#neurodynamic

[teledyn]

Musical neurodynamics | Nature Reviews Neuroscience

"The interaction of certain kinds of sounds with ongoing pattern-forming dynamics results in patterns of perception, action and coordination that we collectively experience as music. Statistically universal structures may have arisen in music because they correspond to stable states of complex, pattern-forming dynamical systems. This analysis of empirical findings from the perspective of neurodynamic principles sheds new light on the neuroscience of music and what makes music powerful."

I have always maintained the so-called Theory of Music was not rooted in the math of Pythagorean ratios. Music is rooted in the physics and physiology of the creature producing it.

Thus Dale Purvis, looking at voice spectra, found embedded nearly-diatonic intervals, perplexed when the third and seventh were somewhat flat. Every competent musician plays with this discrepancy.

Schoenberg claimed that in a hundred years, plumbers would be whistling his melodies. It's been over a century, and I've not found one. When tested, trained 12-tone musicians asked to improvise don't. They statistically emit the holes in diatonic, ie their bodies aim to play what you might call "intensionally unnaturally".

[brightlotusmoon]

Frontiers | The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs

Entropy is a dimensionless quantity that is used for measuring uncertainty about the state of a system but it can also imply physical qualities, where high entropy is synonymous with high disorder. Entropy is applied here in the context of states of consciousness and their associated neurodynamics, with a particular focus on the psychedelic state. The psychedelic state is considered an exemplar of a primitive or primary state of consciousness that preceded the development of modern, adult, human, normal waking consciousness. Based on neuroimaging data with psilocybin, a classic psychedelic drug, it is argued that the defining feature of “primary states” is elevated entropy in certain aspects of brain function, such as the repertoire of functional connectivity motifs that form and fragment across time.

[compneuropapers]

Interesting Papers for Week 47, 2024

The neural basis of swap errors in working memory. Alleman, M., Panichello, M., Buschman, T. J., & Johnston, W. J. (2024). Proceedings of the National Academy of Sciences, 121(33), e2401032121.

Brain region–specific action of ketamine as a rapid antidepressant. Chen, M., Ma, S., Liu, H., Dong, Y., Tang, J., Ni, Z., … Hu, H. (2024). Science, 385(6709).

Predictive sequence learning in the hippocampal formation. Chen, Y., Zhang, H., Cameron, M., & Sejnowski, T. (2024). Neuron, 112(15), 2645-2658.e4.

A neural circuit architecture for rapid learning in goal-directed navigation. Dan, C., Hulse, B. K., Kappagantula, R., Jayaraman, V., & Hermundstad, A. M. (2024). Neuron, 112(15), 2581-2599.e23.

The Consolidation of Newly Learned Movements Depends upon the Somatosensory Cortex in Humans. Ebrahimi, S., van der Voort, B., & Ostry, D. J. (2024). Journal of Neuroscience, 44(32), e0629242024.

The effect of noninstrumental information on reward learning. Embrey, J. R., Li, A. X., Liew, S. X., & Newell, B. R. (2024). Memory & Cognition, 52(5), 1210–1227.

Closed-loop microstimulations of the orbitofrontal cortex during real-life gaze interaction enhance dynamic social attention. Fan, S., Dal Monte, O., Nair, A. R., Fagan, N. A., & Chang, S. W. C. (2024). Neuron, 112(15), 2631-2644.e6.

Attentional selection and communication through coherence: Scope and limitations. Greenwood, P. E., & Ward, L. M. (2024). PLOS Computational Biology, 20(8), e1011431.

Complexity of mental geometry for 3D pose perception. Guo, C., Maruya, A., & Zaidi, Q. (2024). Vision Research, 222, 108438.

Dynamic assemblies of parvalbumin interneurons in brain oscillations. Huang, Y.-C., Chen, H.-C., Lin, Y.-T., Lin, S.-T., Zheng, Q., Abdelfattah, A. S., … Chen, T.-W. (2024). Neuron, 112(15), 2600-2613.e5.

Selective reactivation of value- and place-dependent information during sharp-wave ripples in the intermediate and dorsal hippocampus. Jin, S.-W., Ha, H.-S., & Lee, I. (2024). Science Advances, 10(32).

Cell-class-specific electric field entrainment of neural activity. Lee, S. Y., Kozalakis, K., Baftizadeh, F., Campagnola, L., Jarsky, T., Koch, C., & Anastassiou, C. A. (2024). Neuron, 112(15), 2614-2630.e5.

The critical dynamics of hippocampal seizures. Lepeu, G., van Maren, E., Slabeva, K., Friedrichs-Maeder, C., Fuchs, M., Z’Graggen, W. J., … Baud, M. O. (2024). Nature Communications, 15, 6945.

The cortical amygdala consolidates a socially transmitted long-term memory. Liu, Z., Sun, W., Ng, Y. H., Dong, H., Quake, S. R., & Südhof, T. C. (2024). Nature, 632(8024), 366–374.

Signatures of Bayesian inference emerge from energy-efficient synapses. Malkin, J., O’Donnell, C., Houghton, C. J., & Aitchison, L. (2024). eLife, 12, e92595.3.

Neurodynamical Computing at the Information Boundaries of Intelligent Systems. Monaco, J. D., & Hwang, G. M. (2024). Cognitive Computation, 16(5), 1–13.

A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses. Rançon, U., Masquelier, T., & Cottereau, B. R. (2024). PLOS Computational Biology, 20(8), e1012288.

The right posterior parietal cortex mediates spatial reorienting of attentional choice bias. Sengupta, A., Banerjee, S., Ganesh, S., Grover, S., & Sridharan, D. (2024). Nature Communications, 15, 6938.

Upper bounds for integrated information. Zaeemzadeh, A., & Tononi, G. (2024). PLOS Computational Biology, 20(8), e1012323.

