.

While monitoring the victim's live sessions, the fraudster performs many overlay attacks, such as bank account theft.

C2C connection timing

The scammer does not contact the victim immediately after the victim launches the infected app, despite appearances. Such touch may trigger antivirus software alerts, endangering the fraudster.

In scenario and most other remote overlay scenarios, communication begins when the user hits one of the malware's targets, Spanish- or Portuguese-speaking bank websites.

Initialising C2C communications

The user contacts the C2C server when seeing a virus target list. WIN32 Socket APIs, the most practical way to connect, are used.

Before configuring the socket, the virus enters its IP and destination port.

Receive C2C messages

When the socket is linked and the beacon is sent, the malware awaits C2C server input. Once received, “read” functions handle the message based on its sequence:

These similar algorithms parse C2C communications.

First, “TwYHJk1_wC51Read,” will be discussed:

After receiving a message from the C2C, the virus decodes and compares it to a command string. “|SocketMain|>” is the first compared command in the first “read” function. Other “read” functions provide different comparison instructions.

Note the function at 0x7364A8.

This function must decipher the complete message. The program decodes a string using mathematical processes.

Check out that function's operation.

Encoding C2C messages

Goal

Communication encoding hides fraudulent goals and techniques. This can be done with current or custom communication algorithms. As seen, “GFHHV..” appears encoded because it seems random and meaningless.

Execution

The C2C communications decoding function is simple and decodes C2C server messages. The same encoder is used for C2C server messages.

Decoding will be broken down into these steps:

Convert the first character (“G”GFHHVGCGEFUGAFOFUGCFMFXHVFJ@) to ASCII. The value is 71. Subtract 65 (ASCII “A”) from this number. The result is 6.

Let's revisit 6 later because it repeats value while decoding.

Step 2: ASCII the character (“F” GFHHVGCGEFUGAFOFUGCFMFXHVFJ@). It's worth 70. Remove 65, the ASCII code for “A.” The result is five. Assume X is variable. The two assembly code lines can be represented by this equation:

4X + 4X4 = 25X = 255 = 125

Step 3: Choose the ASCII value for “H”: 72. Subtract “A” from ASCII: outcome 7. Add the previous step's result. 125+7=132. Remove step 1's result and 66 (‘B’) from it. 60 = 132-66-6. “<” in ASCII. First character in decoded string.

Step 4: Repeat Steps 2 and 3 with the next set of characters (“H” & “V” GFHHVG..), attaching the decoded character each time to form the string.

Step 5: String ends with “@” GFHHVGCGEFUGAFOFUGCFMFXHVFJ@. Decoding the encoded text may display the result: |PRINCIPAL|> The malware submitted this string to the C2C server initially.

Something to remember

Encoding and decoding can create the same character from different encoded characters.

AFV and GGC map to P.

A more complicated example follows. The same plaintext may be retrieved by decoding “GFHHVGCGEFUGAFOFUGCFMFXHVFJ” and “AFBHPFVFXFOFTFIFOFVFGFRHPFD.”

If the network is monitored, such encoding and decoding methods make it difficult to understand the malware's behaviour without the decoding algorithm. Because each command has many encoding options, the malware's comparable mode of operation appears unique to the network monitor each time.

C2C instructions

Execution

The sample's next steps are simple after deciphering the instruction. The fraudster can control the victim's mouse, keyboard, and screen, among other things, because each command has a specific operating role.

Main commands

The infection has set up commands for the scammer to perform various tasks on the victim's PC.

A beacon, denoted by “<|PRINCIPAL|>,” is delivered by the virus to the C2C server to indicate network establishment.

Following this first stage, the fraudster has full control over the victim's system and can take any action. Extraction of critical system data is crucial. Using the “<|Info|>” command, basic system information about the victim can be exported. The Windows version, location, browser, and webpage are listed. Malware's example response to this command sent back to the C2C server:

“Info: Chrome, Bank xWin 10At 4:04:12 PM.

This answer says the victim is reading Bank x's homepage on Windows 10 and Chrome at 4:04:12 PM.

This data is stolen for many reasons. Knowing the victim's operating system may make it easier to infiltrate their system with additional malicious tools. Knowing the victim's intended bank site helps the fraudster perform the attack.

Conclusion

Today, remote overlay attacks are one of the most common threats to bank accounts, endangering banks and their clients. These attacks depend on malware-operator communication, which is vital to their strategy. Such assaults require real-time contact. The virus encrypts communication to strengthen its defences and make it harder to reverse. As cybersecurity experts, IBM monitors, examines, and stops these transactions to prevent fraud.

Staying safe

For safety, users should routinely check their apps and uninstall any that seem odd or dangerous.

Unauthorised transactions in cryptocurrency wallets and unexpected login attempts in email accounts should also be checked. Being aware and proactive helps decrease the risks of this evolving assault paradigm.

IBM Trusteer helps you authenticate people, prevent fraud and malware, and build identity trust across the omnichannel consumer experience. Over 500 top firms utilise IBM Trusteer to expand and protect client digital experiences.

For more details visit govindhtech.com

[hydrus]

Version 622

windows

zip

exe

macOS

app

linux

tar.zst

I had an ok week. There's some bug fixes and new duplicate tech for advanced users to test.

full changelog

highlights

The recent resizable ratings changes broke on a particular version of Qt (PyQt6). If you were caught by this, and perhaps even couldn't boot, sorry for the trouble! I do test PyQt6 every week, but this somehow slipped through the cracks.

Ctrl+Shift+O will now launch the options dialog. If your menubar gets messed up because of a setting, this is the new fallback.

You can now paste multiline content into the text input of a 'write/edit' tag autocomplete that has a paste button (e.g. in 'manage tags'), and it'll recognise that and ask if you want to essentially hit the paste button instead and enter those lines as separate tags. If you would do this a lot, a new checkbox in optons->tag editing let's you skip the confirmation.

I improved some PNG colour correction this week. I think it will make about one in twenty PNGs a small shade brighter or darker--not usually enough to notice unless you are in the duplicates system. If you notice any of your PNGs are suddenly crazy bright/dark/wrong, let me know!

A couple of new checkboxes in options->files and trash let you, if you have the archived file delete lock on, pre-re-inbox archived files that are due to be deleted in the archive/delete or duplicate filters. I don't really like writing exceptions for the delete lock, but let's try this method out.

duplicate test

If you are in advanced mode, the manual duplicate filter will have a new '(not) visual duplicates' comparison line in the right hover, along with some mathematical numbers. This is the 'A and B are visual duplicates' tech I have been working on that tries to differentiate resizes/re-encodes from files with significant differences.

