OpenRaiser/Intern-Atlas huggingface dataset:
Toward Platform-based Building Design
Venue: arXiv cs.OH 2023
Year: 2023

The electronic design industry has undergone a significant transformation, transitioning from traditional hand-drawn designs to modern automated design processes. While Computer-Aided Design (CAD) tools emerged alongside the electronic industry, the current building design process has little to no automation. There is a need for a unified platform to address the complexity of building design and provide a more systematic approach. Platform-based design (PBD), originally developed in the electronic industry, enables efficient design processes by promoting the reuse of hardware and software systems. It also facilitates design space exploration while optimizing performance. This paper proposes a modular approach that divides the building into various disciplines and introduces a design flow using the PBD framework to streamline the design process. We also present a case study that demonstrates the use of the PBD framework in the Heating, Ventilation, and Air Conditioning (HVAC) systems design.
Learning Activation Functions for Sparse Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

Sparse Neural Networks (SNNs) can potentially demonstrate similar performance to their dense counterparts while saving significant energy and memory at inference. However, the accuracy drop incurred by SNNs, especially at high pruning ratios, can be an issue in critical deployment conditions. While recent works mitigate this issue through sophisticated pruning techniques, we shift our focus to an overlooked factor: hyperparameters and activation functions. Our analyses have shown that the accuracy drop can additionally be attributed to (i) Using ReLU as the default choice for activation functions unanimously, and (ii) Fine-tuning SNNs with the same hyperparameters as dense counterparts. Thus, we focus on learning a novel way to tune activation functions for sparse networks and combining these with a separate hyperparameter optimization (HPO) regime for sparse networks. By conducting experiments on popular DNN models (LeNet-5, VGG-16, ResNet-18, and EfficientNet-B0) trained on MNIST, CIFAR-10, and ImageNet-16 datasets, we show that the novel combination of these two approaches, dubbed Sparse Activation Function Search, short: SAFS, results in up to 15.53%, 8.88%, and 6.33% absolute improvement in the accuracy for LeNet-5, VGG-16, and ResNet-18 over the default training protocols, especially at high pruning ratios. Our code can be found at https://github.com/automl/SAFS
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification
Venue: arXiv cs.CL 2023
Year: 2023

Africa has over 2000 indigenous languages but they are under-represented in NLP research due to lack of datasets. In recent years, there have been progress in developing labeled corpora for African languages. However, they are often available in a single domain and may not generalize to other domains. In this paper, we focus on the task of sentiment classification for cross domain adaptation. We create a new dataset, NollySenti - based on the Nollywood movie reviews for five languages widely spoken in Nigeria (English, Hausa, Igbo, Nigerian-Pidgin, and Yoruba. We provide an extensive empirical evaluation using classical machine learning methods and pre-trained language models. Leveraging transfer learning, we compare the performance of cross-domain adaptation from Twitter domain, and cross-lingual adaptation from English language. Our evaluation shows that transfer from English in the same target domain leads to more than 5% improvement in accuracy compared to transfer from Twitter in the same language. To further mitigate the domain difference, we leverage machine translation (MT) from English to other Nigerian languages, which leads to a further improvement of 7% over cross-lingual evaluation. While MT to low-resource languages are often of low quality, through human evaluation, we show that most of the translated sentences preserve the sentiment of the original English reviews.
Multi-microservice migration modelling, comparison, and potential in 5G/6G mobile edge computing: A non-average parameter values approach
Venue: arXiv cs.NI 2023
Year: 2023

Cloud, fog, and edge computing integration with future mobile Internet-of-Things (IoT) devices and related applications in 5G/6G networks will become more practical in the coming years. Containers became the de facto virtualization technique that replaced Virtual Memory (VM). Mobile IoT applications, e.g., intelligent transportation and augmented reality, incorporating fog-edge, have increased the demand for a millisecond-scale response and processing time. Edge Computing reduces remote network traffic and latency. These services must run on edge nodes that are physically close to devices. However, classical migration techniques may not meet the requirements of future mission-critical IoT applications. IoT mobile devices have limited resources for running multiple services, and client-server latency worsens when fog-edge services must migrate to maintain proximity in light of device mobility. This study analyzes the performance of the MiGrror migration method and the pre-copy live migration method when the migration of multiple VMs/containers is considered. This paper presents mathematical models for the stated methods and provides migration guidelines and comparisons for services to be implemented as multiple containers, as in microservice-based environments. Experiments demonstrate that MiGrror outperforms the pre-copy technique and, unlike conventional live migrations, can maintain less than 10 milliseconds of downtime and reduce migration time with a minimal bandwidth overhead. The results show that MiGrror can improve service continuity and availability for users. Most significant is that the model can use average and non-average values for different parameters during migration to achieve improved and more accurate results, while other research typically only uses average values. This paper shows that using only average parameter values in migration can lead to inaccurate results.
Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks
Venue: arXiv cs.LG 2023
Year: 2023

