OpenRaiser/Intern-Atlas huggingface dataset:
Proactive Schemes: A Survey of Adversarial Attacks for Social Good
Venue: arXiv cs.CV 2024
Year: 2024

Adversarial attacks in computer vision exploit the vulnerabilities of machine learning models by introducing subtle perturbations to input data, often leading to incorrect predictions or classifications. These attacks have evolved in sophistication with the advent of deep learning, presenting significant challenges in critical applications, which can be harmful for society. However, there is also a rich line of research from a transformative perspective that leverages adversarial techniques for social good. Specifically, we examine the rise of proactive schemes-methods that encrypt input data using additional signals termed templates, to enhance the performance of deep learning models. By embedding these imperceptible templates into digital media, proactive schemes are applied across various applications, from simple image enhancements to complicated deep learning frameworks to aid performance, as compared to the passive schemes, which don't change the input data distribution for their framework. The survey delves into the methodologies behind these proactive schemes, the encryption and learning processes, and their application to modern computer vision and natural language processing applications. Additionally, it discusses the challenges, potential vulnerabilities, and future directions for proactive schemes, ultimately highlighting their potential to foster the responsible and secure advancement of deep learning technologies.
Low Latency Point Cloud Rendering with Learned Splatting
Venue: arXiv cs.CV 2024
Year: 2024

Point cloud is a critical 3D representation with many emerging applications. Because of the point sparsity and irregularity, high-quality rendering of point clouds is challenging and often requires complex computations to recover the continuous surface representation. On the other hand, to avoid visual discomfort, the motion-to-photon latency has to be very short, under 10 ms. Existing rendering solutions lack in either quality or speed. To tackle these challenges, we present a framework that unlocks interactive, free-viewing and high-fidelity point cloud rendering. We train a generic neural network to estimate 3D elliptical Gaussians from arbitrary point clouds and use differentiable surface splatting to render smooth texture and surface normal for arbitrary views. Our approach does not require per-scene optimization, and enable real-time rendering of dynamic point cloud. Experimental results demonstrate the proposed solution enjoys superior visual quality and speed, as well as generalizability to different scene content and robustness to compression artifacts. The code is available at https://github.com/huzi96/gaussian-pcloud-render .
Understanding the Cognitive Complexity in Language Elicited by Product Images
Venue: arXiv cs.CL 2024
Year: 2024

Product images (e.g., a phone) can be used to elicit a diverse set of consumer-reported features expressed through language, including surface-level perceptual attributes (e.g., "white") and more complex ones, like perceived utility (e.g., "battery"). The cognitive complexity of elicited language reveals the nature of cognitive processes and the context required to understand them; cognitive complexity also predicts consumers' subsequent choices. This work offers an approach for measuring and validating the cognitive complexity of human language elicited by product images, providing a tool for understanding the cognitive processes of human as well as virtual respondents simulated by Large Language Models (LLMs). We also introduce a large dataset that includes diverse descriptive labels for product images, including human-rated complexity. We demonstrate that human-rated cognitive complexity can be approximated using a set of natural language models that, combined, roughly capture the complexity construct. Moreover, this approach is minimally supervised and scalable, even in use cases with limited human assessment of complexity.
PANOS: Payload-Aware Navigation in Offroad Scenarios
Venue: arXiv cs.RO 2024
Year: 2024

Nature has evolved humans to walk on different terrains by developing a detailed understanding of their physical characteristics. Similarly, legged robots need to develop their capability to walk on complex terrains with a variety of task-dependent payloads to achieve their goals. However, conventional terrain adaptation methods are susceptible to failure with varying payloads. In this work, we introduce PANOS, a weakly supervised approach that integrates proprioception and exteroception from onboard sensing to achieve a stable gait while walking by a legged robot over various terrains. Our work also provides evidence of its adaptability over varying payloads. We evaluate our method on multiple terrains and payloads using a legged robot. PANOS improves the stability up to 44% without any payload and 53% with 15 lbs payload. We also notice a reduction in the vibration cost of 20% with the payload for various terrain types when compared to state-of-the-art methods.
Friend- and Enemy-oriented Hedonic Games With Strangers Full Version
Venue: arXiv cs.GT 2024
Year: 2024

