Objectively-measured and Subjectively-perceived Financial Literacy

This dissertation consists of three chapters on financial literacy. The chapters examine the impact of financial literacy on households’ risk tolerance and financial planning behavior. Financial literacy was assessed in two dimensions: an objectively-measured financial literacy score, and a subjectively-perceived financial literacy level. The first chapter reviews the literature measuring financial literacy, and raises concerns about the prevalent state of financial-literacy overconfidence, which can lead to underprepared financial planning and irrational financial behavior. This chapter uses the survey data from the Financial Regulatory Authority’s (FINRAs) 2009, 2012 and 2015 National Financial Capability Study (NFCS) to explore how individual and regional characteristics can explain the extent of differences in financial literacy overconfidence. The second chapter examines the role of risk tolerance in the association between financial literacy and financial planning behavior, using NFCS’s three waves survey. The results reveal that both financial literacy (objectively-measured and subjectively-perceived) and risk tolerance are positively associated with financial planning behavior, and that financial literacy can be employed as an effective tool to alleviate individuals’ perception bias in risk tolerance. This chapter uses structural equation models (SEM) with latent variables to extend mediation analysis for the categorical mediator (risk tolerance) and outcomes (financial planning behaviors). The results further underscore the essential mediator role of risk tolerance between financial literacy and financial planning behavior, implying that the current financial literacy program should put more focus on helping participants perceive the optimal degree of risk tolerance. The third chapter uses data from the 2013 Chinese Household Finance Survey (CHFS) to explore the impact of financial literacy on Chinese households’ retirement planning and the demand for commercial[1]insurance. Particular attention is paid to financially excluded subpopulations (rural, illiterate and migrant). These subpopulations are vulnerable concerning the social pension system coverage. The chapter finds that improving financial literacy could help Chinese households better prepare for retirement. Households with a higher objective financial literacy score and who paid more attention to financial information are more likely to have a retirement plan, and have diversified ways to support life after retirement. Moreover, annual household disposable income, family net assets, gender, age, age-squared, family size, number of children, health condition are significant factors explaining how Chinese households choose different ways to support their lives after retirement. Rural and illiterate households depend more on saving and child support after retirement, while urban and literate households are more likely to rely on the social pension plan and retirement pay. Degrees of trust in commercial pension plans is the critical factor determining whether Chinese households are willing to buy commercial insurance and pension plans. This choice can be improved by means of increasing financial knowledge and paying more attention to financial information. [1] Commercial insurance and commercial pension plan in this chapter refers to the health insurance, life insurance and pension plan that Chinese households purchased individually from a commercial company but not received directly from government. The counterpart in the U.S.is the same health insurance, life insurance and pension plan purchased from the marketplace, but not directly covered by an employer or the government

A Novel Joint Gene Set Analysis Framework Improves Identification of Enriched Pathways in Cross Disease Transcriptomic Analysis

Motivation: Gene set enrichment analysis is a widely accepted expression analysis tool which aims at detecting coordinated expression change within a pre-defined gene sets rather than individual genes. The benefit of gene set analysis over individual differentially expressed (DE) gene analysis includes more reproducible and interpretable results and detecting small but consistent change among gene set which could not be detected by DE gene analysis. There have been many successful gene set analysis applications in human diseases. However, when the sample size of a disease study is small and no other public data sets of the same disease are available, it will lead to lack of power to detect pathways of importance to the disease.Results: We have developed a novel joint gene set analysis statistical framework which aims at improving the power of identifying enriched gene sets through integrating multiple similar disease data sets. Through comprehensive simulation studies, we demonstrated that our proposed frameworks obtained much better AUC scores than single data set analysis and another meta-analysis method in identification of enriched pathways. When applied to two real data sets, the proposed framework could retain the enriched gene sets identified by single data set analysis and exclusively obtained up to 200% more disease-related gene sets demonstrating the improved identification power through information shared between similar diseases. We expect that the proposed framework would enable researchers to better explore public data sets when the sample size of their study is limited

Connecting Speech Encoder and Large Language Model for ASR

The impressive capability and versatility of large language models (LLMs) have aroused increasing attention in automatic speech recognition (ASR), with several pioneering studies attempting to build integrated ASR models by connecting a speech encoder with an LLM. This paper presents a comparative study of three commonly used structures as connectors, including fully connected layers, multi-head cross-attention, and Q-Former. Speech encoders from the Whisper model series as well as LLMs from the Vicuna model series with different model sizes were studied. Experiments were performed on the commonly used LibriSpeech, Common Voice, and GigaSpeech datasets, where the LLMs with Q-Formers demonstrated consistent and considerable word error rate (WER) reductions over LLMs with other connector structures. Q-Former-based LLMs can generalise well to out-of-domain datasets, where 12% relative WER reductions over the Whisper baseline ASR model were achieved on the Eval2000 test set without using any in-domain training data from Switchboard. Moreover, a novel segment-level Q-Former is proposed to enable LLMs to recognise speech segments with a duration exceeding the limitation of the encoders, which results in 17% relative WER reductions over other connector structures on 90-second-long speech data

