DCASE 2022 Task 2 tackles domain shifts in ASD for machine condition monitoring.
problem Domain shifts change acoustic characteristics, affecting ASD performance.
method Domain generalization techniques to detect anomalies across unknown domains.
result Two types of domain generalization techniques were identified and analyzed.
As part of the 2016 public evaluation challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), the second task focused on evaluating sound event detection systems using synthetic mixtures of office sounds. This task, which follows the `Event Detection - Office Synthetic' task of DCASE 2013,…
DCASE 2021 ASD task tackles domain-shifted anomalous sound detection.
problem Detecting unknown anomalous sounds under domain-shifted conditions.
method Ensemble of outlier exposure and inlier modeling detectors, feature learning from machine identification.
result Two types of remarkable approaches were adopted by top teams.
To address Task 5 in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 challenge, in this paper, we propose an ensemble learning system. The proposed system consists of three different models, based on convolutional neural network and long short memory recurrent neural network. With extracted …
Enhances ASC using time- and frequency-liked CNNs and bilinear pooling.
problem Improving acoustic scene classification accuracy.
method Harmonic and percussive source separation, two-stream CNN architecture, bilinear pooling.
result Improved accuracy on DCASE 2019 sub task 1a dataset.
CNNs improve generalization to unseen audio devices with increased width, not depth.
problem CNNs are sensitive to specific audio recording devices in acoustic scene classification.
method Investigated the relationship between over-parameterization and generalization in CNNs for audio classification.
result Increasing width improves generalization to unseen devices without increasing the number of parameters.
Acoustic scene classification and related tasks have been dominated by Convolutional Neural Networks (CNNs). Top-performing CNNs use mainly audio spectograms as input and borrow their architectural design primarily from computer vision. A recent study has shown that restricting the receptive field (RF) of CNNs in appro…
A new method detects anomalous sounds using self-supervised learning.
problem Detecting anomalous sounds in noisy data.
method Flow-based self-supervised density estimation using Normalizing Flows.
result The method improves anomaly detection performance significantly.
New dataset for industrial machine malfunction detection with domain shifts.
problem Challenges in detecting anomalies due to domain shifts in industrial sounds.
method Created a dataset with domain shifts for five types of industrial machines.
result Significant performance differences between source and target domains.
This paper describes Task 2 of the DCASE 2018 Challenge, titled "General-purpose audio tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle platform as "Freesound General-Purpose Audio Tagging Challenge". The goal of the task is to build an audio tagging system that can recognize the c…
Distribution mismatches between the data seen at training and at application time remain a major challenge in all application areas of machine learning. We study this problem in the context of machine listening (Task 1b of the DCASE 2019 Challenge). We propose a novel approach to learn domain-invariant classifiers in a…
Paper proposes MTL for weakly labelled SED, improving performance with 2-step attention.
problem Weakly labelled sound event detection.
method Multi-Task Learning framework with 2-step Attention Pooling.
result Improved SED performance with 22.3%, 12.8%, 5.9% gains at 0, 10, 20 dB SNR.
System tackles indeterminacies in automated audio captioning.
problem Word selection and sentence length indeterminacies in automated audio captioning.
method Solves caption generation and sub-indeterminacy problems through multi-task learning to estimate keywords and sentence length.
result Model achieved 20.7 SPIDEr score, significantly outperforming baseline.
This foreword discusses the contributions of Bolyai, Gauss, and Lobachevsky to non-Euclidean geometry.
problem The development of non-Euclidean geometries by Bolyai, Gauss, and Lobachevsky.
method Historical review of the contributions of these mathematicians.
result The foundational work on non-Euclidean geometries by Bolyai, Gauss, and Lobachevsky.
A theory linking invariants, Floer homologies, and Higgs bundles.
problem Understanding complex geometric structures and their invariants.
method Categorification and geometric Langlands correspondence.
result Established a new geometric Langlands correspondence.
India's tax on crypto income sparks debate on its future.
problem Impact of 30% tax on Indian crypto market.
method Discussion of various opinions and predictions.
result Uncertainty about crypto's future in India.
