{"results":[{"id":"crossref_10.1186/s12889-025-25182-x","title":"Inequalities and indoor air pollution: a prospective observational study of particulate matter (PM2.5) levels in 309 UK homes from the Born in Bradford cohort study","authors":[{"name":"Rachael W. Cheung"},{"name":"Lia Chatzidiakou"},{"name":"Tiffany C. Yang"},{"name":"Simon P. O’Meara"},{"name":"David R. Shaw"},{"name":"Denisa Genes"},{"name":"Yunqi Shao"},{"name":"Athina Ruangkanit"},{"name":"Thomas Warburton"},{"name":"Ashish Kumar"},{"name":"Sri Hapsari Budisulistiorini"},{"name":"Chantelle Wood"},{"name":"Gordon McFiggans"},{"name":"Nicola Carslaw"},{"name":"Jacqueline F. Hamilton"},{"name":"Rosemary R. C. McEachan"}],"abstract":"","source":"CrossRef","year":2025,"language":"en","subjects":null,"doi":"10.1186/s12889-025-25182-x","url":"https://doi.org/10.1186/s12889-025-25182-x","pdf_url":"https://link.springer.com/content/pdf/10.1186/s12889-025-25182-x.pdf","is_open_access":true,"citations":3,"published_at":"","score":69.09},{"id":"arxiv_2511.19520","title":"Modeling Bioelectric State Transitions in Glial Cells: An ASAL-Inspired Computational Approach to Glioblastoma Initiation","authors":[{"name":"Wiktoria Agata Pawlak"}],"abstract":"Understanding how glioblastoma (GBM) emerges from initially healthy glial tissue requires models that integrate bioelectrical, metabolic, and multicellular dynamics. This work introduces an ASAL-inspired agent-based framework that simulates bioelectric state transitions in glial cells as a function of mitochondrial efficiency (Meff), ion-channel conductances, gap-junction coupling, and ROS dynamics. Using a 64x64 multicellular grid over 60,000 simulation steps, we show that reducing Meff below a critical threshold (~0.6) drives sustained depolarization, ATP collapse, and elevated ROS, reproducing key electrophysiological signatures associated with GBM. We further apply evolutionary optimization (genetic algorithms and MAP-Elites) to explore resilience, parameter sensitivity, and the emergence of tumor-like attractors. Early evolutionary runs converge toward depolarized, ROS-dominated regimes characterized by weakened electrical coupling and altered ionic transport. These results highlight mitochondrial dysfunction and disrupted bioelectric signaling as sufficient drivers of malignant-like transitions and provide a computational basis for probing the bioelectrical origins of oncogenesis.","source":"arXiv","year":2025,"language":"en","subjects":["physics.bio-ph","cs.NE","q-bio.NC"],"url":"https://arxiv.org/abs/2511.19520","pdf_url":"https://arxiv.org/pdf/2511.19520","is_open_access":true,"published_at":"2025-11-24T04:59:51Z","score":69},{"id":"arxiv_2511.11848","title":"Phase-Coded Memory and Morphological Resonance: A Next-Generation Retrieval-Augmented Generator Architecture","authors":[{"name":"Denis V. Saklakov"}],"abstract":"This paper introduces a cognitive Retrieval-Augmented Generator (RAG) architecture that transcends transformer context-length limitations through phase-coded memory and morphological-semantic resonance. Instead of token embeddings, the system encodes meaning as complex wave patterns with amplitude-phase structure. A three-tier design is presented: a Morphological Mapper that transforms inputs into semantic waveforms, a Field Memory Layer that stores knowledge as distributed holographic traces and retrieves it via phase interference, and a Non-Contextual Generator that produces coherent output guided by resonance rather than fixed context. This approach eliminates sequential token dependence, greatly reduces memory and computational overhead, and enables unlimited effective context through frequency-based semantic access. The paper outlines theoretical foundations, pseudocode implementation, and experimental evidence from related complex-valued neural models, emphasizing substantial energy, storage, and time savings.","source":"arXiv","year":2025,"language":"en","subjects":["cs.NE"],"url":"https://arxiv.org/abs/2511.11848","pdf_url":"https://arxiv.org/pdf/2511.11848","is_open_access":true,"published_at":"2025-11-14T20:12:24Z","score":69},{"id":"arxiv_2404.05021","title":"Context-dependent Causality (the Non-Nonotonic Case)","authors":[{"name":"Nir Billfeld"},{"name":"Moshe Kim"}],"abstract":"We develop a novel identification strategy as