OpenRaiser/Intern-Atlas huggingface dataset:
Beyond Classification: Financial Reasoning in State-of-the-Art Language Models
Venue: arXiv cs.CL 2023
Year: 2023

Large Language Models (LLMs), consisting of 100 billion or more parameters, have demonstrated remarkable ability in complex multi-step reasoning tasks. However, the application of such generic advancements has been limited to a few fields, such as clinical or legal, with the field of financial reasoning remaining largely unexplored. To the best of our knowledge, the ability of LLMs to solve financial reasoning problems has never been dealt with, and whether it can be performed at any scale remains unknown. To address this knowledge gap, this research presents a comprehensive investigation into the potential application of LLMs in the financial domain. The investigation includes a detailed exploration of a range of subjects, including task formulation, synthetic data generation, prompting methods, and evaluation capability. Furthermore, the study benchmarks various GPT variants with parameter scales ranging from 2.8B to 13B, with and without instruction tuning, on diverse dataset sizes. By analyzing the results, we reveal that the ability to generate coherent financial reasoning first emerges at 6B parameters, and continues to improve with better instruction-tuning or larger datasets. Additionally, the study provides a publicly accessible dataset named sFIOG (Synthetic-Financial Investment Opinion Generation), consisting of 11,802 synthetic investment thesis samples, to support further research in the field of financial reasoning. Overall, this research seeks to contribute to the understanding of the efficacy of language models in the field of finance, with a particular emphasis on their ability to engage in sophisticated reasoning and analysis within the context of investment decision-making.
BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms
Venue: arXiv cs.LG 2023
Year: 2023

As deep neural networks (DNNs) are being applied to a wide range of edge intelligent applications, it is critical for edge inference platforms to have both high-throughput and low-latency at the same time. Such edge platforms with multiple DNN models pose new challenges for scheduler designs. First, each request may have different service level objectives (SLOs) to improve quality of service (QoS). Second, the edge platforms should be able to efficiently schedule multiple heterogeneous DNN models so that system utilization can be improved. To meet these two goals, this paper proposes BCEdge, a novel learning-based scheduling framework that takes adaptive batching and concurrent execution of DNN inference services on edge platforms. We define a utility function to evaluate the trade-off between throughput and latency. The scheduler in BCEdge leverages maximum entropy-based deep reinforcement learning (DRL) to maximize utility by 1) co-optimizing batch size and 2) the number of concurrent models automatically. Our prototype implemented on different edge platforms shows that the proposed BCEdge enhances utility by up to 37.6% on average, compared to state-of-the-art solutions, while satisfying SLOs.
How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?
Venue: arXiv cs.CL 2023
Year: 2023

Scaling language models have revolutionized widespread NLP tasks, yet little comprehensively explored few-shot relation extraction with large language models. In this paper, we investigate principal methodologies, in-context learning and data generation, for few-shot relation extraction via GPT-3.5 through exhaustive experiments. To enhance few-shot performance, we further propose task-related instructions and schema-constrained data generation. We observe that in-context learning can achieve performance on par with previous prompt learning approaches, and data generation with the large language model can boost previous solutions to obtain new state-of-the-art few-shot results on four widely-studied relation extraction datasets. We hope our work can inspire future research for the capabilities of large language models in few-shot relation extraction. Code is available in https://github.com/zjunlp/DeepKE/tree/main/example/llm.
Connectivity Analysis of Directed Highway VANETs using Graph Theory
Venue: arXiv cs.NI 2023
Year: 2023

Graph theory is a promising approach in handling the problem of estimating the connectivity probability of vehicular ad-hoc networks (VANETs). With a communication network represented as graph, graph connectivity indicators become valid for connectivity analysis of communication networks as well. In this article, we discuss two different graph-based methods for VANETs connectivity analysis showing that they capture the same behavior as estimated using probabilistic models. The study is, then, extended to include the case of directed VANETs, resulting from the utilization of different communication ranges by different vehicles. Overall, the graph-based methods prove a robust performance, as they can be simply diversified into scenarios that are too complex to acquire a rigid probabilistic model for them.
From Words to Code: Harnessing Data for Program Synthesis from Natural Language
Venue: arXiv cs.DB 2023
Year: 2023