Lifelong learning agents aim to learn multiple tasks sequentially over a lifetime. This involves the ability to exploit previous knowledge when learning new tasks and to avoid forgetting. Modulating masks, a specific type of parameter isolation approach, have recently shown promise in both supervised and reinforcement learning. While lifelong learning algorithms have been investigated mainly within a single-agent approach, a question remains on how multiple agents can share lifelong learning knowledge with each other. We show that the parameter isolation mechanism used by modulating masks is particularly suitable for exchanging knowledge among agents in a distributed and decentralized system of lifelong learners. The key idea is that the isolation of specific task knowledge to specific masks allows agents to transfer only specific knowledge on-demand, resulting in robust and effective distributed lifelong learning. We assume fully distributed and asynchronous scenarios with dynamic agent numbers and connectivity. An on-demand communication protocol ensures agents query their peers for specific masks to be transferred and integrated into their policies when facing each task. Experiments indicate that on-demand mask communication is an effective way to implement distributed lifelong reinforcement learning and provides a lifelong learning benefit with respect to distributed RL baselines such as DD-PPO, IMPALA, and PPO+EWC. The system is particularly robust to connection drops and demonstrates rapid learning due to knowledge exchange.
Annotation-free Audio-Visual Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

The objective of Audio-Visual Segmentation (AVS) is to localise the sounding objects within visual scenes by accurately predicting pixel-wise segmentation masks. To tackle the task, it involves a comprehensive consideration of both the data and model aspects. In this paper, first, we initiate a novel pipeline for generating artificial data for the AVS task without extra manual annotations. We leverage existing image segmentation and audio datasets and match the image-mask pairs with its corresponding audio samples using category labels in segmentation datasets, that allows us to effortlessly compose (image, audio, mask) triplets for training AVS models. The pipeline is annotation-free and scalable to cover a large number of categories. Additionally, we introduce a lightweight model SAMA-AVS which adapts the pre-trained segment anything model~(SAM) to the AVS task. By introducing only a small number of trainable parameters with adapters, the proposed model can effectively achieve adequate audio-visual fusion and interaction in the encoding stage with vast majority of parameters fixed. We conduct extensive experiments, and the results show our proposed model remarkably surpasses other competing methods. Moreover, by using the proposed model pretrained with our synthetic data, the performance on real AVSBench data is further improved, achieving 83.17 mIoU on S4 subset and 66.95 mIoU on MS3 set. The project page is https://jinxiang-liu.github.io/anno-free-AVS/.
Late-Binding Scholarship in the Age of AI: Navigating Legal and Normative Challenges of a New Form of Knowledge Production
Venue: arXiv cs.CY 2023
Year: 2023

Artificial Intelligence (AI) is poised to enable a new leap in the creation of scholarly content. New forms of engagement with AI systems, such as collaborations with large language models like GPT-3, offer affordances that will change the nature of both the scholarly process and the artifacts it produces. This article articulates ways in which those artifacts can be written, distributed, read, organized, and stored that are more dynamic, and potentially more effective, than current academic practices. Specifically, rather than the current "early-binding" process (that is, one in which ideas are fully reduced to a final written form before they leave an author's desk), we propose that there are substantial benefits to a "late-binding" process, in which ideas are written dynamically at the moment of reading. In fact, the paradigm of "binding" knowledge may transition to a new model in which scholarship remains ever "unbound" and evolving. An alternative form for a scholarly work could be encapsulated via several key components: a text abstract of the work's core arguments; hyperlinks to a bibliography of relevant related work; novel data that had been collected and metadata describing those data; algorithms or processes necessary for analyzing those data; a reference to a particular AI model that would serve as a "renderer" of the canonical version of the text; and specified parameters that would allow for a precise, word-for-word reconstruction of the canonical version. Such a form would enable both the rendering of the canonical version, and also the possibility of dynamic AI reimaginings of the text in light of future findings, scholarship unknown to the original authors, alternative theories, and precise tailoring to specific audiences (e.g., children, adults, professionals, amateurs).
Medical Data Asset Management and an Approach for Disease Prediction using Blockchain and Machine Learning
Venue: arXiv cs.CY 2023
Year: 2023

