Tracking and fast imaging of a translational object via Fourier modulation

The tracking and imaging of high-speed moving objects hold significant promise for application in various fields. Single-pixel imaging enables the progressive capture of a fast-moving translational object through motion compensation. However, achieving a balance between a short reconstruction time and a good image quality is challenging. In this study, we present a approach that simultaneously incorporates position encoding and spatial information encoding through the Fourier patterns. The utilization of Fourier patterns with specific spatial frequencies ensures robust and accurate object localization. By exploiting the properties of the Fourier transform, our method achieves a remarkable reduction in time complexity and memory consumption while significantly enhancing image quality. Furthermore, we introduce an optimized sampling strategy specifically tailored for small moving objects, significantly reducing the required dwell time for imaging. The proposed method provides a practical solution for the real-time tracking, imaging and edge detection of translational objects, underscoring its considerable potential for diverse applications.

Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency

We consider Bayesian optimization using Gaussian Process models, also referred to as kernel-based bandit optimization. We study the methodology of exploring the domain using random samples drawn from a distribution. We show that this random exploration approach achieves the optimal error rates. Our analysis is based on novel concentration bounds in an infinite dimensional Hilbert space established in this work, which may be of independent interest. We further develop an algorithm based on random exploration with domain shrinking and establish its order-optimal regret guarantees under both noise-free and noisy settings. In the noise-free setting, our analysis closes the existing gap in regret performance and thereby resolves a COLT open problem. The proposed algorithm also enjoys a computational advantage over prevailing methods due to the random exploration that obviates the expensive optimization of a non-convex acquisition function for choosing the query points at each iteration.

Parallel compressive super-resolution imaging with wide field-of-view based on physics enhanced network

Achieving both high-performance and wide field-of-view (FOV) super-resolution imaging has been attracting increasing attention in recent years. However, such goal suffers from long reconstruction time and huge storage space. Parallel compressive imaging (PCI) provides an efficient solution, but the super-resolution quality and imaging speed are strongly dependent on precise optical transfer function (OTF), modulation masks and reconstruction algorithm. In this work, we propose a wide FOV parallel compressive super-resolution imaging approach based on physics enhanced network. By training the network with the prior OTF of an arbitrary 128x128-pixel region and fine-tuning the network with other OTFs within rest regions of FOV, we realize both mask optimization and super-resolution imaging with up to 1020x1500 wide FOV. Numerical simulations and practical experiments demonstrate the effectiveness and superiority of the proposed approach. We achieve high-quality reconstruction with 4x4 times super-resolution enhancement using only three designed masks to reach real-time imaging speed. The proposed approach promotes the technology of rapid imaging for super-resolution and wide FOV, ranging from infrared to Terahertz.

Can 5G NR Sidelink communications support wireless augmented reality?

Smart glasses that support augmented reality (AR) have the potential to become the consumer's primary medium of connecting to the future internet. For the best quality of user experience, AR glasses must have a small form factor and long battery life, while satisfying the data rate and latency requirements of AR applications. To extend the AR glasses' battery life, the computation and processing involved in AR may be offloaded to a companion device, such as a smartphone, through a wireless connection. Sidelink (SL), i.e., the D2D communication interface of 5G NR, is a potential candidate for this wireless link. In this paper, we use system-level simulations to analyze the feasibility of NR SL for supporting AR. Our simulator incorporates the PHY layer structure and MAC layer resource scheduling of 3GPP SL, standard 3GPP channel models, and MCS configurations. Our results suggest that the current SL standard specifications are insufficient for high-end AR use cases with heavy interaction but can support simpler previews and file transfers. We further propose two enhancements to SL resource allocation, which have the potential to offer significant performance improvements for AR applications.

Evaluating the Efficacy of Supervised Learning vs Large Language Models for Identifying Cognitive Distortions and Suicidal Risks in Chinese Social Media

Large language models, particularly those akin to the rapidly progressing GPT series, are gaining traction for their expansive influence. While there is keen interest in their applicability within medical domains such as psychology, tangible explorations on real-world data remain scant. Concurrently, users on social media platforms are increasingly vocalizing personal sentiments; under specific thematic umbrellas, these sentiments often manifest as negative emotions, sometimes escalating to suicidal inclinations. Timely discernment of such cognitive distortions and suicidal risks is crucial to effectively intervene and potentially avert dire circumstances. Our study ventured into this realm by experimenting on two pivotal tasks: suicidal risk and cognitive distortion identification on Chinese social media platforms. Using supervised learning as a baseline, we examined and contrasted the efficacy of large language models via three distinct strategies: zero-shot, few-shot, and fine-tuning. Our findings revealed a discernible performance gap between the large language models and traditional supervised learning approaches, primarily attributed to the models' inability to fully grasp subtle categories. Notably, while GPT-4 outperforms its counterparts in multiple scenarios, GPT-3.5 shows significant enhancement in suicide risk classification after fine-tuning. To our knowledge, this investigation stands as the maiden attempt at gauging large language models on Chinese social media tasks. This study underscores the forward-looking and transformative implications of using large language models in the field of psychology. It lays the groundwork for future applications in psychological research and practice.

Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals

In the contemporary landscape of social media, an alarming number of users express negative emotions, some of which manifest as strong suicidal intentions. This situation underscores a profound need for trained psychological counselors who can enact effective mental interventions. However, the development of these professionals is often an imperative but time-consuming task. Consequently, the mobilization of non-professionals or volunteers in this capacity emerges as a pressing concern. Leveraging the capabilities of artificial intelligence, and in particular, the recent advances in large language models, offers a viable solution to this challenge. This paper introduces a novel model constructed on the foundation of large language models to fully assist non-professionals in providing psychological interventions on online user discourses. This framework makes it plausible to harness the power of non-professional counselors in a meaningful way. A comprehensive study was conducted involving ten professional psychological counselors of varying expertise, evaluating the system across five critical dimensions. The findings affirm that our system is capable of analyzing patients' issues with relative accuracy and proffering professional-level strategies recommendations, thereby enhancing support for non-professionals. This research serves as a compelling validation of the application of large language models in the field of psychology and lays the groundwork for a new paradigm of community-based mental health support.

Non-parametric Probabilistic Time Series Forecasting via Innovations Representation

Probabilistic time series forecasting predicts the conditional probability distributions of the time series at a future time given past realizations. Such techniques are critical in risk-based decision-making and planning under uncertainties. Existing approaches are primarily based on parametric or semi-parametric time-series models that are restrictive, difficult to validate, and challenging to adapt to varying conditions. This paper proposes a nonparametric method based on the classic notion of {\em innovations} pioneered by Norbert Wiener and Gopinath Kallianpur that causally transforms a nonparametric random process to an independent and identical uniformly distributed {\em innovations process}. We present a machine-learning architecture and a learning algorithm that circumvent two limitations of the original Wiener-Kallianpur innovations representation: (i) the need for known probability distributions of the time series and (ii) the existence of a causal decoder that reproduces the original time series from the innovations representation. We develop a deep-learning approach and a Monte Carlo sampling technique to obtain a generative model for the predicted conditional probability distribution of the time series based on a weak notion of Wiener-Kallianpur innovations representation. The efficacy of the proposed probabilistic forecasting technique is demonstrated on a variety of electricity price datasets, showing marked improvement over leading benchmarks of probabilistic forecasting techniques.

Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach

Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data. It has attracted a growing interest in recent years due to its advantages in terms of privacy considerations, and communication resources. In FL, selected clients train their local models and send a function of the models to the server, which consumes a random processing and transmission time. The server updates the global model and broadcasts it back to the clients. The client selection problem in FL is to schedule a subset of the clients for training and transmission at each given time so as to optimize the learning performance. In this paper, we present a novel multi-armed bandit (MAB)-based approach for client selection to minimize the training latency without harming the ability of the model to generalize, that is, to provide reliable predictions for new observations. We develop a novel algorithm to achieve this goal, dubbed Bandit Scheduling for FL (BSFL). We analyze BSFL theoretically, and show that it achieves a logarithmic regret, defined as the loss of BSFL as compared to a genie that has complete knowledge about the latency means of all clients. Furthermore, simulation results using synthetic and real datasets demonstrate that BSFL is superior to existing methods.

A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret

We consider the problem of online stochastic optimization in a distributed setting with $M$ clients connected through a central server. We develop a distributed online learning algorithm that achieves order-optimal cumulative regret with low communication cost measured in the total number of bits transmitted over the entire learning horizon. This is in contrast to existing studies which focus on the offline measure of simple regret for learning efficiency. The holistic measure for communication cost also departs from the prevailing approach that \emph{separately} tackles the communication frequency and the number of bits in each communication round.

Distributed Linear Bandits under Communication Constraints

We consider distributed linear bandits where $M$ agents learn collaboratively to minimize the overall cumulative regret incurred by all agents. Information exchange is facilitated by a central server, and both the uplink and downlink communications are carried over channels with fixed capacity, which limits the amount of information that can be transmitted in each use of the channels. We investigate the regret-communication trade-off by (i) establishing information-theoretic lower bounds on the required communications (in terms of bits) for achieving a sublinear regret order; (ii) developing an efficient algorithm that achieves the minimum sublinear regret order offered by centralized learning using the minimum order of communications dictated by the information-theoretic lower bounds. For sparse linear bandits, we show a variant of the proposed algorithm offers better regret-communication trade-off by leveraging the sparsity of the problem.