I have tuned my algorithm using about a hundred real pairs, and I'd now like users to test it on more IRL examples. It sometimes gives a false negative (saying files are not visual duplicates, despite them being so), which I am ok with from time to time. What I am really concerned about is a false positive (it saying they are visual duplicates, despite there being a recolour or watermark or other significant change). So, if you do some duplicate filtering work this week, please keep an eye on this line. If it predicts something wrong, I would be interested in being sent that pair so I can test more on my end. Feel free to look at the numbers too, but they are mostly for debug.

Assuming this goes well, I will re-tune this detector, polish its presentation, and enable it for all users, and I will add it as a comparison tool in duplicates auto-resolution.

next week

I think I will keep chipping away at my duplicates auto-resolution todo.

[renatoferreiradasilva]

Problems for Researchers Aiming to Verify the Article’s Claims and Advance Research on the Topic

Theoretical Problems

Hermiticity in Sobolev Spaces

Objective: Formally prove that the operator ( H = -\frac{d^{12}}{dx^{12}} + V(x) ) is self-adjoint in an appropriate Hilbert space (e.g., ( L^2(\mathbb{R}, w(x)dx) )) under periodic or rapidly decaying boundary conditions.

Hint: Use the Kato-Rellich theorem for perturbations of self-adjoint operators.

Asymptotic Spectral Density

Objective: Prove that the eigenvalue density of ( H ) satisfies ( \rho(\lambda) \sim C \lambda^{5/12} ), aligning with the asymptotic law ( N(T) \sim \frac{T}{2\pi} \log \frac{T}{2\pi e} ) for zeta zeros.

Hint: Relate eigenvalue counting to Weyl’s law for differential operators.

PT Symmetry and Eigenvalue Reality

Objective: Investigate whether ( H ) is PT-symmetric and how this symmetry ensures real eigenvalues despite an asymmetric ( V(x) ).

Extension: Study spontaneous PT-symmetry breaking for non-even potentials.

Numerical Problems

Operator Implementation on Adaptive Grids

Objective: Reproduce the article’s results using spectral methods (e.g., Fourier/Chebyshev bases) and compare with finite differences.

Challenge: Ensure numerical stability for ( \frac{d^{12}}{dx^{12}} ) on large domains (( L \gg 1 )).

Machine Learning for ( V(x) ) Optimization

Objective: Train a neural network to optimize ( V(x) ), minimizing ( ||\lambda_n - \text{Im}(s_n)|| ).

Hint: Use physics-informed neural networks (PINNs) with symmetry constraints and regularization.

Large-Scale Eigenvalue Computation

Objective: Compute the first ( 10^6 ) eigenvalues of ( H ) and compare with zeta zeros up to ( \text{Im}(s) \sim 10^{12} ).

Tools: GPU parallelization or HPC clusters.

Statistical Problems

Universality of GUE Statistics

Objective: Verify whether GUE statistics persist for ( H )’s eigenvalues at smaller scales (e.g., ( 10^3 ) eigenvalues) or if non-universal local correlations exist.

Method: Analyze ( n )-point correlation functions for ( n \geq 3 ).

Effect of Stochastic Perturbations

Objective: Introduce noise to ( V(x) ) (e.g., ( V(x) \to V(x) + \epsilon \xi(x) )) and study transitions between GUE, GOE, and Poisson statistics.

Extension: Relate to the hypothesis that zeta zeros are "rigidly" chaotic.

Physics Connection Problems

Modeling as a Quantum Chaotic System

Objective: Simulate wavepacket evolution under ( H ) and compute Lyapunov exponents to confirm classical-quantum chaos.

Tools: Numerical integration of the Schrödinger equation via split-step Fourier methods.

Link to Riemann’s Explicit Formula

Objective: Demonstrate that ( V(x) \propto \sum_p \log p \cdot e^{-x/p} ) implies a direct relationship between ( H )’s spectrum and prime distribution.

Challenge: Connect operator traces to the prime-counting function ( J(x) ).

Generalization Problems

Pseudo-Differential Operators

Objective: Replace ( -\frac{d^{12}}{dx^{12}} ) with ( |\nabla|^\alpha ) (( \alpha \in \mathbb{R}^+ )) and tune ( \alpha ) to better match zeta zeros.

Hint: Use Fourier transforms to discretize non-local operators.

Automorphic ( L )-Functions

Objective: Construct operators for zeros of automorphic ( L )-functions (e.g., Ramanujan’s ( L )-function) and verify GUE statistics.

Advanced Computational Problems

Quantum Computer Implementation

Objective: Encode ( H ) as a Hamiltonian in a quantum circuit (e.g., using ancilla qubits) and estimate eigenvalues via VQE (Variational Quantum Eigensolver).

Challenge: Handle the operator’s high order in low-qubit systems.

Sensitivity Analysis

Objective: Study how small variations in ( V(x) ) affect eigenvalues using spectral sensitivity analysis.

Application: Determine whether RH is "stable" under potential perturbations.

Independent Validation Problems

Independent Reproduction of Results

Objective: Replicate the article’s results using open-source tools (e.g., Python/FEniCS or Mathematica) and publish eigenvalue datasets.

Suggested Platforms: GitHub, Zenodo.

Comparison with Other Operators

Objective: Compare ( H )’s performance against lower/higher-order operators (e.g., 8th or 16th order) in approximating zeta zeros.

Philosophical/Open-Ended Problems

Interpreting the GUE Correlation

Objective: Debate whether GUE adherence supports RH or is merely an emergent coincidence.

Key Question: Can integrable systems exhibit random matrix universality without an underlying Hamiltonian structure?

Connection to Quantum Field Theory

Objective: Investigate whether ( H ) could represent a conformal field theory (CFT) Hamiltonian in 1+1 dimensions, linking RH to the AdS/CFT correspondence.

These problems span technical validations, ambitious generalizations, and interdisciplinary explorations, offering a roadmap for advancing spectral approaches to the Riemann Hypothesis. Each solved problem would not only validate the proposed model but also deepen connections between number theory, mathematical physics, and computational science.

