A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This study uses smartphone data to predict when mood interventions are needed for bipolar disorder.
Mobile technologies offer opportunities for higher resolution monitoring of health conditions. This opportunity seems of particular promise in psychiatry where diagnoses often rely on retrospective and subjective recall of mood states. However, getting actionable information from these rather complex time series is cha…
Recurrent major mood episodes and subsyndromal mood instability cause substantial disability in patients with bipolar disorder. Early identification of mood episodes enabling timely mood stabilisation is an important clinical goal. Recent technological advances allow the prospective reporting of mood in real time enabl…
Along with the advance of opinion mining techniques, public mood has been found to be a key element for stock market prediction. However, how market participants' behavior is affected by public mood has been rarely discussed. Consequently, there has been little progress in leveraging public mood for the asset allocatio…
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbei…
Deep learning diagnoses MS from smartphone data.
The study analyzes sentiment of European tweets during the pandemic.
We propose a new heavy-tailed distribution --- Gaussian-Chain (GC) distribution, which is inspirited by the hierarchical structures prevailing in social organizations. We determine the mean, variance and kurtosis of the Gaussian-Chain distribution to show its heavy-tailed property, and compute the tail distribution tab…
We consider the task of multimodal music mood prediction based on the audio signal and the lyrics of a track. We reproduce the implementation of traditional feature engineering based approaches and propose a new model based on deep learning. We compare the performance of both approaches on a database containing 18,000 …
Study examines how pandemic anxiety affects financial market trust.
Activity and motion analysis has the potential to be used as a diagnostic tool for mental disorders. However, to-date, little work has been performed in turning stratification measures of activity into useful symptom markers. The research presented in this thesis has focused on the identification of objective activity …
Federated learning algorithm reduces global model size by combining local and global representations.
The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a worldwide discussion of the future of our human society with a mixed mood of hope, anxiousness, excitement and fear. We try to dymystify AlphaGo Zero by a qualitative analysis to indicate that AlphaGo Zero can be understood as a specially structured…
In Part III of this study, we apply the price dynamical model with big buyers and big sellers developed in Part I of this paper to the daily closing prices of the top 20 banking and real estate stocks listed in the Hong Kong Stock Exchange. The basic idea is to estimate the strength parameters of the big buyers and the…
AI systems that explain their decisions can be monitored for harmful intentions.
mcanalysis quantifies menstrual cycle effects in health data.
Simple online monitor detects unsafe LLM outputs.
We investigate possible origins of trends using a deterministic threshold model, where we refer to long-term variabilities of price changes (price movements) in financial markets as trends. From the investigation we find two phenomena. One is that the trend of monotonic increase and decrease can be generated by dealers…
This research tackles monitoring machine learning algorithms post-deployment, addressing performativity issues.
Paper improves ETF tail-risk monitoring reliability.
We have applied a Long Short-Term Memory neural network to model S&P 500 volatility, incorporating Google domestic trends as indicators of the public mood and macroeconomic factors. In a held-out test set, our Long Short-Term Memory model gives a mean absolute percentage error of 24.2%, outperforming linear Ridge/Lasso…
PITMonitor monitors model calibration over time with formal error guarantees.
The paper adds explanation to predictive process monitoring.
A new method monitors unstructured 3D shapes without registration.
IDS algorithm optimizes sequential decisions in various monitoring settings.
Focuses on monitoring and explaining models in real-world applications.
Surveying low-cost sensors for air quality monitoring and calibration.
Social media is increasingly used by humans to express their feelings and opinions in the form of short text messages. Detecting sentiments in the text has a wide range of applications including identifying anxiety or depression of individuals and measuring well-being or mood of a community. Sentiments can be expressed…
Neural system optimizes glucose levels in diabetics.
We present a numerical scheme to calculate fluctuation identities for exponential Lévy processes in the continuous monitoring case. This includes the Spitzer identities for touching a single upper or lower barrier, and the more difficult case of the two-barriers exit problem. These identities are given in the Fourier-L…
Optimal probing framework for scalable network monitoring.
The paper proposes a method to create efficient remote monitoring models.
New monitoring method detects ML risk models' performance changes in medical interventions.
Emerging wearable sensors have enabled the unprecedented ability to continuously monitor human activities for healthcare purposes. However, with so many ambient sensors collecting different measurements, it becomes important not only to maintain good monitoring accuracy, but also low power consumption to ensure sustain…
With the growing popularity of wearable devices, the ability to utilize physiological data collected from these devices to predict the wearer's mental state such as mood and stress suggests great clinical applications, yet such a task is extremely challenging. In this paper, we present a general platform for personaliz…
Risk monitoring detects when TTA models degrade at test time.
Paper optimizes a big data and ML risk monitoring system for financial markets.
For using neural networks in safety critical domains, it is important to know if a decision made by a neural network is supported by prior similarities in training. We propose runtime neuron activation pattern monitoring - after the standard training process, one creates a monitor by feeding the training data to the ne…
Structural health monitoring is a condition-based field of study utilised to monitor infrastructure, via sensing systems. It is therefore used in the field of aerospace engineering to assist in monitoring the health of aerospace structures. A difficulty however is that in structural health monitoring the data input is …
A monitoring procedure improves machine learning forecasts for digital platforms.
Novel framework monitors cardiac image segmentation models in real-time.
New Thompson sampling algorithm for stochastic partial monitoring achieves logarithmic regret.
DFKI Cabin Simulator tests visual monitoring functions in vehicles.
Paper monitors DNN accuracy to enhance trustworthiness.
Proposes real-time risk monitoring for machine learning systems under unknown shifts.
A registration-free framework monitors shape and color in 4D point clouds.
IDS optimizes regret in stochastic partial monitoring with linear rewards.
Amazon SageMaker Model Monitor detects drift in deployed ML models.