DeepDrum generates drum rhythms under musical constraints.
problem Generating rhythms that adhere to musical style and constraints.
method Adaptive Neural Network with Conditional Layers containing musical parameters and instrumentation.
result DeepDrum effectively generates rhythms that resemble learned styles while conforming to given constraints.
GenéLive! generates rhythm game commands with musical structure.
problem Generating accurate rhythm commands in music games.
method Deep generative model that considers musical structures.
result Reduced business cost by half at KLab Inc.
Music SketchNet generates missing measures in incomplete music pieces, guided by user input.
problem Generating missing measures in incomplete monophonic musical pieces.
method Introducing SketchVAE for factorized representation of rhythm and pitch, and two discriminative architectures for guided music completion.
result Our approach outperforms state-of-the-art models in both objective and subjective evaluations.
In this paper we provide compelling evidence of cyclical mean reversion and multiperiod stock return predictability over horizons of about 30 years with a half-life of about 15 years. This implies that the US stock market follows a long-term rhythm where a period of above average returns tends to be followed by a perio…
Personalized deep learning reduces inappropriate shocks in VA detection.
problem High inappropriate shock rate in traditional VA detection methods.
method Personalized deep learning framework using CNN for real-time VA detection and collaborative inference.
result 6.6% reduction in inappropriate shock rate compared to traditional methods.
Generates music with coherent rhythm, chords, and melody using LSTM models.
problem Lack of direction and coherence in generated music by neural networks.
method Two-stage LSTM model: first generates harmonic and rhythmic templates, then melodies conditioned on these.
result Subjective test shows improved musical coherence and coherence compared to baselines.
EC^2-VAE generates music analogies by disentangling pitch and rhythm representations.
problem Disentangling music representations for generating creative analogies.
method Explicitly-constrained variational autoencoder (EC^2-VAE) for disentangling pitch and rhythm representations.
result EC^2-VAE enables the generation of music analogies by borrowing representations from different pieces.
Improves music composition with user-defined constraints using continuous time models.
problem Combining sequence models with user-defined constraints in continuous time.
method Introduces a novel particle filter scheme for continuous time point processes.
result The particle filter scheme yields superior results in a human listening test.
PhysioMTL learns personalized HRV rhythms from wearable data, improving prediction and counterfactual analysis.
problem Challenges in interpreting HRV measurements due to variability and stressors.
method PhysioMTL combines Optimal Transport and MTL to learn personalized diurnal rhythms from heterogeneous data.
result PhysioMTL outperforms other methods in predicting unseen subjects and generating counterfactual effects.
Novel ECG classification method using deep time-frequency representation and progressive decision fusion.
problem Challenges in classifying abnormal ECG rhythms due to broad taxonomy, noises, and lack of annotated data.
method Transform ECG signal into time-frequency domain, train scale-specific deep CNNs, and fuse decisions progressively.
result Effective and efficient ECG classification method validated on synthetic and real-world datasets.
Study uses reinforcement learning to optimize metachronal paddling at low Reynolds number.
problem Optimizing metachronal paddling strategies for efficient swimming at low Reynolds numbers.
method Applied reinforcement learning to a swimmer model with varying paddle spacings.
result The reinforcement learning algorithm selects a back-to-front metachronal wave-like stroke as the most efficient, regardless of the number of paddles.
The development of new technology such as wearables that record high-quality single channel ECG, provides an opportunity for ECG screening in a larger population, especially for atrial fibrillation screening. The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), …
Model predicts epileptic seizures with high accuracy using EEG signals.
problem Predicting epileptic seizures with high accuracy for diagnosis and treatment.
method Pearson's product-moment correlation coefficient with a linear classifier on generalized Gaussian modeling.
result 100% effectiveness for sensitivity and specificity greater than 83%.
The starting point of this article is the question "How to retrieve fingerprints of rhythm in written texts?" We address this problem in the case of Brazilian and European Portuguese. These two dialects of Modern Portuguese share the same lexicon and most of the sentences they produce are superficially identical. Yet t…
The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but wheth…
Study combines CNNs and LSTMs for ECG classification, improving performance with attention mechanisms.
problem Improving ECG classification accuracy for weakly supervised tasks.
method Investigates ConvLSTM networks and various attention mechanisms to capture both morphological and temporal features.
result Attention mechanisms improve classification performance and provide interpretable saliency maps.
