OpenRaiser/Intern-Atlas huggingface dataset:
Topological Filtering for Visual Data Mining and Analysis of Complex Networks
Venue: arXiv cs.SI 2023
Year: 2023

The discovery of small world and scale free properties of many real world networks has revolutionized the way we study, analyze, model and process networks. An important way to analyze these complex networks is to visualize them using graph layout algorithms. Due to their large size and complex connectivity, it is difficult to make deductions from the visual representation of these networks. In this paper, we present a method for interactive analysis of large graphs based on topological filtering, network metrics and visualization. We analyze a number of real world networks and draw interesting conclusions using the proposed method.
Plated-Through-Hole Via Design Specifications for 112G Serial Links
Venue: arXiv cs.NI 2023
Year: 2023

An earlier study of a high layer-count test board using plated-through-hole (PTH) vias and a limited quantity of laser vias was shown to be capable of supporting 112 Gb/s PAM-4 links (or equivalent signaling having 28 GHz (Nyquist) bandwidth). This original board design was then rebuilt using a different fabricator, and the test results revealed a significant decrease in the bandwidth of the vias. These results led to the development of a set of design specifications that PCB vendors can easily validate, which will ensure that the use of high layer-count boards with PTH technology are viable for emerging 112 Gb/s PAM-4 links.
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI Feedback
Venue: arXiv cs.NI 2023
Year: 2023

The recent advances in machine learning and deep neural networks have made them attractive candidates for wireless communications functions such as channel estimation, decoding, and downlink channel state information (CSI) compression. However, most of these neural networks are large and inefficient making it a barrier for deployment in practical wireless systems that require low-latency and low memory footprints for individual network functions. To mitigate these limitations, we propose accelerated and compressed efficient neural networks for massive MIMO CSI feedback. Specifically, we have thoroughly investigated the adoption of network pruning, post-training dynamic range quantization, and weight clustering to optimize CSI feedback compression for massive MIMO systems. Furthermore, we have deployed the proposed model compression techniques on commodity hardware and demonstrated that in order to achieve inference gains, specialized libraries that accelerate computations for sparse neural networks are required. Our findings indicate that there is remarkable value in applying these model compression techniques and the proposed joint pruning and quantization approach reduced model size by 86.5% and inference time by 76.2% with minimal impact to model accuracy. These compression methods are crucial to pave the way for practical adoption and deployments of deep learning-based techniques in commercial wireless systems.
Network Visualization of ChatGPT Research: a study based on term and keyword co-occurrence network analysis
Venue: arXiv cs.SI 2023
Year: 2023

The main objective of this paper is to identify the major research areas of ChatGPT through term and keyword co-occurrence network mapping techniques. For conducting the present study, total of 577 publications were retrieved from the Lens database for the network visualization. The findings of the study showed that chatgpt occurrence in maximum number of times followed by its related terms such as artificial intelligence, large language model, gpt, study etc. This study will be helpful to library and information science as well as computer or information technology professionals.
Large Language Models are Edge-Case Fuzzers: Testing Deep Learning Libraries via FuzzGPT
Venue: arXiv cs.SE 2023
Year: 2023

Deep Learning (DL) library bugs affect downstream DL applications, emphasizing the need for reliable systems. Generating valid input programs for fuzzing DL libraries is challenging due to the need for satisfying both language syntax/semantics and constraints for constructing valid computational graphs. Recently, the TitanFuzz work demonstrates that modern Large Language Models (LLMs) can be directly leveraged to implicitly learn all the constraints to generate valid DL programs for fuzzing. However, LLMs tend to generate ordinary programs following similar patterns seen in their massive training corpora, while fuzzing favors unusual inputs that cover edge cases or are unlikely to be manually produced. To fill this gap, this paper proposes FuzzGPT, the first technique to prime LLMs to synthesize unusual programs for fuzzing. FuzzGPT is built on the well-known hypothesis that historical bug-triggering programs may include rare/valuable code ingredients important for bug finding. Traditional techniques leveraging such historical information require intensive human efforts to design dedicated generators and ensure the validity of generated programs. FuzzGPT demonstrates that this process can be fully automated via the intrinsic capabilities of LLMs (including fine-tuning and in-context learning), while being generalizable and applicable to challenging domains. While FuzzGPT can be applied with different LLMs, this paper focuses on the powerful GPT-style models: Codex and CodeGen. Moreover, FuzzGPT also shows the potential of directly leveraging the instruct-following capability of the recent ChatGPT for effective fuzzing. Evaluation on two popular DL libraries (PyTorch and TensorFlow) shows that FuzzGPT can substantially outperform TitanFuzz, detecting 76 bugs, with 49 already confirmed as previously unknown bugs, including 11 high-priority bugs or security vulnerabilities.
A Bibliometric Review of Large Language Models Research from 2017 to 2023
Venue: arXiv cs.DL 2023
Year: 2023