[biomedres]

Application of Hybrid CTC/2D-Attention end-to-end Model in Speech Recognition During the COVID-19 Pandemic

Application of Hybrid CTC/2D-Attention end-to-end Model in Speech Recognition During the COVID-19 Pandemic in Biomedical Journal of Scientific & Technical Research

Speech recognition technology is one of the important research directions in the field of artificial intelligence and other emerging technologies. Its main function is to convert a speech signal directly into a corresponding text. Yu Dong, et al. [1] proposed deep neural network and hidden Markov model, which has achieved better recognition effect than GMM-HMM system in continuous speech recognition task [1]. Then, Based on Recurrent Neural Networks (RNN) [2,3] and Convolutional Neural Networks (CNN) [4-9], deep learning algorithms are gradually coming into the mainstream in speech recognition tasks. And in the actual task they have achieved a very good effect. Recent studies have shown that endto- end speech recognition frameworks have greater potential than traditional frameworks. The first is the Connectionist Temporal Classification (CTC) [10], which enables us to learn each sequence directly from the end-to-end model in this way. It is unnecessary to label the mapping relationship between input sequence and output sequence in the training data in advance so that the endto- end model can achieve better results in the sequential learning tasks such as speech recognition. The second is the encodedecoder model based on the attention mechanism. Transformer [11] is a common model based on the attention mechanism. Currently, many researchers are trying to apply Transformer to the ASR field. Linhao Dong, et al. [12] introduced the Attention mechanism from both the time domain and frequency domain by applying 2D-attention, which converged with a small training cost and achieved a good effect.

And Abdelrahman Mohamed [13] both used the characterization extracted from the convolutional network to replace the previous absolute position coding representation, thus making the feature length as close as possible to the target output length, thus saving calculation, and alleviating the mismatch between the length of the feature sequence and the target sequence. Although the effect is not as good as the RNN model [14], the word error rate is the lowest in the method without language model. Shigeki Karita, et al. [15] made a complete comparison between RNN and Transformer in multiple languages, and the performance of Transformer has certain advantages in every task. Yang Wei, et al. [16] proposed that the hybrid architecture of CTC+attention has certain advancement in the task of Mandarin recognition with accent. In this paper, a hybrid end-to-end architecture model combining Transformer model and CTC is proposed. By adopting joint training and joint decoding, 2DAttention mechanism is introduced from the perspectives of time domain and frequency domain, and the training process of Aishell dataset is studied in the shallow encoder-decoder network.

For more articles in Journals on Biomedical Sciences click here bjstr

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[theenterprisemac]

Looking At - Lockdown Options for the Mac

A quick note before we get started this research is from 2023 and has not been updated. I am just now putting this content out because I finally have a place to put it.

Options Looked At

I picked three options:

Absolute Manage (Home and Office)

HiddenApp

Prey Project

I picked Absolute Manage because that is something I am familiar with from the enterprise space. Home and office version because I am not paying for the enterprise product. HiddenApp because it was on the Jamf Marketplace (tho it seems to have been replaced with Senturo, but HiddenApp is still around so who knows). Prey was an obvious include because it's a popular option that's cheap.

What are we evaluating?

I am only looking at how the product locks down the Mac in the case of an outside of geofence, lost or stolen situation. I am not commenting on any of the other functionality.

tl;dr

For those of you not interested in the why's, how's and wheretofor's the conclusion is that Absolute Manage is significantly better than either of the other options. Its lock down is more effective and more robust against tampering etc.

What's wrong with HiddenApp?

The password to unlock the Mac is stored as an MD5 hash with no salt or any other protection.

You can, if you disconnect from the network, change the password to whatever you want it to be by simply changing the hash in the device_locked.status file found in /usr/local/hidden. You need to be an admin, but that is more and more common in the Mac space even in the enterprise.

The lock down is triggered by a Launch Daemon and therefore doesn't activate immediately. I have seen it take multiple minutes to lock the screen–giving you more than enough time to stop it.

The HiddenApp itself is not obfuscated so you can easily reverse engineer any part you need.

If the user is an admin not only can they change the lock password, but they can also prevent their machine from ever locking by simply controlling the missing marker file. You can also of course simply remove HiddenApp since it has no special protection. If you are not on the network once you stop the lock down–HiddenApp can't fix itself without network help.

What's wrong with Prey?

Prey like HiddenApp has a weak method of storing the password to unlock the computer. The method used is: string input converted to utf-8 then base64 encoded and then md5 hashed and returned as a hex digest. You can find this by looking at: /lib/agent/actions/lock/mac/prey-lock in the Prey folder (this is a Python3 file). So you can easily break this scheme due to it being MD5 you just need to base64 encode your wordlist first.

The password hash is easily obtained from looking at the output of sudo ps -A with the window expanded. The password is in the command-line arguments passed to prey-actions.app with -lock flag.

The lock can be bypassed with Safe Mode and with SSH.

The application itself is built from a lot of JavaScript/node.js code. This also means its trivial to reverse engineer.

The application makes no effort to hide itself or obscure what it is doing.

What's right with Absolute Manage Home and Office?

Unlike the other two options Absolute Manage uses a robust lock down based on a SecurityAgentPlugin that runs at login. The lock down is therefore immediate and is hard to bypass by comparison.

The password is not as robust as the other options (4-6 digit pin), but given that the lockdown is immediate during login you don't have the same ability to block it or tamper with it. Keep in mind this is the personal product–so the pin lock makes some limited sense.

The application does a good job obscuring itself and what it is doing.

The only effective bypass I found was if SSH is enabled, then you can SSH in and bypass the lock. I put in a feature suggestion that they disable SSH as part of the lock down.

The product is much more difficult to get rid of, because it stores its components in multiple locations and generally tries to hide itself.

Safe mode does not get around the lock out unlike some of the other products.

The biggest issue I found was the time between issuing a lock command and it being enforced on the endpoint was excessively long-hours in many cases. Observed times as long as 15 hours between issuing the lock command it taking place. This could have been my setup so take it with a grain of salt.

Conclusion

The asset management tool space is a crowded one, and if you are looking for a good product that locks down stolen or otherwise improperly stationed assets you need to take great care to verify what you are buying. Of the three products I picked only one was remotely serviceable, and unless you dive into the details of how the products work it is easy to mistake bad solutions for good ones.

