OpenRaiser/Intern-Atlas huggingface dataset:
Pruning random resistive memory for optimizing analogue AI
Venue: arXiv cs.ET 2023
Year: 2023

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and environmental sustainability. One promising solution is to revisit analogue computing, a technique that predates digital computing and exploits emerging analogue electronic devices, such as resistive memory, which features in-memory computing, high scalability, and nonvolatility. However, analogue computing still faces the same challenges as before: programming nonidealities and expensive programming due to the underlying devices physics. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network. Software-wise, the topology of a randomly weighted neural network is optimized by pruning connections rather than precisely tuning resistive memory weights. Hardware-wise, we reveal the physical origin of the programming stochasticity using transmission electron microscopy, which is leveraged for large-scale and low-cost implementation of an overparameterized random neural network containing high-performance sub-networks. We implemented the co-design on a 40nm 256K resistive memory macro, observing 17.3% and 19.9% accuracy improvements in image and audio classification on FashionMNIST and Spoken digits datasets, as well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE datasets, respectively. This is accompanied by 82.1%, 51.2%, and 99.8% improvement in energy efficiency thanks to analogue in-memory computing. By embracing the intrinsic stochasticity and in-memory computing, this work may solve the biggest obstacle of analogue computing systems and thus unleash their immense potential for next-generation AI hardware.
MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model
Venue: arXiv cs.CV 2023
Year: 2023

Over the past few years, self-supervised monocular depth estimation that does not depend on ground-truth during the training phase has received widespread attention. Most efforts focus on designing different types of network architectures and loss functions or handling edge cases, e.g., occlusion and dynamic objects. In this work, we introduce a novel self-supervised depth estimation framework, dubbed MonoDiffusion, by formulating it as an iterative denoising process. Because the depth ground-truth is unavailable in the training phase, we develop a pseudo ground-truth diffusion process to assist the diffusion in MonoDiffusion. The pseudo ground-truth diffusion gradually adds noise to the depth map generated by a pre-trained teacher model. Moreover,the teacher model allows applying a distillation loss to guide the denoised depth. Further, we develop a masked visual condition mechanism to enhance the denoising ability of model. Extensive experiments are conducted on the KITTI and Make3D datasets and the proposed MonoDiffusion outperforms prior state-of-the-art competitors. The source code will be available at https://github.com/ShuweiShao/MonoDiffusion.
Sketch-based Video Object Segmentation: Benchmark and Analysis
Venue: arXiv cs.CV 2023
Year: 2023

Reference-based video object segmentation is an emerging topic which aims to segment the corresponding target object in each video frame referred by a given reference, such as a language expression or a photo mask. However, language expressions can sometimes be vague in conveying an intended concept and ambiguous when similar objects in one frame are hard to distinguish by language. Meanwhile, photo masks are costly to annotate and less practical to provide in a real application. This paper introduces a new task of sketch-based video object segmentation, an associated benchmark, and a strong baseline. Our benchmark includes three datasets, Sketch-DAVIS16, Sketch-DAVIS17 and Sketch-YouTube-VOS, which exploit human-drawn sketches as an informative yet low-cost reference for video object segmentation. We take advantage of STCN, a popular baseline of semi-supervised VOS task, and evaluate what the most effective design for incorporating a sketch reference is. Experimental results show sketch is more effective yet annotation-efficient than other references, such as photo masks, language and scribble.
A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market
Venue: arXiv cs.LG 2023
Year: 2023

The South Australia region of the Australian National Electricity Market (NEM) displays some of the highest levels of price volatility observed in modern electricity markets. This paper outlines an approach to probabilistic forecasting under these extreme conditions, including spike filtration and several post-processing steps. We propose using quantile regression as an ensemble tool for probabilistic forecasting, with our combined forecasts achieving superior results compared to all constituent models. Within our ensemble framework, we demonstrate that averaging models with varying training length periods leads to a more adaptive model and increased prediction accuracy. The applicability of the final model is evaluated by comparing our median forecasts with the point forecasts available from the Australian NEM operator, with our model outperforming these NEM forecasts by a significant margin.
Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud
Venue: arXiv cs.CV 2023
Year: 2023

In deformable object manipulation, we often want to interact with specific segments of an object that are only defined in non-deformed models of the object. We thus require a system that can recognize and locate these segments in sensor data of deformed real world objects. This is normally done using deformable object registration, which is problem specific and complex to tune. Recent methods utilize neural occupancy functions to improve deformable object registration by registering to an object reconstruction. Going one step further, we propose a system that in addition to reconstruction learns segmentation of the reconstructed object. As the resulting output already contains the information about the segments, we can skip the registration process. Tested on a variety of deformable objects in simulation and the real world, we demonstrate that our method learns to robustly find these segments. We also introduce a simple sampling algorithm to generate better training data for occupancy learning.
Efficient bandwidth extension of musical signals using a differentiable harmonic plus noise model
Venue: arXiv cs.SD 2023
Year: 2023