We introduce friend- and enemy-oriented hedonic games with strangers (FOHGS and EOHGS respectively), two classes of hedonic games wherein agents are classified as friends, enemies, or strangers under the assumption that strangers will become either friends or enemies ex post facto. For several notions of stability in FOHGS and EOHGS, we characterize the hardness of verification for possible and necessary stability. We characterize the hardness of deciding whether possibly and necessarily X stable partitions exist for a given stability notion X. We prove that necessarily internally stable partitions always exist and provide sufficient conditions for necessary contractual individual stability.
Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)
Venue: arXiv cs.IR 2024
Year: 2024

Learning to rank (LTR) is widely employed in web searches to prioritize pertinent webpages from retrieved content based on input queries. However, traditional LTR models encounter two principal obstacles that lead to suboptimal performance: (1) the lack of well-annotated query-webpage pairs with ranking scores covering a diverse range of search query popularities, which hampers their ability to address queries across the popularity spectrum, and (2) inadequately trained models that fail to induce generalized representations for LTR, resulting in overfitting. To address these challenges, we propose a \emph{\uline{G}enerative \uline{S}emi-\uline{S}upervised \uline{P}re-trained} (GS2P) LTR model. We conduct extensive offline experiments on both a publicly available dataset and a real-world dataset collected from a large-scale search engine. Furthermore, we deploy GS2P in a large-scale web search engine with realistic traffic, where we observe significant improvements in the real-world application.
Omni 3D: BEOL-Compatible 3D Logic with Omnipresent Power, Signal, and Clock
Venue: arXiv cs.ET 2024
Year: 2024

This paper presents Omni 3D - a 3D-stacked device architecture that is naturally enabled by back-end-of-line (BEOL)-compatible transistors. Omni 3D arbitrarily interleaves metal layers for both signal/power with FETs in 3D (i.e., nFETs and pFETs are stacked in 3D). Thus, signal/power routing layers have fine-grained, all-sided access to the FET active regions maximizing 3D standard cell design flexibility. This is in sharp contrast to approaches such as back-side power delivery networks (BSPDNs), complementary FETs (CFETs), and stacked FETs. Importantly, the routing flexibility of Omni 3D is enabled by double-side routing and an interleaved metal (IM) layer for inter- and intra-cell routing, respectively. In this work, we explore Omni 3D variants (e.g., both with and without the IM layer) and optimize these variants using a virtual-source BEOL-FET compact model. We establish a physical design flow that efficiently utilizes the double-side routing in Omni 3D and perform a thorough design-technology-co-optimization (DTCO) of Omni 3D device architecture on several design points. From our design flow, we project 2.0x improvement in the energy-delay product and 1.5x reduction in area compared to the state-of-the-art CFETs with BSPDNs.
Random Forest Regression Feature Importance for Climate Impact Pathway Detection
Venue: arXiv cs.LG 2024
Year: 2024

