OpenRaiser/Intern-Atlas huggingface dataset:
Investigating the changes in BOLD responses during viewing of images with varied complexity: An fMRI time-series based analysis on human vision
Venue: arXiv cs.CV 2023
Year: 2023

Functional MRI (fMRI) is widely used to examine brain functionality by detecting alteration in oxygenated blood flow that arises with brain activity. This work aims to investigate the neurological variation of human brain responses during viewing of images with varied complexity using fMRI time series (TS) analysis. Publicly available BOLD5000 dataset is used for this purpose which contains fMRI scans while viewing 5254 distinct images of diverse categories, drawn from three standard computer vision datasets: COCO, Imagenet and SUN. To understand vision, it is important to study how brain functions while looking at images of diverse complexities. Our first study employs classical machine learning and deep learning strategies to classify image complexity-specific fMRI TS, represents instances when images from COCO, Imagenet and SUN datasets are seen. The implementation of this classification across visual datasets holds great significance, as it provides valuable insights into the fluctuations in BOLD signals when perceiving images of varying complexities. Subsequently, temporal semantic segmentation is also performed on whole fMRI TS to segment these time instances. The obtained result of this analysis has established a baseline in studying how differently human brain functions while looking into images of diverse complexities. Therefore, accurate identification and distinguishing of variations in BOLD signals from fMRI TS data serves as a critical initial step in vision studies, providing insightful explanations for how static images with diverse complexities are perceived.
AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices
Venue: arXiv cs.DC 2023
Year: 2023

Mobile video applications today have attracted significant attention. Deep learning model (e.g. deep neural network, DNN) compression is widely used to enable on-device inference for facilitating robust and private mobile video applications. The compressed DNN, however, is vulnerable to the agnostic data drift of the live video captured from the dynamically changing mobile scenarios. To combat the data drift, mobile ends rely on edge servers to continuously evolve and re-compress the DNN with freshly collected data. We design a framework, AdaEvo, that efficiently supports the resource-limited edge server handling mobile DNN evolution tasks from multiple mobile ends. The key goal of AdaEvo is to maximize the average quality of experience (QoE), e.g. the proportion of high-quality DNN service time to the entire life cycle, for all mobile ends. Specifically, it estimates the DNN accuracy drops at the mobile end without labels and performs a dedicated video frame sampling strategy to control the size of retraining data. In addition, it balances the limited computing and memory resources on the edge server and the competition between asynchronous tasks initiated by different mobile users. With an extensive evaluation of real-world videos from mobile scenarios and across four diverse mobile tasks, experimental results show that AdaEvo enables up to 34% accuracy improvement and 32% average QoE improvement.
SAF-Net: Self-Attention Fusion Network for Myocardial Infarction Detection using Multi-View Echocardiography
Venue: arXiv cs.LG 2023
Year: 2023

Myocardial infarction (MI) is a severe case of coronary artery disease (CAD) and ultimately, its detection is substantial to prevent progressive damage to the myocardium. In this study, we propose a novel view-fusion model named self-attention fusion network (SAF-Net) to detect MI from multi-view echocardiography recordings. The proposed framework utilizes apical 2-chamber (A2C) and apical 4-chamber (A4C) view echocardiography recordings for classification. Three reference frames are extracted from each recording of both views and deployed pre-trained deep networks to extract highly representative features. The SAF-Net model utilizes a self-attention mechanism to learn dependencies in extracted feature vectors. The proposed model is computationally efficient thanks to its compact architecture having three main parts: a feature embedding to reduce dimensionality, self-attention for view-pooling, and dense layers for the classification. Experimental evaluation is performed using the HMC-QU-TAU dataset which consists of 160 patients with A2C and A4C view echocardiography recordings. The proposed SAF-Net model achieves a high-performance level with 88.26% precision, 77.64% sensitivity, and 78.13% accuracy. The results demonstrate that the SAF-Net model achieves the most accurate MI detection over multi-view echocardiography recordings.
P2I-NET: Mapping Camera Pose to Image via Adversarial Learning for New View Synthesis in Real Indoor Environments
Venue: arXiv cs.CV 2023
Year: 2023

