OpenRaiser/Intern-Atlas huggingface dataset:
Deciphering Urban Morphogenesis: A Morphospace Approach
Venue: arXiv cs.CY 2024
Year: 2024

Cities emerged independently across different world regions and historical periods, raising fundamental questions: How did the first urban settlements develop? What social and spatial conditions enabled their emergence? Are these processes universal or context-dependent? Moreover, what distinguishes cities from other human settlements? This paper investigates the drivers behind the creation of cities through a hybrid approach that integrates urban theory, the biological concept of morphospace (the space of all possible configurations), and archaeological evidence. It explores the transition from sedentary hunter-gatherer communities to urban societies, highlighting fundamental forces converging to produce increasingly complex divisions of labour as a central driver of urbanization. Morphogenesis is conceptualized as a trajectory through morphospace, governed by structure-seeking selection processes that balance density, permeability, and information as critical dimensions. The study highlights the non-ergodic nature of urban morphogenesis, where configurations are progressively selected based on their fitness to support the diversifying interactions between mutually dependent agents. The morphospace framework effectively distinguishes between theoretical spatial configurations, non-urban and proto-urban settlements, and contemporary cities. This analysis supports the proposition that cities emerge and evolve as solutions balancing density, permeability, and informational organization, enabling them to support increasingly complex societal functions.
GPAI Evaluations Standards Taskforce: Towards Effective AI Governance
Venue: arXiv cs.CY 2024
Year: 2024

General-purpose AI evaluations have been proposed as a promising way of identifying and mitigating systemic risks posed by AI development and deployment. While GPAI evaluations play an increasingly central role in institutional decision- and policy-making -- including by way of the European Union AI Act's mandate to conduct evaluations on GPAI models presenting systemic risk -- no standards exist to date to promote their quality or legitimacy. To strengthen GPAI evaluations in the EU, which currently constitutes the first and only jurisdiction that mandates GPAI evaluations, we outline four desiderata for GPAI evaluations: internal validity, external validity, reproducibility, and portability. To uphold these desiderata in a dynamic environment of continuously evolving risks, we propose a dedicated EU GPAI Evaluation Standards Taskforce, to be housed within the bodies established by the EU AI Act. We outline the responsibilities of the Taskforce, specify the GPAI provider commitments that would facilitate Taskforce success, discuss the potential impact of the Taskforce on global AI governance, and address potential sources of failure that policymakers should heed.
Interactive and Expressive Code-Augmented Planning with Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making, but often struggle with complex, long-horizon planning tasks. Recent techniques have sought to structure LLM outputs using control flow and other code-adjacent techniques to improve planning performance. These techniques include using variables (to track important information) and functions (to divide complex tasks into smaller re-usable sub-tasks). However, purely code-based approaches can be error-prone and insufficient for handling ambiguous or unstructured data. To address these challenges, we propose REPL-Plan, an LLM planning approach that is fully code-expressive (it can utilize all the benefits of code) while also being dynamic (it can flexibly adapt from errors and use the LLM for fuzzy situations). In REPL-Plan, an LLM solves tasks by interacting with a Read-Eval-Print Loop (REPL), which iteratively executes and evaluates code, similar to language shells or interactive code notebooks, allowing the model to flexibly correct errors and handle tasks dynamically. We demonstrate that REPL-Plan achieves strong results across various planning domains compared to previous methods.
Static Reuse Profile Estimation for Array Applications
Venue: arXiv cs.PF 2024
Year: 2024

Reuse distance analysis is a widely recognized method for application characterization that illustrates cache locality. Although there are various techniques to calculate the reuse profile from dynamic memory traces, it is both time and space-consuming due to the requirement to collect dynamic memory traces at runtime. In contrast, static analysis reuse profile estimation is a promisingly faster approach since it is calculated at compile time without running the program or collecting memory traces. This work presents a static analysis technique to estimate the reuse profile of loop-based programs. For an input program, we generate a basic block-level control flow graph and the execution count by analyzing the LLVM IR of the program. We present the memory accesses of the application kernel in a compact bracketed format and use a recursive algorithm to predict the reuse distance histogram. We deploy a separate predictor that unrolls the loop(s) for smaller bounds and generates a temporary reuse distance profile for those small cases. Using these smaller profiles, the reuse profile is extrapolated for the actual loop bound(s). We use this reuse profile to predict the cache hit rate. Results show that our model can predict cache hit rates with an average accuracy of 95% relative to the dynamic reuse profile methods.
Data-Driven Multi-step Nonlinear Model Predictive Control for Industrial Heavy Load Hydraulic Robot
Venue: arXiv cs.RO 2024
Year: 2024