well as a new estimator for context-dependent causal inference in non-parametric triangular models with non-separable disturbances. Departing from the common practice, our analysis does not rely on the strict monotonicity assumption. Our key contribution lies in leveraging on diffusion models to formulate the structural equations as a system evolving from noise accumulation to account for the influence of the latent context (confounder) on the outcome. Our identifiability strategy involves a system of Fredholm integral equations expressing the distributional relationship between a latent context variable and a vector of observables. These integral equations involve an unknown kernel and are governed by a set of structural form functions, inducing a non-monotonic inverse problem. We prove that if the kernel density can be represented as an infinite mixture of Gaussians, then there exists a unique solution for the unknown function. This is a significant result, as it shows that it is possible to solve a non-monotonic inverse problem even when the kernel is unknown. On the methodological front we leverage on a novel and enriched Contaminated Generative Adversarial (Neural) Networks (CONGAN) which we provide as a solution to the non-monotonic inverse problem.","source":"arXiv","year":2024,"language":"en","subjects":["econ.EM"],"url":"https://arxiv.org/abs/2404.05021","pdf_url":"https://arxiv.org/pdf/2404.05021","is_open_access":true,"published_at":"2024-04-07T17:25:24Z","score":68},{"id":"doaj_10.46298/jdmdh.6492","title":"OCR17: Ground Truth and Models for 17th c. French Prints (and hopefully more)","authors":[{"name":"Simon Gabay"},{"name":"Thibault Clérice"},{"name":"Christian Reul"}],"abstract":"Machine learning begins with machine teaching: in the following paper, we present the data that we have prepared to kick-start the training of reliable OCR models for 17th century prints written in French. The construction of a representative corpus is a major challenge: we need to gather documents from different decades and of different genres to cover as many sizes, weights and styles as possible. Historical prints containing glyphs and typefaces that have now disappeared, transcription is a complex act, for which we present guidelines. Finally, we provide preliminary results based on these training data and experiments to improve them.","source":"DOAJ","year":2023,"language":"","subjects":["History of scholarship and learning. The humanities","Bibliography. Library science. Information resources"],"doi":"10.46298/jdmdh.6492","url":"http://jdmdh.episciences.org/6492/pdf","pdf_url":"http://jdmdh.episciences.org/6492/pdf","is_open_access":true,"published_at":"","score":67},{"id":"arxiv_2006.10748","title":"Genetic Programming visitation scheduling solution can deliver a less austere COVID-19 pandemic population lockdown","authors":[{"name":"Daniel Howard"}],"abstract":"A computational methodology is introduced to minimize infection opportunities for people suffering some degree of lockdown in response to a pandemic, as is the 2020 COVID-19 pandemic. Persons use their mobile phone or computational device to request trips to places of their need or interest indicating a rough time of day: `morning', `afternoon', `night' or `any time' when they would like to undertake these outings as well as the desired place to visit. An artificial intelligence methodology which is a variant of Genetic Programming studies all requests and responds with specific time allocations for such visits that minimize the overall risks of infection, hospitalization and death of people. A number of alternatives for this computation are presented and results of numerical experiments involving over 230 people of various ages and background health levels in over 1700 visits that take place over three consecutive days. A novel partial infection model is introduced to discuss these proof of concept solutions which are compared to round robin uninformed time scheduling for visits to places. The computations indicate vast improvements with far fewer dead and hospitalized. These auger well for a more realistic study using accurate infection models with the view to test deployment in the real world. The input that drives the infection model is the degree of infection by taxonomic class, such as the information