FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models

Large Language models (LLMs) usually rely on extensive training datasets. In the financial domain, creating numerical reasoning datasets that include a mix of tables and long text often involves substantial manual annotation expenses. To address the limited data resources and reduce the annotation cost, we introduce FinLLMs, a method for generating financial question-answering data based on common financial formulas using Large Language Models. First, we compile a list of common financial formulas and construct a graph based on the variables these formulas employ. We then augment the formula set by combining those that share identical variables as new elements. Specifically, we explore formulas obtained by manual annotation and merge those formulas with shared variables by traversing the constructed graph. Finally, utilizing GPT-3.5, we generate financial question-answering data that encompasses both tabular information and long textual content, building on the collected formula set. Our experiments demonstrate that synthetic data generated by FinLLMs effectively enhances the performance of several large-scale numerical reasoning models in the financial domain, outperforming two established benchmark financial question-answering datasets.

Deep Learning-based Embedded Intrusion Detection System for Automotive CAN

Rising complexity of in-vehicle electronics is enabling new capabilities like autonomous driving and active safety. However, rising automation also increases risk of security threats which is compounded by lack of in-built security measures in legacy networks like CAN, allowing attackers to observe, tamper and modify information shared over such broadcast networks. Various intrusion detection approaches have been proposed to detect and tackle such threats, with machine learning models proving highly effective. However, deploying machine learning models will require high processing power through high-end processors or GPUs to perform them close to line rate. In this paper, we propose a hybrid FPGA-based ECU approach that can transparently integrate IDS functionality through a dedicated off-the-shelf hardware accelerator that implements a deep-CNN intrusion detection model. Our results show that the proposed approach provides an average accuracy of over 99% across multiple attack datasets with 0.64% false detection rates while consuming 94% less energy and achieving 51.8% reduction in per-message processing latency when compared to IDS implementations on GPUs.

CFASL: Composite Factor-Aligned Symmetry Learning for Disentanglement in Variational AutoEncoder

Symmetries of input and latent vectors have provided valuable insights for disentanglement learning in VAEs.However, only a few works were proposed as an unsupervised method, and even these works require known factor information in training data. We propose a novel method, Composite Factor-Aligned Symmetry Learning (CFASL), which is integrated into VAEs for learning symmetry-based disentanglement in unsupervised learning without any knowledge of the dataset factor information.CFASL incorporates three novel features for learning symmetry-based disentanglement: 1) Injecting inductive bias to align latent vector dimensions to factor-aligned symmetries within an explicit learnable symmetry codebook 2) Learning a composite symmetry to express unknown factors change between two random samples by learning factor-aligned symmetries within the codebook 3) Inducing group equivariant encoder and decoder in training VAEs with the two conditions. In addition, we propose an extended evaluation metric for multi-factor changes in comparison to disentanglement evaluation in VAEs. In quantitative and in-depth qualitative analysis, CFASL demonstrates a significant improvement of disentanglement in single-factor change, and multi-factor change conditions compared to state-of-the-art methods.

Bias-Conflict Sample Synthesis and Adversarial Removal Debias Strategy for Temporal Sentence Grounding in Video

Temporal Sentence Grounding in Video (TSGV) is troubled by dataset bias issue, which is caused by the uneven temporal distribution of the target moments for samples with similar semantic components in input videos or query texts. Existing methods resort to utilizing prior knowledge about bias to artificially break this uneven distribution, which only removes a limited amount of significant language biases. In this work, we propose the bias-conflict sample synthesis and adversarial removal debias strategy (BSSARD), which dynamically generates bias-conflict samples by explicitly leveraging potentially spurious correlations between single-modality features and the temporal position of the target moments. Through adversarial training, its bias generators continuously introduce biases and generate bias-conflict samples to deceive its grounding model. Meanwhile, the grounding model continuously eliminates the introduced biases, which requires it to model multi-modality alignment information. BSSARD will cover most kinds of coupling relationships and disrupt language and visual biases simultaneously. Extensive experiments on Charades-CD and ActivityNet-CD demonstrate the promising debiasing capability of BSSARD. Source codes are available at https://github.com/qzhb/BSSARD.

Notes on Information Propagation in Noisy Multichannel Data Models: Insights into Sensor Selection and Fusion in Multimodal Biomedical Applications

Multimodality and multichannel monitoring have become increasingly popular and accessible in engineering, Internet of Things, wearable devices, and biomedical applications. In these contexts, given the diverse and complex nature of data modalities, the relevance of sensor fusion and sensor selection is heightened. In this note, we study the problem of channel/modality selection and fusion from an information theoretical perspective, focusing on linear and nonlinear signal mixtures corrupted by additive Gaussian noise. We revisit and extend well-known properties of linear noisy data models in estimation and information theory, providing practical insights that assist in the decision-making process between channel (modality) selection and fusion. Using the notion of multichannel signal-to-noise ratio, we derive conditions under which, selection or fusion of multimodal/multichannel data can be beneficial or redundant. This contributes to a better understanding of how to optimize sensor fusion and selection from a theoretical standpoint, aiming to enhance multimodal/multichannel system design, especially for biomedical multichannel/multimodal applications.