Automating complex industrial robots requires precise nonlinear control and efficient energy management. This paper introduces a data-driven nonlinear model predictive control (NMPC) framework to optimize control under multiple objectives. To enhance the prediction accuracy of the dynamic model, we design a single-shot multi-step prediction (SSMP) model based on long short-term memory (LSTM) and multilayer perceptrons (MLP), which can directly obtain the predictive horizon without iterative repetition and reduce computational pressure. Moreover, we combine offline and online models to address disturbances stemming from environmental interactions, similar to the superposition of the robot's free and forced responses. The online model learns the system's variations from the prediction mismatches of the offline model and updates its weights in real time. The proposed hybrid predictive model simplifies the relationship between inputs and outputs into matrix multiplication, which can quickly obtain the derivative. Therefore, the solution for the control signal sequence employs a gradient descent method with an adaptive learning rate, allowing the NMPC cost function to be formulated as a convex function incorporating critical states. The learning rate is dynamically adjusted based on state errors to counteract the inherent prediction inaccuracies of neural networks. The controller outputs the average value of the control signal sequence instead of the first value. Simulations and experiments on a 22-ton hydraulic excavator have validated the effectiveness of our method, showing that the proposed NMPC approach can be widely applied to industrial systems, including nonlinear control and energy management.
Iterative decoding of short BCH codes and its post-processing
Venue: arXiv cs.IT 2024
Year: 2024

Effective iterative decoding of short BCH codes faces two primary challenges: identifying an appropriate parity-check matrix and accelerating decoder convergence. To address these issues, we propose a systematic scheme to derive an optimized parity-check matrix through a heuristic approach. This involves a series of binary sum and row shift operations, resulting in a low-density, quasi-regular column weight distribution with a reduced number of shortest cycles in the underlying redundant Tanner graph. For the revised normalized min-sum decoder, we concurrently integrate three types of random permutations into the alternated messages across iterations, leading to significantly faster convergence compared to existing methods. Furthermore, by utilizing the iterative trajectories of failed normalized min-sum decoding, we enhance the reliability measurement of codeword bits with the assistance of a neural network model from prior work, which accommodates more failures for the post-processing of ordered statistics decoding. Additionally, we report the types of undetected errors for the design of iterative decoders for short BCH codes, which potentially challenge efforts to approach the maximum likelihood limit. Extensive simulations demonstrate that the proposed hybrid framework achieves an attractive balance between performance, latency, and complexity.
Dissecting Conditional Branch Predictors of Apple Firestorm and Qualcomm Oryon for Software Optimization and Architectural Analysis
Venue: arXiv cs.AR 2024
Year: 2024

Branch predictor (BP) is a critical component of modern processors, and its accurate modeling is essential for compilers and applications. However, processor vendors have disclosed limited details about their BP implementations. Recent advancements in reverse engineering the BP of general-purpose processors have enabled the creation of more accurate BP models. Nonetheless, we have identified critical deficiencies in the existing methods. For instance, they impose strong assumptions on the branch history update function and the index/tag functions of key BP components, limiting their applicability to a broader range of processors, including those from Apple and Qualcomm. In this paper, we design a more general branch prediction reverse engineering pipeline that can additionally recover the conditional branch predictors (CBPs) of Apple Firestorm and Qualcomm Oryon microarchitectures, and subsequently build accurate CBP models. Leveraging these models, we uncover two previously undisclosed effects that impair branch prediction accuracy and propose related solutions, resulting in up to 14% MPKI reduction and 7% performance improvement in representative applications. Furthermore, we conduct a comprehensive comparison of the known Intel/Apple/Qualcomm CBPs using a unified standalone branch predictor simulator, which facilitates a deeper understanding of CBP behavior.
Transforming Static Images Using Generative Models for Video Salient Object Detection
Venue: arXiv cs.CV 2024
Year: 2024

In many video processing tasks, leveraging large-scale image datasets is a common strategy, as image data is more abundant and facilitates comprehensive knowledge transfer. A typical approach for simulating video from static images involves applying spatial transformations, such as affine transformations and spline warping, to create sequences that mimic temporal progression. However, in tasks like video salient object detection, where both appearance and motion cues are critical, these basic image-to-video techniques fail to produce realistic optical flows that capture the independent motion properties of each object. In this study, we show that image-to-video diffusion models can generate realistic transformations of static images while understanding the contextual relationships between image components. This ability allows the model to generate plausible optical flows, preserving semantic integrity while reflecting the independent motion of scene elements. By augmenting individual images in this way, we create large-scale image-flow pairs that significantly enhance model training. Our approach achieves state-of-the-art performance across all public benchmark datasets, outperforming existing approaches.
Dehazing-aided Multi-Rate Multi-Modal Pose Estimation Framework for Mitigating Visual Disturbances in Extreme Underwater Domain
Venue: arXiv cs.RO 2024
Year: 2024