Integration of history information Drives Serial Dependence and Stabilizes Working Memory Representations. Zhang, Z., & Lewis-Peacock, J. A. (2024). Journal of Neuroscience, 44(32), e2399232024.

[just-like-her-father]

(Listen to the music to enhance the reading experience.)

Eight years ago, Florence greeted Charlotte Stark not with fanfare, but with quiet curiosity. A name whispered along marble corridors of old Italian banking halls, in the leather-scented salons of private innovation clubs, and in university courtyards where theory wrestled with practice. Back then, she was an outsider. Today, she is Florence’s beating heart of intellect, innovation, and influence — a sovereign force whose dominion spans the realm of economic reformation, cognitive technology, and futurist philosophy.

The transformation was not gradual; it was exponential.

She arrived in 2017 — twenty-four, enigmatic, American-born but philosophically borderless. Charlotte Stark, then a polymath fresh off a controversial exit from a U.S. think tank, stepped into Florence with a singular mission: to redefine how cities think, build, and thrive.

In her first public appearance, held in the minimalist atrium of the Istituto per le Scienze Cognitive Avanzate, Charlotte addressed an audience of jaded economists and optimistic engineers. They expected tech jargon and futurist fluff. What they got was clarity wrapped in elegance:

“Economics is not the study of money,” she said, eyes calm, voice measured. “It is the study of vision. Currency is just the applause.”

That quote would go on to become the opening line of The Stark Doctrine, a widely circulated economic paper that challenged the traditional GDP framework and introduced the Vision-Impact Gradient — a new metric for evaluating a nation’s worth by its ability to manifest intent into scalable change.

In less than two years, Stark Novae, her self-founded think-and-do tank, had revitalized a decaying Florentine industrial park and turned it into a cybernetic incubator zone. Her work fused predictive AI, sustainable energy models, and economic behavioral theory. What struck most was not just what she built — but how.

She implemented Italy’s first decentralized AI-governed green grid in a consortium of Tuscan towns. Energy costs dropped. Community trust surged. Stark Novae was suddenly not just admired, it was followed.

In a 2020 interview at TechFlorence, she stunned the room by asking:

“Why are we still romanticizing fossil energy in a city that gave us the Renaissance? If Leonardo da Vinci were alive today, he’d be programming synthetic photosynthesis, not painting ceilings.”

Florence, a city that once resisted outsiders’ dominance, embraced her. Even the most traditional Italian institutions — the Accademia delle Scienze, the Chamber of Commerce, and the Vatican’s AI-Ethics Council — sought her counsel.

As the world began turning to Florence for innovation models, Charlotte became the epicenter. She didn’t chase markets; markets began to orbit her.

Her public lectures drew thousands — but it was her closed-door midnight salons that rewrote policy. In the candlelit backrooms of converted convents, she’d gather philosophers, bioengineers, quantum coders, and chefs. Conversations ranged from post-human cognition to the future of bread.

A local journalist once called her “the high priestess of synthesis — she speaks like she’s explaining the future to the past.”

In 2023, she co-authored The Cognitive City, a blueprint for cities run on adaptive neural logic. That document, now translated into 18 languages, became required reading in design schools and U.N. developmental summits.

She was no longer just a thinker. She was a shaper.

Her empire expanded: A NeuroDynamics Lab outside Siena. A Civic Ethics Simulator used by mayors across Europe. A Reality Layer Protocol — a semi-augmented environment designed to re-train human attention spans — quietly beta-tested in schools under her nonprofit, Synapse Florence.

And she never lost the flair.

Riding through Oltrarno, in tailored trousers and fingerless gloves, she became as much a part of Florence’s daily myth as Brunelleschi’s dome. She quoted Foucault at wine tastings, debated political economy in vintage cafés, and had standing Tuesday breakfasts with local grandmothers who adored her fluent Italian and her deep love for saffron risotto.

The world watched as Charlotte took the stage at the Telekinesis - Intellect Union Conference 2025, held poetically at Galileo’s restored observatory.

Dressed in stark ivory and soft steel blue, she walked to the podium with the solemn grace of someone about to shift a paradigm.

“Telekinesis is not a fantasy,” she opened. “It’s the final frontier of cognitive bandwidth. The mind, if given the right conditions and interface, is the most efficient processor known to man. The question is not how — but why haven’t we yet?”

She then unveiled NeuroBridge v1.4, a functioning prototype of a brain-interface conduit that allowed short-distance object manipulation through trained intent pathways. The crowd — a constellation of Nobel Laureates, policy giants, and disbelieving scientists — stood breathless as she demonstrated lifting a titanium sphere, three inches above the platform, without touching it.

The interface, according to her, was still in infancy. But the implications were seismic: intent-based interaction, neural-syntactic reprogramming, and even post-verbal cognition.

She didn’t seek applause. She simply nodded and said:

“Human potential is not capped by biology. It is capped by permission.”

Florence erupted.

Within days, investment surged. NeuroBridge became a joint project with Italian state labs, and Charlotte launched the Stark Initiative for Cognitive Sovereignty — aiming to give marginalized communities access to emerging brain-tech tools.

Today, Florence refers to her simply as La Signora della Mente — The Lady of the Mind. Her face is painted on murals next to da Vinci. Her quotes are engraved on bridges. Teenagers cite her like she’s Socrates with better hair.

She chairs four international panels. Advises two European presidents. Sleeps four hours. Meditates in hidden monasteries. Dines with artisans. Still walks into every room like she owns the blueprints of the universe.

And what of her next move?

A recent cryptic post from her official channel read:

“The future is not being invented. It’s being remembered. Like something we lost in a past life and are finally learning to rebuild.”

In Florence, Charlotte Stark is no longer a guest.

She is the standard.

