OpenRaiser/Intern-Atlas huggingface dataset:
Geometric constellation shaping for wireless optical intensity channels: An information-theoretic approach
Venue: arXiv cs.IT 2024
Year: 2024

A simple geometric shaping method is proposed for optical wireless communication systems based on intensity modulation and direct detection (IM/DD) from an information-theoretic perspective. Constellations consisting of equiprobable levels with exponential-like distribution are obtained, which possesses asymptotic optimality in the sense that the high-SNR capacity of average-intensity constrained optical intensity channel can be approached by such constellations with increasing size. All $2^b$ levels ($b\in\mathbb N$) of the obtained constellation can be represented by a basic level and $b+2$ bits, thereby reducing the required resolution of the digital-to-analog converter (DAC) without affecting the asymptotic optimality. Achievable information rate evaluations verify the asymptotic optimality. As an example, error performance results of a simple $16$-level LDPC coded modulation scheme show that a shaping gain of $0.65$ dB can be obtained by applying the proposed constellation design. This method can also be applied to more specific IM/DD channel models, since it only requires a near-optimal continuous input distribution.
Hybrid Quadratic Programming -- Pullback Bundle Dynamical Systems Control
Venue: arXiv cs.RO 2024
Year: 2024

Dynamical System (DS)-based closed-loop control is a simple and effective way to generate reactive motion policies that well generalize to the robotic workspace, while retaining stability guarantees. Lately the formalism has been expanded in order to handle arbitrary geometry curved spaces, namely manifolds, beyond the standard flat Euclidean space. Despite the many different ways proposed to handle DS on manifolds, it is still unclear how to apply such structures on real robotic systems. In this preliminary study, we propose a way to combine modern optimal control techniques with a geometry-based formulation of DS. The advantage of such approach is two fold. First, it yields a torque-based control for compliant and adaptive motions; second, it generates dynamical systems consistent with the controlled system's dynamics. The salient point of the approach is that the complexity of designing a proper constrained-based optimal control problem, to ensure that dynamics move on a manifold while avoiding obstacles or self-collisions, is "outsourced" to the geometric DS. Constraints are implicitly embedded into the structure of the space in which the DS evolves. The optimal control, on the other hand, provides a torque-based control interface, and ensures dynamical consistency of the generated output. The whole can be achieved with minimal computational overhead since most of the computational complexity is delegated to the closed-form geometric DS.
Ubiquitous Robot Control Through Multimodal Motion Capture Using Smartwatch and Smartphone Data
Venue: arXiv cs.RO 2024
Year: 2024

We present an open-source library for seamless robot control through motion capture using smartphones and smartwatches. Our library features three modes: Watch Only Mode, enabling control with a single smartwatch; Upper Arm Mode, offering heightened accuracy by incorporating the smartphone attached to the upper arm; and Pocket Mode, determining body orientation via the smartphone placed in any pocket. These modes are applied in two real-robot tasks, showcasing placement accuracy within 2 cm compared to a gold-standard motion capture system. WearMoCap stands as a suitable alternative to conventional motion capture systems, particularly in environments where ubiquity is essential. The library is available at: www.github.com/wearable-motion-capture.
The Application of Procedurally Generated Libraries in Immersive Virtual Reality
Venue: arXiv cs.HC 2024
Year: 2024

While digital libraries offer essential benefits for the digital age such as constant availability and accessibility, they often fail in providing a similarly enjoyable browsing experience that benefits engagement and serendipitous exploration inherent to traditional library experiences. In this paper, we propose a virtual reality (VR) library environment that emulates a traditional library promoting an engaging browsing experience that benefits browsing enjoyment and serendipity. The implemented system uses procedural content generation to dynamically generate a library-like environment, wherein each room corresponds to a specific category of the digital library. To assess the usability of the implemented system, we conducted an A-B study comparing the VR environment with the web interface of Project Gutenberg. The results of the study suggest that while the physical demand for using the VR environment is higher, the system can significantly benefit the browsing experience and entice users curiosity. Furthermore, the study suggests that the system can increase user engagement and users generally felt that using the system was rewarding.
Learning-based legged locomotion; state of the art and future perspectives
Venue: arXiv cs.RO 2024
Year: 2024

Legged locomotion holds the premise of universal mobility, a critical capability for many real-world robotic applications. Both model-based and learning-based approaches have advanced the field of legged locomotion in the past three decades. In recent years, however, a number of factors have dramatically accelerated progress in learning-based methods, including the rise of deep learning, rapid progress in simulating robotic systems, and the availability of high-performance and affordable hardware. This article aims to give a brief history of the field, to summarize recent efforts in learning locomotion skills for quadrupeds, and to provide researchers new to the area with an understanding of the key issues involved. With the recent proliferation of humanoid robots, we further outline the rapid rise of analogous methods for bipedal locomotion. We conclude with a discussion of open problems as well as related societal impact.
Linear Index for Logarithmic Search-Time for any String under any Internal Node in Suffix Trees
Venue: arXiv cs.DS 2024
Year: 2024