Disturbances to the climate system, both natural and anthropogenic, have far reaching impacts that are not always easy to identify or quantify using traditional climate science analyses or causal modeling techniques. In this paper, we develop a novel technique for discovering and ranking the chain of spatio-temporal downstream impacts of a climate source, referred to herein as a source-impact pathway, using Random Forest Regression (RFR) and SHapley Additive exPlanation (SHAP) feature importances. Rather than utilizing RFR for classification or regression tasks (the most common use case for RFR), we propose a fundamentally new workflow in which we: (i) train random forest (RF) regressors on a set of spatio-temporal features of interest, (ii) calculate their pair-wise feature importances using the SHAP weights associated with those features, and (iii) translate these feature importances into a weighted pathway network (i.e., a weighted directed graph), which can be used to trace out and rank interdependencies between climate features and/or modalities. Importantly, while herein we employ RFR and SHAP feature importance in steps (i) and (ii) of our algorithm, our novel workflow is in no way tied to these approaches, which could be replaced with any regression method and sensitivity method. We adopt a tiered verification approach to verify our new pathway identification methodology. In this approach, we apply our method to ensembles of data generated by running two increasingly complex benchmarks: (i) a set of synthetic coupled equations, and (ii) a fully coupled simulation of the 1991 eruption of Mount Pinatubo in the Philippines performed using a modified version 2 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv2). We find that our RFR feature importance-based approach can accurately detect known pathways of impact for both test cases.
CasFT: Future Trend Modeling for Information Popularity Prediction with Dynamic Cues-Driven Diffusion Models
Venue: arXiv cs.AI 2024
Year: 2024

The rapid spread of diverse information on online social platforms has prompted both academia and industry to realize the importance of predicting content popularity, which could benefit a wide range of applications, such as recommendation systems and strategic decision-making. Recent works mainly focused on extracting spatiotemporal patterns inherent in the information diffusion process within a given observation period so as to predict its popularity over a future period of time. However, these works often overlook the future popularity trend, as future popularity could either increase exponentially or stagnate, introducing uncertainties to the prediction performance. Additionally, how to transfer the preceding-term dynamics learned from the observed diffusion process into future-term trends remains an unexplored challenge. Against this background, we propose CasFT, which leverages observed information Cascades and dynamic cues extracted via neural ODEs as conditions to guide the generation of Future popularity-increasing Trends through a diffusion model. These generated trends are then combined with the spatiotemporal patterns in the observed information cascade to make the final popularity prediction. Extensive experiments conducted on three real-world datasets demonstrate that CasFT significantly improves the prediction accuracy, compared to state-of-the-art approaches, yielding 2.2%-19.3% improvement across different datasets.
Stochastic Subsampling With Average Pooling
Venue: arXiv cs.LG 2024
Year: 2024

Regularization of deep neural networks has been an important issue to achieve higher generalization performance without overfitting problems. Although the popular method of Dropout provides a regularization effect, it causes inconsistent properties in the output, which may degrade the performance of deep neural networks. In this study, we propose a new module called stochastic average pooling, which incorporates Dropout-like stochasticity in pooling. We describe the properties of stochastic subsampling and average pooling and leverage them to design a module without any inconsistency problem. The stochastic average pooling achieves a regularization effect without any potential performance degradation due to the inconsistency issue and can easily be plugged into existing architectures of deep neural networks. Experiments demonstrate that replacing existing average pooling with stochastic average pooling yields consistent improvements across a variety of tasks, datasets, and models.
PIFS-Rec: Process-In-Fabric-Switch for Large-Scale Recommendation System Inferences
Venue: arXiv cs.AR 2024
Year: 2024

Deep Learning Recommendation Models (DLRMs) have become increasingly popular and prevalent in today's datacenters, consuming most of the AI inference cycles. The performance of DLRMs is heavily influenced by available bandwidth due to their large vector sizes in embedding tables and concurrent accesses. To achieve substantial improvements over existing solutions, novel approaches towards DLRM optimization are needed, especially, in the context of emerging interconnect technologies like CXL. This study delves into exploring CXL-enabled systems, implementing a process-in-fabric-switch (PIFS) solution to accelerate DLRMs while optimizing their memory and bandwidth scalability. We present an in-depth characterization of industry-scale DLRM workloads running on CXL-ready systems, identifying the predominant bottlenecks in existing CXL systems. We, therefore, propose PIFS-Rec, a PIFS-based scheme that implements near-data processing through downstream ports of the fabric switch. PIFS-Rec achieves a latency that is 3.89x lower than Pond, an industry-standard CXL-based system, and also outperforms BEACON, a state-of-the-art scheme, by 2.03x.
Topic-aware Causal Intervention for Counterfactual Detection
Venue: arXiv cs.CL 2024
Year: 2024

