OpenRaiser/Intern-Atlas huggingface dataset:
Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification
Venue: arXiv cs.CV 2024
Year: 2024

This paper introduces Point-GN, a novel non-parametric network for efficient and accurate 3D point cloud classification. Unlike conventional deep learning models that rely on a large number of trainable parameters, Point-GN leverages non-learnable components-specifically, Farthest Point Sampling (FPS), k-Nearest Neighbors (k-NN), and Gaussian Positional Encoding (GPE)-to extract both local and global geometric features. This design eliminates the need for additional training while maintaining high performance, making Point-GN particularly suited for real-time, resource-constrained applications. We evaluate Point-GN on two benchmark datasets, ModelNet40 and ScanObjectNN, achieving classification accuracies of 85.29% and 85.89%, respectively, while significantly reducing computational complexity. Point-GN outperforms existing non-parametric methods and matches the performance of fully trained models, all with zero learnable parameters. Our results demonstrate that Point-GN is a promising solution for 3D point cloud classification in practical, real-time environments.
Lightweight Multiplane Images Network for Real-Time Stereoscopic Conversion from Planar Video
Venue: arXiv cs.CV 2024
Year: 2024

With the rapid development of stereoscopic display technologies, especially glasses-free 3D screens, and virtual reality devices, stereoscopic conversion has become an important task to address the lack of high-quality stereoscopic image and video resources. Current stereoscopic conversion algorithms typically struggle to balance reconstruction performance and inference efficiency. This paper proposes a planar video real-time stereoscopic conversion network based on multi-plane images (MPI), which consists of a detail branch for generating MPI and a depth-semantic branch for perceiving depth information. Unlike models that depend on explicit depth map inputs, the proposed method employs a lightweight depth-semantic branch to extract depth-aware features implicitly. To optimize the lightweight branch, a heavy training but light inference strategy is adopted, which involves designing a coarse-to-fine auxiliary branch that is only used during the training stage. In addition, the proposed method simplifies the MPI rendering process for stereoscopic conversion scenarios to further accelerate the inference. Experimental results demonstrate that the proposed method can achieve comparable performance to some state-of-the-art (SOTA) models and support real-time inference at 2K resolution. Compared to the SOTA TMPI algorithm, the proposed method obtains similar subjective quality while achieving over $40\times$ inference acceleration.
Completing the Functional Approach in Object-Oriented Languages
Venue: arXiv cs.PL 2024
Year: 2024

Over the last two decades practically all object-oriented programming languages have introduced features that are well-known from functional programming languages. But many features that were introduced were fragmentary. In Java-TX we address the latter features and propose a completion. Java-TX (i.e. Type eXtended) is a language based on Java. The predominant new features are global type inference and real function types for lambda expressions. Global type inference means that all type annotations can be omitted, and the compiler infers them without losing the static type property. We introduce the function types in a similar fashion as in Scala but additionally integrated them into the Java target-typing as proposed in the so-called strawman approach. In this paper, we provide an integrated presentation of all Java-TX features. The focus is therby on the automatic inference of type parameters for classes and their methods, and on the heterogeneous translation of function types, which permits the preservation of the argument and return types in bytecode.
Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware
Venue: arXiv cs.NE 2024
Year: 2024