[justforbooks]

Mike Lynch

British tech entrepreneur who sold his Autonomy software group to Hewlett-Packard and was later cleared after a long-running US fraud case

Mike Lynch, who has died aged 59 in the wreck of his yacht, was sometimes described as “Britain’s Bill Gates”. It was a huge exaggeration, but Lynch could claim two parallels with Gates: he developed world-leading technology (in his case in machine learning or AI) and, unlike so many UK scientists, he learned how to turn it into commercial success.

Such was this success that his company, Autonomy, was valued at $11bn when he sold it to Hewlett-Packard in 2011, but the fall-out from the sale would come to overshadow his technological achievements, and lead to a national debate about the circumstances in which UK citizens may be extradited to the US.

Lynch founded Autonomy with two partners in 1996. Its software enabled a computer to search huge quantities of diverse information, including phone calls, emails and videos, and recognise words. He told the Independent in 1999: “The way our technology works is to look at words and understand the relationships because it has seen a lot of content before. When it sees the word ‘star’ in the context of film, it knows it has nothing to do with the word moon. Because it works from text, it can deal with slang and with different languages.”

Autonomy became a leading company in Cambridge’s Silicon Fen cluster and established a base in San Francisco. “We knew we had to be successful in America. It was a question of ‘Go West young man, go to San Francisco and be ignored.’ They found it hard to believe that anyone from England could have anything powerful.” Lynch found what he called the “cold-hearted schmooze” to secure funding tough.

But Autonomy’s software, enabling computers to identify and match themes and ideas, and sort mammoth amounts of data, was licensed to more than 500 customers, including the US State Department and the BBC. It was listed on Nasdaq in 1998 and on the FTSE 100 in November 2000, although its value of £5.1bn would be halved within a few months in the collapse of the technology boom and accusations of over-promotion. In 2005 it bought a major US rival, Verity, for $500m.

Lynch’s profile rose with it. In 2006 he was appointed OBE for services to enterprise and the following year joined the board of the BBC. In 2011 he became a member of the government’s Council for Science and Technology, and was named the most influential person in UK IT by Computer Weekly. In 2014 he was elected a fellow of the Royal Society.

Though quietly spoken, he had a reputation for toughness, coloured by a liking for James Bond, which led to Autonomy conference rooms being named after Bond villains, and a tank of piranha fish in reception. (Lynch claimed it belonged to one of his business partners.) Challenged about a company culture where people were “a little fanatical”, he replied: “This is not the place for you if you want to work 9 to 5 and don’t love your work.”

Born in Ilford, east London, to Michael, a firefighter, and Dolores, a nurse, and brought up in Chelmsford, Lynch won a scholarship to the independent Bancroft’s school in Woodford Green, before taking a natural sciences degree at Cambridge, where his PhD in artificial neural networks, a form of machine learning, has been widely studied since.

A saxophone player and jazz lover, he set up his first business, Lynett Systems, while still a student, to produce electronic equipment for the music industry. Later he would attribute some loss of hearing to adjusting synthesisers for bands. He quoted his own experience to highlight the difficulties of finding funding for startup businesses in Britain. He finally negotiated a £2,000 loan from one of the managers of Genesis in a Soho bar.

Lynch’s next venture came out of his research. In 1991 he founded Cambridge Neurodynamics, specialising in computer-based fingerprint recognition. Then he established Autonomy.

The pinnacle of his success appeared to come in October 2011 when Autonomy was purchased by Hewlett-Packard for $11bn and Lynch made an estimated $800m. Shortly afterwards he established a new company, Invoke Capital, for investment in tech companies, and he and his wife, Angela Bacares, whom he had married in 2001, invested about £200m in Darktrace, a cybersecurity company.

But just 13 months after the Autonomy sale, HP announced an $8.8bn writedown of the assets “due to serious accounting improprieties, disclosure failures and outright misrepresentations” which it claimed had artificially inflated the company’s value. The authorities investigated, and while the UK Serious Fraud Office found insufficient evidence, in 2018 the US authorities indicted Lynch for fraud. Soon after, Autonomy’s chief financial officer, Sushovan Hussain, was found guilty of fraud and sentenced to five years in prison.

In March 2019 HP followed up with a civil action for fraud in London. Lynch spent days in the witness box as the civil action stretched over nine months. It ended in January 2022 with the judge ruling that HP had substantially succeeded, but that damages would be much less than the $5bn they had claimed.

Meanwhile the US authorities sought Lynch’s extradition on criminal charges of conspiracy and fraud. In spite of representations by senior politicians and accusations that the US authorities were attempting to exercise “extraterritorial jurisdiction”, a district judge ruled in favour of extradition.

An application for judicial review and a further appeal failed, and in May 2023 Lynch was flown to the US to be held under house arrest in San Francisco, with the prospect of a 25-year sentence.

Charged with wire fraud, securities fraud and conspiracy, on 18 March this year Lynch pleaded not guilty, alongside his former vice-president of finance, Stephen Chamberlain. On 6 June, they were found not guilty of all charges. Chamberlain died after being hit by a car on 17 August.

Lynch declared that he wanted to get back to what he loved doing – innovating. But he had little opportunity to do so. He soon embarked on a voyage to celebrate his acquittal, with family, colleagues and business associates. It ended with the sinking of his yacht, Bayesian – named after the 18th-century mathematician, Thomas Bayes, whose work on probability had informed much of his thinking – in a violent storm off the coast of Sicily.

Lynch is survived by his wife and elder daughter, Esme. Their other daughter, Hannah, was also on board the Bayesian.

🔔 Michael Richard Lynch, technology entrepreneur, born 16 June 1965; died 19 August 2024

Daily inspiration. Discover more photos at Just for Books…?