This paper delves into the potential of DU-VIO, a dehazing-aided hybrid multi-rate multi-modal Visual-Inertial Odometry (VIO) estimation framework, designed to thrive in the challenging realm of extreme underwater environments. The cutting-edge DU-VIO framework is incorporating a GAN-based pre-processing module and a hybrid CNN-LSTM module for precise pose estimation, using visibility-enhanced underwater images and raw IMU data. Accurate pose estimation is paramount for various underwater robotics and exploration applications. However, underwater visibility is often compromised by suspended particles and attenuation effects, rendering visual-inertial pose estimation a formidable challenge. DU-VIO aims to overcome these limitations by effectively removing visual disturbances from raw image data, enhancing the quality of image features used for pose estimation. We demonstrate the effectiveness of DU-VIO by calculating RMSE scores for translation and rotation vectors in comparison to their reference values. These scores are then compared to those of a base model using a modified AQUALOC Dataset. This study's significance lies in its potential to revolutionize underwater robotics and exploration. DU-VIO offers a robust solution to the persistent challenge of underwater visibility, significantly improving the accuracy of pose estimation. This research contributes valuable insights and tools for advancing underwater technology, with far-reaching implications for scientific research, environmental monitoring, and industrial applications.
Towards Smart Fronthauling Management: Experimental Insights from a 5G Testbed
Venue: arXiv cs.NI 2024
Year: 2024

The fronthaul connection is a key component of Centralized RAN (C-RAN) architectures, consistently required to handle high capacity demands. However, this critical feature is at risk when the transport link relies on wireless technology. Fortunately, solutions exist to enhance the reliability of wireless links. In this paper, we recall the theoretical fronthaul model, present a dynamic reconfiguration strategy and perform a conclusive experiment. Specifically, we showcase the setup of a wireless fronthaul testbed and discuss the resulting measurements. For this task, we leveraged the commercial hardware provided by the High-Frequency Campus Lab (HFCL), a private 5G network with millimeter wave (mmWave) radio access interface. Our experiments provide original data on the fronthaul utilization in this real deployment, demonstrating both a good accordance with the theoretical model discussed in [1] and the viability of one stabilizing solution.
Experimental comparison of graph-based approximate nearest neighbor search algorithms on edge devices
Venue: arXiv cs.DS 2024
Year: 2024

In this paper, we present an experimental comparison of various graph-based approximate nearest neighbor (ANN) search algorithms deployed on edge devices for real-time nearest neighbor search applications, such as smart city infrastructure and autonomous vehicles. To the best of our knowledge, this specific comparative analysis has not been previously conducted. While existing research has explored graph-based ANN algorithms, it has often been limited to single-threaded implementations on standard commodity hardware. Our study leverages the full computational and storage capabilities of edge devices, incorporating additional metrics such as insertion and deletion latency of new vectors and power consumption. This comprehensive evaluation aims to provide valuable insights into the performance and suitability of these algorithms for edge-based real-time tracking systems enhanced by nearest-neighbor search algorithms.
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting
Venue: arXiv cs.LG 2024
Year: 2024

Multiple federated learning (FL) methods are proposed for traffic flow forecasting (TFF) to avoid heavy-transmission and privacy-leaking concerns resulting from the disclosure of raw data in centralized methods. However, these FL methods adopt offline learning which may yield subpar performance, when concept drift occurs, i.e., distributions of historical and future data vary. Online learning can detect concept drift during model training, thus more applicable to TFF. Nevertheless, the existing federated online learning method for TFF fails to efficiently solve the concept drift problem and causes tremendous computing and communication overhead. Therefore, we propose a novel method named Resource-Efficient Federated Online Learning (REFOL) for TFF, which guarantees prediction performance in a communication-lightweight and computation-efficient way. Specifically, we design a data-driven client participation mechanism to detect the occurrence of concept drift and determine clients' participation necessity. Subsequently, we propose an adaptive online optimization strategy, which guarantees prediction performance and meanwhile avoids meaningless model updates. Then, a graph convolution-based model aggregation mechanism is designed, aiming to assess participants' contribution based on spatial correlation without importing extra communication and computing consumption on clients. Finally, we conduct extensive experiments on real-world datasets to demonstrate the superiority of REFOL in terms of prediction improvement and resource economization.
Determining the covering radius of all generalized Zetterberg codes in odd characteristic
Venue: arXiv cs.IT 2024
Year: 2024

