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Textbooks Are All You Need: Conclusion and References

NeRF Editing and Inpainting Techniques: Conclusion and References

AutoStory: Generating Diverse Storytelling Images with Minimal Human Effort: Experiments

Fine-tuned LLMs Know More, Hallucinate Less With Few-Shot Sequence-to-Sequence Semantic Parsing 

Enabling Acoustic Audience Feedback in Large Virtual Events: Conclusions & References

Authors: (1) JunJie Wee, Department of Mathematics, Michigan State University; (2) Jiahui Chen, Department of Mathematical Sciences, University of Arkansas; (3) Kelin Xia, Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University & xiakelin@ntu.edu.sg; (4)Guo-Wei Wei, 1Department of Mathematics, Michigan State University, Department of Biochemistry and Molecular Biology, Michigan State University, Department of Electrical and Computer Engineering, Michigan State University & xiakelin@ntu.edu.sg. Table of Links Abstract & Introduction Results Discussion Conclusion Materials and Methods Software and resources, Code and Data Availability Supporting Information, Acknowledgments & References Abstract Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite of tremendous effort, machine learning prediction of protein solubility changes upon mutation remains a challenging task as indicated by the poor scores of normalized Correct Prediction Ratio (CPR). Part of the challenge stems from the fact that there is no three-dimensional (3D) structures for the wild-type and mutant proteins. This work integrates persistent Laplacians and pre-trained Transformer for the task. The Transformer, pretrained with hunderds of millions of protein sequences, embeds wild-type and mutant sequences, while persistent Laplacians track the topological invariant change and homotopic shape evolution induced by mutations in 3D protein structures, which are rendered from AlphaFold2. The resulting machine learning model was trained on an extensive data set labeled with three solubility types. Our model outperforms all existing predictive methods and improves the state-of-the-art up to 15% Mutation, Protein solubility, Persistent Laplacian, Transformer, AlphaFold2. Keywords: 1 Introduction Genetic mutations alter the genome sequence, leading to changes in the corresponding amino acid sequence of a protein. These alternations have far-reaching implications on the protein’s structure, function, and stability, affecting attributes such as folding stability, binding affinity, and solubility. The consequences of protein mutations have been extensively studied in diverse fields such as evolutionary biology, cancer biology, immunology, directed evolution, and protein engineering [1]. Understanding the impact of genetic mutations on protein solubility is crucial in various fields, including protein engineering, drug discovery, and biotechnology. Accurately analyzing and predicting the impact of mutations on protein solubility is therefore crucial in many fields, facilitating the engineering of proteins with desirable functions. There are numerous intricately interconnected factors impacting protein solubility, ranging from amino acid sequence arrangement, post-translational modifications, protein-protein interactions, to environmental conditions, such as solvent type, ion type and concentration, the presence of small molecules, temperature, etc. Unfortunately, the existing data set does not contain sufficient information. This complexity poses significant challenges for the accurate prediction and modeling of protein solubility, often requiring multifaceted computational approaches for reliable outcomes. Computational predictions serve as a valuable complement to experimental mutagenesis analysis of protein stability changes upon mutation. Such computational approaches offer several advantages, including being economical, efficient, and provide a viable alternative to laborintensive site-directed mutagenesis experiments [2]. As a result, the development of accurate and reliable computational techniques for mutational impact prediction could substantially enhance the throughput and accessibility of research in protein engineering and drug discovery Over the years, a variety of computational methods have been developed to explore the effects of mutations on protein solubility, including but not limited to CamSol [3], OptSolMut [4], PON-Sol [5], SODA [6], Solubis [7], and others as summarized in a recent review [8]. CamSol employs an algorithm to construct a residue-specific solubility profile, although no explicit method has been made publicly available. OptSolMut is trained on 137 samples, each featuring single or multiple mutations affecting solubility or aggregation. PON-Sol utilizes a random forest model trained on a dataset of 406 single amino acid substitutions labeled as solubilityincreasing, solubility-decreasing, or exhibiting no change in solubility. SODA, which is based on the PON-Sol data, specifically