Recent advances in personalized image generation have enabled pre-trained text-to-image models to learn new concepts from specific image sets. However, these methods often necessitate extensive test-time finetuning for each new concept, leading to inefficiencies in both time and scalability. To address this challenge, we introduce Instant-Booth, an innovative approach leveraging existing text-to-image models for instantaneous text-guided image personalization, eliminating the need for test-time finetuning. This efficiency is achieved through two primary innovations. Firstly, we utilize an image encoder that transforms input images into a global embedding to grasp the general concept. Secondly, we integrate new adapter layers into the pre-trained model, enhancing its ability to capture intricate identity details while maintaining language coherence. Significantly, our model is trained exclusively on textimage pairs, without reliance on concept-specific paired images. When benchmarked against existing finetuning-based personalization techniques like DreamBooth and TextualInversion, InstantBooth not only shows comparable proficiency in aligning language with image, maintaining image quality, and preserving the identity but also boasts a 100-fold increase in generation speed. Project Page: https://jshi31.github.io/InstantBooth/

The ubiquity and remarkable technological progress of wearable consumer devices and mobile-computing platforms (smart phone, smart watch, tablet), along with the multitude of sensor modalities available, have enabled continuous monitoring of patients and their daily activities. Such rich, longitudinal information can be mined for physiological and behavioral signatures of cognitive impairment and provide new avenues for detecting MCI in a timely and cost-effective manner. In this work, we present a platform for remote and unobtrusive monitoring of symptoms related to cognitive impairment using several consumer-grade smart devices. We demonstrate how the platform has been used to collect a total of 16TB of data during the Lilly Exploratory Digital Assessment Study, a 12-week feasibility study which monitored 31 people with cognitive impairment and 82 without cognitive impairment in free living conditions. We describe how careful data unification, time-alignment, and imputation techniques can handle missing data rates inherent in real-world settings and ultimately show utility of these disparate data in differentiating symptomatics from healthy controls based on features computed purely from device data.

While recent work has shown that a worker’s performance can be more accurately modeled by temporal correlation in task performance, a fundamental challenge remains in the need for expert gold labels to evaluate a worker’s performance. To solve this problem, we explore two methods of utilizing limited gold labels, initial training and periodic updating. Furthermore, we present a novel way of learning a prediction model in the absence of gold labels with uncertaintyaware learning and soft-label updating. Our experiment with a real crowdsourcing dataset demonstrates that periodic updating tends to show better performance than initial training when the number of gold labels are very limited (< 25).

While temporal behavioral patterns can be discerned to underlie real crowd work, prior studies have typically modeled worker performance under a simplified i.i.d. assumption. To better model such temporal worker behavior, we propose a time-series label prediction model for crowd work. This latent variable model captures and summarizes past worker behavior, enabling us to better predict the quality of each worker's next label. Given inherent uncertainty in prediction, we also investigate a decision reject option to balance the tradeoff between prediction accuracy vs. coverage. Results show our model improves accuracy of both label prediction on real crowd worker data, as well as data quality overall.

In crowdsourced relevance judging, each crowd worker typically judges only a small number of examples, yielding a sparse and imbalanced set of judgments in which relatively few workers influence output consensus labels, particularly with simple consensus methods like majority voting. We show how probabilistic matrix factorization, a standard approach in collaborative filtering, can be used to infer missing worker judgments such that all workers influence output labels. Given complete worker judgments inferred by PMF, we evaluate impact in unsupervised and supervised scenarios. In the supervised case, we consider both weighted voting and worker selection strategies based on worker accuracy. Experiments on crowd judgments from the 2010 TREC Relevance Feedback Track show promise of the PMF approach merits further investigation and analysis.

The volatile and stochastic characteristics of securities make it challenging to predict even tomorrow's stock prices. Better estimation of stock trends can be accomplished using both the significant and well-constructed set of features. Moreover, the prediction capability will gain momentum as we build the right model to capture unobservable attributes of the varying tendencies. In this paper, we propose a Binary Stock Event Model (BSEM) and generate features sets based on it in order to better predict the future trends of the stock market. We apply two learning models such as a Bayesian Naive Classifier and a Support Vector Machine to prove the efficiency of our approach in the aspects of prediction accuracy and computational cost. Our experiments demonstrate that the prediction accuracies are around 70-80% in one day predictions. In addition, our back-testing proves that our trading model outperforms well-known technical indicator based trading strategies with regards to cumulative returns by 30%-100%. As a result, this paper suggests that our BSEM based stock forecasting shows its excellence with regards to prediction accuracy and cumulative returns in a real world dataset.