This paper introduces a model of environmental acoustic scenes which adopts a morphological approach by ab-stracting temporal structures of acoustic scenes. To demonstrate its potential, this model is employed to evaluate the performance of a large set of acoustic events detection systems. This model allows us to expli…
Survey on non-positively curved cube complexes and geometric group theory.
problem None explicitly stated, focuses on introduction.
method Lecture notes and mini-course teaching.
result Introduction to non-positively curved cube complexes and geometric group theory.
We describe a novel weakly labeled Audio Event Classification approach based on a self-supervised attention model. The weakly labeled framework is used to eliminate the need for expensive data labeling procedure and self-supervised attention is deployed to help a model distinguish between relevant and irrelevant parts …
Intangible investment becomes a strong predictor of stock returns over time.
problem Understanding the role of intangible investment in stock returns over different periods.
method Comparing intangible investment's predictive power over two distinct periods (1963-1992 and 1993-2022) using orthogonal factors.
result Intangible investment's predictive power for stock returns has significantly increased over time, becoming a main predictor for recent periods.
Paper discusses ASD challenge for machine condition monitoring.
problem Detecting unknown anomalous sounds without labeled data.
method Design and evaluation of a large-scale ASD dataset, novel approaches.
result Several novel approaches developed, evaluation results analyzed.
Study compares three portfolio optimization methods on Indian stocks.
problem Optimizing portfolios for the Indian stock market.
method Three portfolio optimization methods (MVP, HRP, HERC) applied to 15 sectors.
result Identified portfolios with highest cumulative return, lowest volatility, and best Sharpe Ratio.
Study analyzes European energy markets' reactions to 2022 events using Bayesian methods.
problem Detecting structural breakpoints in energy and financial markets during turbulent times.
method Combines Hurst exponent for market efficiency, BEAST for abrupt changes, and seasonal/trend analysis.
result Markets exhibit varied reactions to critical events, affecting their trends and breakpoints.
New approach to robustly reliable learners against instance-targeted attacks.
problem Addressing data poisoning attacks on robustly reliable learners.
method Defining regularized robustly reliable learners and efficient algorithms.
result Efficient algorithms for robustly reliable learners with sublinear runtime.
This paper considers a semi-supervised learning framework for weakly labeled polyphonic sound event detection problems for the DCASE 2019 challenge's task4 by combining both the tri-training and adversarial learning. The goal of the task4 is to detect onsets and offsets of multiple sound events in a single audio clip. …
New Langlands duality conjectures for 3-manifold skein modules.
problem Understanding Langlands duality in 3-manifold skein modules.
method Combining representation theory of double affine Hecke algebras and 1-form symmetry structure.
result Recent special cases confirmed, with detailed proofs.
Discussing hybrid models in Bayesian networks.
problem Improving accuracy in network modeling.
method Hybrid semiparametric Bayesian approach.
result Enhanced model performance in complex networks.
Study validates numerical method for singular FBSDEs convergence.
problem Solving singular FBSDEs and associated PDEs.
method Particles approximation for transport operator and tree approximation for diffusion operator.
result Convergence rate of numerical method proved under reasonable conditions.
Piecewise normalizing flows improve multi-modal distribution modeling.
problem Improving accuracy in modeling multi-modal distributions.
method Divide target distribution into clusters, train flows to match standard normal base.
result Piecewise flows outperform standard approaches in accuracy.
This study evaluates different portfolio designs for Indian stocks.
problem Optimizing portfolio weights for risk and return in volatile stock markets.
method Three portfolio design approaches: risk minimization, risk optimization, and equal weighting. Historical data from 2017-2022 used.
result Equal-weight portfolios outperformed other designs in most sectors.
This paper extends 3D results to higher dimensions, proving compactness for PIC1 pinched manifolds.
problem Proving compactness for higher-dimensional manifolds with specific curvature conditions.
method Constructing Ricci flows for non-compact PIC1 pinched manifolds to prove compactness.
result Proves that PIC1 pinched manifolds of non-negative complex sectional curvature must be flat or compact.
Study quantifies events leading to Terra project failure in 2022.
problem Terra project's fragility and dependency on Anchor protocol.
method Systematic review of social media news, hourly and transaction data analysis, network science techniques.
result Identified trigger events and analyzed dependency structures using network science.