In the present medical services, the board, clinical well-being records are as electronic clinical record (EHR/EMR) frameworks. These frameworks store patients' clinical histories in a computerized design. Notwithstanding, a patient's clinical information is gained in a productive and ideal way and is demonstrated to be troublesome through these records. Powerlessness constantly prevents the well-being of the board from getting data, less use of data obtained, unmanageable protection controls, and unfortunate information resource security. In this paper, we present an effective and safe clinical information resource, the executives' framework involving Blockchain, to determine these issues. Blockchain innovation facilitates the openness of all such records by keeping a block for each patient. This paper proposes an engineering utilizing an off-chain arrangement that will empower specialists and patients to get records in a protected manner. Blockchain makes clinical records permanent and scrambles them for information honesty. Clients can notice their well-being records, yet just patients own the confidential key and can impart it to those they want. Smart contracts likewise help our information proprietors to deal with their information access in a permission way. The eventual outcome will be seen as a web and portable connection point to get to, identify, and guarantee high-security information handily. In this adventure, we will give deals with any consequences regarding the issues associated with clinical consideration data and the chiefs using AI and Blockchain. Removing only the imperative information from the data is possible with the use of AI. This is done using arranged estimations. At the point when this data is taken care of, the accompanying issue is information sharing and its constancy.
Bits of Grass: Does GPT already know how to write like Whitman?
Venue: arXiv cs.CL 2023
Year: 2023

This study examines the ability of GPT-3.5, GPT-3.5-turbo (ChatGPT) and GPT-4 models to generate poems in the style of specific authors using zero-shot and many-shot prompts (which use the maximum context length of 8192 tokens). We assess the performance of models that are not fine-tuned for generating poetry in the style of specific authors, via automated evaluation. Our findings indicate that without fine-tuning, even when provided with the maximum number of 17 poem examples (8192 tokens) in the prompt, these models do not generate poetry in the desired style.
Using Symbolic Computation to Analyze Zero-Hopf Bifurcations of Polynomial Differential Systems
Venue: arXiv cs.SC 2023
Year: 2023

This paper is devoted to the study of infinitesimal limit cycles that can bifurcate from zero-Hopf equilibria of differential systems based on the averaging method. We develop an efficient symbolic program using Maple for computing the averaged functions of any order for continuous differential systems in arbitrary dimension. The program allows us to systematically analyze zero-Hopf bifurcations of polynomial differential systems using symbolic computation methods. We show that for the first-order averaging, $\ell\in\{0,1,\ldots,2^{n-3}\}$ limit cycles can bifurcate from the zero-Hopf equilibrium for the general class of perturbed differential systems and up to the second-order averaging, the maximum number of limit cycles can be determined by computing the mixed volume of a polynomial system obtained from the averaged functions. A number of examples are presented to demonstrate the effectiveness of the proposed algorithmic approach.
Prophet Secretary Against the Online Optimal
Venue: arXiv cs.GT 2023
Year: 2023

We study the prophet secretary problem, a well-studied variant of the classic prophet inequality, where values are drawn from independent known distributions but arrive in uniformly random order. Upon seeing a value at each step, the decision-maker has to either select it and stop or irrevocably discard it. Traditionally, the chosen benchmark is the expected reward of the prophet, who knows all the values in advance and can always select the maximum one. %% In this work, we study the prophet secretary problem against a less pessimistic but equally well-motivated benchmark; the \emph{online} optimal. Here, the main goal is to find polynomial-time algorithms that guarantee near-optimal expected reward. As a warm-up, we present a quasi-polynomial time approximation scheme (QPTAS) achieving a $(1-\e)$-approximation in $O(n^{\text{poly} \log n\cdot f(\e)})$ time through careful discretization and non-trivial bundling processes. Using the toolbox developed for the QPTAS, coupled with a novel \emph{frontloading} technique that enables us to reduce the number of decisions we need to make, we are able to remove the dependence on $n$ in the exponent and obtain a polynomial time approximation scheme (PTAS) for this problem.
Towards Generalizable Data Protection With Transferable Unlearnable Examples
Venue: arXiv cs.CR 2023
Year: 2023

Artificial Intelligence (AI) is making a profound impact in almost every domain. One of the crucial factors contributing to this success has been the access to an abundance of high-quality data for constructing machine learning models. Lately, as the role of data in artificial intelligence has been significantly magnified, concerns have arisen regarding the secure utilization of data, particularly in the context of unauthorized data usage. To mitigate data exploitation, data unlearning have been introduced to render data unexploitable. However, current unlearnable examples lack the generalization required for wide applicability. In this paper, we present a novel, generalizable data protection method by generating transferable unlearnable examples. To the best of our knowledge, this is the first solution that examines data privacy from the perspective of data distribution. Through extensive experimentation, we substantiate the enhanced generalizable protection capabilities of our proposed method.
DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilities
Venue: arXiv cs.LG 2023
Year: 2023