Creating programs to correctly manipulate data is a difficult task, as the underlying programming languages and APIs can be challenging to learn for many users who are not skilled programmers. Large language models (LLMs) demonstrate remarkable potential for generating code from natural language, but in the data manipulation domain, apart from the natural language (NL) description of the intended task, we also have the dataset on which the task is to be performed, or the "data context". Existing approaches have utilized data context in a limited way by simply adding relevant information from the input data into the prompts sent to the LLM. In this work, we utilize the available input data to execute the candidate programs generated by the LLMs and gather their outputs. We introduce semantic reranking, a technique to rerank the programs generated by LLMs based on three signals coming the program outputs: (a) semantic filtering and well-formedness based score tuning: do programs even generate well-formed outputs, (b) semantic interleaving: how do the outputs from different candidates compare to each other, and (c) output-based score tuning: how do the outputs compare to outputs predicted for the same task. We provide theoretical justification for semantic interleaving. We also introduce temperature mixing, where we combine samples generated by LLMs using both high and low temperatures. We extensively evaluate our approach in three domains, namely databases (SQL), data science (Pandas) and business intelligence (Excel's Power Query M) on a variety of new and existing benchmarks. We observe substantial gains across domains, with improvements of up to 45% in top-1 accuracy and 34% in top-3 accuracy.
On the Collaborative Object Transportation Using Leader Follower Approach
Venue: arXiv cs.RO 2023
Year: 2023

In this paper we address the multi-agent collaborative object transportation problem in a partially known environment with obstacles under a specified goal condition. We propose a leader follower approach for two mobile manipulators collaboratively transporting an object along specified desired trajectories. The proposed approach treats the mobile manipulation system as two independent subsystems: a mobile platform and a manipulator arm and uses their kinematics model for trajectory tracking. In this work we considered that the mobile platform is subject to non-holonomic constraints, with a manipulator carrying a rigid load. The desired trajectories of the end points of the load are obtained from Probabilistic RoadMap-based planning approach. Our method combines Proportional Navigation Guidance-based approach with a proposed Stop-and-Sync algorithm to reach sufficiently close to the desired trajectory, the deviation due to the non-holonomic constraints is compensated by the manipulator arm. A leader follower approach for computing inverse kinematics solution for the position of the end-effector of the manipulator arm is proposed to maintain the load rigidity. Further, we compare the proposed approach with other approaches to analyse the efficacy of our algorithm.
Predicting blood pressure under circumstances of missing data: An analysis of missing data patterns and imputation methods using NHANES
Venue: arXiv cs.LG 2023
Year: 2023

The World Health Organization defines cardio-vascular disease (CVD) as "a group of disorders of the heart and blood vessels," including coronary heart disease and stroke (WHO 21). CVD is affected by "intermediate risk factors" such as raised blood pressure, raised blood glucose, raised blood lipids, and obesity. These are predominantly influenced by lifestyle and behaviour, including physical inactivity, unhealthy diets, high intake of salt, and tobacco and alcohol use. However, genetics and social/environmental factors such as poverty, stress, and racism also play an important role. Researchers studying the behavioural and environmental factors associated with these "intermediate risk factors" need access to high quality and detailed information on diet and physical activity. However, missing data are a pervasive problem in clinical and public health research, affecting both randomized trials and observational studies. Reasons for missing data can vary substantially across studies because of loss to follow-up, missed study visits, refusal to answer survey questions, or an unrecorded measurement during an office visit. One method of handling missing values is to simply delete observations for which there is missingness (called Complete Case Analysis). This is rarely used as deleting the data point containing missing data (List wise deletion) results in a smaller number of samples and thus affects accuracy. Additional methods of handling missing data exists, such as summarizing the variables with its observed values (Available Case Analysis). Motivated by the pervasiveness of missing data in the NHANES dataset, we will conduct an analysis of imputation methods under different simulated patterns of missing data. We will then apply these imputation methods to create a complete dataset upon which we can use ordinary least squares to predict blood pressure from diet and physical activity.
Modeling the sense of presence of remote participants in hybrid communication and its application to the design of avatar robot behavior
Venue: arXiv cs.HC 2023
Year: 2023

We formulated the sense of the presence of a remote participant in hybrid communication using a Bayesian framework. We also applied the knowledge gained from the simulation with the Bayesian model to the avatar robot's intervention behavior and encouraged the local participants to speak by intervening in the remote participant's behavior using an avatar robot. We then modeled the influence of the avatar robot's behavior on the local participants' statements using an active inference framework that included the presence of a remote participant as a latent variable. Based on the simulation results, we designed the gaze behavior of an avatar robot. Finally, we examined the effectiveness of the designed gaze behavior of the avatar robot. The gaze behavior expressed more of the remote participant's attention and interest in local participants, but local participants expressed fewer opinions in the meeting tasks. The results suggest that gaze behavior increased the presence of the remote participant and discouraged the local participant from speaking in the context of the experimental task. We believe that presence has a sufficiently large influence on whether participants want to express an opinion. It is worth investigating the influence of presence and its control methods using Bayesian models.
Predict NAS Multi-Task by Stacking Ensemble Models using GP-NAS
Venue: arXiv cs.LG 2023
Year: 2023