[geoslogic]

if you’re reading this, you lowkey neurodynamic

[richardvarey]

Our brain and body literally sync to music

New research suggests that music perception and enjoyment arise from natural brain and body oscillations that sync with rhythm, melody, and harmony. https://neurosciencenews.com/music-brain-body-28802/ Musical neurodynamics explores how our brains process, respond to, and create music. This fascinating field sits at the intersection of neuroscience, music theory, and cognitive psychology.Neural…

[wellandable]

The Truth About Nerve Pain: How Abnormal Neurodynamics Affect You & How Manual Therapy Can Help

Understanding Nerve Pain and How Manual Therapy Can Help

Nerve pain can feel frustrating, unpredictable, and even overwhelming. If you’ve ever experienced sharp pain, tingling, numbness, or weakness that worsens with certain movements or positions, you might be dealing with abnormal neurodynamics—a condition where nerves don’t move as they should.

The good news? Manual therapy can help restore normal nerve function, reducing pain and improving movement. At Well+Able Integrated Health in Kamloops, we specialize in hands-on techniques that support your nervous system, helping you feel better and move more easily.

What Is Abnormal Neurodynamics?

Your nerves aren’t just passive wires in your body. They need to move freely within muscles, joints, and connective tissues. Unlike muscles that stretch, nerves telescope, meaning they slide and lengthen within their protective sheaths. When something restricts this movement—like inflammation, tight muscles, or scar tissue—it can lead to pain, numbness, or weakness. This is known as abnormal neurodynamics.

What Causes Nerve Pain?

Nerve pain can develop for several reasons, including:

Nerve compression: Tight muscles, joints, or fascia can put pressure on a nerve, leading to pain and dysfunction.

Scar tissue or adhesions: After an injury or surgery, nerves can get stuck to surrounding tissues, limiting movement.

Inflammation and swelling: Increased pressure around a nerve can make it more sensitive.

Muscle imbalances: Weak or overly tight muscles can pull on nerves, affecting how they move and function.

These issues can affect any nerve in the body, but common areas include the neck, lower back, arms, and legs.

Signs You May Have a Nerve Mobility Issue

If you experience any of the following symptoms, your nerves may not be moving properly:

Sharp, burning, or aching pain that worsens with movement.

Numbness, tingling, or a "pins and needles" sensation in your hands, arms, or legs.

Weakness or difficulty gripping objects or lifting your foot while walking.

Pain that changes with certain positions—for example, stretching or bending your neck might make it worse.

Learn more about how nerve pain affects your daily life.

How Manual Therapy Supports Healthy Nerve Function

At Well+Able Integrated Health, our treatment approach focuses on restoring healthy nerve movement through techniques that:

✔ Relieve Nerve Entrapments – Soft tissue techniques release pressure from tight muscles and fascia. ✔ Encourage Normal Nerve Movement – Neurodynamic mobilization techniques improve nerve mobility. ✔ Reduce Pain Sensitivity – Gentle manual therapy helps calm irritated nerves. ✔ Correct Muscle Imbalances – Strengthening weak muscles and stretching tight ones supports healthy movement. ✔ Incorporate Nerve-Friendly Exercises – Specific exercises and stretches can help maintain long-term nerve health.

Explore how personalized treatment plans can help.

Find Relief from Nerve Pain in Kamloops

If nerve pain is interfering with your daily life, you don’t have to just live with it. Manual therapy offers a natural, research-backed way to reduce pain and restore movement.

At Well+Able Integrated Health, we have a special interest in treating nerve pain, chronic pain, and movement-related discomfort. Our hands-on approach helps you feel and move better without relying on medication or surgery.

Want to learn more? Check out how registered massage therapy supports pain relief.

Book an appointment today at Well+Able Integrated Health in Kamloops! Schedule your session here or call 250-317-2899 to speak with our team.

[drmikewatts]

IEEE Transactions on Emerging Topics in Computational Intelligence Volume 9, Issue 1, February 2025

1) A Survey of Human-Object Interaction Detection With Deep Learning

Author(s): Geng Han, Jiachen Zhao, Lele Zhang, Fang Deng

Pages: 3 - 26

2) Exploring the Horizons of Meta-Learning in Neural Networks: A Survey of the State-of-the-Art

Author(s): Asit Barman, Swalpa Kumar Roy, Swagatam Das, Paramartha Dutta

Pages: 27 - 42

3) Micro Many-Objective Evolutionary Algorithm With Knowledge Transfer

Author(s): Hu Peng, Zhongtian Luo, Tian Fang, Qingfu Zhang

Pages: 43 - 56

4) MoAR-CNN: Multi-Objective Adversarially Robust Convolutional Neural Network for SAR Image Classification

Author(s): Hai-Nan Wei, Guo-Qiang Zeng, Kang-Di Lu, Guang-Gang Geng, Jian Weng

Pages: 57 - 74

5) Prescribed-Time Optimal Consensus for Switched Stochastic Multiagent Systems: Reinforcement Learning Strategy

Author(s): Weiwei Guang, Xin Wang, Lihua Tan, Jian Sun, Tingwen Huang

Pages: 75 - 86

6) SR-ABR: Super Resolution Integrated ABR Algorithm for Cloud-Based Video Streaming

Author(s): Haiqiao Wu, Dapeng Oliver Wu, Peng Gong

Pages: 87 - 98

7) 3D-IMMC: Incomplete Multi-Modal 3D Shape Clustering via Cross Mapping and Dual Adaptive Fusion

Author(s): Tianyi Qin, Bo Peng, Jianjun Lei, Jiahui Song, Liying Xu, Qingming Huang

Pages: 99 - 108

8) A Co-Evolutionary Dual Niching Differential Evolution Algorithm for Nonlinear Equation Systems Optimization

Author(s): Shuijia Li, Rui Wang, Wenyin Gong, Zuowen Liao, Ling Wang

Pages: 109 - 118

9) A Collaborative Multi-Component Optimization Model Based on Pattern Sequence Similarity for Electricity Demand Prediction

Author(s): Xiaoyong Tang, Juan Zhang, Ronghui Cao, Wenzheng Liu

Pages: 119 - 130

10) A Deep Reinforcement Learning-Based Adaptive Large Neighborhood Search for Capacitated Electric Vehicle Routing Problems