The task of bandwidth extension addresses the generation of missing high frequencies of audio signals based on knowledge of the low-frequency part of the sound. This task applies to various problems, such as audio coding or audio restoration. In this article, we focus on efficient bandwidth extension of monophonic and polyphonic musical signals using a differentiable digital signal processing (DDSP) model. Such a model is composed of a neural network part with relatively few parameters trained to infer the parameters of a differentiable digital signal processing model, which efficiently generates the output full-band audio signal. We first address bandwidth extension of monophonic signals, and then propose two methods to explicitely handle polyphonic signals. The benefits of the proposed models are first demonstrated on monophonic and polyphonic synthetic data against a baseline and a deep-learning-based resnet model. The models are next evaluated on recorded monophonic and polyphonic data, for a wide variety of instruments and musical genres. We show that all proposed models surpass a higher complexity deep learning model for an objective metric computed in the frequency domain. A MUSHRA listening test confirms the superiority of the proposed approach in terms of perceptual quality.
Differentially Private Approximate Pattern Matching
Venue: arXiv cs.DS 2023
Year: 2023

In this paper, we consider the $k$-approximate pattern matching problem under differential privacy, where the goal is to report or count all substrings of a given string $S$ which have a Hamming distance at most $k$ to a pattern $P$, or decide whether such a substring exists. In our definition of privacy, individual positions of the string $S$ are protected. To be able to answer queries under differential privacy, we allow some slack on $k$, i.e. we allow reporting or counting substrings of $S$ with a distance at most $(1+γ)k+α$ to $P$, for a multiplicative error $γ$ and an additive error $α$. We analyze which values of $α$ and $γ$ are necessary or sufficient to solve the $k$-approximate pattern matching problem while satisfying $ε$-differential privacy. Let $n$ denote the length of $S$. We give 1) an $ε$-differentially private algorithm with an additive error of $O(ε^{-1}\log n)$ and no multiplicative error for the existence variant; 2) an $ε$-differentially private algorithm with an additive error $O(ε^{-1}\max(k,\log n)\cdot\log n)$ for the counting variant; 3) an $ε$-differentially private algorithm with an additive error of $O(ε^{-1}\log n)$ and multiplicative error $O(1)$ for the reporting variant for a special class of patterns. The error bounds hold with high probability. All of these algorithms return a witness, that is, if there exists a substring of $S$ with distance at most $k$ to $P$, then the algorithm returns a substring of $S$ with distance at most $(1+γ)k+α$ to $P$. Further, we complement these results by a lower bound, showing that any algorithm for the existence variant which also returns a witness must have an additive error of $Ω(ε^{-1}\log n)$ with constant probability.
Qualifying System F-sub
Venue: arXiv cs.PL 2023
Year: 2023

Type qualifiers offer a lightweight mechanism for enriching existing type systems to enforce additional, desirable, program invariants. They do so by offering a restricted but effective form of subtyping. While the theory of type qualifiers is well understood and present in many programming languages today, polymorphism over type qualifiers is an area that is less examined. We explore how such a polymorphic system could arise by constructing a calculus System F<:Q which combines the higher-rank bounded polymorphism of System F<: with the theory of type qualifiers. We explore how the ideas used to construct System F<:Q can be reused in situations where type qualifiers naturally arise -- in reference immutability, function colouring, and capture checking. Finally, we re-examine other qualifier systems in the literature in light of the observations presented while developing System F<:Q.
The Last Decade in Review: Tracing the Evolution of Safety Assurance Cases through a Comprehensive Bibliometric Analysis
Venue: arXiv cs.SE 2023
Year: 2023