Counterfactual statements, which describe events that did not or cannot take place, are beneficial to numerous NLP applications. Hence, we consider the problem of counterfactual detection (CFD) and seek to enhance the CFD models. Previous models are reliant on clue phrases to predict counterfactuality, so they suffer from significant performance drop when clue phrase hints do not exist during testing. Moreover, these models tend to predict non-counterfactuals over counterfactuals. To address these issues, we propose to integrate neural topic model into the CFD model to capture the global semantics of the input statement. We continue to causally intervene the hidden representations of the CFD model to balance the effect of the class labels. Extensive experiments show that our approach outperforms previous state-of-the-art CFD and bias-resolving methods in both the CFD and other bias-sensitive tasks.
Numerical Approximation Capacity of Neural Networks with Bounded Parameters: Do Limits Exist, and How Can They Be Measured?
Venue: arXiv cs.LG 2024
Year: 2024

The Universal Approximation Theorem posits that neural networks can theoretically possess unlimited approximation capacity with a suitable activation function and a freely chosen or trained set of parameters. However, a more practical scenario arises when these neural parameters, especially the nonlinear weights and biases, are bounded. This leads us to question: \textbf{Does the approximation capacity of a neural network remain universal, or does it have a limit when the parameters are practically bounded? And if it has a limit, how can it be measured?} Our theoretical study indicates that while universal approximation is theoretically feasible, in practical numerical scenarios, Deep Neural Networks (DNNs) with any analytic activation functions (such as Tanh and Sigmoid) can only be approximated by a finite-dimensional vector space under a bounded nonlinear parameter space (NP space), whether in a continuous or discrete sense. Based on this study, we introduce the concepts of \textit{$ε$ outer measure} and \textit{Numerical Span Dimension (NSdim)} to quantify the approximation capacity limit of a family of networks both theoretically and practically. Furthermore, drawing on our new theoretical study and adopting a fresh perspective, we strive to understand the relationship between back-propagation neural networks and random parameter networks (such as the Extreme Learning Machine (ELM)) with both finite and infinite width. We also aim to provide fresh insights into regularization, the trade-off between width and depth, parameter space, width redundancy, condensation, and other related important issues.
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning
Venue: arXiv cs.AI 2024
Year: 2024

The significant portion of diabetic patients was affected due to major blindness caused by Diabetic retinopathy (DR). For diabetic retinopathy, lesion segmentation, and detection the comprehensive examination is delved into the deep learning techniques application. The study conducted a systematic literature review using the PRISMA analysis and 62 articles has been investigated in the research. By including CNN-based models for DR grading, and feature fusion several deep-learning methodologies are explored during the study. For enhancing effectiveness in classification accuracy and robustness the data augmentation and ensemble learning strategies are scrutinized. By demonstrating the superior performance compared to individual models the efficacy of ensemble learning methods is investigated. The potential ensemble approaches in DR diagnosis are shown by the integration of multiple pre-trained networks with custom classifiers that yield high specificity. The diverse deep-learning techniques that are employed for detecting DR lesions are discussed within the diabetic retinopathy lesions segmentation and detection section. By emphasizing the requirement for continued research and integration into clinical practice deep learning shows promise for personalized healthcare and early detection of diabetics.
Super Level Sets and Exponential Decay: A Synergistic Approach to Stable Neural Network Training
Venue: arXiv cs.LG 2024
Year: 2024