With the electrification of transportation, the rising uptake of electric vehicles (EVs) might stress distribution networks significantly, leaving their performance degraded and stability jeopardized. To accommodate these new loads cost-effectively, modern power grids require coordinated or ``smart'' charging strategies capable of optimizing EV charging scheduling in a scalable and efficient fashion. With this in view, the present work focuses on reservation management programs for large-scale, networked EV charging stations. We formulate a time-coupled binary optimization problem that maximizes EV users' total welfare gain while accounting for the network's available power capacity and stations' occupancy limits. To tackle the problem at scale while retaining high solution quality, a data-driven optimization framework combining techniques from the fields of Deep Learning and Approximation Algorithms is introduced. The framework's key ingredient is a novel input-output processing scheme for neural networks that allows direct extrapolation to problem sizes substantially larger than those included in the training set. Extensive numerical simulations based on synthetic and real-world data traces verify the effectiveness and superiority of the presented approach over two representative scheduling algorithms. Lastly, we round up the contributions by listing several immediate extensions to the proposed framework and outlining the prospects for further exploration.
Non-volatile Reconfigurable Digital Optical Diffractive Neural Network Based on Phase Change Material
Venue: arXiv cs.ET 2023
Year: 2023

Optical diffractive neural networks have triggered extensive research with their low power consumption and high speed in image processing. In this work, we propose a reconfigurable digital all-optical diffractive neural network (R-ODNN) structure. The optical neurons are built with Sb2Se3 phase-change material, making our network reconfigurable, digital, and non-volatile. Using three digital diffractive layers with 14,400 neurons on each and 10 photodetectors connected to a resistor network, our model achieves 94.46% accuracy for handwritten digit recognition. We also performed full-vector simulations and discussed the impact of errors to demonstrate the feasibility and robustness of the R-ODNN.
Assessing Exoplanet Habitability through Data-driven Approaches: A Comprehensive Literature Review
Venue: arXiv cs.OH 2023
Year: 2023

The exploration and study of exoplanets remain at the frontier of astronomical research, challenging scientists to continuously innovate and refine methodologies to navigate the vast, complex data these celestial bodies produce. This literature the review aims to illuminate the emerging trends and advancements within this sphere, specifically focusing on the interplay between exoplanet detection, classification, and visualization, and the the increasingly pivotal role of machine learning and computational models. Our journey through this realm of exploration commences with a comprehensive analysis of fifteen meticulously selected, seminal papers in the field. These papers, each representing a distinct facet of exoplanet research, collectively offer a multi-dimensional perspective on the current state of the field. They provide valuable insights into the innovative application of machine learning techniques to overcome the challenges posed by the analysis and interpretation of astronomical data. From the application of Support Vector Machines (SVM) to Deep Learning models, the review encapsulates the broad spectrum of machine learning approaches employed in exoplanet research. The review also seeks to unravel the story woven by the data within these papers, detailing the triumphs and tribulations of the field. It highlights the increasing reliance on diverse datasets, such as Kepler and TESS, and the push for improved accuracy in exoplanet detection and classification models. The narrative concludes with key takeaways and insights, drawing together the threads of research to present a cohesive picture of the direction in which the field is moving. This literature review, therefore, serves not just as an academic exploration, but also as a narrative of scientific discovery and innovation in the quest to understand our cosmic neighborhood.
Information-Ordered Bottlenecks for Adaptive Semantic Compression
Venue: arXiv cs.LG 2023
Year: 2023

We present the information-ordered bottleneck (IOB), a neural layer designed to adaptively compress data into latent variables ordered by likelihood maximization. Without retraining, IOB nodes can be truncated at any bottleneck width, capturing the most crucial information in the first latent variables. Unifying several previous approaches, we show that IOBs achieve near-optimal compression for a given encoding architecture and can assign ordering to latent signals in a manner that is semantically meaningful. IOBs demonstrate a remarkable ability to compress embeddings of image and text data, leveraging the performance of SOTA architectures such as CNNs, transformers, and diffusion models. Moreover, we introduce a novel theory for estimating global intrinsic dimensionality with IOBs and show that they recover SOTA dimensionality estimates for complex synthetic data. Furthermore, we showcase the utility of these models for exploratory analysis through applications on heterogeneous datasets, enabling computer-aided discovery of dataset complexity.
TrustSER: On the Trustworthiness of Fine-tuning Pre-trained Speech Embeddings For Speech Emotion Recognition
Venue: arXiv cs.SD 2023
Year: 2023

Recent studies have explored the use of pre-trained embeddings for speech emotion recognition (SER), achieving comparable performance to conventional methods that rely on low-level knowledge-inspired acoustic features. These embeddings are often generated from models trained on large-scale speech datasets using self-supervised or weakly-supervised learning objectives. Despite the significant advancements made in SER through the use of pre-trained embeddings, there is a limited understanding of the trustworthiness of these methods, including privacy breaches, unfair performance, vulnerability to adversarial attacks, and computational cost, all of which may hinder the real-world deployment of these systems. In response, we introduce TrustSER, a general framework designed to evaluate the trustworthiness of SER systems using deep learning methods, with a focus on privacy, safety, fairness, and sustainability, offering unique insights into future research in the field of SER. Our code is publicly available under: https://github.com/usc-sail/trust-ser.