targets samples with decreasing solubility [6]. Solubis is an optimization tool that increases protein solubility and integrates interaction analysis from FoldX [2], aggregation prediction from TANGO [9], and structural analysis from YASARA [10]. Recently, PON-Sol2 [11] extended the original PON-Sol dataset and employed a gradient boosting algorithm for sequence-based predictions. Despite of intensive effort, the current prediction accuracy in terms of normalized Correct Prediction Ratio (CPR) remains very low, calling for innovative strategies. Topological data analysis (TDA) is a relatively new approach for data science. Its main technique is persistent homology [12, 13]. The essential idea of persistent homology is to construct a multiscale analysis of data in terms of topological invariants. The resulting changes of topological invariants over scales can be used to characterize the intricate structures of data, leading to an unusually powerful approach in describing protein structure, flexibility, and folding [14]. Persistent homology was integrated with machine learning for the classification of proteins in 2015 [15], which was one the first integrations of TDA and machine learning, and the predictions of mutation-induced protein stability changes [16, 17] and protein-protein binding free energy changes [18, 19]. One of the major achievements of TDA is its winning of D3R Grand Challenges, an annual worldwide competition series in computer-aided drug design [20, 21]. A nearly comprehensive summary of the early success of TDA in biological science was given in a review [22]. However, persistent homology only tracks the changes in topological invariants and cannot capture homotopic shape evolution of data over scales or induced by mutations. To overcome this limitation, Wei and coworkers introduced persistent combinatorial Laplacians, also called persistent spectral graphs, on point clouds [23] and evolutionary de Rham-Hodge method on manifolds [24] in 2019. The essence of these methods is the persistent topological Laplacians (PTLs) either on point clouds or on manifolds. PTLs not only fully capture the topological invariants in its harmonic spectra as those given by persistent homology, but also capture the homotopic shape evolution of data during the multiscale analysis or a mutation process. PTLs were applied to the predictions of protein flexibility [25] and protein-ligand binding free energies [26], protein–protein interactions[27, 28], and protein engineering [1]. The most remarkable accomplishment by persistent Laplacian is its accurate forecasting of emerging dominant SARS-CoV-2 variants BA.4 and BA.5 about two months in advance [29]. However, TDA approaches depend on the biomolcular structures, which may not be available. In fact, many proteins involved in the present study do not have 3D structures. In recent years, advanced natural language processing (NLP) models, including Transformers and long short-term memory (LSTM), have been widely implemented across various domains to extract protein sequence information. For example, Tasks Assessing Protein Embedding (TAPE) introduced three different architectures, namely transformer, dilated residual network (ResNet), and LSTM [30]. Additionally, LSTM-based models like Bepler [31] and UniRep [32] have been developed. Additionally, large-scale protein transformer models trained on extensive datasets comprising hundreds of millions of sequences have made significant advancements in the field. These models, including Evolutionary Scale Modeling (ESM) [33] and ProtTrans [34, 35], have exhibited exceptional performance in capturing a variety of protein properties. ESM, for instance, allows for fine-tuning based on either downstream task data or local multiple sequence alignments [36]. In the present work, we leverage the pre-trained ESM transformer model to extract crucial information from protein sequences. In this work, we will integrate transformer-based sequence embedding and persistent topological Laplacians to predict protein solubility changes upon mutation. While sequence-based models can be applied without 3D structural information, the PTL-based features require highquality structures. We generate 3D structures of wild type proteins from AlphaFold2 [37] to facilitate topological embedding. By combining both embedding approaches, they naturally complement each other in classifying protein solubility changes upon mutation. These embeddings are fed into an ensemble classifier, gradient boosted trees (GBT), to build a machine learning model, called TopLapGBT. We validate TopLapGBT on the classification of protein solubility changes upon mutation. We demonstrate that this integrated machine learning model gives rise to a substantial improvement as compared to existing state-of-the-art models. Residue-Similarity plots are also applied to assess how well the TopLapGBT model classify three solubility labels. This paper is under CC 4.0 license. available on arxiv