Novel ECG classification for AF using spectro-temporal Kalman filtering and deep CNN.
problem Atrial fibrillation (AF) detection in ECG signals.
method Spectro-temporal representation using Kalman filter and deep convolutional neural networks.
result Proposed method achieves an overall F1 score of 80.2% on the PhysioNet/Computing in Cardiology (CinC) 2017 dataset.
Study limits of circadian synchronization under different light signals.
problem Disruption of circadian rhythms due to misalignment with external light signals.
method Matrix-free approach for locating periodic steady states, numerical continuation, bifurcation diagrams, unsupervised learning.
result Limits of circadian synchronization to external light signals of different frequency and duty cycle.
Algorithm finds critical subsequences in ECG beats for classification.
problem Classifying ECG beats accurately and understanding why.
method Optimized implementation of nearest neighbor algorithm with DTW distance.
result Algorithm discovers important subsequences for ECG classification.
Two-step model estimates DLMO using both daily and frequent data.
problem Expensive and time-consuming DLMO measurement.
method Two-step framework combining daily and frequent data.
result Two-step model with two time-scale features has lower errors.
A big challenge in algorithmic composition is to devise a model that is both easily trainable and able to reproduce the long-range temporal dependencies typical of music. Here we investigate how artificial neural networks can be trained on a large corpus of melodies and turned into automated music composers able to gen…
Considering that a trader or a trading algorithm interacting with markets during continuous auctions can be modeled by an iterating procedure adjusting the price at which he posts orders at a given rhythm, this paper proposes a procedure minimizing his costs. We prove the a.s. convergence of the algorithm under assumpt…
Paper analyzes privacy-aware mobility behavior using entropy metrics.
problem Intrusive user location tracking makes it easy to identify users.
method Proposes spatio-temporal entropy to quantify mobility, uses GAMs to study effects of variables.
result Global GAM provides more accurate predictions of spatio-temporal entropy.
Dance Dance Revolution (DDR) is a popular rhythm-based video game. Players perform steps on a dance platform in synchronization with music as directed by on-screen step charts. While many step charts are available in standardized packs, players may grow tired of existing charts, or wish to dance to a song for which no …
The classical literature on optimal liquidation, rooted in Almgren-Chriss models, tackles the optimal liquidation problem using a trade-off between market impact and price risk. Therefore, it only answers the general question of the optimal liquidation rhythm. The very question of the actual way to proceed with liquida…
Based on the cumulated experience over the past 25 years in the field of Brain-Computer Interface (BCI) we can now envision a new generation of BCI. Such BCIs will not require training; instead they will be smartly initialized using remote massive databases and will adapt to the user fast and effectively in the first m…
Market activity scales near a constant of 0.632 in intrinsic time.
problem Understanding the stability of market scaling laws.
method Modeling market directional changes as a memoryless exponential hazard process and identifying the intrinsic time scaling constant.
result The intrinsic time scaling constant is 1−1/e=0.632. This paper develops synthetic mobility datasets to protect privacy while maintaining realism.
problem Privacy concerns restrict sharing real mobility datasets, leading to lack of reproducibility.
method The paper benchmarks RNNs, GANs, and copulas to generate realistic synthetic trajectories.
result The approach generates trajectories that are statistically and semantically similar to real-world data.
Method detects multi-timescale consumer spending patterns from receipts.
problem Understanding and managing consumer behavior in high-dimensional data.
method Non-negative tensor factorization (NTF) to extract multi-timescale expenditure patterns.
result Consumption patterns are characterized based on spending behavior over different timescales.
CTEF fits ellipsoids to noisy data in any dimension.
problem Fitting ellipsoids to noisy data in arbitrary dimensions.
method Uses the Cayley transform to fit ellipsoids.
result CTEF outperforms other methods, especially when data are not uniformly distributed.
Models learn to transform musical scores into expressive performances.
problem Creating large volumes of paired data for sequence transformations.
method Adapted GAN techniques to sequences, creating paired data through simple transformations.
result Models can plausibly invert transformations like quantization and removing voices.