Given a new $6DoF$ camera pose in an indoor environment, we study the challenging problem of predicting the view from that pose based on a set of reference RGBD views. Existing explicit or implicit 3D geometry construction methods are computationally expensive while those based on learning have predominantly focused on isolated views of object categories with regular geometric structure. Differing from the traditional \textit{render-inpaint} approach to new view synthesis in the real indoor environment, we propose a conditional generative adversarial neural network (P2I-NET) to directly predict the new view from the given pose. P2I-NET learns the conditional distribution of the images of the environment for establishing the correspondence between the camera pose and its view of the environment, and achieves this through a number of innovative designs in its architecture and training lost function. Two auxiliary discriminator constraints are introduced for enforcing the consistency between the pose of the generated image and that of the corresponding real world image in both the latent feature space and the real world pose space. Additionally a deep convolutional neural network (CNN) is introduced to further reinforce this consistency in the pixel space. We have performed extensive new view synthesis experiments on real indoor datasets. Results show that P2I-NET has superior performance against a number of NeRF based strong baseline models. In particular, we show that P2I-NET is 40 to 100 times faster than these competitor techniques while synthesising similar quality images. Furthermore, we contribute a new publicly available indoor environment dataset containing 22 high resolution RGBD videos where each frame also has accurate camera pose parameters.
From LAION-5B to LAION-EO: Filtering Billions of Images Using Anchor Datasets for Satellite Image Extraction
Venue: arXiv cs.CV 2023
Year: 2023

Large datasets, such as LAION-5B, contain a diverse distribution of images shared online. However, extraction of domain-specific subsets of large image corpora is challenging. The extraction approach based on an anchor dataset, combined with further filtering, is proposed here and demonstrated for the domain of satellite imagery. This results in the release of LAION-EO, a dataset sourced from the web containing pairs of text and satellite images in high (pixel-wise) resolution. The paper outlines the acquisition procedure as well as some of the features of the dataset.
Direct Models for Simultaneous Translation and Automatic Subtitling: FBK@IWSLT2023
Venue: arXiv cs.CL 2023
Year: 2023

This paper describes the FBK's participation in the Simultaneous Translation and Automatic Subtitling tracks of the IWSLT 2023 Evaluation Campaign. Our submission focused on the use of direct architectures to perform both tasks: for the simultaneous one, we leveraged the knowledge already acquired by offline-trained models and directly applied a policy to obtain the real-time inference; for the subtitling one, we adapted the direct ST model to produce well-formed subtitles and exploited the same architecture to produce timestamps needed for the subtitle synchronization with audiovisual content. Our English-German SimulST system shows a reduced computational-aware latency compared to the one achieved by the top-ranked systems in the 2021 and 2022 rounds of the task, with gains of up to 3.5 BLEU. Our automatic subtitling system outperforms the only existing solution based on a direct system by 3.7 and 1.7 SubER in English-German and English-Spanish respectively.
Developing automatic verbatim transcripts for international multilingual meetings: an end-to-end solution
Venue: arXiv cs.CL 2023
Year: 2023

This paper presents an end-to-end solution for the creation of fully automated conference meeting transcripts and their machine translations into various languages. This tool has been developed at the World Intellectual Property Organization (WIPO) using in-house developed speech-to-text (S2T) and machine translation (MT) components. Beyond describing data collection and fine-tuning, resulting in a highly customized and robust system, this paper describes the architecture and evolution of the technical components as well as highlights the business impact and benefits from the user side. We also point out particular challenges in the evolution and adoption of the system and how the new approach created a new product and replaced existing established workflows in conference management documentation.
Utilization of machine learning for the detection of self-admitted vulnerabilities
Venue: arXiv cs.SE 2023
Year: 2023

Motivation: Technical debt is a metaphor that describes not-quite-right code introduced for short-term needs. Developers are aware of it and admit it in source code comments, which is called Self- Admitted Technical Debt (SATD). Therefore, SATD indicates weak code that developers are aware of. Problem statement: Inspecting source code is time-consuming; automatically inspecting source code for its vulnerabilities is a crucial aspect of developing software. It helps practitioners reduce the time-consuming process and focus on vulnerable aspects of the source code. Proposal: Accurately identify and better understand the semantics of self-admitted technical debt (SATD) by leveraging NLP and NL-PL approaches to detect vulnerabilities and the related SATD. Finally, a CI/CD pipeline will be proposed to make the vulnerability discovery process easily accessible to practitioners.
NLPBench: Evaluating Large Language Models on Solving NLP Problems
Venue: arXiv cs.CL 2023
Year: 2023

Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP). Despite these successes, there remains a dearth of research dedicated to the NLP problem-solving abilities of LLMs. To fill the gap in this area, we present a unique benchmarking dataset, NLPBench, comprising 378 college-level NLP questions spanning various NLP topics sourced from Yale University's prior final exams. NLPBench includes questions with context, in which multiple sub-questions share the same public information, and diverse question types, including multiple choice, short answer, and math. Our evaluation, centered on LLMs such as GPT-3.5/4, PaLM-2, and LLAMA-2, incorporates advanced prompting strategies like the chain-of-thought (CoT) and tree-of-thought (ToT). Our study reveals that the effectiveness of the advanced prompting strategies can be inconsistent, occasionally damaging LLM performance, especially in smaller models like the LLAMA-2 (13b). Furthermore, our manual assessment illuminated specific shortcomings in LLMs' scientific problem-solving skills, with weaknesses in logical decomposition and reasoning notably affecting results.
Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems
Venue: arXiv cs.IR 2023
Year: 2023