[hopelin]

How to adjust the speed of a geared stepper motor

1.A brief explanation of a geared stepper motor A geared stepper motor is an electromechanical device that converts electrical pulses into discontinuous mechanical motion. ‌It achieves precise position control by receiving digital control signals (electrical pulse signals) and converting them into corresponding angular displacements or linear displacements. The main feature of a geared stepper motor is that its output angular displacement or linear displacement is proportional to the number of input pulses, and its speed is proportional to the pulse frequency, which makes it important in systems that require high-precision motion control. As an important component of a stepper motor, the gearbox achieves precise control of speed and torque through its precise control function, thereby ensuring the irreplaceability of stepper motors in open-loop high-resolution positioning systems. ‌

2.Advantages of geared stepper motors 1.Reduce speed and increase output torque: Through different reduction ratios, geared stepper motors can reduce the speed of the input shaft while increasing the output torque, which is very useful for applications that require large torque output. ‌2.Compact structure and small footprint: The design of the geared stepper motor makes its structure very compact, thereby reducing the required installation space. ‌ ‌ 3.High transmission efficiency and precise speed ratio‌: The geared stepper motor has high transmission efficiency and can ensure the accuracy of the speed ratio, which is critical for applications that require precise control of speed and position. ‌4.Reduce the inertia of the load‌: By reducing speed, the geared stepper motor can reduce the inertia of the load, which is very beneficial for applications that require fast response and precise control. ‌5.Provide more accurate timing than the chain system, reduce friction loss and noise‌: Compared with the chain system, the geared stepper motor provides more accurate timing, while reducing friction loss and noise, improving the reliability and durability of the system.

3.Methods for adjusting the speed of the geared stepper motor 1.Change the main frequency of the controller‌: By adjusting the main frequency of the controller to change the pulse frequency output to the motor, the speed of the motor is controlled. The higher the main frequency, the higher the output pulse frequency, and the faster the motor speed. ‌2.Use PWM (pulse width modulation) control‌: Use the PWM signal to control the duty cycle of the pulse output, and then adjust the pulse frequency to achieve fine adjustment of the motor speed. ‌3.Table lookup speed regulation‌: According to the set speed value, by looking up the corresponding table of the pre-set speed value and pulse frequency, select the appropriate pulse frequency output to achieve a specific speed. ‌4.Adopting S-type acceleration curve‌: By adopting S-type acceleration curve, the motor can be smoothly accelerated and decelerated, providing better start and stop control. ‌5.Closed-loop control system‌: The real-time speed of the motor is fed back by the encoder, and the output of the pulse frequency is adjusted after comparison with the set value to achieve accurate speed tracking and enhance control accuracy.

4.Drive mode of gear stepper motor 1.Single voltage drive‌: In this mode, only one direction voltage is used to power the winding during the operation of the motor winding. The advantages of this mode are simple circuit structure, few components, low cost and high reliability. However, due to the increased power consumption, the efficiency of the entire power drive circuit is low, and it is only suitable for driving low-power stepper motors‌. ‌2.High and low voltage drive‌: In order to enable the winding to quickly reach the set current when power is on, and the winding current quickly decays to zero when power is off, while maintaining high efficiency, high and low voltage drive modes have emerged. This method uses high voltage power supply at the front edge of conduction to increase the front rise rate of the current, and uses low voltage to maintain the current of the winding after the front edge. High and low voltage drive can obtain better high-frequency characteristics, but may cause oscillation at low frequencies. ‌3.Full-step drive: including single-phase full-step drive and two-phase full-step drive. Full-step drive changes the direction and magnitude of the current to make the stepper motor rotate according to the set step angle. The advantage of this method is that it is simple and intuitive, and it is suitable for occasions where the torque requirement is not high. However, due to its relatively small torque, it may need to be used in combination with other drive methods. ‌4.Half-step drive: By changing the direction and magnitude of the current, the stepper motor rotates according to half a step angle. Compared with full-step drive, half-step drive has higher resolution and smoother movement, and is suitable for occasions with higher positioning requirements. ‌5.Micro-step drive: By controlling the magnitude and direction of the current in segments, the stepper motor rotates according to a smaller micro-step angle. Microstepping drives can achieve higher resolution and smoother motion, and are suitable for situations where high-precision positioning and control are required.

[citynewsglobe]

[ad_1] Messenger RNA (mRNA) know-how has taken the world by storm, primarily on account of its breakthrough in vaccine growth. At its core, mRNA is a genetic materials instructing cells to provide particular proteins. This know-how has enabled us to develop vaccines at unprecedented speeds and with exceptional efficacy. A number of corporations are on the forefront of making and offering mRNA merchandise that energy these medical improvements. However what precisely makes mRNA know-how so revolutionary? In contrast to conventional vaccines that always use weakened types of a virus, mRNA vaccines educate our cells to provide a protein that triggers an immune response. This methodology streamlines the manufacturing course of and reduces the chance of antagonistic reactions, making it a safer and extra versatile different for a lot of. The mRNA sequence will be quickly designed and synthesized within the lab, considerably lowering the time required in comparison with older vaccine applied sciences. That is significantly essential in responding to rising infectious illnesses the place time is of the essence. Moreover, the flexibleness of mRNA permits for fast updates to vaccines, making it simpler to fight new variants and mutations of viruses which will come up. mRNA Past Vaccines Whereas vaccines have introduced mRNA know-how into the limelight, its potential functions lengthen far past. Researchers are exploring its use in treating numerous cancers. By encoding cancer-fighting proteins, mRNA therapies can exactly goal and destroy malignant cells. This method assaults most cancers extra successfully and permits for focusing on particular varieties of cancerous cells whereas leaving wholesome cells unaffected. This preemptive focusing on is important for lowering the tough unwanted side effects generally related to conventional most cancers remedies like chemotherapy and radiation. Furthermore, mRNA know-how provides promising options for genetic problems. This method might right genetic mutations, providing hope to numerous sufferers worldwide. Think about remedies for problems like cystic fibrosis or muscular dystrophy turning into not simply extra accessible but in addition simpler. The flexibleness of mRNA means it might doubtlessly be programmed to right or exchange defective genes, offering a extra tailor-made and potent answer than standard therapies. The prospect of treating such debilitating circumstances on the genetic degree might considerably enhance the standard of life for thousands and thousands of people globally. Moreover, the power to modulate gene expression by mRNA might open up new therapeutic avenues for quite a few different genetic and bought illnesses. Present Analysis and Developments The tutorial neighborhood is abuzz with present analysis centered on the functions of mRNA know-how. Main pharmaceutical corporations and analysis establishments are investing closely in mRNA-based remedies. This inflow of sources is sparking many research and scientific trials to develop the scope of mRNA functions. Many of those tasks are exploring how mRNA will be stabilized and delivered extra effectively to focus on cells, which has been one of many main technical challenges. For example, current research are investigating the usage of mRNA for personalised most cancers vaccines. This innovation might revolutionize oncology by providing tailor-made remedies particular to a person’s most cancers profile. Such personalised approaches enhance remedy efficacy and scale back the chance of relapse. By creating vaccines distinctive to a affected person’s genetic make-up or the particular traits of their tumor, the chance of a profitable consequence will increase dramatically. As well as, this personalised drugs might help keep away from the one-size-fits-all limitations of standard remedies, providing hope for higher affected person outcomes. The continuing analysis into mRNA’s functions in different fields