that may arise from population testing for COVID-19 or, alternatively, any contamination model. The taxonomy class assumed in the computations is the likely level of infection by age group.","source":"arXiv","year":2020,"language":"en","subjects":["cs.NE","physics.soc-ph"],"url":"https://arxiv.org/abs/2006.10748","pdf_url":"https://arxiv.org/pdf/2006.10748","is_open_access":true,"published_at":"2020-06-17T22:03:31Z","score":64},{"id":"arxiv_1810.13166","title":"Don't forget, there is more than forgetting: new metrics for Continual Learning","authors":[{"name":"Natalia Díaz-Rodríguez"},{"name":"Vincenzo Lomonaco"},{"name":"David Filliat"},{"name":"Davide Maltoni"}],"abstract":"Continual learning consists of algorithms that learn from a stream of data/tasks continuously and adaptively thought time, enabling the incremental development of ever more complex knowledge and skills. The lack of consensus in evaluating continual learning algorithms and the almost exclusive focus on forgetting motivate us to propose a more comprehensive set of implementation independent metrics accounting for several factors we believe have practical implications worth considering in the deployment of real AI systems that learn continually: accuracy or performance over time, backward and forward knowledge transfer, memory overhead as well as computational efficiency. Drawing inspiration from the standard Multi-Attribute Value Theory (MAVT) we further propose to fuse these metrics into a single score for ranking purposes and we evaluate our proposal with five continual learning strategies on the iCIFAR-100 continual learning benchmark.","source":"arXiv","year":2018,"language":"en","subjects":["cs.AI","cs.CV","cs.LG","cs.NE"],"url":"https://arxiv.org/abs/1810.13166","pdf_url":"https://arxiv.org/pdf/1810.13166","is_open_access":true,"published_at":"2018-10-31T09:15:02Z","score":62},{"id":"arxiv_1706.09556","title":"Vision-based Detection of Acoustic Timed Events: a Case Study on Clarinet Note Onsets","authors":[{"name":"A. Bazzica"},{"name":"J. C. van Gemert"},{"name":"C. C. S. Liem"},{"name":"A. Hanjalic"}],"abstract":"Acoustic events often have a visual counterpart. Knowledge of visual information can aid the understanding of complex auditory scenes, even when only a stereo mixdown is available in the audio domain, \\eg identifying which musicians are playing in large musical ensembles. In this paper, we consider a vision-based approach to note onset detection. As a case study we focus on challenging, real-world clarinetist videos and carry out preliminary experiments on a 3D convolutional neural network based on multiple streams and purposely avoiding temporal pooling. We release an audiovisual dataset with 4.5 hours of clarinetist videos together with cleaned annotations which include about 36,000 onsets and the coordinates for a number of salient points and regions of interest. By performing several training trials on our dataset, we learned that the problem is challenging. We found that the CNN model is highly sensitive to the optimization algorithm and hyper-parameters, and that treating the problem as binary classification may prevent the joint optimization of precision and recall. To encourage further research, we publicly share our dataset, annotations and all models and detail which issues we came across during our preliminary experiments.","source":"arXiv","year":2017,"language":"en","subjects":["cs.NE","cs.MM","cs.SD"],"url":"https://arxiv.org/abs/1706.09556","pdf_url":"https://arxiv.org/pdf/1706.09556","is_open_access":true,"published_at":"2017-06-29T02:43:37Z","score":61},{"id":"arxiv_1706.09559","title":"Audio Spectrogram Representations for Processing with Convolutional Neural Networks","authors":[{"name":"L. Wyse"}],"abstract":"One of the decisions that arise when designing a neural network for any application is how the data should be represented in order to be presented to, and possibly generated by, a neural network. For audio, the choice is less obvious than it seems to be for visual images, and a variety of representations have been used for different applications including the raw digitized sample stream, hand-crafted features, machine discovered features, MFCCs and variants that include deltas, and a variety of spectral representations. This paper reviews some of