Suffix trees are key and efficient data structure for solving string problems. A suffix tree is a compressed trie containing all the suffixes of a given text of length $n$ with a linear construction cost. In this work, we introduce an algorithm to build a linear index that allows finding a pattern of any length under any internal node in a suffix tree in O(logn) time.
3D WholeBody Pose Estimation based on Semantic Graph Attention Network and Distance Information
Venue: arXiv cs.CV 2024
Year: 2024

In recent years, a plethora of diverse methods have been proposed for 3D pose estimation. Among these, self-attention mechanisms and graph convolutions have both been proven to be effective and practical methods. Recognizing the strengths of those two techniques, we have developed a novel Semantic Graph Attention Network which can benefit from the ability of self-attention to capture global context, while also utilizing the graph convolutions to handle the local connectivity and structural constraints of the skeleton. We also design a Body Part Decoder that assists in extracting and refining the information related to specific segments of the body. Furthermore, our approach incorporates Distance Information, enhancing our model's capability to comprehend and accurately predict spatial relationships. Finally, we introduce a Geometry Loss who makes a critical constraint on the structural skeleton of the body, ensuring that the model's predictions adhere to the natural limits of human posture. The experimental results validate the effectiveness of our approach, demonstrating that every element within the system is essential for improving pose estimation outcomes. With comparison to state-of-the-art, the proposed work not only meets but exceeds the existing benchmarks.
A Survey of Generative Information Retrieval
Venue: arXiv cs.IR 2024
Year: 2024

Generative Retrieval (GR) is an emerging paradigm in information retrieval that leverages generative models to directly map queries to relevant document identifiers (DocIDs) without the need for traditional query processing or document reranking. This survey provides a comprehensive overview of GR, highlighting key developments, indexing and retrieval strategies, and challenges. We discuss various document identifier strategies, including numerical and string-based identifiers, and explore different document representation methods. Our primary contribution lies in outlining future research directions that could profoundly impact the field: improving the quality of query generation, exploring learnable document identifiers, enhancing scalability, and integrating GR with multi-task learning frameworks. By examining state-of-the-art GR techniques and their applications, this survey aims to provide a foundational understanding of GR and inspire further innovations in this transformative approach to information retrieval. We also make the complementary materials such as paper collection publicly available at https://github.com/MiuLab/GenIR-Survey/
Structures and Techniques for Streaming Dynamic Graph Processing on Decentralized Message-Driven Systems
Venue: arXiv cs.DC 2024
Year: 2024

The paper presents structures and techniques aimed towards co-designing scalable asynchronous and decentralized dynamic graph processing for fine-grain memory-driven architectures. It uses asynchronous active messages, in the form of actions that send ``work to data'', with a programming and execution model that allows spawning tasks from within the data-parallelism combined with a data-structure that parallelizes vertex object across many scratchpad memory-coupled cores and yet provides a single programming abstraction to the data object. The graph is constructed by streaming new edges using novel message delivery mechanisms and language constructs that work together to pass data and control using abstraction of actions, continuations and local control objects (LCOs) such as futures. It results in very fine-grain updates to a hierarchical dynamic vertex data structure, which subsequently triggers a user application action to update the results of any previous computation without recomputing from scratch. In our experiments we use BFS to demonstrate our concept design, and document challenges and opportunities.
Scaling Up Deep Clustering Methods Beyond ImageNet-1K
Venue: arXiv cs.CV 2024
Year: 2024

Deep image clustering methods are typically evaluated on small-scale balanced classification datasets while feature-based $k$-means has been applied on proprietary billion-scale datasets. In this work, we explore the performance of feature-based deep clustering approaches on large-scale benchmarks whilst disentangling the impact of the following data-related factors: i) class imbalance, ii) class granularity, iii) easy-to-recognize classes, and iv) the ability to capture multiple classes. Consequently, we develop multiple new benchmarks based on ImageNet21K. Our experimental analysis reveals that feature-based $k$-means is often unfairly evaluated on balanced datasets. However, deep clustering methods outperform $k$-means across most large-scale benchmarks. Interestingly, $k$-means underperforms on easy-to-classify benchmarks by large margins. The performance gap, however, diminishes on the highest data regimes such as ImageNet21K. Finally, we find that non-primary cluster predictions capture meaningful classes (i.e. coarser classes).
The hop-like problem nature -- unveiling and modelling new features of real-world problems
Venue: arXiv cs.NE 2024
Year: 2024