, corresponding to regenerative drugs and autoimmunity, additionally exhibits nice promise, doubtlessly reworking the panorama of medical remedy. Challenges and Moral Issues Regardless of the promise, mRNA know-how faces numerous challenges and moral concerns. One important problem is the steadiness and supply of mRNA molecules. Making certain that mRNA reaches the goal cells with out degradation is essential for the success of those therapies. Advances in nanoparticle supply techniques and different applied sciences are being explored to guard mRNA molecules in transit and enhance cell uptake. Moreover, producing mRNA at scale whereas sustaining prime quality and purity is a serious hurdle researchers and producers attempt to beat. Ethically, there are considerations concerning genetic privateness and the long-term results of mRNA remedies. Transparency and sturdy moral pointers will likely be paramount to addressing public considerations as this know-how evolves. Researchers and policymakers should collaborate to make sure moral requirements maintain tempo with technological developments. Points corresponding to information safety, the potential for genetic discrimination, and knowledgeable consent have gotten more and more vital because the know-how progresses. Addressing these considerations responsibly will likely be essential for sustaining public belief and guaranteeing the equitable distribution of mRNA-based therapies. Moreover, guaranteeing that these superior therapies are accessible to all inhabitants segments, no matter socio-economic standing, is significant for stopping healthcare disparities. Future Instructions and Societal Impression The way forward for mRNA know-how is shiny, with potential functions extending to varied illnesses. Researchers are optimistic that mRNA may very well be leveraged for circumstances like cystic fibrosis, muscular dystrophy, and different persistent sicknesses. Think about a future the place genetic mutations will be corrected with a easy injection or persistent illnesses are managed extra successfully with fewer unwanted side effects. This opens new avenues for remedy and brings hope to thousands and thousands of sufferers and households affected by these circumstances. As researchers delve deeper, the realm of epigenetic modifications by mRNA might emerge, offering much more focused and complicated remedy choices. Furthermore, the societal impression of widespread mRNA adoption may very well be transformative. Think about a world the place speedy vaccine deployment stops pandemics of their tracks or the place genetic problems are routinely corrected. The probabilities are countless and will herald a brand new period in drugs. As we proceed to discover and develop mRNA know-how’s functions, we're enhancing healthcare and basically redefining it. The advances might result in extra equitable healthcare options, lowering disparities in remedy entry and outcomes throughout totally different populations worldwide. The submit Revolutionizing Healthcare: The Promise of mRNA Expertise appeared first on Vamonde. [ad_2] Supply hyperlink

[juliebowie]

Understanding What are Vector Databases and their Importance

Summary: Vector databases manage high-dimensional data efficiently, using advanced indexing for fast similarity searches. They are essential for handling unstructured data and are widely used in applications like recommendation systems and NLP.

Introduction

Vector databases store and manage data as high-dimensional vectors, enabling efficient similarity searches and complex queries. They excel in handling unstructured data, such as images, text, and audio, by transforming them into numerical vectors for rapid retrieval and analysis.

In today's data-driven world, understanding vector databases is crucial because they power advanced technologies like recommendation systems, semantic search, and machine learning applications. This blog aims to clarify how vector databases work, their benefits, and their growing significance in modern data management and analysis.

Read Blog: Exploring Differences: Database vs Data Warehouse.

What are Vector Databases?

Vector databases are specialised databases designed to store and manage high-dimensional data. Unlike traditional databases that handle structured data, vector databases focus on representing data as vectors in a multidimensional space. This representation allows for efficient similarity searches and complex data retrieval operations, making them essential for unstructured or semi-structured data applications.

Key Features

Vector databases excel at managing high-dimensional data, which is crucial for tasks involving large feature sets or complex data representations. These databases can handle various applications, from image and text analysis to recommendation systems, by converting data into vector format.

One of the standout features of vector databases is their ability to perform similarity searches. They allow users to find items most similar to a given query vector, making them ideal for content-based search and personalisation applications.

To handle vast amounts of data, vector databases utilise advanced indexing mechanisms such as KD-trees and locality-sensitive hashing (LSH). These indexing techniques enhance search efficiency by quickly narrowing down the possible matches, thus optimising retrieval times and resource usage.

How Vector Databases Work

Understanding how vector databases function requires a closer look at their data representation, indexing mechanisms, and query processing methods. These components work together to enable efficient and accurate retrieval of high-dimensional data.

Data Representation

In vector databases, data is represented as vectors, which are arrays of numbers. Each vector encodes specific features of an item, such as the attributes of an image or the semantic meaning of a text. 

For instance, in image search, each image might be transformed into a vector that captures its visual characteristics. Similarly, text documents are converted into vectors based on their semantic content. This vector representation allows the database to handle complex, high-dimensional data efficiently.

Indexing Mechanisms

Vector databases utilise various indexing techniques to speed up the search and retrieval processes. One common method is the KD-tree, which partitions the data space into regions, making it quicker to locate points of interest. 

Another technique is Locality-Sensitive Hashing (LSH), which hashes vectors into buckets based on their proximity, allowing for rapid approximate nearest neighbor searches. These indexing methods help manage large datasets by reducing the number of comparisons needed during a query.

Query Processing

Query processing in vector databases focuses on similarity searches and nearest neighbor retrieval. When a query vector is submitted, the database uses the indexing structure to quickly find vectors that are close to the query vector. 

This involves calculating distances or similarities between vectors, such as using Euclidean distance or cosine similarity. The database returns results based on the proximity of the vectors, allowing users to retrieve items that are most similar to the query, whether they are images, texts, or other data types.

By combining these techniques, vector databases offer powerful and efficient tools for managing and querying high-dimensional data.

Use Cases of Vector Databases

Vector databases excel in various practical applications by leveraging their ability to handle high-dimensional data efficiently. Here’s a look at some key use cases:

Recommendation Systems

Vector databases play a crucial role in recommendation systems by enabling personalised suggestions based on user preferences. By representing user profiles and items as vectors, these databases can quickly identify and recommend items similar to those previously interacted with. This method enhances user experience by providing highly relevant recommendations.