these representations and issues that arise, focusing particularly on spectrograms for generating audio using neural networks for style transfer.","source":"arXiv","year":2017,"language":"en","subjects":["cs.SD","cs.LG","cs.MM","cs.NE"],"url":"https://arxiv.org/abs/1706.09559","pdf_url":"https://arxiv.org/pdf/1706.09559","is_open_access":true,"published_at":"2017-06-29T03:04:06Z","score":61},{"id":"arxiv_1706.09558","title":"Talking Drums: Generating drum grooves with neural networks","authors":[{"name":"P. Hutchings"}],"abstract":"Presented is a method of generating a full drum kit part for a provided kick-drum sequence. A sequence to sequence neural network model used in natural language translation was adopted to encode multiple musical styles and an online survey was developed to test different techniques for sampling the output of the softmax function. The strongest results were found using a sampling technique that drew from the three most probable outputs at each subdivision of the drum pattern but the consistency of output was found to be heavily dependent on style.","source":"arXiv","year":2017,"language":"en","subjects":["cs.SD","cs.MM","cs.NE"],"url":"https://arxiv.org/abs/1706.09558","pdf_url":"https://arxiv.org/pdf/1706.09558","is_open_access":true,"published_at":"2017-06-29T03:03:35Z","score":61},{"id":"arxiv_1706.09552","title":"Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations","authors":[{"name":"H. V. Koops"},{"name":"W. B. de Haas"},{"name":"J. Bransen"},{"name":"A. Volk"}],"abstract":"The increasing accuracy of automatic chord estimation systems, the availability of vast amounts of heterogeneous reference annotations, and insights from annotator subjectivity research make chord label personalization increasingly important. Nevertheless, automatic chord estimation systems are historically exclusively trained and evaluated on a single reference annotation. We introduce a first approach to automatic chord label personalization by modeling subjectivity through deep learning of a harmonic interval-based chord label representation. After integrating these representations from multiple annotators, we can accurately personalize chord labels for individual annotators from a single model and the annotators' chord label vocabulary. Furthermore, we show that chord personalization using multiple reference annotations outperforms using a single reference annotation.","source":"arXiv","year":2017,"language":"en","subjects":["cs.SD","cs.MM","cs.NE"],"url":"https://arxiv.org/abs/1706.09552","pdf_url":"https://arxiv.org/pdf/1706.09552","is_open_access":true,"published_at":"2017-06-29T02:38:02Z","score":61},{"id":"arxiv_1706.09555","title":"Music Signal Processing Using Vector Product Neural Networks","authors":[{"name":"Z. C. Fan"},{"name":"T. S. Chan"},{"name":"Y. H. Yang"},{"name":"J. S. R. Jang"}],"abstract":"We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.","source":"arXiv","year":2017,"language":"en","subjects":["cs.SD","cs.LG","cs.MM","cs.NE"],"url":"https://arxiv.org/abs/1706.09555","pdf_url":"https://arxiv.org/pdf/1706.09555","is_open_access":true,"published_at":"2017-06-29T02:41:30Z","score":61},{"id":"arxiv_1706.09551","title":"Toward Inverse Control of Physics-Based Sound Synthesis","authors":[{"name":"A. Pfalz"},{"name":"E. Berdahl"}],"abstract":"Long Short-Term Memory networks (LSTMs) can be trained to realize inverse control of physics-based sound synthesizers. Physics-based sound synthesizers simulate the laws of physics to produce output sound according to input gesture signals. When a user's gestures are measured in real time, she or he can use them to control physics-based sound synthesizers, thereby creating simulated virtual instruments. An intriguing question is how to program a computer to learn to play such physics-based models. This work demonstrates that LSTMs can be trained to accomplish this inverse control task with four physics-based sound synthesizers.","source":"arXiv","year":2017,"language":"en","subjects":["cs.SD","cs.MM","cs.NE"],"url":"https://arxiv.org/abs/1706.09551","pdf_url":"https://arxiv.org/pdf/1706.09551","is_open_access":true,"published_at":"2017-06-29T02:33:56Z","score":61},{"id":"arxiv_1706.09088","title":"Modeling Musical Context with Word2vec","authors":[{"name":"Dorien Herremans"},{"name":"Ching-Hua