Acoustic scene classification is the task of identifying the scene from which the audio signal is recorded. Convolutional neural network (CNN) models are widely adopted with proven successes in acoustic scene classification. However, there is little insight on how an audio scene is perceived in CNN, as what have been d…
GC 2022 challenges real-time trend detection in financial tick data.
problem Efficiently detect trading trends in high-volume financial tick data.
method Real-time complex event processing of tick data, focusing on trend indicators and patterns.
result Participants must build reusable and practical solutions for real-life trading decisions.
Acoustic Scene Classification (ASC) is a challenging task, as a single scene may involve multiple events that contain complex sound patterns. For example, a cooking scene may contain several sound sources including silverware clinking, chopping, frying, etc. What complicates ASC more is that classes of different activi…
Proves Gerber statistic is always non-negative.
problem Verifying the positive semi-definiteness of Gerber statistic.
method Analytical proof of both forms of Gerber statistic.
result Gerber statistic is positive semi-definite.
In this paper we propose a novel environmental sound classification approach incorporating unsupervised feature learning from codebook via spherical K-Means++ algorithm and a new architecture for high-level data augmentation. The audio signal is transformed into a 2D representation using a discrete wavelet transform …
This work proposes using Conformal Prediction to improve OOD detection scores and vice versa.
problem Improper evaluation of OOD detection scores due to finite sample size.
method Defining new conformal AUROC and FRP@TPR95 metrics and using OOD scores as non-conformity scores.
result Improved evaluation metrics and better interpretation of OOD scores.
Study uses high-frequency data to predict ruble depreciation during crisis.
problem Predicting ruble depreciation during the Russian invasion of Ukraine.
method Uses intraday high-frequency data (google searches and implied volatility) to model exchange rate fluctuations.
result Implied volatility is more effective than attention in predicting ruble depreciation.
Paper develops Bayesian inference for discrete-choice mnp models with Gaussian priors.
problem Estimating parameters of discrete-choice multinomial probit models with Gaussian priors.
method Adapts Fasano and Durante's results to a specific mnp model with zero mean and independent Gaussian priors, simplifying posterior distribution parameters and providing a new variational algorithm.
result Simplified expressions for posterior distribution parameters and a novel variational algorithm.
In this paper, we present an acoustic scene classification framework based on a large-margin factorized convolutional neural network (CNN). We adopt the factorized CNN to learn the patterns in the time-frequency domain by factorizing the 2D kernel into two separate 1D kernels. The factorized kernel leads to learn the m…
Corrects a paper on Alexander modules and answers a related question.
problem Peripheral elements in reduced Alexander modules
method Analyzes and corrects a paper on Alexander modules
result Answers a question and corrects a minor error in the original paper
Study optimal rates for multiclass classification, resolving open questions.
problem Optimal rates for multiclass classification with any label space.
method Establishes optimal rates for all hypothesis classes, defining new tree structures.
result Optimal rates for multiclass classification with no infinite DSL trees.
The paper extends the market price of risk for electricity swap contracts, incorporating jump risk.
problem Pricing electricity swap contracts with consideration of jump risk.
method Introducing a Merton type model with jumps and transferring to the physical measure, comparing arithmetic and geometric averaging.
result A decomposition of swap's market price of risk into classical and market price of risk components.
Study analyzes Airbnb booking lead times during global crises using a new metric.
problem Disruptions in booking behaviors during global crises affect forecasting accuracy.
method Normalized L1 (Manhattan) distance to assess lead time divergences.
result Identified two-phase disruption: abrupt change at pandemic onset followed by partial recovery.
This paper tackles inventory control with general arrival dynamics and post-processing, improving profitability.
problem Inventory control with arbitrary arrival dynamics and post-processing constraints.
method Formulated as an exogenous decision process, incorporating deep generative models for arrivals, and applying supervised learning techniques.
result Improves profitability over production baselines and real-world A/B test data.
Salzmann's legacy in mathematics documented.
problem Mathematical challenges faced by Salzmann.
method Comprehensive review of Salzmann's work and bibliography.
result Salzmann's far-reaching influence in mathematics.
Sullivan discusses his contributions to math and physics.
problem None explicitly stated in the abstract.
method Personal overview of Dennis Sullivan's work.
result Sullivan's work spans mathematics and physics.