Safety assurance is of paramount importance across various domains, including automotive, aerospace, and nuclear energy, where the reliability and acceptability of mission-critical systems are imperative. This assurance is effectively realized through the utilization of Safety Assurance Cases. The use of safety assurance cases allows for verifying the correctness of the created systems capabilities, preventing system failure. The latter may result in loss of life, severe injuries, large-scale environmental damage, property destruction, and major economic loss. Still, the emergence of complex technologies such as cyber-physical systems (CPSs), characterized by their heterogeneity, autonomy, machine learning capabilities, and the uncertainty of their operational environments poses significant challenges for safety assurance activities. Several papers have tried to propose solutions to tackle these challenges, but to the best of our knowledge, no secondary study investigates the trends, patterns, and relationships characterizing the safety case scientific literature. This makes it difficult to have a holistic view of the safety case landscape and to identify the most promising future research directions. In this paper, we, therefore, rely on state-of-the-art bibliometric tools(e.g., VosViewer) to conduct a bibliometric analysis that allows us to generate valuable insights, identify key authors and venues, and gain a birds eye view of the current state of research in the safety assurance area. By revealing knowledge gaps and highlighting potential avenues for future research, our analysis provides an essential foundation for researchers, corporate safety analysts, and regulators seeking to embrace or enhance safety practices that align with their specific needs and objectives.
STEM Rebalance: A Novel Approach for Tackling Imbalanced Datasets using SMOTE, Edited Nearest Neighbour, and Mixup
Venue: arXiv cs.LG 2023
Year: 2023

Imbalanced datasets in medical imaging are characterized by skewed class proportions and scarcity of abnormal cases. When trained using such data, models tend to assign higher probabilities to normal cases, leading to biased performance. Common oversampling techniques such as SMOTE rely on local information and can introduce marginalization issues. This paper investigates the potential of using Mixup augmentation that combines two training examples along with their corresponding labels to generate new data points as a generic vicinal distribution. To this end, we propose STEM, which combines SMOTE-ENN and Mixup at the instance level. This integration enables us to effectively leverage the entire distribution of minority classes, thereby mitigating both between-class and within-class imbalances. We focus on the breast cancer problem, where imbalanced datasets are prevalent. The results demonstrate the effectiveness of STEM, which achieves AUC values of 0.96 and 0.99 in the Digital Database for Screening Mammography and Wisconsin Breast Cancer (Diagnostics) datasets, respectively. Moreover, this method shows promising potential when applied with an ensemble of machine learning (ML) classifiers.
Causality Diagrams using Hybrid Vector Clocks
Venue: arXiv cs.DC 2023
Year: 2023

Causality in distributed systems is a concept that has long been explored and numerous approaches have been made to use causality as a way to trace distributed system execution. Traditional approaches usually used system profiling and newer approaches profiled clocks of systems to detect failures and construct timelines that caused those failures. Since the advent of logical clocks, these profiles have become more and more accurate with ways to characterize concurrency and distributions, with accurate diagrams for message passing. Vector clocks addressed the shortcomings of using traditional logical clocks, by storing information about other processes in the system as well. Hybrid vector clocks are a novel approach to this concept where clocks need not store all the process information. Rather, we store information of processes within an acceptable skew of the focused process. This gives us an efficient way of profiling with substantially reduced costs to the system. Building on this idea, we propose the idea of building causal traces using information generated from the hybrid vector clock. The hybrid vector clock would provide us with a strong sense of concurrency and distribution, and we theorize that all the information generated from the clock is sufficient to develop a causal trace for debugging. We post-process and parse the clocks generated from an execution trace to develop a swimlane on a web interface, that traces the points of failure of a distributed system. We also provide an API to reuse this concept for any generic distributed system framework.
Fast Normalized Cross-Correlation for Template Matching with Rotations
Venue: arXiv cs.CV 2023
Year: 2023

Normalized cross-correlation is the reference approach to carry out template matching on images. When it is computed in Fourier space, it can handle efficiently template translations but it cannot do so with template rotations. Including rotations requires sampling the whole space of rotations, repeating the computation of the correlation each time. This article develops an alternative mathematical theory to handle efficiently, at the same time, rotations and translations. Our proposal has a reduced computational complexity because it does not require to repeatedly sample the space of rotations. To do so, we integrate the information relative to all rotated versions of the template into a unique symmetric tensor template -which is computed only once per template-. Afterward, we demonstrate that the correlation between the image to be processed with the independent tensor components of the tensorial template contains enough information to recover template instance positions and rotations. Our proposed method has the potential to speed up conventional template matching computations by a factor of several magnitude orders for the case of 3D images.
GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation
Venue: arXiv cs.CV 2023
Year: 2023

We present MM-Navigator, a GPT-4V-based agent for the smartphone graphical user interface (GUI) navigation task. MM-Navigator can interact with a smartphone screen as human users, and determine subsequent actions to fulfill given instructions. Our findings demonstrate that large multimodal models (LMMs), specifically GPT-4V, excel in zero-shot GUI navigation through its advanced screen interpretation, action reasoning, and precise action localization capabilities. We first benchmark MM-Navigator on our collected iOS screen dataset. According to human assessments, the system exhibited a 91\% accuracy rate in generating reasonable action descriptions and a 75\% accuracy rate in executing the correct actions for single-step instructions on iOS. Additionally, we evaluate the model on a subset of an Android screen navigation dataset, where the model outperforms previous GUI navigators in a zero-shot fashion. Our benchmark and detailed analyses aim to lay a robust groundwork for future research into the GUI navigation task. The project page is at https://github.com/zzxslp/MM-Navigator.
To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning
Venue: arXiv cs.CV 2023
Year: 2023