Spiking neural networks (SNNs) emulated on dedicated neuromorphic accelerators promise to offer energy-efficient signal processing. However, the neuromorphic advantage over traditional algorithms still remains to be demonstrated in real-world applications. Here, we describe an intensity-modulation, direct-detection (IM/DD) task that is relevant to high-speed optical communication systems used in data centers. Compared to other machine learning-inspired benchmarks, the task offers several advantages. First, the dataset is inherently time-dependent, i.e., there is a time dimension that can be natively mapped to the dynamic evolution of SNNs. Second, small-scale SNNs can achieve the target accuracy required by technical communication standards. Third, due to the small scale and the defined target accuracy, the task facilitates the optimization for real-world aspects, such as energy efficiency, resource requirements, and system complexity.
Fine-Grained Behavior Simulation with Role-Playing Large Language Model on Social Media
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have demonstrated impressive capabilities in role-playing tasks. However, there is limited research on whether LLMs can accurately simulate user behavior in real-world scenarios, such as social media. This requires models to effectively analyze a user's history and simulate their role. In this paper, we introduce \textbf{FineRob}, a novel fine-grained behavior simulation dataset. We collect the complete behavioral history of 1,866 distinct users across three social media platforms. Each behavior is decomposed into three fine-grained elements: object, type, and content, resulting in 78.6k QA records. Based on FineRob, we identify two dominant reasoning patterns in LLMs' behavior simulation processes and propose the \textbf{OM-CoT} fine-tuning method to enhance the capability. Through comprehensive experiments, we conduct an in-depth analysis of key factors of behavior simulation and also demonstrate the effectiveness of OM-CoT approach\footnote{Code and dataset are available at \url{https://github.com/linkseed18612254945/FineRob}}
Resource Slicing through Intelligent Orchestration of Energy-aware IoT services in Edge-Cloud Continuum
Venue: arXiv cs.DC 2024
Year: 2024

The rapid growth of the Internet of Things (IoT) applications inflicts high requirements for computing resources and network bandwidth. A growing number of service providers are applying edge-cloud computing to improve the quality of their services. Deploying IoT applications to optimal computing nodes to minimize energy consumption and enhance system performance remains an open challenge. In this paper, we present an intelligent orchestration concept for breaking down IoT applications into granular microservices, called nanoservices, and deploying them in an energy-aware manner to optimal computing nodes in the edge-cloud continuum by applying resource and network slicing methods. With this consolidated slicing scheme, we can efficiently allocate network and compute resources to meet the needs of these nanoservices.
Revisiting Atomic Patterns for Elliptic Curve Scalar Multiplication Revealing Inherent Vulnerability to Simple SCA
Venue: arXiv cs.CR 2024
Year: 2024

Elliptic Curve Scalar Multiplication denoted as kP operation is the basic operation in all Elliptic Curve based cryptographic protocols. The atomicity principle and different atomic patterns for kP algorithms were proposed in the past as countermeasures against simple side-channel analysis. In this work, we investigated the resistance of a kP algorithm implemented in hardware using Longa's atomic patterns. We analysed its simulated power trace. We show in the example of our kP implementation for the NIST EC P-256 that the field squaring operations are distinguishable from the field multiplications even if they are performed by the same field multiplier, due to the addressing of the second multiplicand. This inherent vulnerability of atomic patterns can be successfully exploited for revealing the scalar k.
Benchmarking terminology building capabilities of ChatGPT on an English-Russian Fashion Corpus
Venue: arXiv cs.CL 2024
Year: 2024

This paper compares the accuracy of the terms extracted using SketchEngine, TBXTools and ChatGPT. In addition, it evaluates the quality of the definitions produced by ChatGPT for these terms. The research is carried out on a comparable corpus of fashion magazines written in English and Russian collected from the web. A gold standard for the fashion terminology was also developed by identifying web pages that can be harvested automatically and contain definitions of terms from the fashion domain in English and Russian. This gold standard was used to evaluate the quality of the extracted terms and of the definitions produced. Our evaluation shows that TBXTools and SketchEngine, while capable of high recall, suffer from reduced precision as the number of terms increases, which affects their overall performance. Conversely, ChatGPT demonstrates superior performance, maintaining or improving precision as more terms are considered. Analysis of the definitions produced by ChatGPT for 60 commonly used terms in English and Russian shows that ChatGPT maintains a reasonable level of accuracy and fidelity across languages, but sometimes the definitions in both languages miss crucial specifics and include unnecessary deviations. Our research reveals that no single tool excels universally; each has strengths suited to particular aspects of terminology extraction and application.
Controlling the Mutation in Large Language Models for the Efficient Evolution of Algorithms
Venue: arXiv cs.NE 2024
Year: 2024