Cold-start recommendation is one of the major challenges faced by recommender systems (RS). Herein, we focus on the user cold-start problem. Recently, methods utilizing side information or meta-learning have been used to model cold-start users. However, it is difficult to deploy these methods to industrial RS. There has not been much research that pays attention to the user cold-start problem in the matching stage. In this paper, we propose Cold & Warm Net based on expert models who are responsible for modeling cold-start and warm-up users respectively. A gate network is applied to incorporate the results from two experts. Furthermore, dynamic knowledge distillation acting as a teacher selector is introduced to assist experts in better learning user representation. With comprehensive mutual information, features highly relevant to user behavior are selected for the bias net which explicitly models user behavior bias. Finally, we evaluate our Cold & Warm Net on public datasets in comparison to models commonly applied in the matching stage and it outperforms other models on all user types. The proposed model has also been deployed on an industrial short video platform and achieves a significant increase in app dwell time and user retention rate.
Joint Sampling and Optimisation for Inverse Rendering
Venue: arXiv cs.GR 2023
Year: 2023

When dealing with difficult inverse problems such as inverse rendering, using Monte Carlo estimated gradients to optimise parameters can slow down convergence due to variance. Averaging many gradient samples in each iteration reduces this variance trivially. However, for problems that require thousands of optimisation iterations, the computational cost of this approach rises quickly. We derive a theoretical framework for interleaving sampling and optimisation. We update and reuse past samples with low-variance finite-difference estimators that describe the change in the estimated gradients between each iteration. By combining proportional and finite-difference samples, we continuously reduce the variance of our novel gradient meta-estimators throughout the optimisation process. We investigate how our estimator interlinks with Adam and derive a stable combination. We implement our method for inverse path tracing and demonstrate how our estimator speeds up convergence on difficult optimisation tasks.
Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python
Venue: arXiv cs.SE 2023
Year: 2023

Machine learning (ML) has the potential to revolutionize a wide range of research areas and industries, but many ML projects never progress past the proof-of-concept stage. To address this issue, we introduce Model Share AI (AIMS), an easy-to-use MLOps platform designed to streamline collaborative model development, model provenance tracking, and model deployment, as well as a host of other functions aiming to maximize the real-world impact of ML research. AIMS features collaborative project spaces and a standardized model evaluation process that ranks model submissions based on their performance on unseen evaluation data, enabling collaborative model development and crowd-sourcing. Model performance and various model metadata are automatically captured to facilitate provenance tracking and allow users to learn from and build on previous submissions. Additionally, AIMS allows users to deploy ML models built in Scikit-Learn, TensorFlow Keras, PyTorch, and ONNX into live REST APIs and automatically generated web apps with minimal code. The ability to deploy models with minimal effort and to make them accessible to non-technical end-users through web apps has the potential to make ML research more applicable to real-world challenges.
Making Logical Relations More Relatable (Proof Pearl)
Venue: arXiv cs.PL 2023
Year: 2023

Mechanical proofs by logical relations often involve tedious reasoning about substitution. In this paper, we show that this is not necessarily the case, by developing, in Agda, a proof that all simply typed lambda calculus expressions evaluate to values. A formalization of the proof is remarkably short (~40 lines of code), making for an excellent introduction to the technique of proofs by logical relations not only on paper but also in a mechanized setting. We then show that this process extends to more sophisticated reasoning by also proving the totality of normalization by evaluation. Although these proofs are not new, we believe presenting them will empower both new and experienced programming language theorists in their use of logical relations.
Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

We develop a neural network architecture which, trained in an unsupervised manner as a denoising diffusion model, simultaneously learns to both generate and segment images. Learning is driven entirely by the denoising diffusion objective, without any annotation or prior knowledge about regions during training. A computational bottleneck, built into the neural architecture, encourages the denoising network to partition an input into regions, denoise them in parallel, and combine the results. Our trained model generates both synthetic images and, by simple examination of its internal predicted partitions, a semantic segmentation of those images. Without any finetuning, we directly apply our unsupervised model to the downstream task of segmenting real images via noising and subsequently denoising them. Experiments demonstrate that our model achieves accurate unsupervised image segmentation and high-quality synthetic image generation across multiple datasets.
Temporal graph models fail to capture global temporal dynamics
Venue: arXiv cs.IR 2023
Year: 2023