Image and Video Search

In visual search engines, vector databases facilitate quick and accurate image and video retrieval. By converting images and videos into vector representations, these databases can perform similarity searches, allowing users to find visually similar content. This is particularly useful in applications like reverse image search and content-based image retrieval.

Natural Language Processing

Vector databases are integral to natural language processing (NLP) tasks, such as semantic search and language models. They store vector embeddings of words, phrases, or documents, enabling systems to understand and process text based on semantic similarity. This capability improves the accuracy of search results and enhances language understanding in various applications.

Anomaly Detection

For anomaly detection, vector databases help in identifying outliers by comparing the vector representations of data points. By analysing deviations from typical patterns, these databases can detect unusual or unexpected data behavior, which is valuable for fraud detection, network security, and system health monitoring.

Benefits of Vector Databases

Vector databases offer several key advantages that make them invaluable for modern data management. They enhance both performance and adaptability, making them a preferred choice for many applications.

Efficiency: Vector databases significantly boost search speed and accuracy by leveraging advanced indexing techniques and optimised algorithms for similarity searches.

Scalability: These databases excel at handling large-scale data efficiently, ensuring that performance remains consistent even as data volumes grow.

Flexibility: They adapt well to various data types and queries, supporting diverse applications from image recognition to natural language processing.

Challenges and Considerations

Vector databases present unique challenges that can impact their effectiveness:

Complexity: Setting up and managing vector databases can be intricate, requiring specialised knowledge of vector indexing and data management techniques.

Data Quality: Ensuring high-quality data involves meticulous preprocessing and accurate vector representation, which can be challenging to achieve.

Performance: Optimising performance necessitates careful consideration of computational resources and tuning to handle large-scale data efficiently.

Addressing these challenges is crucial for leveraging the full potential of vector databases in real-world applications.

Future Trends and Developments

As vector databases continue to evolve, several exciting trends and technological advancements are shaping their future. These developments are expected to enhance their capabilities and broaden their applications.

Advancements in Vector Databases

One of the key trends is the integration of advanced machine learning algorithms with vector databases. This integration enhances the accuracy of similarity searches and improves the efficiency of indexing large datasets. 

Additionally, the rise of distributed vector databases allows for more scalable solutions, handling enormous volumes of data with reduced latency. Innovations in hardware, such as GPUs and TPUs, also contribute to faster processing and real-time data analysis.

Potential Impact

These advancements are set to revolutionise various industries. In e-commerce, improved recommendation systems will offer more personalised user experiences, driving higher engagement and sales. 

In healthcare, enhanced data retrieval capabilities will support better diagnostics and personalised treatments. Moreover, advancements in vector databases will enable more sophisticated AI and machine learning models, leading to breakthroughs in natural language processing and computer vision. 

As these technologies mature, they will unlock new opportunities and applications across diverse sectors, significantly impacting how businesses and organisations leverage data.

Frequently Asked Questions

What are vector databases? 

Vector databases store data as high-dimensional vectors, enabling efficient similarity searches and complex queries. They are ideal for handling unstructured data like images, text, and audio by transforming it into numerical vectors.

How do vector databases work? 

Vector databases represent data as vectors and use advanced indexing techniques, like KD-trees and Locality-Sensitive Hashing (LSH), for fast similarity searches. They calculate distances between vectors to retrieve the most similar items.

What are the benefits of using vector databases? 

Vector databases enhance search speed and accuracy with advanced indexing techniques. They are scalable, flexible, and effective for applications like recommendation systems, image search, and natural language processing.

Conclusion

Vector databases play a crucial role in managing and querying high-dimensional data. They excel in handling unstructured data types, such as images, text, and audio, by converting them into vectors. 

Their advanced indexing techniques and efficient similarity searches make them indispensable for modern data applications, including recommendation systems and NLP. As technology evolves, vector databases will continue to enhance data management, driving innovations across various industries.

[govindhtech]

Quantum Kernel Methods In Quantum ML For IoT Data Analytics

IoT Data Prediction Improves with Quantum Machine Learning Kernels.

Quantum kernel techniques

Recent research examines how quantum computation could improve the processing and interpretation of the growing volume of data produced by networked IoT devices. Scientists are curious if quantum kernel approaches can improve machine learning tasks like categorising and forecasting data. The use of projected quantum kernels (PQKs) to classify IoT data has been extensively studied by Francesco D'Amore and colleagues.

Beyond kernel approaches, quantum machine learning

Machine learning has great opportunities and challenges as IoT data grows. The study team focused on constructing prediction models with a quantum algorithm-compatible dataset to tackle this. This method avoided the lengthy pre-processing needed to adapt classical datasets to quantum methods.

The study focused on quantum kernel approaches. Kernel approaches tackle problems by implicitly mapping information into a higher-dimensional space. The Projected Kernel (PQK) quantum algorithm encodes data into a Hilbert space, which represents all quantum system states. For analysis, this quantum form is projected onto classical space. This method uses quantum computational principles without data organised for quantum processing.

This study uses a real IoT dataset. Even though many quantum machine learning studies use fabricated or simplified data, realistic issues must be used to assess quantum approaches' applicability. The dataset, a representative sample of smart building data, contained sensor readings of office ambient conditions.

This dataset can also be used with quantum algorithms without complex dimensionality reduction. For better accuracy and processing efficiency, choose a directly compatible dataset. The research shows how these methods might improve smart building occupancy forecast accuracy, addressing a major difficulty in quantum machine learning: the lack of quantum-compatible datasets.

The study stressed proper feature maps. Feature maps are needed to efficiently encapsulate classical data from IoT devices for quantum computation. These maps transform raw data for machine learning. Feature map selection strongly impacts model performance and quantum algorithm data learning. The study examined how many feature maps encoded conventional IoT data into a quantum state to investigate how different encoding strategies affect learning and generalisation.

A PQK approach

Research team benchmarked PQK method extensively. They compared its performance to classical kernel methods and Support Vector Machines. Understanding the quantum technique's pros and cons and assessing its efficacy requires these comparisons. The results suggest that PQK may increase prediction performance, laying the groundwork for comparison with classical methods.

The study found that PQK improves IoT data prediction, although the researchers urged further research. Future research should focus on scaling these algorithms to handle larger and more complex datasets. PQK's noise resistance, a property of near-term quantum technology, must also be examined.

This quantum-inspired strategy will be tested by expanding IoT applications beyond smart buildings and comparing performance to deep neural networks.