Benchmarks are essential tools for the optimizer's development. Using them, we can check for what kind of problems a given optimizer is effective or not. Since the objective of the Evolutionary Computation field is to support the tools to solve hard, real-world problems, the benchmarks that resemble their features seem particularly valuable. Therefore, we propose a hop-based analysis of the optimization process. We apply this analysis to the NP-hard, large-scale real-world problem. Its results indicate the existence of some of the features of the well-known Leading Ones problem. To model these features well, we propose the Leading Blocks Problem (LBP), which is more general than Leading Ones and some of the benchmarks inspired by this problem. LBP allows for the assembly of new types of hard optimization problems that are not handled well by the considered state-of-the-art genetic algorithm (GA). Finally, the experiments reveal what kind of mechanisms must be proposed to improve GAs' effectiveness while solving LBP and the considered real-world problem.
Constraint-based Adversarial Example Synthesis
Venue: arXiv cs.CR 2024
Year: 2024

In the era of rapid advancements in artificial intelligence (AI), neural network models have achieved notable breakthroughs. However, concerns arise regarding their vulnerability to adversarial attacks. This study focuses on enhancing Concolic Testing, a specialized technique for testing Python programs implementing neural networks. The extended tool, PyCT, now accommodates a broader range of neural network operations, including floating-point and activation function computations. By systematically generating prediction path constraints, the research facilitates the identification of potential adversarial examples. Demonstrating effectiveness across various neural network architectures, the study highlights the vulnerability of Python-based neural network models to adversarial attacks. This research contributes to securing AI-powered applications by emphasizing the need for robust testing methodologies to detect and mitigate potential adversarial threats. It underscores the importance of rigorous testing techniques in fortifying neural network models for reliable applications in Python.
Report on Methods and Applications for Crafting 3D Humans
Venue: arXiv cs.GR 2024
Year: 2024

This paper presents an in-depth exploration of 3D human model and avatar generation technology, propelled by the rapid advancements in large-scale models and artificial intelligence. The paper reviews the comprehensive process of 3D human model generation, from scanning to rendering, and highlights the pivotal role these models play in entertainment, VR, AR, healthcare, and education. We underscore the significance of diffusion models in generating high-fidelity images and videos. It emphasizes the indispensable nature of 3D human models in enhancing user experiences and functionalities across various fields. Furthermore, this paper anticipates the potential of integrating large-scale models with deep learning to revolutionize 3D content generation, offering insights into the future prospects of the technology. It concludes by emphasizing the importance of continuous innovation in the field, suggesting that ongoing advancements will significantly expand the capabilities and applications of 3D human models and avatars.
SVFF: An Automated Framework for SR-IOV Virtual Function Management in FPGA Accelerated Virtualized Environments
Venue: arXiv cs.OS 2024
Year: 2024

FPGA accelerator devices have emerged as a powerful platform for implementing high-performance and scalable solutions in a wide range of industries, leveraging their reconfigurability and virtualization capabilities. Virtualization, in particular, offers several benefits including improved security by resource isolation and sharing, and SR-IOV is the main solution for enabling it on FPGAs. This paper introduces the SR-IOV Virtual Function Framework (SVFF), a solution that aims to simplify and enhance the management of Virtual Functions (VFs) on PCIe-attached FPGA devices in Linux and QEMU/KVM environments, solving the lack of SR-IOV re-configuration support on guests. The framework leverages the SR-IOV support in the Xilinx Queue-based Direct Memory Access (QDMA) to automate the creation, attachment, detachment, and reconfiguration of VFs to different Virtual Machines (VMs). A novel pause functionality for the VFIO device has been implemented in QEMU to enable the detachment of VFs from the host without detaching them from the guest, making reconfiguration of VFs transparent for guests that already have a VF attached to them without any performance loss. The proposed solution offers the ability to automatically and seamlessly assign a set of VFs to different VMs and adjust the configuration on the fly. Thanks to the pause functionality, it also offers the ability to attach additional VFs to new VMs without affecting devices already attached to other VMs.
AGALE: A Graph-Aware Continual Learning Evaluation Framework
Venue: arXiv cs.LG 2024
Year: 2024