A recently released Temporal Graph Benchmark is analyzed in the context of Dynamic Link Property Prediction. We outline our observations and propose a trivial optimization-free baseline of "recently popular nodes" outperforming other methods on medium and large-size datasets in the Temporal Graph Benchmark. We propose two measures based on Wasserstein distance which can quantify the strength of short-term and long-term global dynamics of datasets. By analyzing our unexpectedly strong baseline, we show how standard negative sampling evaluation can be unsuitable for datasets with strong temporal dynamics. We also show how simple negative-sampling can lead to model degeneration during training, resulting in impossible to rank, fully saturated predictions of temporal graph networks. We propose improved negative sampling schemes for both training and evaluation and prove their usefulness. We conduct a comparison with a model trained non-contrastively without negative sampling. Our results provide a challenging baseline and indicate that temporal graph network architectures need deep rethinking for usage in problems with significant global dynamics, such as social media, cryptocurrency markets or e-commerce. We open-source the code for baselines, measures and proposed negative sampling schemes.
Importance-Weighted Offline Learning Done Right
Venue: arXiv cs.LG 2023
Year: 2023

We study the problem of offline policy optimization in stochastic contextual bandit problems, where the goal is to learn a near-optimal policy based on a dataset of decision data collected by a suboptimal behavior policy. Rather than making any structural assumptions on the reward function, we assume access to a given policy class and aim to compete with the best comparator policy within this class. In this setting, a standard approach is to compute importance-weighted estimators of the value of each policy, and select a policy that minimizes the estimated value up to a "pessimistic" adjustment subtracted from the estimates to reduce their random fluctuations. In this paper, we show that a simple alternative approach based on the "implicit exploration" estimator of \citet{Neu2015} yields performance guarantees that are superior in nearly all possible terms to all previous results. Most notably, we remove an extremely restrictive "uniform coverage" assumption made in all previous works. These improvements are made possible by the observation that the upper and lower tails importance-weighted estimators behave very differently from each other, and their careful control can massively improve on previous results that were all based on symmetric two-sided concentration inequalities. We also extend our results to infinite policy classes in a PAC-Bayesian fashion, and showcase the robustness of our algorithm to the choice of hyper-parameters by means of numerical simulations.
Model-based design of temporal analysis of products (TAP) reactors: A simulated case study in oxidative propane dehydrogenation
Venue: arXiv cs.CE 2023
Year: 2023

Temporal analysis of products (TAP) reactors enable experiments that probe numerous kinetic processes within a single set of experimental data through variations in pulse intensity, delay, or temperature. Selecting additional TAP experiments often involves arbitrary selection of reaction conditions or the use of chemical intuition. To make experiment selection in TAP more robust, we explore the efficacy of model-based design of experiments (MBDoE) for precision in TAP reactor kinetic modeling. We successfully applied this approach to a case study of synthetic oxidative propane dehydrogenation (OPDH) that involves pulses of propane and oxygen. We found that experiments identified as optimal through the MBDoE for precision generally reduce parameter uncertainties to a higher degree than alternative experiments. The performance of MBDoE for model divergence was also explored for OPDH, with the relevant active sites (catalyst structure) being unknown. An experiment that maximized the divergence between the three proposed mechanisms was identified and led to clear mechanism discrimination. However, re-optimization of kinetic parameters eliminated the ability to discriminate. The findings yield insight into the prospects and limitations of MBDoE for TAP and transient kinetic experiments.
Large Language Model Routing with Benchmark Datasets
Venue: arXiv cs.CL 2023
Year: 2023

There is a rapidly growing number of open-source Large Language Models (LLMs) and benchmark datasets to compare them. While some models dominate these benchmarks, no single model typically achieves the best accuracy in all tasks and use cases. In this work, we address the challenge of selecting the best LLM out of a collection of models for new tasks. We propose a new formulation for the problem, in which benchmark datasets are repurposed to learn a "router" model for this LLM selection, and we show that this problem can be reduced to a collection of binary classification tasks. We demonstrate the utility and limitations of learning model routers from various benchmark datasets, where we consistently improve performance upon using any single model for all tasks.
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
Venue: arXiv cs.CV 2023
Year: 2023

Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face challenges when it comes to generating highly aesthetic images. This creates the need for aesthetic alignment post pre-training. In this paper, we propose quality-tuning to effectively guide a pre-trained model to exclusively generate highly visually appealing images, while maintaining generality across visual concepts. Our key insight is that supervised fine-tuning with a set of surprisingly small but extremely visually appealing images can significantly improve the generation quality. We pre-train a latent diffusion model on $1.1$ billion image-text pairs and fine-tune it with only a few thousand carefully selected high-quality images. The resulting model, Emu, achieves a win rate of $82.9\%$ compared with its pre-trained only counterpart. Compared to the state-of-the-art SDXLv1.0, Emu is preferred $68.4\%$ and $71.3\%$ of the time on visual appeal on the standard PartiPrompts and our Open User Input benchmark based on the real-world usage of text-to-image models. In addition, we show that quality-tuning is a generic approach that is also effective for other architectures, including pixel diffusion and masked generative transformer models.