Accurately predicting the performance of architecture with small sample training is an important but not easy task. How to analysis and train dataset to overcome overfitting is the core problem we should deal with. Meanwhile if there is the mult-task problem, we should also think about if we can take advantage of their correlation and estimate as fast as we can. In this track, Super Network builds a search space based on ViT-Base. The search space contain depth, num-heads, mpl-ratio and embed-dim. What we done firstly are pre-processing the data based on our understanding of this problem which can reduce complexity of problem and probability of over fitting. Then we tried different kind of models and different way to combine them. Finally we choose stacking ensemble models using GP-NAS with cross validation. Our stacking model ranked 1st in CVPR 2022 Track 2 Challenge.
Exploring Xenophobic Events through GDELT Data Analysis
Venue: arXiv cs.CY 2023
Year: 2023

This study explores xenophobic events related to refugees and migration using the GDELT 2.0 database and APIs through visualizations. We conducted two case studies -- the first being an analysis of refugee-related news following the death of a two-year-old Syrian boy, Alan Kurdi, and the second a surge in news articles in March 2021 based on the data obtained from GDELT API. In addition to the two case studies, we present a discussion of our exploratory data analysis steps and the challenges encountered while working with GDELT data and its tools.
Multimodal Data Augmentation for Image Captioning using Diffusion Models
Venue: arXiv cs.CV 2023
Year: 2023

Image captioning, an important vision-language task, often requires a tremendous number of finely labeled image-caption pairs for learning the underlying alignment between images and texts. In this paper, we proposed a multimodal data augmentation method, leveraging a recent text-to-image model called Stable Diffusion, to expand the training set via high-quality generation of image-caption pairs. Extensive experiments on the MS COCO dataset demonstrate the advantages of our approach over several benchmark methods, and particularly a significant boost when having fewer training instances. In addition, models trained on our augmented datasets also outperform prior unpaired image captioning methods by a large margin. Finally, further improvement regarding the training efficiency and effectiveness can be obtained after intentionally filtering the generated data based on quality assessment.
Toward Textual Transform Coding
Venue: arXiv cs.IT 2023
Year: 2023

Inspired by recent work on compression with and for young humans, the success of transform-based approaches to information processing, and the rise of powerful language-based AI, we propose \emph{textual transform coding}. It shares some of its key properties with traditional transform-based coding underlying much of our current multimedia compression technologies. It can form the basis for compression at bit rates until recently considered uselessly low, and for boosting human satisfaction from reconstructions at more traditional bit rates.
Localization using Multi-Focal Spatial Attention for Masked Face Recognition
Venue: arXiv cs.CV 2023
Year: 2023

Since the beginning of world-wide COVID-19 pandemic, facial masks have been recommended to limit the spread of the disease. However, these masks hide certain facial attributes. Hence, it has become difficult for existing face recognition systems to perform identity verification on masked faces. In this context, it is necessary to develop masked Face Recognition (MFR) for contactless biometric recognition systems. Thus, in this paper, we propose Complementary Attention Learning and Multi-Focal Spatial Attention that precisely removes masked region by training complementary spatial attention to focus on two distinct regions: masked regions and backgrounds. In our method, standard spatial attention and networks focus on unmasked regions, and extract mask-invariant features while minimizing the loss of the conventional Face Recognition (FR) performance. For conventional FR, we evaluate the performance on the IJB-C, Age-DB, CALFW, and CPLFW datasets. We evaluate the MFR performance on the ICCV2021-MFR/Insightface track, and demonstrate the improved performance on the both MFR and FR datasets. Additionally, we empirically verify that spatial attention of proposed method is more precisely activated in unmasked regions.
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization
Venue: arXiv cs.CL 2023
Year: 2023

Recent pre-trained language models (PLMs) achieve promising results in existing abstractive summarization datasets. However, existing summarization benchmarks overlap in time with the standard pre-training corpora and finetuning datasets. Hence, the strong performance of PLMs may rely on the parametric knowledge that is memorized during pre-training and fine-tuning. Moreover, the knowledge memorized by PLMs may quickly become outdated, which affects the generalization performance of PLMs on future data. In this work, we propose TempoSum, a novel benchmark that contains data samples from 2010 to 2022, to understand the temporal generalization ability of abstractive summarization models. Through extensive human evaluation, we show that parametric knowledge stored in summarization models significantly affects the faithfulness of the generated summaries on future data. Moreover, existing faithfulness enhancement methods cannot reliably improve the faithfulness of summarization models on future data. Finally, we discuss several recommendations to the research community on how to evaluate and improve the temporal generalization capability of text summarization models.
SeqAug: Sequential Feature Resampling as a modality agnostic augmentation method
Venue: arXiv cs.CL 2023
Year: 2023