The study “Assessing Projected Quantum Kernels for the Classification of IoT Data” describes these findings. Information is more accessible.

This study covers classical machine learning, dataset creation, feature maps, Hilbert space, IoT devices, Quantum kernel approaches, prediction models, projected kernel, quantum algorithms, and quantum machine learning. This work advances quantum machine learning by showing how quantum computing could transform many sectors.

[secourses]

Zero to Hero Stable Diffusion 3 Tutorial with Amazing SwarmUI SD Web UI that Utilizes ComfyUI

Zero to Hero Stable Diffusion 3 Tutorial with Amazing SwarmUI SD Web UI that Utilizes ComfyUI : https://youtu.be/HKX8_F1Er_w

Do not skip any part of this tutorial to master how to use Stable Diffusion 3 (SD3) with the most advanced generative AI open source APP SwarmUI. Automatic1111 SD Web UI or Fooocus are not supporting the #SD3 yet. Therefore, I am starting to make tutorials for SwarmUI as well. #StableSwarmUI is officially developed by the StabilityAI and your mind will be blown after you watch this tutorial and learn its amazing features. StableSwarmUI uses #ComfyUI as the back end thus it has all the good features of ComfyUI and it brings you easy to use features of Automatic1111 #StableDiffusion Web UI with them. I really liked SwarmUI and planning to do more tutorials for it.

🔗 The Public Post (no login or account required) Shown In The Video With The Links ➡️ https://www.patreon.com/posts/stableswarmui-3-106135985

0:00 Introduction to the Stable Diffusion 3 (SD3) and SwarmUI and what is in the tutorial 4:12 Architecture and features of SD3 5:05 What each different model files of Stable Diffusion 3 means 6:26 How to download and install SwarmUI on Windows for SD3 and all other Stable Diffusion models 8:42 What kind of folder path you should use when installing SwarmUI 10:28 If you get installation error how to notice and fix it 11:49 Installation has been completed and now how to start using SwarmUI 12:29 Which settings I change before start using SwarmUI and how to change your theme like dark, white, gray 12:56 How to make SwarmUI save generated images as PNG 13:08 How to find description of each settings and configuration 13:28 How to download SD3 model and start using on Windows 13:38 How to use model downloader utility of SwarmUI 14:17 How to set models folder paths and link your existing models folders in SwarmUI 14:35 Explanation of Root folder path in SwarmUI 14:52 VAE of SD3 do we need to download? 15:25 Generate and model section of the SwarmUI to generate images and how to select your base model 16:02 Setting up parameters and what they do to generate images 17:06 Which sampling method is best for SD3 17:22 Information about SD3 text encoders and their comparison 18:14 First time generating an image with SD3 19:36 How to regenerate same image 20:17 How to see image generation speed and step speed and more information 20:29 Stable Diffusion 3 it per second speed on RTX 3090 TI 20:39 How to see VRAM usage on Windows 10 22:08 And testing and comparing different text encoders for SD3 22:36 How to use FP16 version of T5 XXL text encoder instead of default FP8 version 25:27 The image generation speed when using best config for SD3 26:37 Why VAE of the SD3 is many times better than previous Stable Diffusion models, 4 vs 8 vs 16 vs 32 channels VAE 27:40 How to and where to download best AI upscaler models 29:10 How to use refiner and upscaler models to improve and upscale generated images 29:21 How to restart and start SwarmUI 32:01 The folders where the generated images are saved 32:13 Image history feature of SwarmUI 33:10 Upscaled image comparison 34:01 How to download all upscaler models at once 34:34 Presets feature in depth 36:55 How to generate forever / infinite times

37:13 Non-tiled upscale caused issues 38:36 How to compare tiled vs non-tiled upscale and decide best 39:05 275 SwarmUI presets (cloned from Fooocus) I prepared and the scripts I coded to prepare them and how to import those presets 42:10 Model browser feature 43:25 How to generate TensorRT engine for huge speed up 43:47 How to update SwarmUI 44:27 Prompt syntax and advanced features 45:35 How to use Wildcards (random prompts) feature 46:47 How to see full details / metadata of generated images 47:13 Full guide for extremely powerful grid image generation (like X/Y/Z plot) 47:35 How to put all downloaded upscalers from zip file 51:37 How to see what is happening at the server logs 53:04 How to continue grid generation process after interruption 54:32 How to open grid generation after it has been completed and how to use it 56:13 Example of tiled upscaling seaming problem

1:00:30 Full guide for image history 1:02:22 How to directly delete images and star them 1:03:20 How to use SD 1.5 and SDXL models and LoRAs 1:06:24 Which sampler method is best 1:06:43 How to use image to image 1:08:43 How to use edit image / inpainting 1:10:38 How to use amazing segmentation feature to automatically inpaint any part of images 1:15:55 How to use segmentation on existing images for inpainting and get perfect results with different seeds 1:18:19 More detailed information regarding upscaling and tiling and SD3 1:20:08 Seams perfect explanation and example and how to fix it 1:21:09 How to use queue system 1:21:23 How to use multiple GPUs with adding more backends 1:24:38 Loading model in low VRAM mode 1:25:10 How to fix colors over saturation 1:27:00 Best image generation configuration for SD3 1:27:44 How to apply upscale to your older generated images quickly via preset 1:28:39 Other amazing features of SwarmUI 1:28:49 Clip tokenization and rare token OHWX

[howcanvpnbeusedtoprotectdata]

which vpn is the most secure

🔒🌍✨ Get 3 Months FREE VPN - Secure & Private Internet Access Worldwide! Click Here ✨🌍🔒

which vpn is the most secure

VPN security features

Title: Enhancing Online Security: Exploring Vital VPN Security Features

In today's digital landscape, safeguarding your online activities is paramount. With the proliferation of cyber threats and the increasing need for data privacy, utilizing Virtual Private Network (VPN) services has become essential. VPNs offer a secure pathway for data transmission over the internet, shielding users from potential risks. Understanding the key security features of VPNs is crucial for maximizing protection.

Encryption stands as the cornerstone of VPN security. Leading VPN providers employ robust encryption protocols, such as AES (Advanced Encryption Standard), to encode data during transmission. This ensures that even if intercepted, the data remains indecipherable to unauthorized parties, bolstering user privacy.

Another critical feature is a strict no-logs policy. Reputable VPN services refrain from logging users' online activities or storing any identifiable information. This commitment to anonymity enhances user trust and further safeguards sensitive data from potential breaches.