In recent years, continual learning (CL) techniques have made significant progress in learning from streaming data while preserving knowledge across sequential tasks, particularly in the realm of euclidean data. To foster fair evaluation and recognize challenges in CL settings, several evaluation frameworks have been proposed, focusing mainly on the single- and multi-label classification task on euclidean data. However, these evaluation frameworks are not trivially applicable when the input data is graph-structured, as they do not consider the topological structure inherent in graphs. Existing continual graph learning (CGL) evaluation frameworks have predominantly focussed on single-label scenarios in the node classification (NC) task. This focus has overlooked the complexities of multi-label scenarios, where nodes may exhibit affiliations with multiple labels, simultaneously participating in multiple tasks. We develop a graph-aware evaluation (\agale) framework that accommodates both single-labeled and multi-labeled nodes, addressing the limitations of previous evaluation frameworks. In particular, we define new incremental settings and devise data partitioning algorithms tailored to CGL datasets. We perform extensive experiments comparing methods from the domains of continual learning, continual graph learning, and dynamic graph learning (DGL). We theoretically analyze \agale and provide new insights about the role of homophily in the performance of compared methods. We release our framework at https://github.com/Tianqi-py/AGALE.
Multi-word Term Embeddings Improve Lexical Product Retrieval
Venue: arXiv cs.IR 2024
Year: 2024

Product search is uniquely different from search for documents, Internet resources or vacancies, therefore it requires the development of specialized search systems. The present work describes the H1 embdedding model, designed for an offline term indexing of product descriptions at e-commerce platforms. The model is compared to other state-of-the-art (SoTA) embedding models within a framework of hybrid product search system that incorporates the advantages of lexical methods for product retrieval and semantic embedding-based methods. We propose an approach to building semantically rich term vocabularies for search indexes. Compared to other production semantic models, H1 paired with the proposed approach stands out due to its ability to process multi-word product terms as one token. As an example, for search queries "new balance shoes", "gloria jeans kids wear" brand entity will be represented as one token - "new balance", "gloria jeans". This results in an increased precision of the system without affecting the recall. The hybrid search system with proposed model scores mAP@12 = 56.1% and R@1k = 86.6% on the WANDS public dataset, beating other SoTA analogues.
animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics
Venue: arXiv cs.SD 2024
Year: 2024

Bioacoustic research, vital for understanding animal behavior, conservation, and ecology, faces a monumental challenge: analyzing vast datasets where animal vocalizations are rare. While deep learning techniques are becoming standard, adapting them to bioacoustics remains difficult. We address this with animal2vec, an interpretable large transformer model, and a self-supervised training scheme tailored for sparse and unbalanced bioacoustic data. It learns from unlabeled audio and then refines its understanding with labeled data. Furthermore, we introduce and publicly release MeerKAT: Meerkat Kalahari Audio Transcripts, a dataset of meerkat (Suricata suricatta) vocalizations with millisecond-resolution annotations, the largest labeled dataset on non-human terrestrial mammals currently available. Our model outperforms existing methods on MeerKAT and the publicly available NIPS4Bplus birdsong dataset. Moreover, animal2vec performs well even with limited labeled data (few-shot learning). animal2vec and MeerKAT provide a new reference point for bioacoustic research, enabling scientists to analyze large amounts of data even with scarce ground truth information.
SCALLER: Standard Cell Assembled and Local Layout Effect-based Ring Oscillators
Venue: arXiv cs.CR 2024
Year: 2024

This letter presents a technique that enables very fine tunability of the frequency of Ring Oscillators (ROs). Multiple ROs with different numbers of tunable elements were designed and fabricated in a 65nm CMOS technology. A tunable element consists of two inverters under different local layout effects (LLEs) and a multiplexer. LLEs impact the transient response of inverters deterministically and allow to establish a fine tunable mechanism even in the presence of large process variation. The entire RO is digital and its layout is standard-cell compatible. We demonstrate the tunability of multi-stage ROs with post-silicon measurements of oscillation frequencies in the range of 80-900MHz and tuning steps of 90KHz
3D Trajectory Design for Energy-constrained Aerial CRNs Under Probabilistic LoS Channel
Venue: arXiv cs.IT 2024
Year: 2024

Unmanned aerial vehicles (UAVs) have been attracting significant attention because there is a high probability of line-of-sight links being obtained between them and terrestrial nodes in high-rise urban areas. In this work, we investigate cognitive radio networks (CRNs) by jointly designing three-dimensional (3D) trajectory, the transmit power of the UAV, and user scheduling. Considering the UAV's onboard energy consumption, an optimization problem is formulated in which the average achievable rate of the considered system is maximized by jointly optimizing the UAV's 3D trajectory, transmission power, and user scheduling. Due to the non-convex optimization problem, a lower bound on the average achievable rate is utilized to reduce the complexity of the solution. Subsequently, the original optimization problem is decoupled into four subproblems by using block coordinate descent, and each subproblem is transformed into manageable convex optimization problems by introducing slack variables and successive convex approximation. Numerical results validate the effectiveness of our proposed algorithm and demonstrate that the 3D trajectories of UAVs can enhance the average achievable rate of aerial CRNs.