Persistent Laplacian and Pre-Trained Transformer for Protein Solubility Changes Upon Mutation

This paper is available on arxiv under CC 4.0 license. Authors: (1) Berrenur Saylam; (2) Ozlem Durmaz ¨ ˙Incel. Table of Links Highlights Abstract & Introduction Federated Learning: Collaborative and Privacy-Preserving Model Training State of the Art Federated Learning Software Platforms and Testbeds Sensors and Edge-Sensing Devices Federated Learning Applications on Sensing Devices Discussion Conclusions, Acknowledgment, and References Abstract The ability to monitor ambient characteristics, interact with them, and derive information about the surroundings has been made possible by the rapid proliferation of edge sensing devices like IoT, mobile, and wearable devices and their measuring capabilities with integrated sensors. Even though these devices are small and have less capacity for data storage and processing, they produce vast amounts of data. Some example application areas where sensor data is collected and processed include healthcare, environmental (including air quality and pollution levels), automotive, industrial, aerospace, and agricultural applications. These enormous volumes of sensing data collected from the edge devices are analyzed using a variety of Machine Learning (ML) and Deep Learning (DL) approaches. However, analyzing them on the cloud or a server presents challenges related to privacy, hardware, and connectivity limitations. Federated Learning (FL) is emerging as a solution to these problems while preserving privacy by jointly training a model without sharing raw data. In this paper, we review the FL strategies from the perspective of edge sensing devices to get over the limitations of conventional machine learning techniques. We focus on the key FL principles, software frameworks, and testbeds. We also explore the current sensor technologies, properties of the sensing devices and sensing applications where FL is utilized. We conclude with a discussion on open issues and future research directions on FL for further studies. Federated Learning, Edge Computing, Sensing Devices, Mobile Sensing, Sensors Keywords: 1. Introduction Sensor-integrated devices, such as IoT devices, wearables, smartphones, drones, and robots, make it possible to collect vast amounts of sensing data to learn more about or extract knowledge from the environment and the surrounding users. Such edge devices can have varied resources regarding memory, battery capacity, and computing power. Even though some devices may appear more potent regarding computing capabilities, they are still considered edge devices because extra resources might not be provided Dey et al. (2013). Many use cases and application areas utilize the sensing data from these edge devices. For example, GPS data from a phone can be used to determine how congested the local traffic is, or motion-sensor data from a wearable can be used to determine a person’s daily activity patterns Atzori et al. (2010). Mainly, machine learning techniques are commonly applied to sensing data. The total amount of collected data has increased as these devices are used by masses and connected via wireless interfaces, making it easy to transfer data for further processing. In traditional learning methods, data is usually gathered in one location on a server-type machine, and then learning algorithms are trained and applied to the aggregated data. A typical machine learning pipeline involves gathering data from various sensors to extract a significant result following an objective. However, several challenges arise for sensor data collection and processing with a variety of devices. Since different devices have different hardware, even sensor data acquisition may differ. There may also be differences in the volume of data gathered, the data sampling frequencies, the battery capacity of the devices, and the cost of communication used to transfer the data to the central data processing unit. It is also challenging to get meaningful results without sufficient, highquality data, which is often the case with sensor data. For example, each device may not capture the same data distribution due to its placement or sensing trajectory. For instance, the human activity recognition field corresponds to classifying performed activity types. Since various people may have different characteristics, these types may vary amongst individuals. As a result, learning algorithms’ outcomes, mainly supervised ones, may be skewed because the training data does not contain a diverse set of classes. This requirement forces building a model with