For an integer $s\ge 1$, let $\mathcal{C}_s(q_0)$ be the generalized Zetterberg code of length $q_0^s+1$ over the finite field $\F_{q_0}$ of odd characteristic. Recently, Shi, Helleseth, and Özbudak (IEEE Trans. Inf. Theory 69(11): 7025-7048, 2023) determined the covering radius of $\mathcal{C}_s(q_0)$ for $q_0^s \not \equiv 7 \pmod{8}$, and left the remaining case as an open problem. In this paper, we develop a general technique involving arithmetic of finite fields and algebraic curves over finite fields to determine the covering radius of all generalized Zetterberg codes for $q_0^s \equiv 7 \pmod{8}$, which therefore solves this open problem. We also introduce the concept of twisted half generalized Zetterberg codes of length $\frac{q_0^s+1}{2}$, and show the same results hold for them. As a result, we obtain some quasi-perfect codes.
GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification
Venue: arXiv cs.LG 2024
Year: 2024

Graph neural networks (GNNs) have emerged as powerful models for learning representations of graph data showing state of the art results in various tasks. Nevertheless, the superiority of these methods is usually supported by either evaluating their performance on small subset of benchmark datasets or by reasoning about their expressive power in terms of certain graph isomorphism tests. In this paper we critically analyse both these aspects through a transductive setting for the task of node classification. First, we delve deeper into the case of multi-label node classification which offers a more realistic scenario and has been ignored in most of the related works. Through analysing the training dynamics for GNN methods we highlight the failure of GNNs to learn over multi-label graph datasets even for the case of abundant training data. Second, we show that specifically for transductive node classification, even the most expressive GNN may fail to learn in absence of node attributes and without using explicit label information as input. To overcome this deficit, we propose a straightforward approach, referred to as GNN-MultiFix, that integrates the feature, label, and positional information of a node. GNN-MultiFix demonstrates significant improvement across all the multi-label datasets. We release our code at https://anonymous.4open.science/r/Graph-MultiFix-4121.
Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote Sensing
Venue: arXiv cs.CV 2024
Year: 2024

Remote sensing imagery offers rich spectral data across extensive areas for Earth observation. Many attempts have been made to leverage these data with transfer learning to develop scalable alternatives for estimating socio-economic conditions, reducing reliance on expensive survey-collected data. However, much of this research has primarily focused on daytime satellite imagery due to the limitation that most pre-trained models are trained on 3-band RGB images. Consequently, modeling techniques for spectral bands beyond the visible spectrum have not been thoroughly investigated. Additionally, quantifying uncertainty in remote sensing regression has been less explored, yet it is essential for more informed targeting and iterative collection of ground truth survey data. In this paper, we introduce a novel framework that leverages generic foundational vision models to process remote sensing imagery using combinations of three spectral bands to exploit multi-spectral data. We also employ methods such as heteroscedastic regression and Bayesian modeling to generate uncertainty estimates for the predictions. Experimental results demonstrate that our method outperforms existing models that use RGB or multi-spectral models with unstructured band usage. Moreover, our framework helps identify uncertain predictions, guiding future ground truth data acquisition.
Multi-terminal Strong Coordination subject to Secrecy Constraints
Venue: arXiv cs.IT 2024
Year: 2024

A fundamental problem in decentralized networked systems is to coordinate actions of different agents so that they reach a state of agreement. In such applications, it is additionally desirable that the actions at various nodes may not be anticipated by malicious eavesdroppers. Motivated by this, we investigate the problem of secure multi-terminal strong coordination aided by a multiple-access wiretap channel. In this setup, independent and identically distributed copies of correlated sources are observed by two transmitters who encode the channel inputs to the MAC-WT. The legitimate receiver observing the channel output and side information correlated with the sources must produce approximately i.i.d. copies of an output variable jointly distributed with the sources. Furthermore, we demand that an external eavesdropper learns essentially nothin g about the sources and the simulated output sequence by observing its own MAC-WT output. This setting is aided by the presence of independent pairwise shared randomness between each encoder and the legitimate decoder, that is unavailable to the eavesdropper. We derive an achievable rate region based on a combination of coordination coding and wiretap coding, along with an outer bound. The inner bound is shown to be tight and a complete characterization is derived for the special case when the sources are conditionally independent given the decoder side information and the legitimate channel is composed of deterministic links. Further, we also analyze a more general scenario with possible encoder cooperation, where one of the encoders can non-causally crib from the other encoders input, for which an achievable rate region is proposed. We then explicitly compute the rate regions for an example both with and without cribbing between the encoders, and demonstrate that cribbing strictly improves upon the achievable rate region.