Author(s): Chao Wang, Mengmeng Cao, Hao Jiang, Xiaoshu Xiang, Xingyi Zhang

Pages: 131 - 144

11) A Diversified Population Migration-Based Multiobjective Evolutionary Algorithm for Dynamic Community Detection

Author(s): Lei Zhang, Chaofan Qin, Haipeng Yang, Zishan Xiong, Renzhi Cao, Fan Cheng

Pages: 145 - 159

12) A Hub-Based Self-Organizing Algorithm for Feedforward Small-World Neural Network

Author(s): Wenjing Li, Can Chen, Junfei Qiao

Pages: 160 - 175

13) A New-Type Zeroing Neural Network Model and Its Application in Dynamic Cryptography

Author(s): Jingcan Zhu, Jie Jin, Chaoyang Chen, Lianghong Wu, Ming Lu, Aijia Ouyang

Pages: 176 - 191

14) FeaMix: Feature Mix With Memory Batch Based on Self-Consistency Learning for Code Generation and Code Translation

Author(s): Shuai Zhao, Jie Tian, Jie Fu, Jie Chen, Jinming Wen

Pages: 192 - 201

15) MUSTER: A Multi-Scale Transformer-Based Decoder for Semantic Segmentation

Author(s): Jing Xu, Wentao Shi, Pan Gao, Qizhu Li, Zhengwei Wang

Pages: 202 - 212

16) Feature Selection Using Generalized Multi-Granulation Dominance Neighborhood Rough Set Based on Weight Partition

Author(s): Weihua Xu, Qinyuan Bu

Pages: 213 - 227

17) A Collaborative Neurodynamic Algorithm for Quadratic Unconstrained Binary Optimization

Author(s): Hongzong Li, Jun Wang

Pages: 228 - 239

18) Global Cross-Attention Network for Single-Sensor Multispectral Imaging

Author(s): Nianzeng Yuan, Junhuai Li, Bangyong Sun

Pages: 240 - 252

19) OccludedInst: An Efficient Instance Segmentation Network for Automatic Driving Occlusion Scenes

Author(s): Hai Wang, Shilin Zhu, Long Chen, Yicheng Li, Yingfeng Cai

Pages: 253 - 270

20) 3D Skeleton-Based Non-Autoregressive Human Motion Prediction Using Encoder-Decoder Attention-Based Model

Author(s): Mayank Lovanshi, Vivek Tiwari, Rajesh Ingle, Swati Jain

Pages: 271 - 280

21) GF-LRP: A Method for Explaining Predictions Made by Variational Graph Auto-Encoders

Author(s): Esther Rodrigo-Bonet, Nikos Deligiannis

Pages: 281 - 291

22) Neuromorphic Auditory Perception by Neural Spiketrum

Author(s): Huajin Tang, Pengjie Gu, Jayawan Wijekoon, MHD Anas Alsakkal, Ziming Wang, Jiangrong Shen, Rui Yan, Gang Pan

Pages: 292 - 303

23) Semi-Supervised Contrastive Learning for Time Series Classification in Healthcare

Author(s): Xiaofeng Liu, Zhihong Liu, Jie Li, Xiang Zhang

Pages: 318 - 331

24) Bi-Level Model Management Strategy for Solving Expensive Multi-Objective Optimization Problems

Author(s): Fei Li, Yujie Yang, Yuhao Liu, Yuanchao Liu, Muyun Qian

Pages: 332 - 346

25) Transfer Optimization for Heterogeneous Drone Delivery and Pickup Problem

Author(s): Xupeng Wen, Guohua Wu, Jiao Liu, Yew-Soon Ong

Pages: 347 - 364

26) Comprehensive Multisource Learning Network for Cross-Subject Multimodal Emotion Recognition

Author(s): Chuangquan Chen, Zhencheng Li, Kit Ian Kou, Jie Du, Chen Li, Hongtao Wang, Chi-Man Vong

Pages: 365 - 380

27) Graph Learning With Riemannian Optimization for Multi-View Integrative Clustering

Author(s): Aparajita Khan, Pradipta Maji

Pages: 381 - 393

28) Applying a Higher Number of Output Membership Functions to Enhance the Precision of a Fuzzy System

Author(s): Salah-ud-din Khokhar, Akif Nadeem, Arslan A. Rizvi, Muhammad Yasir Noor

Pages: 394 - 405

29) Tensorlized Multi-Kernel Clustering via Consensus Tensor Decomposition

Author(s): Fei Qi, Junyu Li, Yue Zhang, Weitian Huang, Bin Hu, Hongmin Cai

Pages: 406 - 418

30) Balancing Security and Correctness in Code Generation: An Empirical Study on Commercial Large Language Models

Author(s): Gavin S. Black, Bhaskar P. Rimal, Varghese Mathew Vaidyan

Pages: 419 - 430

31) Camouflage Is All You Need: Evaluating and Enhancing Transformer Models Robustness Against Camouflage Adversarial Attacks

Author(s): Álvaro Huertas-García, Alejandro Martín, Javier Huertas-Tato, David Camacho

Pages: 431 - 443

32) Deep Learning Surrogate Models of JULES-INFERNO for Wildfire Prediction on a Global Scale

Author(s): Sibo Cheng, Hector Chassagnon, Matthew Kasoar, Yike Guo, Rossella Arcucci

Pages: 444 - 454

33) Dual Completion Learning for Incomplete Multi-View Clustering

Author(s): Qiangqiang Shen, Xuanqi Zhang, Shuqin Wang, Yuanman Li, Yongsheng Liang, Yongyong Chen

Pages: 455 - 467

34) Active Learning-Based Backtracking Attack Against Source Location Privacy of Cyber-Physical System

Author(s): Zhen Hong, Minjie Chen, Rui Wang, Mingyuan Yan, Dehua Zheng, Changting Lin, Jie Su, Meng Han