numerous classes, preferably with a significant amount of data for each class. Besides, analyzing the gathered data at one location brings another challenge: typically, this raises a privacy concern. The GDPR (General Data Protection Regulation Voigt and Von dem Bussche (2017)) is a recentlyintroduced example law to secure users’ data. Additionally, the collection of data at one location increases the expense of communication. Furthermore, applying algorithms as a global model may not be effective from a security perspective since the whole system can be corrupted with one successful cyber attack, especially for medical domains. Therefore, gathering a large volume of data in one location is not practical. An alternative to centralized sensor data processing is to run the machine learning models directly on edge devices. However, edge devices have hardware limitations that reduce their ability to execute complex learning algorithms, particularly deep architectures. Deep learning algorithms require higher processing power and energy in addition to memory. Consequently, we require new mechanisms to adapt the centralized and on-device models to modest computation restrictions Capra et al. (2019). Considering that resource efficiency and privacy are the two critical factors in many sensing applications for edge devices, combining data from many sources while maintaining data privacy is necessary to produce resource-efficient learning systems with such devices. A promising paradigm for cooperative and privacy-preserving machine learning in dispersed contexts is federated learning (FL) Yang et al. (2019). FL has attracted considerable attention in several disciplines since it allows training on local data while keeping it private and decentralized. Google presented FL in 2016 as a solution to these problems while protecting user privacy. The goal is to jointly train a model on each device without sharing raw data and produce meaningful results by only sharing model parameters with a central orchestrating unit Yu et al. (2017). This paper aims to explore Federated Learning, a novel approach to learning to provide a state-of-the-art assessment from the viewpoint of sensorbased edge devices and provide an overview of FL applications on sensing devices. In literature, some survey studies Nguyen et al. (2021); Aledhari et al. (2020) focus on federated learning with IoT devices. In this article, we examine FL from a broader perspective including sensing edge devices, such as IoT sensors, wearables, and other mobile devices. Most current surveys Nguyen et al. (2021); Xu et al. (2021); Wu et al. (2020); Sannara et al. (2021) on FL offer overviews of one specific application case without providing the overall state in the literature. Sensors are measuring devices used in various applications, including medical, environmental, automotive, industrial, aerospace, and agriculture. They detect changes in their environment, enabling precise monitoring, control, and decision-making processes. Medical sensors like ECGs and pulse oximeters help diagnose heart conditions, while environmental sensors assess water quality, pH, and dissolved oxygen. Automotive applications use light sensors, rain sensors, flow sensors, vibration sensors, and gyroscopes for stability and navigation accuracy. The diverse range of sensors used in various domains contributes to producing multimodal sensor data. In this paper, we revisit this hot topic from the standpoint of boosting sensing devices, considering the practice of many sensing domains. Our contribution is divided into two parts: a comprehensive review of the state of the art on FL, including major sensing application domains, and state-of-the-art frameworks along with testbeds for the application of FL, which are not generally included in other surveys. Furthermore, we provide extensive discussions that are prominent for further studies. The structure of this paper is shown in Figure 1. In Section 2, we describe the FL concept and working procedure, along with its primary challenges, methods, and measurements. We introduce the most cutting-edge platforms for using FL algorithms and a collection of testbeds to provide applicability and encourage future studies in Section 3. Section 4 describes the different kinds of sensors and devices and their properties. In Section 5, we put together the application areas and current usage of FL techniques in real life. We discuss the state-of-the-art approaches for sensing devices, suggest further directions in Section 6, and conclude with Section 7. This paper is under a CC 4.0 license. available on Arxiv