The integration of Large Language Models (LLMs) with evolutionary computation (EC) has introduced a promising paradigm for automating the design of metaheuristic algorithms. However, existing frameworks, such as the Large Language Model Evolutionary Algorithm (LLaMEA), often lack precise control over mutation mechanisms, leading to inefficiencies in solution space exploration and potentially suboptimal convergence. This paper introduces a novel approach to mutation control within LLM-driven evolutionary frameworks, inspired by theory of genetic algorithms. Specifically, we propose dynamic mutation prompts that adaptively regulate mutation rates, leveraging a heavy-tailed power-law distribution to balance exploration and exploitation. Experiments using GPT-3.5-turbo and GPT-4o models demonstrate that GPT-3.5-turbo fails to adhere to the specific mutation instructions, while GPT-4o is able to adapt its mutation based on the prompt engineered dynamic prompts. Further experiments show that the introduction of these dynamic rates can improve the convergence speed and adaptability of LLaMEA, when using GPT-4o. This work sets the starting point for better controlled LLM-based mutations in code optimization tasks, paving the way for further advancements in automated metaheuristic design.
Electrode and electroactive polymer layout design using topology optimization
Venue: arXiv cs.CE 2024
Year: 2024

When electrically stimulated, electroactive polymers (EAPs) respond with mechanical deformation. The goal of this work is to design electrode and EAP layouts simultaneously in structures by using density-based, multi-material topology optimization. In this novel approach the layout of electrodes and EAP material are not given a priori but is a result from the topology optimization. Material interpolation based on exponential functions is introduced, allowing a large flexibility to control the material interpolation. The electric field in the surrounding free space is modeled using a truncated extended domain method. Numerical examples that demonstrates the method's ability to design arbitrary EAP and electrode layouts are presented. In these optimized structures, electrode material is continuously connected from the electrical sources to opposite sides of the EAP material and thereby concentrating the electric field to the EAP material which drives the deformation.
Composed Image Retrieval for Training-Free Domain Conversion
Venue: arXiv cs.CV 2024
Year: 2024

This work addresses composed image retrieval in the context of domain conversion, where the content of a query image is retrieved in the domain specified by the query text. We show that a strong vision-language model provides sufficient descriptive power without additional training. The query image is mapped to the text input space using textual inversion. Unlike common practice that invert in the continuous space of text tokens, we use the discrete word space via a nearest-neighbor search in a text vocabulary. With this inversion, the image is softly mapped across the vocabulary and is made more robust using retrieval-based augmentation. Database images are retrieved by a weighted ensemble of text queries combining mapped words with the domain text. Our method outperforms prior art by a large margin on standard and newly introduced benchmarks. Code: https://github.com/NikosEfth/freedom
Scalable Bayesian Tensor Ring Factorization for Multiway Data Analysis
Venue: arXiv cs.LG 2024
Year: 2024

Tensor decompositions play a crucial role in numerous applications related to multi-way data analysis. By employing a Bayesian framework with sparsity-inducing priors, Bayesian Tensor Ring (BTR) factorization offers probabilistic estimates and an effective approach for automatically adapting the tensor ring rank during the learning process. However, previous BTR method employs an Automatic Relevance Determination (ARD) prior, which can lead to sub-optimal solutions. Besides, it solely focuses on continuous data, whereas many applications involve discrete data. More importantly, it relies on the Coordinate-Ascent Variational Inference (CAVI) algorithm, which is inadequate for handling large tensors with extensive observations. These limitations greatly limit its application scales and scopes, making it suitable only for small-scale problems, such as image/video completion. To address these issues, we propose a novel BTR model that incorporates a nonparametric Multiplicative Gamma Process (MGP) prior, known for its superior accuracy in identifying latent structures. To handle discrete data, we introduce the Pólya-Gamma augmentation for closed-form updates. Furthermore, we develop an efficient Gibbs sampler for consistent posterior simulation, which reduces the computational complexity of previous VI algorithm by two orders, and an online EM algorithm that is scalable to extremely large tensors. To showcase the advantages of our model, we conduct extensive experiments on both simulation data and real-world applications.
Exploring trends in audio mixes and masters: Insights from a dataset analysis
Venue: arXiv cs.SD 2024
Year: 2024