Large language models (LLMs) are a class of language models that have demonstrated outstanding performance across a range of natural language processing (NLP) tasks and have become a highly sought-after research area, because of their ability to generate human-like language and their potential to revolutionize science and technology. In this study, we conduct bibliometric and discourse analyses of scholarly literature on LLMs. Synthesizing over 5,000 publications, this paper serves as a roadmap for researchers, practitioners, and policymakers to navigate the current landscape of LLMs research. We present the research trends from 2017 to early 2023, identifying patterns in research paradigms and collaborations. We start with analyzing the core algorithm developments and NLP tasks that are fundamental in LLMs research. We then investigate the applications of LLMs in various fields and domains including medicine, engineering, social science, and humanities. Our review also reveals the dynamic, fast-paced evolution of LLMs research. Overall, this paper offers valuable insights into the current state, impact, and potential of LLMs research and its applications.
Effective Theory of Transformers at Initialization
Venue: arXiv cs.LG 2023
Year: 2023

We perform an effective-theory analysis of forward-backward signal propagation in wide and deep Transformers, i.e., residual neural networks with multi-head self-attention blocks and multilayer perceptron blocks. This analysis suggests particular width scalings of initialization and training hyperparameters for these models. We then take up such suggestions, training Vision and Language Transformers in practical setups.
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data
Venue: arXiv cs.CV 2023
Year: 2023

Scaling up weakly-supervised datasets has shown to be highly effective in the image-text domain and has contributed to most of the recent state-of-the-art computer vision and multimodal neural networks. However, existing large-scale video-text datasets and mining techniques suffer from several limitations, such as the scarcity of aligned data, the lack of diversity in the data, and the difficulty of collecting aligned data. Currently popular video-text data mining approach via automatic speech recognition (ASR) used in HowTo100M provides low-quality captions that often do not refer to the video content. Other mining approaches do not provide proper language descriptions (video tags) and are biased toward short clips (alt text). In this work, we show how recent advances in image captioning allow us to pre-train high-quality video models without any parallel video-text data. We pre-train several video captioning models that are based on an OPT language model and a TimeSformer visual backbone. We fine-tune these networks on several video captioning datasets. First, we demonstrate that image captioning pseudolabels work better for pre-training than the existing HowTo100M ASR captions. Second, we show that pre-training on both images and videos produces a significantly better network (+4 CIDER on MSR-VTT) than pre-training on a single modality. Our methods are complementary to the existing pre-training or data mining approaches and can be used in a variety of settings. Given the efficacy of the pseudolabeling method, we are planning to publicly release the generated captions.
A Complete V-Equational System for Graded lambda-Calculus
Venue: arXiv cs.LO 2023
Year: 2023

Modern programming frequently requires generalised notions of program equivalence based on a metric or a similar structure. Previous work addressed this challenge by introducing the notion of a V-equation, i.e. an equation labelled by an element of a quantale V, which covers inter alia (ultra-)metric, classical, and fuzzy (in)equations. It also introduced a V-equational system for the linear variant of lambda-calculus where any given resource must be used exactly once. In this paper we drop the (often too strict) linearity constraint by adding graded modal types which allow multiple uses of a resource in a controlled manner. We show that such a control, whilst providing more expressivity to the programmer, also interacts more richly with V-equations than the linear or Cartesian cases. Our main result is the introduction of a sound and complete V-equational system for a lambda-calculus with graded modal types interpreted by what we call a Lipschitz exponential comonad. We also show how to build such comonads canonically via a universal construction, and use our results to derive graded metric equational systems (and corresponding models) for programs with timed and probabilistic behaviour.
OpenContrails: Benchmarking Contrail Detection on GOES-16 ABI
Venue: arXiv cs.CV 2023
Year: 2023

Contrails (condensation trails) are line-shaped ice clouds caused by aircraft and are likely the largest contributor of aviation-induced climate change. Contrail avoidance is potentially an inexpensive way to significantly reduce the climate impact of aviation. An automated contrail detection system is an essential tool to develop and evaluate contrail avoidance systems. In this paper, we present a human-labeled dataset named OpenContrails to train and evaluate contrail detection models based on GOES-16 Advanced Baseline Imager (ABI) data. We propose and evaluate a contrail detection model that incorporates temporal context for improved detection accuracy. The human labeled dataset and the contrail detection outputs are publicly available on Google Cloud Storage at gs://goes_contrails_dataset.
I2I: Initializing Adapters with Improvised Knowledge
Venue: arXiv cs.CL 2023
Year: 2023