Edge Devices: What Are They and How Do They Work?

Revolutionizing Creative Text Generation with Quality-Diversity through AI Feedback

In this article, we’ll tackle some mind-boggling questions that even can’t wrap their heads around. Seriously, all they do is slap the word ‘quantum’ on everything and call it a day. But hey, let’s dive in and see if we can untangle this entanglement. Marvel Will advancement in leave Cryptography in the dust? Quantum Computing Will it be able to know the private keys? How will it affect public blockchains like Bitcoin and Ethereum? and, Will our assets be safe? This article doesn’t require any pre-requisite; I’ll explain everything as we proceed. Let’s address Quantum Computing first. Quantum computing Quantum computing is a new paradigm of computing that uses quantum mechanical phenomena, such as superposition and entanglement, to perform operations on data. Quantum computers can potentially solve some problems much faster than classical computers, such as factoring large numbers or searching large databases. Now let’s compare our quantum computer with our day-to-day classical computer. For example, a 2048-bit RSA key, which is commonly used for secure communication, requires factoring a 617-digit number. (100000000000000000000000000000) => This is only 30 digit number, by the way. It is estimated that a classical computer would take billions of years to factor a 2048-bit RSA key using the best-known algorithms. In contrast, a quantum computer running Shor’s algorithm could factor a 2048-bit RSA key in a matter of hours or days, depending on the number of qubits and the quality of the quantum hardware. Classical computers take billions of years. By the way, our whole universe is only years old, and we’ve been walking around the planet for 6 million years. On the other hand, a quantum computer will crack this while we are gone for a long drive, and when we come back, all our assets are gone. Fascinating, isn’t it? 13.8 billion Just Joking. https://giphy.com/gifs/high-quality-highqualitygifs-ygzdQq98HgcLBCacep?embedable=true Here’s the twist, though. Building a large-scale quantum computer capable of running Shor’s algorithm is a significant engineering challenge, and no such computer currently exists. Unfortunately, or maybe fortunately, the largest quantum computers today have around 100 qubits, which is not enough to factor a 2048-bit RSA key. You see, you’ll need at the very least damn logical qubits to factor a 2048-bit RSA key in a few days or less. 4,000 Phew, that was close. So, Not really until we develop a way to protect ourselves; the cryptographic community is well aware of these potential threats and is actively working on developing quantum-safe cryptography. are we safe? Quantum-safe cryptographic algorithms are designed to be resistant to attacks from both classical and quantum computers. These new algorithms are being developed and standardized to replace vulnerable ones, ensuring the continued security of digital communication and assets. One example of quantum-safe cryptography is , which relies on the hardness of certain problems in lattice theory that are believed to be resistant to quantum attacks. Other candidates include , , and . lattice-based cryptography code-based cryptography multivariate cryptography hash-based signatures You might be wondering, but how is factoring a prime number has to do anything with the crypto industry? https://giphy.com/gifs/reaction-a5viI92PAF89q?embedable=true Factoring large prime numbers is an essential part of many cryptographic systems, including the widely used RSA encryption and digital signature schemes. These systems rely on the fact that it is computationally infeasible to factor large numbers into their prime factors using classical computers. In RSA encryption, a public key is generated by multiplying two large prime numbers, and the private key is derived from the factors of this product. The security of RSA encryption relies on the fact that it is difficult to factor the product of two large prime numbers into its factors, making it difficult for an attacker to obtain the private key and decrypt messages. Similarly, in digital signature schemes like RSA and Elliptic Curve Cryptography (ECC), the private key is used to sign messages, and the public key is used to verify the signature. The security of these schemes also relies on the difficulty of factoring large numbers. If a quantum computer capable of running Shor’s algorithm becomes available, it could efficiently factor large numbers and break the security of these cryptographic systems. This is why the development of quantum-safe cryptographic algorithms is crucial to ensure the continued security of digital communication and assets in the face of quantum computing advancements. https://giphy.com/gifs/rodneydangerfield-thinking-math-rodney-gEvab1ilmJjA82FaSV?embedable=true What is Shor’s algorithm? Shor’s algorithm is a quantum algorithm for factoring large numbers efficiently. It was developed by Peter Shor in 1994 and is one of the most famous quantum algorithms. The algorithm works by finding the , which is a mathematical property that repeats after a certain number of iterations. In the case of factoring large numbers, the function used is the , which takes a base number, raises it to a power, and then takes the remainder when divided by a large number. period of a function modular exponentiation function This is way too complex to explain in this article. I’ll write a dedicated article in future to explain it. Please don’t be disappointed. Is there any other way apart from factoring the prime number that our quantum computer can do to break blockchain security? Well, it can disrupt the consensus mechanism that is used to generate blocks after coming to an agreement with the majority of the participants in the network. Quantum computers could use their speed advantage to manipulate the consensus algorithms that blockchain networks use to agree on the state of the ledger. For example, an attacker with a quantum computer could perform a 51% attack on a proof-of-work blockchain, such as Bitcoin, by outpacing the honest nodes in finding new blocks and creating a longer chain. This would allow the attacker to double-spend transactions or rewrite the history of the ledger. However, quantum computing could also offer some benefits for blockchain technology, such as enhancing its performance, scalability, and efficiency. For example, quantum computers could enable new levels of computational power and data analysis for blockchain applications, such as smart contracts, supply chain management, or healthcare records. Who knows, maybe we’ll combine them together to form ! Quantum Blockchain It could use quantum encryption and signatures to protect the transactions and quantum consensus algorithms to generate the blocks. Quantum blockchain could also use quantum cloud computing platforms to make blockchain more accessible and affordable. In summary, It could threaten its security by breaking its cryptography and disrupting its consensus, but it could also enhance its performance, scalability, and efficiency by enabling new forms of computation, encryption, and signatures. Quantum blockchain is a possible solution that integrates both technologies to create a more secure and functional network. quantum computing poses both challenges and opportunities for blockchain technology. I thank you for taking the time to read my article. Also published here. Dive into insightful articles on decentralized systems, Tech, and AI trends: . Do visit to show your support. https://www.0xkishan.com

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