On the Wiener disorder problem

In the Wiener disorder problem, the drift of a Wiener process changes suddenly at some unknown and unobservable disorder time. The objective is to detect this change as quickly as possible after it happens. Earlier work on the Bayesian formulation of this problem brings optimal (or asymptotically optimal) detection rules assuming that the prior distribution of the change time is given at time zero, and additional information is received by observing the Wiener process only. Here, we consider a different information structure where possible causes of this disorder are observed. More precisely, we assume that we also observe an arrival/counting process representing external shocks. The disorder happens because of these shocks, and the change time coincides with one of the arrival times. Such a formulation arises, for example, from detecting a change in financial data caused by major financial events, or detecting damages in structures caused by earthquakes. In this paper, we formulate the problem in a Bayesian framework assuming that those observable shocks form a Poisson process. We present an optimal detection rule that minimizes a linear Bayes risk, which includes the expected detection delay and the probability of early false alarms. We also give the solution of the ``variational formulation'' where the objective is to minimize the detection delay over all stopping rules for which the false alarm probability does not exceed a given constant.Comment: Published in at http://dx.doi.org/10.1214/09-AAP655 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Biologically-inspired neural coding of sound onset for a musical sound classification task

A biologically-inspired neural coding scheme for the early auditory system is outlined. The cochlea response is simulated with a passive gammatone filterbank. The output of each bandpass filter is spike-encoded using a zero-crossing based method over a range of sensitivity levels. The scheme is inspired by the highly parallellised nature of the auditory nerve innervation within the cochlea. A key aspect of early auditory processing is simulated, namely that of onset detection, using leaky integrate-and-fire neuron models. Finally, a time-domain neural network (the echo state network) is used to tackle the what task of auditory perception using the output of the onset detection neuron alone

Zero-Day Aware Decision Fusion-Based Model for Crypto-Ransomware Early Detection

Crypto-ransomware employs the cryptography to lock user personal files and demands ransom to release them. By utilizing several technological utilities like cyber-currency and cloud-based developing platforms, crypto-ransomware has gained high popularity among adversaries. Motivated by the monetary revenue, crypto-ransomware developers continuously produce many variants of such malicious programs to evade the detection. Consequently, the rate of crypto-ransomware novel attacks is continuously increasing. As such, it is imperative for detection solutions to be able to discover these novel attacks, also called zero-day attacks. While anomaly detection-based solutions are able to deal with this problem, they suffer the high rate of false alarms. Thus, this paper puts forward a detection model that incorporates anomaly with behavioral detection approaches. In this model, two types of detection estimators were built. The first type is an ensemble of behavioral-based classifiers whereas the second type is an anomaly-based estimator. The decisions of both types of estimators were combined using fusion technique. The proposed model is able to detect the novel attack while maintaining low false alarms rate. By applying the proposed model, the detection rate was increased from 96% to 99% and the false positive rate was as low as 2.4 %

Transfer Learning for Power Outage Detection Task with Limited Training Data

Early detection of power outages is crucial for maintaining a reliable power distribution system. This research investigates the use of transfer learning and language models in detecting outages with limited labeled data. By leveraging pretraining and transfer learning, models can generalize to unseen classes. Using a curated balanced dataset of social media tweets related to power outages, we conducted experiments using zero-shot and few-shot learning. Our hypothesis is that Language Models pretrained with limited data could achieve high performance in outage detection tasks over baseline models. Results show that while classical models outperform zero-shot Language Models, few-shot fine-tuning significantly improves their performance. For example, with 10% fine-tuning, BERT achieves 81.3% accuracy (+15.3%), and GPT achieves 74.5% accuracy (+8.5%). This has practical implications for analyzing and localizing outages in scenarios with limited data availability. Our evaluation provides insights into the potential of few-shot fine-tuning with Language Models for power outage detection, highlighting their strengths and limitations. This research contributes to the knowledge base of leveraging advanced natural language processing techniques for managing critical infrastructure

SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model

With the development of large language models, many remarkable linguistic systems like ChatGPT have thrived and achieved astonishing success on many tasks, showing the incredible power of foundation models. In the spirit of unleashing the capability of foundation models on vision tasks, the Segment Anything Model (SAM), a vision foundation model for image segmentation, has been proposed recently and presents strong zero-shot ability on many downstream 2D tasks. However, whether SAM can be adapted to 3D vision tasks has yet to be explored, especially 3D object detection. With this inspiration, we explore adapting the zero-shot ability of SAM to 3D object detection in this paper. We propose a SAM-powered BEV processing pipeline to detect objects and get promising results on the large-scale Waymo open dataset. As an early attempt, our method takes a step toward 3D object detection with vision foundation models and presents the opportunity to unleash their power on 3D vision tasks. The code is released at https://github.com/DYZhang09/SAM3D.Comment: Technical Report. The code is released at https://github.com/DYZhang09/SAM3

Probing the distribution of dark matter in the Abell 901/902 supercluster with weak lensing

We present a weak shear analysis of the Abell 901/902 supercluster, composed of three rich clusters at z=0.16. Using a deep R-band image from the 0.5 x 0.5 degree MPG/ESO Wide Field Imager together with supplementary B-band observations, we build up a comprehensive picture of the light and mass distributions in this region. We find that, on average, the light from the early-type galaxies traces the dark matter fairly well, although one cluster is a notable exception to this rule. The clusters themselves exhibit a range of mass-to-light (M/L) ratios, X-ray properties, and galaxy populations. We attempt to model the relation between the total mass and the light from the early-type galaxies with a simple scale-independent linear biasing model. We find M/L_B=130h for the early type galaxies with zero stochasticity, which, if taken at face value, would imply Omega_m < 0.1. However, this linear relation breaks down on small scales and on scales equivalent to the average cluster separation (approximately 1 Mpc), demonstrating that a single M/L ratio is not adequate to fully describe the mass-light relation in the supercluster. Rather, the scatter in M/L ratios observed for the clusters supports a model incorporating non-linear biasing or stochastic processes. Finally, there is a clear detection of filamentary structure connecting two of the clusters, seen in both the galaxy and dark matter distributions, and we discuss the effects of cluster-cluster and cluster-filament interactions as a means to reconcile the disparate descriptions of the supercluster.Comment: 23 pages, 19 figures. ApJ, accepte