Adapters present a promising solution to the catastrophic forgetting problem in continual learning. However, training independent Adapter modules for every new task misses an opportunity for cross-task knowledge transfer. We propose Improvise to Initialize (I2I), a continual learning algorithm that initializes Adapters for incoming tasks by distilling knowledge from previously-learned tasks' Adapters. We evaluate I2I on CLiMB, a multimodal continual learning benchmark, by conducting experiments on sequences of visual question answering tasks. Adapters trained with I2I consistently achieve better task accuracy than independently-trained Adapters, demonstrating that our algorithm facilitates knowledge transfer between task Adapters. I2I also results in better cross-task knowledge transfer than the state-of-the-art AdapterFusion without incurring the associated parametric cost.
Redrafting Requirements Modeling Using a Single Multilevel Diagram
Venue: arXiv cs.SE 2023
Year: 2023

The complexity of software-based systems has increased significantly, especially with regards to capturing requirements along with dependencies among requirements. A conceptual model is a way of thinking about and making sense of the real world s complexities. In this paper, we focused on two approaches in this context: (a) multiple models applied to the same system with simultaneous usage of dissimilar notations vs. (b) a single model that utilizes a single framework of notations. In the first approach, inconsistencies arise among models that require a great deal of painstaking discipline and coordination between them. The multiple-model notion is based on the claim that it is not possible to present all application views in a single representation, so diverse models are used, with each model representing a different view. This article advocates a second approach that utilizes a single model with multilevel (static/dynamic and behavioral) specification. To substantiate this approach s feasibility, we embrace the occurrence-only model, which comprises (a) Stoic ontology, (b) thinging machine (TM) language and (c) Lupascian logic. In this paper, we focus on TM modeling as the mechanism of single-model building. We claim that a TM can be a unifying diagrammatic language for virtually all current modeling languages. To demonstrate such a claim, we redraft almost all the diagrammatic representations in The Handbook of Requirements Modeling of the International Requirements Engineering Board. This redrafting includes context, class, activity, use case, data flow and state diagrams. The results seem to indicate that there are no difficulties in representing all views in TM.
A Diffusion-based Method for Multi-turn Compositional Image Generation
Venue: arXiv cs.CV 2023
Year: 2023

Multi-turn compositional image generation (M-CIG) is a challenging task that aims to iteratively manipulate a reference image given a modification text. While most of the existing methods for M-CIG are based on generative adversarial networks (GANs), recent advances in image generation have demonstrated the superiority of diffusion models over GANs. In this paper, we propose a diffusion-based method for M-CIG named conditional denoising diffusion with image compositional matching (CDD-ICM). We leverage CLIP as the backbone of image and text encoders, and incorporate a gated fusion mechanism, originally proposed for question answering, to compositionally fuse the reference image and the modification text at each turn of M-CIG. We introduce a conditioning scheme to generate the target image based on the fusion results. To prioritize the semantic quality of the generated target image, we learn an auxiliary image compositional match (ICM) objective, along with the conditional denoising diffusion (CDD) objective in a multi-task learning framework. Additionally, we also perform ICM guidance and classifier-free guidance to improve performance. Experimental results show that CDD-ICM achieves state-of-the-art results on two benchmark datasets for M-CIG, i.e., CoDraw and i-CLEVR.
Ericson: An Interactive Open-Domain Conversational Search Agent
Venue: arXiv cs.CL 2023
Year: 2023

Open-domain conversational search (ODCS) aims to provide valuable, up-to-date information, while maintaining natural conversations to help users refine and ultimately answer information needs. However, creating an effective and robust ODCS agent is challenging. In this paper, we present a fully functional ODCS system, Ericson, which includes state-of-the-art question answering and information retrieval components, as well as intent inference and dialogue management models for proactive question refinement and recommendations. Our system was stress-tested in the Amazon Alexa Prize, by engaging in live conversations with thousands of Alexa users, thus providing empirical basis for the analysis of the ODCS system in real settings. Our interaction data analysis revealed that accurate intent classification, encouraging user engagement, and careful proactive recommendations contribute most to the users satisfaction. Our study further identifies limitations of the existing search techniques, and can serve as a building block for the next generation of ODCS agents.
Code Critters: A Block-Based Testing Game
Venue: arXiv cs.SE 2023
Year: 2023

Learning to program has become common in schools, higher education and individual learning. Although testing is an important aspect of programming, it is often neglected in education due to a perceived lack of time and knowledge, or simply because testing is considered less important or fun. To make testing more engaging, we therefore introduce Code Critters, a Tower Defense game based on testing concepts: The aim of the game is to place magic mines along the route taken by small "critters" from their home to a tower, such that the mines distinguish between critters executing correct code from those executing buggy code. Code is shown and edited using a block-based language to make the game accessible for younger learners. The mines encode test inputs as well as test oracles, thus making testing an integral and fun component of the game.
ERRA: An Embodied Representation and Reasoning Architecture for Long-horizon Language-conditioned Manipulation Tasks
Venue: arXiv cs.RO 2023
Year: 2023