The objective of this paper is to enhance the optimization process for neural networks by developing a dynamic learning rate algorithm that effectively integrates exponential decay and advanced anti-overfitting strategies. Our primary contribution is the establishment of a theoretical framework where we demonstrate that the optimization landscape, under the influence of our algorithm, exhibits unique stability characteristics defined by Lyapunov stability principles. Specifically, we prove that the superlevel sets of the loss function, as influenced by our adaptive learning rate, are always connected, ensuring consistent training dynamics. Furthermore, we establish the "equiconnectedness" property of these superlevel sets, which maintains uniform stability across varying training conditions and epochs. This paper contributes to the theoretical understanding of dynamic learning rate mechanisms in neural networks and also pave the way for the development of more efficient and reliable neural optimization techniques. This study intends to formalize and validate the equiconnectedness of loss function as superlevel sets in the context of neural network training, opening newer avenues for future research in adaptive machine learning algorithms. We leverage previous theoretical discoveries to propose training mechanisms that can effectively handle complex and high-dimensional data landscapes, particularly in applications requiring high precision and reliability.
Analytical assessment of workers' safety concerning direct and indirect ways of getting infected by dangerous pathogen
Venue: arXiv cs.CE 2024
Year: 2024

Developing safety policies to protect large groups of individuals working in indoor environments from disease spread is an important and challenging task. To address this issue, we investigate the scenario of workers becoming infected by a dangerous airborne pathogen in a near-real-life industrial environment. We present a simple analytical model based on observations made during the recent COVID-19 pandemic and business expectations concerning worker protection. The model can be adapted to address other epidemic or non-epidemic threats, including hazardous vapors from industrial processes. In the presented model, we consider both direct and indirect modes of infection. Direct infection occurs through direct contact with an infected individual, while indirect infection results from contact with a contaminated environment, including airborne pathogens in enclosed spaces or contaminated surfaces. Our analysis utilizes a simplified droplet/aerosol diffusion model, validated by droplet spread simulations. This model can be easily applied to new scenarios and has modest computational requirements compared to full simulations. Thus, it can be implemented within an automated protection ecosystem in an industrial setting, where rapid assessment of potential danger is required, and calculations must be performed almost in real-time. We validate general research findings on disease spread using a simple agent-based model. Based on our results, we outline a set of countermeasures for infection prevention, which could serve as the foundation for a prevention policy suited to industrial scenarios.
Demo2Vec: Learning Region Embedding with Demographic Information
Venue: arXiv cs.LG 2024
Year: 2024

Demographic data, such as income, education level, and employment rate, contain valuable information of urban regions, yet few studies have integrated demographic information to generate region embedding. In this study, we show how the simple and easy-to-access demographic data can improve the quality of state-of-the-art region embedding and provide better predictive performances in urban areas across three common urban tasks, namely check-in prediction, crime rate prediction, and house price prediction. We find that existing pre-train methods based on KL divergence are potentially biased towards mobility information and propose to use Jenson-Shannon divergence as a more appropriate loss function for multi-view representation learning. Experimental results from both New York and Chicago show that mobility + income is the best pre-train data combination, providing up to 10.22\% better predictive performances than existing models. Considering that mobility big data can be hardly accessible in many developing cities, we suggest geographic proximity + income to be a simple but effective data combination for region embedding pre-training.
Dispute resolution in legal mediation with quantitative argumentation
Venue: arXiv cs.AI 2024
Year: 2024

Mediation is often treated as an extension of negotiation, without taking into account the unique role that norms and facts play in legal mediation. Additionally, current approaches for updating argument acceptability in response to changing variables frequently require the introduction of new arguments or the removal of existing ones, which can be inefficient and cumbersome in decision-making processes within legal disputes. In this paper, our contribution is two-fold. First, we introduce a QuAM (Quantitative Argumentation Mediate) framework, which integrates the parties' knowledge and the mediator's knowledge, including facts and legal norms, when determining the acceptability of a mediation goal. Second, we develop a new formalism to model the relationship between the acceptability of a goal argument and the values assigned to a variable associated with the argument. We use a real-world legal mediation as a running example to illustrate our approach.