Chuan"}],"abstract":"We present a semantic vector space model for capturing complex polyphonic musical context. A word2vec model based on a skip-gram representation with negative sampling was used to model slices of music from a dataset of Beethoven's piano sonatas. A visualization of the reduced vector space using t-distributed stochastic neighbor embedding shows that the resulting embedded vector space captures tonal relationships, even without any explicit information about the musical contents of the slices. Secondly, an excerpt of the Moonlight Sonata from Beethoven was altered by replacing slices based on context similarity. The resulting music shows that the selected slice based on similar word2vec context also has a relatively short tonal distance from the original slice.","source":"arXiv","year":2017,"language":"en","subjects":["cs.SD","cs.IR","cs.MM","cs.NE"],"url":"https://arxiv.org/abs/1706.09088","pdf_url":"https://arxiv.org/pdf/1706.09088","is_open_access":true,"published_at":"2017-06-28T00:46:50Z","score":61},{"id":"arxiv_1706.09553","title":"Transforming Musical Signals through a Genre Classifying Convolutional Neural Network","authors":[{"name":"S. Geng"},{"name":"G. Ren"},{"name":"M. Ogihara"}],"abstract":"Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.","source":"arXiv","year":2017,"language":"en","subjects":["cs.SD","cs.LG","cs.MM","cs.NE"],"url":"https://arxiv.org/abs/1706.09553","pdf_url":"https://arxiv.org/pdf/1706.09553","is_open_access":true,"published_at":"2017-06-29T02:39:00Z","score":61},{"id":"arxiv_1706.09557","title":"Machine listening intelligence","authors":[{"name":"C. E. Cella"}],"abstract":"This manifesto paper will introduce machine listening intelligence, an integrated research framework for acoustic and musical signals modelling, based on signal processing, deep learning and computational musicology.","source":"arXiv","year":2017,"language":"en","subjects":["cs.SD","cs.LG"],"url":"https://arxiv.org/abs/1706.09557","pdf_url":"https://arxiv.org/pdf/1706.09557","is_open_access":true,"published_at":"2017-06-29T02:52:25Z","score":61},{"id":"arxiv_1707.02746","title":"Backpropagation in matrix notation","authors":[{"name":"N. M. Mishachev"}],"abstract":"In this note we calculate the gradient of the network function in matrix notation.","source":"arXiv","year":2017,"language":"en","subjects":["cs.NE"],"url":"https://arxiv.org/abs/1707.02746","pdf_url":"https://arxiv.org/pdf/1707.02746","is_open_access":true,"published_at":"2017-07-10T08:44:46Z","score":61},{"id":"arxiv_1503.01524","title":"Genetic optimization of the Hyperloop route through the Grapevine","authors":[{"name":"Casey J. Handmer"}],"abstract":"We demonstrate a genetic algorithm that employs a versatile fitness function to optimize route selection for the Hyperloop, a proposed high speed passenger transportation system.","source":"arXiv","year":2015,"language":"en","subjects":["cs.NE"],"url":"https://arxiv.org/abs/1503.01524","pdf_url":"https://arxiv.org/pdf/1503.01524","is_open_access":true,"published_at":"2015-03-05T03:29:16Z","score":59},{"id":"arxiv_1501.02128","title":"Introduction and Ranking Results of the ICSI 2014 Competition on Single Objective Optimization","authors":[{"name":"Ying Tan"},{"name":"Junzhi Li"},{"name":"Zhongyang Zheng"}],"abstract":"This technical report includes the introduction and ranking results of the ICSI 2014 Competition on Single Objective Optimization.","source":"arXiv","year":2015,"language":"en","subjects":["cs.NE"],"url":"https://arxiv.org/abs/1501.02128","pdf_url":"https://arxiv.org/pdf/1501.02128","is_open_access":true,"published_at":"2015-01-09T13:21:11Z","score":59},{"id":"arxiv_1103.5081","title":"Using Variable Threshold to Increase Capacity in a Feedback Neural Network","authors":[{"name":"Praveen Kuruvada"}],"abstract":"The article presents new results on the use of variable thresholds to increase the capacity of a feedback neural network. Non-binary networks are also considered in this analysis.","source":"arXiv","year":2011,"language":"en","subjects":["cs.NE"],"url":"https://arxiv.org/abs/1103.5081","pdf_url":"https://arxiv.org/pdf/1103.5081","is_open_access":true,"published_at":"2011-03-25T20:59:13Z","score":55}],"total":157945,"page":1,"page_size":20,"sources":["arXiv","DOAJ","CrossRef"],"query":"cs.NE"}