Pages: 468 - 479

35) Hierarchical Encoding Method for Retinal Segmentation Evolutionary Architecture Search

Author(s): Huangxu Sheng, Hai-Lin Liu, Yutao Lai, Shaoda Zeng, Lei Chen

Pages: 480 - 493

36) CycleFusion: Automatic Annotation and Graph-to-Graph Transaction Based Cycle-Consistent Adversarial Network for Infrared and Visible Image Fusion

Author(s): Yueying Luo, Wenbo Liu, Kangjian He, Dan Xu, Hongzhen Shi, Hao Zhang

Pages: 494 - 508

37) Explicit and Implicit Box Equivariance Learning for Weakly-Supervised Rotated Object Detection

Author(s): Linfei Wang, Yibing Zhan, Xu Lin, Baosheng Yu, Liang Ding, Jianqing Zhu, Dapeng Tao

Pages: 509 - 521

38) Deep Reinforcement Learning-Based Feature Extraction and Encoding for Finger-Vein Verification

Author(s): Yantao Li, Chao Fan, Huafeng Qin, Shaojiang Deng, Mounim A. El-Yacoubi, Gang Zhou

Pages: 522 - 536

39) Global Bipartite Exact Consensus of Unknown Nonlinear Multi-Agent Systems With Switching Topologies: Iterative Learning Approach

Author(s): Mengdan Liang, Junmin Li

Pages: 537 - 551

40) APR-Net Tracker: Attention Pyramidal Residual Network for Visual Object Tracking

Author(s): Bing Liu, Di Yuan, Xiaofang Li

Pages: 552 - 564

41) Symmetric Regularized Sequential Latent Variable Models With Adversarial Neural Networks

Author(s): Jin Huang, Ming Xiao

Pages: 565 - 575

42) StreamSoNGv2: Online Classification of Data Streams Using Growing Neural Gas

Author(s): Jeffrey J. Dale, James M. Keller, Aquila P. A. Galusha

Pages: 576 - 589

43) FCPFS: Fuzzy Granular Ball Clustering-Based Partial Multilabel Feature Selection With Fuzzy Mutual Information

Author(s): Lin Sun, Qifeng Zhang, Weiping Ding, Tianxiang Wang, Jiucheng Xu

Pages: 590 - 606

44) An Automatic Paper-Reviewer Recommendation Algorithm Based on Depth and Breadth

Author(s): Xiulin Zheng, Peipei Li, Xindong Wu

Pages: 607 - 616

45) Efficient and Robust Sparse Linear Discriminant Analysis for Data Classification

Author(s): Jingjing Liu, Manlong Feng, Xianchao Xiu, Wanquan Liu, Xiaoyang Zeng

Pages: 617 - 629

46) Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions

Author(s): Tianxu Wu, Shuo Ye, Shuhuang Chen, Qinmu Peng, Xinge You

Pages: 630 - 640

47) Efficient Low-Light Light Field Enhancement With Progressive Feature Interaction

Author(s): Xin Luo, Gaosheng Liu, Zhi Lu, Kun Li, Jingyu Yang

Pages: 641 - 653

48) Circuit Implementation of Memristive Fuzzy Logic for Blood Pressure Grading Quantification

Author(s): Ya Li, Shaojun Ji, Qinghui Hong

Pages: 654 - 667

49) Learning From Pairwise Confidence Comparisons and Unlabeled Data

Author(s): Junpeng Li, Shuying Huang, Changchun Hua, Yana Yang

Pages: 668 - 680

50) Subspace Sequentially Iterative Leaning for Semi-Supervised SVM

Author(s): Jiajun Wen, Xi Chen, Heng Kong, Junhong Zhang, Zhihui Lai, Linlin Shen

Pages: 681 - 694

51) Clickbait Detection via Prompt-Tuning With Titles Only

Author(s): Ye Wang, Yi Zhu, Yun Li, Jipeng Qiang, Yunhao Yuan, Xindong Wu

Pages: 695 - 705

52) TROPE: Triplet-Guided Feature Refinement for Person Re-Identification

Author(s): Divya Singh, Jimson Mathew, Mayank Agarwal, Mahesh Govind

Pages: 706 - 716

53) Deep Spiking Neural Networks Driven by Adaptive Interval Membrane Potential for Temporal Credit Assignment Problem

Author(s): Jiaqiang Jiang, Haohui Ding, Haixia Wang, Rui Yan

Pages: 717 - 728

54) Transfer Learning-Based Region Statistical Data Completion via Double Graphs

Author(s): Shengwen Li, Suzhen Huang, Xuyang Cheng, Renyao Chen, Yi Zhou, Shunping Zhou, Hong Yao, Junfang Gong

Pages: 729 - 739

55) COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport

Author(s): Linhao Zhang, Li Jin, Guangluan Xu, Xiaoyu Li, Xian Sun

Pages: 740 - 756

56) Least Information Spectral GAN With Time-Series Data Augmentation for Industrial IoT

Author(s): Joonho Seon, Seongwoo Lee, Young Ghyu Sun, Soo Hyun Kim, Dong In Kim, Jin Young Kim

Pages: 757 - 769

57) Gp3Former: Gaussian Prior Tri-Cascaded Transformer for Video Instance Segmentation in Livestreaming Scenarios

Author(s): Wensheng Li, Jing Zhang, Li Zhuo

Pages: 770 - 784

58) Open-Space Emergency Guiding With Individual Density Prediction Based on Internet of Things Localization

Author(s): Lien-Wu Chen, Hao-Wei Huang, Yi-Ju Chen, Ming-Fong Tsai

Pages: 785 - 797

59) Consistency and Diversity Induced Tensorized Multi-View Subspace Clustering

Author(s): Chunming Xiao, Yonghui Huang, Haonan Huang, Qibin Zhao, Guoxu Zhou

Pages: 798 - 809

60) Convert Cross-Domain Classification Into Few-Shot Learning: A Unified Prompt-Tuning Framework for Unsupervised Domain Adaptation