SALMONN: Towards Generic Hearing Abilities for Large Language Models

Hearing is arguably an essential ability of artificial intelligence (AI) agents in the physical world, which refers to the perception and understanding of general auditory information consisting of at least three types of sounds: speech, audio events, and music. In this paper, we propose SALMONN, a speech audio language music open neural network, built by integrating a pre-trained text-based large language model (LLM) with speech and audio encoders into a single multimodal model. SALMONN enables the LLM to directly process and understand general audio inputs and achieve competitive performances on a number of speech and audio tasks used in training, such as automatic speech recognition and translation, auditory-information-based question answering, emotion recognition, speaker verification, and music and audio captioning etc. SALMONN also has a diverse set of emergent abilities unseen in the training, which includes but is not limited to speech translation to untrained languages, speech-based slot filling, spoken-query-based question answering, audio-based storytelling, and speech audio co-reasoning etc. The presence of cross-modal emergent abilities is studied, and a novel few-shot activation tuning approach is proposed to activate such abilities. To our knowledge, SALMONN is the first model of its type and can be regarded as a step towards AI with generic hearing abilities. The source code, model checkpoints and data are available at https://github.com/bytedance/SALMONN

Fine-grained Audio-Visual Joint Representations for Multimodal Large Language Models

Audio-visual large language models (LLM) have drawn significant attention, yet the fine-grained combination of both input streams is rather under-explored, which is challenging but necessary for LLMs to understand general video inputs. To this end, a fine-grained audio-visual joint representation (FAVOR) learning framework for multimodal LLMs is proposed in this paper, which extends a text-based LLM to simultaneously perceive speech and audio events in the audio input stream and images or videos in the visual input stream, at the frame level. To fuse the audio and visual feature streams into joint representations and to align the joint space with the LLM input embedding space, we propose a causal Q-Former structure with a causal attention module to enhance the capture of causal relations of the audio-visual frames across time. An audio-visual evaluation benchmark (AVEB) is also proposed which comprises six representative single-modal tasks with five cross-modal tasks reflecting audio-visual co-reasoning abilities. While achieving competitive single-modal performance on audio, speech and image tasks in AVEB, FAVOR achieved over 20% accuracy improvements on the video question-answering task when fine-grained information or temporal causal reasoning is required. FAVOR, in addition, demonstrated remarkable video comprehension and reasoning abilities on tasks that are unprecedented by other multimodal LLMs. An interactive demo of FAVOR is available at https://github.com/BriansIDP/AudioVisualLLM.git, and the training code and model checkpoints will be released soon

Image-Guided Autonomous Guidewire Navigation in Robot-Assisted Endovascular Interventions using Reinforcement Learning

Autonomous robots in endovascular interventions possess the potential to navigate guidewires with safety and reliability, while reducing human error and shortening surgical time. However, current methods of guidewire navigation based on Reinforcement Learning (RL) depend on manual demonstration data or magnetic guidance. In this work, we propose an Image-guided Autonomous Guidewire Navigation (IAGN) method. Specifically, we introduce BDA-star, a path planning algorithm with boundary distance constraints, for the trajectory planning of guidewire navigation. We established an IAGN-RL environment where the observations are real-time guidewire feeding images highlighting the position of the guidewire tip and the planned path. We proposed a reward function based on the distances from both the guidewire tip to the planned path and the target to evaluate the agent's actions. Furthermore, in policy network, we employ a pre-trained convolutional neural network to extract features, mitigating stability issues and slow convergence rates associated with direct learning from raw pixels. Experiments conducted on the aortic simulation IAGN platform demonstrated that the proposed method, targeting the left subclavian artery and the brachiocephalic artery, achieved a 100% guidewire navigation success rate, along with reduced movement and retraction distances and trajectories tend to the center of the vessels

Quantum storage of entangled photons at telecom wavelengths in a crystal

The quantum internet -- in synergy with the internet that we use today -- promises an enabling platform for next-generation information processing, including exponentially speed-up distributed computation, secure communication, and high-precision metrology. The key ingredients for realizing such a global network are the distribution and storage of quantum entanglement. As quantum networks are likely to be based on existing fibre networks, telecom-wavelength entangled photons and corresponding quantum memories are of central interest. Recently, ${\rm ^{167}Er^{3+}}$ ions have been identified as a promising candidate for an efficient, broadband quantum memory at telecom wavelength. However, to date, no storage of entangled photons, the crucial step of quantum memory using these ions, has been reported. Here, we demonstrate the storage and recall of the entangled state of two telecom photons generated from an integrated photonic chip based on silicon nitride. Combining the natural narrow linewidth of the entangled photons and long storage time of ${\rm ^{167}Er^{3+}}$ ions, we achieve storage time of 400 ns, more than one order of magnitude longer than in previous works. Successful storage of entanglement in the crystal is certified by a violation of an entanglement witness by more than 12 standard deviations (-0.161 $\pm$ 0.012) at 400 ns storage time. These results pave the way for realizing quantum networks based on solid-state devices.Comment: 15 pages, 11 figure