Data augmentation is a prevalent technique for improving performance in various machine learning applications. We propose SeqAug, a modality-agnostic augmentation method that is tailored towards sequences of extracted features. The core idea of SeqAug is to augment the sequence by resampling from the underlying feature distribution. Resampling is performed by randomly selecting feature dimensions and permuting them along the temporal axis. Experiments on CMU-MOSEI verify that SeqAug is modality agnostic; it can be successfully applied to a single modality or multiple modalities. We further verify its compatibility with both recurrent and transformer architectures, and also demonstrate comparable to state-of-the-art results.
Neural Network Training and Non-Differentiable Objective Functions
Venue: arXiv cs.CV 2023
Year: 2023

Many important computer vision tasks are naturally formulated to have a non-differentiable objective. Therefore, the standard, dominant training procedure of a neural network is not applicable since back-propagation requires the gradients of the objective with respect to the output of the model. Most deep learning methods side-step the problem sub-optimally by using a proxy loss for training, which was originally designed for another task and is not tailored to the specifics of the objective. The proxy loss functions may or may not align well with the original non-differentiable objective. An appropriate proxy has to be designed for a novel task, which may not be feasible for a non-specialist. This thesis makes four main contributions toward bridging the gap between the non-differentiable objective and the training loss function. Throughout the thesis, we refer to a loss function as a surrogate loss if it is a differentiable approximation of the non-differentiable objective. Note that we use the terms objective and evaluation metric interchangeably. The contributions of this thesis make the training of neural networks more scalable -- to new tasks in a nearly labor-free manner when the evaluation metric is decomposable, which will help researchers with novel tasks. For non-decomposable evaluation metrics, the differentiable components developed for the recall@k surrogate, such as sorting and counting, can also be used for creating new surrogates.
Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks using Novel Sterile Training Technique
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we investigate novel data collection and training techniques towards improving classification accuracy of non-moving (static) hand gestures using a convolutional neural network (CNN) and frequency-modulated-continuous-wave (FMCW) millimeter-wave (mmWave) radars. Recently, non-contact hand pose and static gesture recognition have received considerable attention in many applications ranging from human-computer interaction (HCI), augmented/virtual reality (AR/VR), and even therapeutic range of motion for medical applications. While most current solutions rely on optical or depth cameras, these methods require ideal lighting and temperature conditions. mmWave radar devices have recently emerged as a promising alternative offering low-cost system-on-chip sensors whose output signals contain precise spatial information even in non-ideal imaging conditions. Additionally, deep convolutional neural networks have been employed extensively in image recognition by learning both feature extraction and classification simultaneously. However, little work has been done towards static gesture recognition using mmWave radars and CNNs due to the difficulty involved in extracting meaningful features from the radar return signal, and the results are inferior compared with dynamic gesture classification. This article presents an efficient data collection approach and a novel technique for deep CNN training by introducing ``sterile'' images which aid in distinguishing distinct features among the static gestures and subsequently improve the classification accuracy. Applying the proposed data collection and training methods yields an increase in classification rate of static hand gestures from $85\%$ to $93\%$ and $90\%$ to $95\%$ for range and range-angle profiles, respectively.
Contact Edit: Artist Tools for Intuitive Modeling of Hand-Object Interactions
Venue: arXiv cs.GR 2023
Year: 2023

Posing high-contact interactions is challenging and time-consuming, with hand-object interactions being especially difficult due to the large number of degrees of freedom (DOF) of the hand and the fact that humans are experts at judging hand poses. This paper addresses this challenge by elevating contact areas to first-class primitives. We provide \textit{end-to-end art-directable} (EAD) tools to model interactions based on contact areas, directly manipulate contact areas, and compute corresponding poses automatically. To make these operations intuitive and fast, we present a novel axis-based contact model that supports real-time approximately isometry-preserving operations on triangulated surfaces, permits movement between surfaces, and is both robust and scalable to large areas. We show that use of our contact model facilitates high quality posing even for unconstrained, high-DOF custom rigs intended for traditional keyframe-based animation pipelines. We additionally evaluate our approach with comparisons to prior art, ablation studies, user studies, qualitative assessments, and extensions to full-body interaction.