Neural network generates music scores directly from polyphonic audio.
problem Transcribing music scores directly from polyphonic audio.
method Convolutional Recurrent Neural Network (CRNN) with CTC loss function.
result Model can learn to transcribe scores directly from audio signals.
TimbreTron transfers musical timbre using CQT and WaveNet.
problem Transfer musical timbre while preserving pitch, rhythm, and loudness.
method Apply image domain style transfer to CQT representation, then generate high-quality waveform with WaveNet.
result TimbreTron recognizably transfers timbre while preserving musical content.
This paper uses fuzzy C-Means clustering and sonification to analyze heart rate variability.
problem Identifying suitable features from HRV analysis for sonification.
method Unsupervised machine learning (fuzzy C-Means clustering) and sonification techniques.
result Improves sonification interpretability by selecting appropriate HRV features.
Time series analysis is used to understand and predict dynamic processes, including evolving demands in business, weather, markets, and biological rhythms. Exponential smoothing is used in all these domains to obtain simple interpretable models of time series and to forecast future values. Despite its popularity, expon…
Investigation of the underlying physics or biology from empirical data requires a quantifiable notion of similarity - when do two observed data sets indicate nearly identical generating processes, and when they do not. The discriminating characteristics to look for in data is often determined by heuristics designed by …
WeSinger improves singing voice synthesis with data augmentation and specialized modules.
problem Improving the accuracy and naturalness of synthesized singing voices.
method Developed a multi-singer Chinese neural singing voice synthesis system with deep bi-directional LSTM, Transformer, LPCNet, and data augmentation.
result WeSinger achieves state-of-the-art performance on the Opencpop corpus.
Music FaderNets learns high-level musical qualities from low-level attributes.
problem Learning high-level musical qualities from limited data and subjective labels.
method Model low-level attributes through feature disentanglement and latent regularization; infer high-level features from low-level representations using GM-VAEs.
result Model successfully learns intrinsic relationships between high-level features and low-level attributes with minimal labeled data.
Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to…
Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement. Therefore, th…
New framework tackles deep financial reporting bottleneck by improving hallucination and coherence.
problem Statistical smoothing trap in LLMs limits deep financial reporting quality.
method DeepNews Framework integrates information foraging, schema-guided planning, and adversarial prompting.
result DeepNews system achieves 25% acceptance rate in blind test, significantly outperforming SOTA.
Brain computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct ch…
New method creates indistinguishable but misclassified ECG signals.
problem Adversarial examples fool deep neural networks in ECG classification.
method Developed a technique to generate smoothed adversarial examples for single-lead ECG.
result Adversarial examples are not unique and can be collated and perturbed.
Research uses activity analysis to identify mental health symptoms.
problem Identifying mental health symptoms using objective activity metrics.
method Proposes a framework for mHealth monitoring of psychiatric patients based on physical activity time series.
result Identifies distinct behavioural phenotypes and measures for mood assessment.
Study uses synthetic data to estimate credit risk for underbanked consumers in Istanbul.
problem Estimating credit risk for underbanked consumers lacking formal credit records.
method Created synthetic dataset, used retrieval augmented generation, trained CatBoost, LightGBM, and XGBoost models.
result Alternative financial data improves credit risk estimation, raising AUC by 13%.
AI agents on social networks rarely engage in extended conversations.
problem Understanding the persistence of interactions in AI-agent social networks.
method Analysis of Moltbook, a social network of AI agents, using interaction half-life and spectral tests.
result Most comments on Moltbook receive a direct reply within seconds, indicating a ``fast response or silence'' regime.
Proposes a model to understand urban dynamics from mega-metropolises.
problem Understanding residents mobility patterns in mega-metropolises.
method Neighbor-Regularized and context-aware Non-negative Tensor Factorization (NR-cNTF).
result NR-cNTF accurately captures city rhythms and spatial communities.
Automated process links oral health to systemic conditions using machine learning.
problem Correlating oral health with systemic health conditions.
method Intraoral fluorescent biomarker imaging, machine learning segmentation, and clinical examination.
result Machine learning classifier achieved AUC of 0.677, indicating a learned association between disease signatures in images and periodontal disease.