Furthermore, VPNs offer a diverse range of security protocols, including OpenVPN, L2TP/IPsec, and IKEv2/IPsec, among others. These protocols establish secure connections between the user's device and the VPN server, mitigating risks associated with data interception or manipulation.

Kill switch functionality is yet another vital aspect of VPN security. In the event of a VPN connection drop, the kill switch automatically halts internet traffic to prevent any data leaks. This feature ensures continuous protection, even under unstable network conditions.

Moreover, DNS leak protection fortifies VPN security by preventing the exposure of users' browsing activities through DNS queries. By routing DNS requests through encrypted tunnels, VPNs maintain confidentiality and shield users from potential surveillance or tracking.

In conclusion, VPN security features play a pivotal role in safeguarding users' online presence and sensitive information. By prioritizing encryption, adopting a strict no-logs policy, implementing robust security protocols, integrating kill switch functionality, and offering DNS leak protection, VPN providers empower users with comprehensive protection against cyber threats. Embracing these features enables individuals and organizations alike to navigate the digital realm with confidence and peace of mind.

Encryption strength comparison

When it comes to protecting sensitive information online, encryption plays a crucial role in safeguarding data from unauthorized access. Encryption strength is a fundamental aspect that determines how secure your data is against potential cyber threats. In this article, we will delve into the comparison of encryption strengths to help you understand the different levels of security they offer.

One of the most commonly used encryption methods is AES (Advanced Encryption Standard). AES comes in key lengths of 128, 192, and 256 bits, with 256-bit AES being the most secure option. It is widely used by governments, banks, and security-conscious organizations due to its robust encryption strength.

Another encryption algorithm worth mentioning is RSA (Rivest-Shamir-Adleman), which is based on the difficulty of factoring large prime numbers. RSA key lengths typically range from 1024 to 4096 bits, with longer keys providing stronger encryption.

In comparison, ECC (Elliptic Curve Cryptography) is another encryption scheme that offers strong security with shorter key lengths compared to RSA. ECC is gaining popularity due to its efficiency and ability to provide equivalent security with smaller key sizes.

Overall, when comparing encryption strengths, it is crucial to consider factors such as key length, algorithm complexity, and resistance to attacks. While AES, RSA, and ECC are all strong encryption methods, choosing the right one depends on the specific security requirements of your data and the level of protection needed. By understanding the differences in encryption strengths, you can make informed decisions to enhance the security of your sensitive information in the digital realm.

Secure VPN protocols

Title: Exploring Secure VPN Protocols: Safeguarding Your Online Privacy

In the digital age, where cyber threats loom large, safeguarding your online privacy is paramount. Virtual Private Networks (VPNs) have emerged as essential tools for ensuring secure and private internet browsing. However, not all VPN protocols are created equal when it comes to security. Understanding secure VPN protocols is crucial for making informed decisions about protecting your data.

One of the most widely used and trusted VPN protocols is OpenVPN. Known for its open-source nature and robust encryption, OpenVPN offers a high level of security and is compatible with various platforms. Its flexibility and reliability make it a popular choice among VPN providers and users alike.

Another secure VPN protocol gaining traction is IKEv2/IPsec (Internet Key Exchange version 2/IP Security). Designed with mobile devices in mind, IKEv2/IPsec provides fast and stable connections, making it ideal for users on the go. With built-in support for features like mobility and multi-homing, IKEv2/IPsec ensures seamless connectivity while maintaining strong security measures.

For users prioritizing speed without compromising security, WireGuard is a promising option. Lauded for its simplicity and efficiency, WireGuard boasts faster speeds compared to traditional VPN protocols while still offering robust encryption. Its minimalistic design and focus on performance make it an attractive choice for those seeking a balance between speed and security.

When it comes to securing your online activities, choosing the right VPN protocol is essential. Whether you prioritize open-source reliability, mobile compatibility, or blazing-fast speeds, there's a secure VPN protocol to suit your needs. By understanding the strengths and weaknesses of each protocol, you can make an informed decision to ensure your online privacy and security are never compromised.

No-logs policy assessment

A no-logs policy is a critical feature to consider when choosing a service provider, such as a VPN, web hosting provider, or messaging app. This policy ensures that the provider does not collect or store any user data or activity logs that could potentially be used to identify individuals.

When assessing a no-logs policy, there are several key factors to consider. First and foremost, it is essential to determine the extent of the data that the provider claims not to log. This includes not only browsing history and IP addresses but also metadata, connection timestamps, and any other information that could be used to track users.

Additionally, transparency is key when evaluating a provider's no-logs policy. A trustworthy service will clearly outline what data is not collected and how it is handled. Look for detailed privacy policies and terms of service that explicitly state the provider's commitment to user privacy.

Furthermore, consider the provider's track record and reputation. Research user reviews and independent audits to determine if the provider has upheld its no-logs policy in the past. A proven history of protecting user privacy is a strong indicator of a reliable service.

In conclusion, a thorough assessment of a provider's no-logs policy is essential for safeguarding your online privacy. By choosing a service with a robust commitment to user anonymity and data protection, you can browse the internet with confidence and peace of mind.

Server location impact

Server Location Impact: Understanding the Importance for Website Performance

When it comes to website performance, one factor that often gets overlooked is the location of the server hosting the website. The geographical location of a server can have a significant impact on the speed, reliability, and overall performance of a website. Understanding this impact is crucial for website owners and developers striving to provide the best possible user experience.

First and foremost, server location affects website loading times. The physical distance between the user and the server can result in latency—the delay between the user's request and the server's response. For example, if a user in New York is trying to access a website hosted on a server in Australia, there will inevitably be a delay in loading times due to the long distance data must travel.

Moreover, server location can impact a website's search engine ranking. Search engines like Google take into account website speed as a ranking factor. Websites hosted on servers closer to their target audience tend to load faster, providing a better user experience and thus receiving a potential boost in search engine rankings.

Additionally, server location plays a crucial role in ensuring reliability and stability. Websites hosted on servers in areas prone to natural disasters or political instability may experience more frequent downtime and interruptions in service. Choosing a server location with reliable infrastructure and favorable environmental conditions can help minimize these risks.

In conclusion, the impact of server location on website performance cannot be overstated. Website owners and developers should carefully consider the geographical location of their servers to optimize loading times, improve search engine rankings, and ensure reliability. By prioritizing server location as part of their overall web hosting strategy, they can provide users with a seamless and enjoyable browsing experience.