Decentralized Interference-Aware Codebook Learning in Millimeter Wave MIMO Systems

Beam codebooks are integral components of the future millimeter wave (mmWave) multiple input multiple output (MIMO) system to relax the reliance on the instantaneous channel state information (CSI). The design of these codebooks, therefore, becomes one of the fundamental problems for these systems, and the well-designed codebooks play key roles in enabling efficient and reliable communications. Prior work has primarily focused on the codebook learning problem within a single cell/network and under stationary interference. In this work, we generalize the interference-aware codebook learning problem to networks with multiple cells/basestations. One of the key differences compared to the single-cell codebook learning problem is that the underlying environment becomes non-stationary, as the behavior of one base station will influence the learning of the others. Moreover, to encompass some of the challenging scenarios, information exchange between the different learning nodes is not allowed, which leads to a fully decentralized system with significantly increased learning difficulties. To tackle the non-stationarity, the averaging of the measurements is used to estimate the interference nulling performance of a particular beam, based on which a decision rule is provided. Furthermore, we theoretically justify the adoption of such estimator and prove that it is a sufficient statistic for the underlying quantity of interest in an asymptotic sense. Finally, a novel reward function based on averaging is proposed to fully decouple the learning of the multiple agents running at different nodes. Simulation results show that the developed solution is capable of learning well-shaped codebook patterns for different networks that significantly suppress the interference without information exchange, highlighting ...

Navigating the Thin Line: Examining User Behavior in Search to Detect Engagement and Backfire Effects

Opinionated users often seek information that aligns with their preexisting beliefs while dismissing contradictory evidence due to confirmation bias. This conduct hinders their ability to consider alternative stances when searching the web. Despite this, few studies have analyzed how the diversification of search results on disputed topics influences the search behavior of highly opinionated users. To this end, we present a preregistered user study (n = 257) investigating whether different levels (low and high) of bias metrics and search results presentation (with or without AI-predicted stances labels) can affect the stance diversity consumption and search behavior of opinionated users on three debated topics (i.e., atheism, intellectual property rights, and school uniforms). Our results show that exposing participants to (counter-attitudinally) biased search results increases their consumption of attitude-opposing content, but we also found that bias was associated with a trend toward overall fewer interactions within the search page. We also found that 19% of users interacted with queries and search pages but did not select any search results. When we removed these participants in a post-hoc analysis, we found that stance labels increased the diversity of stances consumed by users, particularly when the search results were biased. Our findings highlight the need for future research to explore distinct search scenario settings to gain insight into opinionated users' behavior.

Wideband Beamforming for RIS Assisted Near-Field Communications

A near-field wideband beamforming scheme is investigated for reconfigurable intelligent surface (RIS) assisted multiple-input multiple-output (MIMO) systems, in which a deep learning-based end-to-end (E2E) optimization framework is proposed to maximize the system spectral efficiency. To deal with the near-field double beam split effect, the base station is equipped with frequency-dependent hybrid precoding architecture by introducing sub-connected true time delay (TTD) units, while two specific RIS architectures, namely true time delay-based RIS (TTD-RIS) and virtual subarray-based RIS (SA-RIS), are exploited to realize the frequency-dependent passive beamforming at the RIS. Furthermore, the efficient E2E beamforming models without explicit channel state information are proposed, which jointly exploits the uplink channel training module and the downlink wideband beamforming module. In the proposed network architecture of the E2E models, the classical communication signal processing methods, i.e., polarized filtering and sparsity transform, are leveraged to develop a signal-guided beamforming network. Numerical results show that the proposed E2E models have superior beamforming performance and robustness to conventional beamforming benchmarks. Furthermore, the tradeoff between the beamforming gain and the hardware complexity is investigated for different frequency-dependent RIS architectures, in which the TTD-RIS can achieve better spectral efficiency than the SA-RIS while requiring additional energy consumption and hardware cost.

A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model

In online advertising scenario, sellers often create multiple creatives to provide comprehensive demonstrations, making it essential to present the most appealing design to maximize the Click-Through Rate (CTR). However, sellers generally struggle to consider users preferences for creative design, leading to the relatively lower aesthetics and quantities compared to Artificial Intelligence (AI)-based approaches. Traditional AI-based approaches still face the same problem of not considering user information while having limited aesthetic knowledge from designers. In fact that fusing the user information, the generated creatives can be more attractive because different users may have different preferences. To optimize the results, the generated creatives in traditional methods are then ranked by another module named creative ranking model. The ranking model can predict the CTR score for each creative considering user features. However, the two above stages are regarded as two different tasks and are optimized separately. In this paper, we proposed a new automated Creative Generation pipeline for Click-Through Rate (CG4CTR) with the goal of improving CTR during the creative generation stage. Our contributions have 4 parts: 1) The inpainting mode in stable diffusion is firstly applied to creative generation task in online advertising scene. A self-cyclic generation pipeline is proposed to ensure the convergence of training. 2) Prompt model is designed to generate individualized creatives for different user groups, which can further improve the diversity and quality. 3) Reward model comprehensively considers the multimodal features of image and text to improve the effectiveness of creative ranking task, and it is also critical in self-cyclic pipeline. 4) The significant benefits obtained in online and offline experiments verify the significance of our proposed method.

FreGrad: Lightweight and Fast Frequency-aware Diffusion Vocoder

The goal of this paper is to generate realistic audio with a lightweight and fast diffusion-based vocoder, named FreGrad. Our framework consists of the following three key components: (1) We employ discrete wavelet transform that decomposes a complicated waveform into sub-band wavelets, which helps FreGrad to operate on a simple and concise feature space, (2) We design a frequency-aware dilated convolution that elevates frequency awareness, resulting in generating speech with accurate frequency information, and (3) We introduce a bag of tricks that boosts the generation quality of the proposed model. In our experiments, FreGrad achieves 3.7 times faster training time and 2.2 times faster inference speed compared to our baseline while reducing the model size by 0.6 times (only 1.78M parameters) without sacrificing the output quality. Audio samples are available at: https://mm.kaist.ac.kr/projects/FreGrad.