This letter introduces ERRA, an embodied learning architecture that enables robots to jointly obtain three fundamental capabilities (reasoning, planning, and interaction) for solving long-horizon language-conditioned manipulation tasks. ERRA is based on tightly-coupled probabilistic inferences at two granularity levels. Coarse-resolution inference is formulated as sequence generation through a large language model, which infers action language from natural language instruction and environment state. The robot then zooms to the fine-resolution inference part to perform the concrete action corresponding to the action language. Fine-resolution inference is constructed as a Markov decision process, which takes action language and environmental sensing as observations and outputs the action. The results of action execution in environments provide feedback for subsequent coarse-resolution reasoning. Such coarse-to-fine inference allows the robot to decompose and achieve long-horizon tasks interactively. In extensive experiments, we show that ERRA can complete various long-horizon manipulation tasks specified by abstract language instructions. We also demonstrate successful generalization to the novel but similar natural language instructions.
Deep Perceptual Similarity is Adaptable to Ambiguous Contexts
Venue: arXiv cs.CV 2023
Year: 2023

The concept of image similarity is ambiguous, and images can be similar in one context and not in another. This ambiguity motivates the creation of metrics for specific contexts. This work explores the ability of deep perceptual similarity (DPS) metrics to adapt to a given context. DPS metrics use the deep features of neural networks for comparing images. These metrics have been successful on datasets that leverage the average human perception in limited settings. But the question remains if they could be adapted to specific similarity contexts. No single metric can suit all similarity contexts, and previous rule-based metrics are labor-intensive to rewrite for new contexts. On the other hand, DPS metrics use neural networks that might be retrained for each context. However, retraining networks takes resources and might ruin performance on previous tasks. This work examines the adaptability of DPS metrics by training ImageNet pretrained CNNs to measure similarity according to given contexts. Contexts are created by randomly ranking six image distortions. Distortions later in the ranking are considered more disruptive to similarity when applied to an image for that context. This also gives insight into whether the pretrained features capture different similarity contexts. The adapted metrics are evaluated on a perceptual similarity dataset to evaluate if adapting to a ranking affects their prior performance. The findings show that DPS metrics can be adapted with high performance. While the adapted metrics have difficulties with the same contexts as baselines, performance is improved in 99% of cases. Finally, it is shown that the adaption is not significantly detrimental to prior performance on perceptual similarity. The implementation of this work is available online: https://github.com/LTU-Machine-Learning/Analysis-of-Deep-Perceptual-Loss-Networks
Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention
Venue: arXiv cs.CV 2023
Year: 2023

Although face recognition has made impressive progress in recent years, we ignore the racial bias of the recognition system when we pursue a high level of accuracy. Previous work found that for different races, face recognition networks focus on different facial regions, and the sensitive regions of darker-skinned people are much smaller. Based on this discovery, we propose a new de-bias method based on gradient attention, called Gradient Attention Balance Network (GABN). Specifically, we use the gradient attention map (GAM) of the face recognition network to track the sensitive facial regions and make the GAMs of different races tend to be consistent through adversarial learning. This method mitigates the bias by making the network focus on similar facial regions. In addition, we also use masks to erase the Top-N sensitive facial regions, forcing the network to allocate its attention to a larger facial region. This method expands the sensitive region of darker-skinned people and further reduces the gap between GAM of darker-skinned people and GAM of Caucasians. Extensive experiments show that GABN successfully mitigates racial bias in face recognition and learns more balanced performance for people of different races.
Influence of Dataset Parameters on the Performance of Direct UE Positioning via Deep Learning
Venue: arXiv cs.IT 2023
Year: 2023

User equipment (UE) positioning accuracy is of paramount importance in current and future communications standard. However, traditional methods tend to perform poorly in non line of sight (NLoS) scenarios. As a result, deep learning is a candidate to enhance the UE positioning accuracy in NLoS environments. In this paper, we study the efficiency of deep learning on the 3GPP indoor factory (InF) statistical channel. More specifically, we analyse the impacts of several key elements on the positioning accuracy: the type of radio data used, the number of base stations (BS), the size of the training dataset, and the generalization ability of a trained model.
FASTAGEDS: Fast Approximate Graph Entity Dependency Discovery
Venue: arXiv cs.DB 2023
Year: 2023

This paper studies the discovery of approximate rules in property graphs. We propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) at almost hold, to tolerate errors and inconsistencies that exist in real-world graphs. We present a new characterisation of GED satisfaction, and devise a depth-first search strategy to traverse the search space of candidate rules efficiently. Further, we perform experiments to demonstrate the feasibility and scalability of our solution, FASTAGEDS, with three real-world graphs.