Existing visual instruction tuning methods typically prompt large language models with textual descriptions to generate instruction-following data. Despite the promising performance achieved, these descriptions are derived from image annotations, which are oftentimes coarse-grained. Furthermore, the instructions might even contradict the visual content without observing the entire visual context. To address this challenge, we introduce a fine-grained visual instruction dataset, LVIS-Instruct4V, which contains 220K visually aligned and context-aware instructions produced by prompting the powerful GPT-4V with images from LVIS. Through experimental validation and case studies, we demonstrate that high-quality visual instructional data could improve the performance of LLaVA-1.5, a state-of-the-art large multimodal model, across a wide spectrum of benchmarks by clear margins. Notably, by simply replacing the LLaVA-Instruct with our LVIS-Instruct4V, we achieve better results than LLaVA on most challenging LMM benchmarks, e.g., LLaVA$^w$ (76.7 vs. 70.7) and MM-Vet (40.2 vs. 35.4). We release our data and model at https://github.com/X2FD/LVIS-INSTRUCT4V.
Algorithms for Object Detection in Substations
Venue: arXiv cs.CV 2023
Year: 2023

Inspection of high-voltage power equipment is an effective way to ensure power supply reliability. Object recognition, one of the key technologies in automatic power equipment inspection, attracts attention of many researchers and engineers. Although quite a few existing models have some their own advantages, object relationship between equipment which is very important in this task is scarcely considered. This paper combining object relationship modeling and Transformer Model proposes a Relation Transformer Model. It has four parts -- backbone, encoder, decoder and prediction heads. With this structure, the proposed method shows in experiments a much better performance than other three commonly used models in object recognition in substation, largely promoting the development of automatic power equipment inspection.
Relative intrinsic dimensionality is intrinsic to learning
Venue: arXiv cs.LG 2023
Year: 2023

High dimensional data can have a surprising property: pairs of data points may be easily separated from each other, or even from arbitrary subsets, with high probability using just simple linear classifiers. However, this is more of a rule of thumb than a reliable property as high dimensionality alone is neither necessary nor sufficient for successful learning. Here, we introduce a new notion of the intrinsic dimension of a data distribution, which precisely captures the separability properties of the data. For this intrinsic dimension, the rule of thumb above becomes a law: high intrinsic dimension guarantees highly separable data. We extend this notion to that of the relative intrinsic dimension of two data distributions, which we show provides both upper and lower bounds on the probability of successfully learning and generalising in a binary classification problem
Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions
Venue: arXiv cs.CL 2023
Year: 2023

Recent advances in Large Language Models (LLMs) have presented new opportunities for integrating Artificial General Intelligence (AGI) into biological research and education. This study evaluated the capabilities of leading LLMs, including GPT-4, GPT-3.5, PaLM2, Claude2, and SenseNova, in answering conceptual biology questions. The models were tested on a 108-question multiple-choice exam covering biology topics in molecular biology, biological techniques, metabolic engineering, and synthetic biology. Among the models, GPT-4 achieved the highest average score of 90 and demonstrated the greatest consistency across trials with different prompts. The results indicated GPT-4's proficiency in logical reasoning and its potential to aid biology research through capabilities like data analysis, hypothesis generation, and knowledge integration. However, further development and validation are still required before the promise of LLMs in accelerating biological discovery can be realized.
Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources
Venue: arXiv cs.CL 2023
Year: 2023

To address the data scarcity issue in Conversational question answering (ConvQA), a dialog inpainting method, which utilizes documents to generate ConvQA datasets, has been proposed. However, the original dialog inpainting model is trained solely on the dialog reconstruction task, resulting in the generation of questions with low contextual relevance due to insufficient learning of question-answer alignment. To overcome this limitation, we propose a novel framework called Dialogizer, which has the capability to automatically generate ConvQA datasets with high contextual relevance from textual sources. The framework incorporates two training tasks: question-answer matching (QAM) and topic-aware dialog generation (TDG). Moreover, re-ranking is conducted during the inference phase based on the contextual relevance of the generated questions. Using our framework, we produce four ConvQA datasets by utilizing documents from multiple domains as the primary source. Through automatic evaluation using diverse metrics, as well as human evaluation, we validate that our proposed framework exhibits the ability to generate datasets of higher quality compared to the baseline dialog inpainting model.