Author(s): Yi Zhu, Hui Shen, Yun Li, Jipeng Qiang, Yunhao Yuan, Xindong Wu

Pages: 810 - 821

61) Differentiable Collaborative Patches for Neural Scene Representations

Author(s): Heng Zhang, Lifeng Zhu

Pages: 822 - 831

62) Adaptive Neural Network Optimal Backstepping Control of Strict Feedback Nonlinear Systems via Reinforcement Learning

Author(s): Mei Zhong, Jinde Cao, Heng Liu

Pages: 832 - 847

63) ARC: A Layer Replacement Compression Method Based on Fine-Grained Self-Attention Distillation for Compressing Pre-Trained Language Models

Author(s): Daohan Yu, Liqing Qiu

Pages: 848 - 860

64) Generative Adversarial Network Based Image-Scaling Attack and Defense Modeling

Author(s): Junjian Li, Honglong Chen, Zhe Li, Anqing Zhang, Xiaomeng Wang, Xingang Wang, Feng Xia

Pages: 861 - 873

65) Global and Cluster Structural Balance via a Priority Strategy Based Memetic Algorithm

Author(s): Yifei Sun, Zhuo Liu, Yaochu Jin, Xin Sun, Yifei Cao, Jie Yang

Pages: 874 - 888

66) Efficient Message Passing Algorithm and Architecture Co-Design for Graph Neural Networks

Author(s): Xiaofeng Zou, Cen Chen, Luochuan Zhang, Shengyang Li, Joey Tianyi Zhou, Wei Wei, Kenli Li

Pages: 889 - 903

67) Targeted Mining Precise-Positioning Episode Rules

Author(s): Jian Zhu, Xiaoye Chen, Wensheng Gan, Zefeng Chen, Philip S. Yu

Pages: 904 - 917

68) Two-Stage Deep Feature Selection Method Using Voting Differential Evolution Algorithm for Pneumonia Detection From Chest X-Ray Images

Author(s): Haibin Ouyang, Dongmei Liu, Steven Li, Weiping Ding, Zhi-Hui Zhan

Pages: 918 - 932

69) Learning EEG Motor Characteristics via Temporal-Spatial Representations

Author(s): Tian-Yu Xiang, Xiao-Hu Zhou, Xiao-Liang Xie, Shi-Qi Liu, Hong-Jun Yang, Zhen-Qiu Feng, Mei-Jiang Gui, Hao Li, De-Xing Huang, Xiu-Ling Liu, Zeng-Guang Hou

Pages: 933 - 945

70) DualC: Drug-Drug Interaction Prediction Based on Dual Latent Feature Extractions

Author(s): Lin Guo, Xiujuan Lei, Lian Liu, Ming Chen, Yi Pan

Pages: 946 - 960

71) ESAI: Efficient Split Artificial Intelligence via Early Exiting Using Neural Architecture Search

Author(s): Behnam Zeinali, Di Zhuang, J. Morris Chang

Pages: 961 - 971

72) Early Time Series Anomaly Prediction With Multi-Objective Optimization

Author(s): Ting-En Chao, Yu Huang, Hao Dai, Gary G. Yen, Vincent S. Tseng

Pages: 972 - 987

73) Enhancing Accuracy-Privacy Trade-Off in Differentially Private Split Learning

Author(s): Ngoc Duy Pham, Khoa T. Phan, Naveen Chilamkurti

Pages: 988 - 1000

74) Evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network

Author(s): Gan Ruan, Leandro L. Minku, Zhao Xu, Xin Yao

Pages: 1001 - 1018

75) Evolutionary Sequential Transfer Learning for Multi-Objective Feature Selection in Classification

Author(s): Jiabin Lin, Qi Chen, Bing Xue, Mengjie Zhang

Pages: 1019 - 1033

76) Feature Autonomous Screening and Sequence Integration Network for Medical Image Classification

Author(s): Hongfeng You, Xiaobing Chen, Kun Yu, Guangbo Fu, Fei Mao, Xin Ning, Xiao Bai, Weiwei Cai

Pages: 1034 - 1048

77) FedLaw: Value-Aware Federated Learning With Individual Fairness and Coalition Stability

Author(s): Jianfeng Lu, Hangjian Zhang, Pan Zhou, Xiong Wang, Chen Wang, Dapeng Oliver Wu

Pages: 1049 - 1062

78) From Bag-of-Words to Transformers: A Comparative Study for Text Classification in Healthcare Discussions in Social Media

Author(s): Enrico De Santis, Alessio Martino, Francesca Ronci, Antonello Rizzi

Pages: 1063 - 1077

79) Fuzzy Composite Learning Control of Uncertain Fractional-Order Nonlinear Systems Using Disturbance Observer

Author(s): Zhiye Bai, Shenggang Li, Heng Liu

Pages: 1078 - 1090

[unclassable]

Since this summer our bandcamp profile have no more free download credits so I have uploaded the more downloaded albums on the netlabel archive https://archive.org/details/le-colibri-necrophile?sort=-date and the free download on bandcamp may be back in september.

[civilmentor1]

CURRENT AFFAIRS - 3 August 2024

1. Nano-MIND technology Context: In a ground-breaking experiment, researchers at the Center for Nanomedicine at the Institute for Basic Science (IBS) and Yonsei University in South Korea have demonstrated the ability to control specific brain regions in mice using magnetic fields. This innovation, called Nano-MIND (Magnetogenetic Interface for NeuroDynamics) technology, uses magnetic fields and…

[jeffreyrobertpalinjr]

Scientists 'Mind Controlled' Mice Remotely

At the mere flick of a magnetic field, mice engineered with nanoparticle-activated 'switches' inside their brains were driven to feed, socialize, and act like clucky new mothers in an experiment designed to test an innovative research tool.

While 'mind control' animal experiments are far from new, they have generally relied on cumbersome electrodes tethering the subject to an external system, which not only requires invasive surgery but also sets limits on how freely the test subject can move about.

In what is claimed to be a breakthrough in neurology, researchers have developed a method for targeting pathways in the brain using a combination of genetics, nanoparticles, and magnetic fields.

They call the technology Nano-MIND, an acronym for Magnetogenetic Interface for NeuroDynamics. And while mind-control is a coarse but relatively accurate way of describing it, the system in its current form is intended to provide researchers with a means of remotely activating neural circuits for a range of research applications.

This is the world's first technology to freely control specific brain regions using magnetic fields. Magnetic stimulation is an emerging field of research in neurology, where washing the brain with pulses of electromagnetism broadly massages whole regions into subtly changing their behavior.

To target specific circuits, the researchers took a leaf out of another field of research called optogenetics, which genetically engineers mechanisms into cells that can be readily activated by a light source. In this case, the team integrated ion channels into targeted populations of brain cells in mice. Instead of delivering light through a localized fiber, as in optogenetics, the ion channels could be switched on magnetically with a twist of a tiny actuator. All that's required is a surrounding field that's strong enough to pull at the nanoparticle.

Similar nanotechnology may even treat poor mental health in humans or play a significant role in therapies for debilitating neurological conditions, thereby returning complete control of a person's mind back to the individual.

[vestatimes]

Heart-Brain Neurodynamics: The Making of Emotions

[neuropoetizando]

fazem dois meses que você me disse adeus. tentei ligar para seu telefone, enviei emails... eu deixei você ir embora e abandonar o barco.

fico pensando, será que foi apenas um sonho? será que não significou nada?

tu prometeu ficar, disse que faria mas não fez, que por mim voltaria mas você se foi.

eu sinto sua falta todos os dias, sinto falta até do seu roncado do outro lado da linha. tu ficava comigo até as crises de ansiedade, pânico passar...

me trouxe tantos sorrisos e momentos felizes. vivemos intensamente um ano.

foram tantos planos... e agora tô chorando.

eu sei que muitas pessoas disseram o oposto sobre você, mas eu pude te conhecer de verdade, eu vi seu rosto, vi o verdadeiro B, que sempre acreditei, porém nunca toquei fisicamente.

sinto tanto tua falta. tanto que dói.

- neuropoetizando

[inkintheinternet]

The Intrusion of Sophisticated Technology

By Arjuwan Lakkdawala

Ink in the Internet

Humans began with hand tools, the simplest being rocks and then later spears. Present day technology has come a shockingly long way from the Stone Age.

But the big question is…where is it going? I won’t say where it will end? Because movies and books about dystopian societies have already predicted this many times. That at some point world war 3 will break out and nukes will bring the end of technology and society as we know it.

I’m thinking where technology will be in 20 or 30 years in terms of advancement.

In my previous article I vaguely mentioned people being concerned with Artificial Intelligence outsmarting humans, and making their contribution to the further development of the human race obsolete.

And so people might think of becoming cyborgs to keep up with AI.

The idea of giving up our brains or even half of it to machines sounds absolutely ridiculous to me. But I see indeed a road going in that direction.

Some entrepreneurs are seriously endorsing the merging of HI and AI.

Human Intelligence and Artificial Intelligence.

The article in The Atlantic ‘What will our lives be like as cyborgs?’ by Tim O’Reilly speaks at length about the augmentation of the human mind and body by AI and machines.

We are on the brink of a big change and it’s definitely going to be the digital takeover of our world.

The movie The Matrix has a good analogy to explain what we are experiencing.

In it the lead character Neo discovers the world he is living in is a simulation, everything is an illusion, a sensory augmentation, to make the ultimate virtual alternative reality.

The grim real picture is that humans are sustained in tubes and only their minds are free in the sensory fake world.

If you are one of the people still not fully aware of the rapidly growing digitization of the world, then you might have a Neo moment, by waking up to realise the world is becoming less detached from reality and more an alternative reality.

People are identifying according to their imagination rather than orientation. There is overwhelming reliance on artificial intelligence in not just the computation of big data. But applications are coming up for even the most trivial mental and physical tasks.

Another analogy is in the movie The Truman Show, where Jim Carrey is watched his whole life as he lives in a fake world.

There is a camera peeping at him from every angle, his life events are orchestrated.

The hundreds of algorithms that pick up data from our every digital interaction, and the companies using them to gain big data, and then manipulate the information for sales in advertising, or to convince us that merging with AI even to a physical level is safe, are sort of controlling and monitoring us like in the movie.

This breach of privacy is a nightmare we are living and participating in whether we know it or not.

FaceApp which went viral, ages and changes people’s features. The altered images are so realistic, so are the Deepfake videos of people saying things they never said.

The digital world is spiralling out of control, and so is the intrusion of technology on our lives.

Recently the famous technology entrepreneur, investor, and engineer, Elon Musk unveiled threads that can be implanted in the human brain, his intentions as he says is to help those who have body paralysis to control things with their thoughts. It would improve the lives of those who accept it. But the safety of the threads is still to be officially certified.

His company NeuraLink wants to merge neuroscience with computer science.

I don’t think anyone can question the relevance of this technology with sufferers of paralysis.

But can the epidemic of losing jobs to Artificial Intelligence in the not so distant future pressure underprivileged job seekers, or over ambitious technology enthusiast to experiment with possibly advanced versions of the threads or other sophisticated machines to merge directly with our brains?

I think we can clearly see the road open to the next take over from AI to cyborgs.

I don’t endorse becoming cyborgs, no machine can compete with the human brain. And I don’t think it’s possible to temper with the brain physically without damaging it.

Copyright ©Arjuwan Lakkdawala 2019