We present an analysis of a dataset of audio metrics and aesthetic considerations about mixes and masters provided by the web platform MixCheck studio. The platform is designed for educational purposes, primarily targeting amateur music producers, and aimed at analysing their recordings prior to them being released. The analysis focuses on the following data points: integrated loudness, mono compatibility, presence of clipping and phase issues, compression and tonal profile across 30 user-specified genres. Both mixed (mixes) and mastered audio (masters) are included in the analysis, where mixes refer to the initial combination and balance of individual tracks, and masters refer to the final refined version optimized for distribution. Results show that loudness-related issues along with dynamics issues are the most prevalent, particularly in mastered audio. However mastered audio presents better results in compression than just mixed audio. Additionally, results show that mastered audio has a lower percentage of stereo field and phase issues.
DiffStyleTTS: Diffusion-based Hierarchical Prosody Modeling for Text-to-Speech with Diverse and Controllable Styles
Venue: arXiv cs.SD 2024
Year: 2024

Human speech exhibits rich and flexible prosodic variations. To address the one-to-many mapping problem from text to prosody in a reasonable and flexible manner, we propose DiffStyleTTS, a multi-speaker acoustic model based on a conditional diffusion module and an improved classifier-free guidance, which hierarchically models speech prosodic features, and controls different prosodic styles to guide prosody prediction. Experiments show that our method outperforms all baselines in naturalness and achieves superior synthesis speed compared to three diffusion-based baselines. Additionally, by adjusting the guiding scale, DiffStyleTTS effectively controls the guidance intensity of the synthetic prosody.
An even simpler hard variant of Not-All-Equal 3-SAT
Venue: arXiv cs.CC 2024
Year: 2024

We show that Not-All-Equal 3-Sat remains NP-complete when restricted to instances that simultaneously satisfy the following properties: (i) The clauses are given as the disjoint union of k partitions, for any fixed $k \geq 4$, of the variable set into subsets of size 3, and (ii) each pair of distinct clauses shares at most one variable. Property (i) implies that each variable appears in exactly $k$ clauses and each clause consists of exactly 3 unnegated variables. Therewith, we improve upon our earlier result (Darmann and Döcker, 2020). Complementing the hardness result for at least $4$ partitions, we show that for $k\leq 3$ the corresponding decision problem is in P. In particular, for $k\in \{1,2\}$, all instances that satisfy Property (i) are nae-satisfiable. By the well-known correspondence between Not-All-Equal 3-Sat and hypergraph coloring, we obtain the following corollary of our results: For $k\geq 4$, Bicolorability is NP-complete for linear 3-uniform $k$-regular hypergraphs even if the edges are given as a decomposition into $k$ perfect matchings; with the same restrictions, for $k \leq 3$ Bicolorability is in P, and for $k \in \{1,2\}$ all such hypergraphs are bicolorable. Finally, we deduce from a construction in the work by Pilz (Pilz, 2019) that every instance of Positive Planar Not-All-Equal Sat with at least three distinct variables per clause is nae-satisfiable. Hence, when restricted to instances with a planar incidence graph, each of the above variants of Not-All-Equal 3-Sat turns into a trivial decision problem.
Scarf's Algorithm on Arborescence Hypergraphs
Venue: arXiv cs.DM 2024
Year: 2024

Scarf's algorithm--a pivoting procedure that finds a dominating extreme point in a down-monotone polytope--can be used to show the existence of a fractional stable matching in hypergraphs. The problem of finding a fractional stable matching in a hypergraph, however, is PPAD-complete. In this work, we study the behavior of Scarf's algorithm on arborescence hypergraphs, the family of hypergraphs in which hyperedges correspond to the paths of an arborescence. For arborescence hypergraphs, we prove that Scarf's algorithm can be implemented to find an integral stable matching in polynomial time. En route to our result, we uncover novel structural properties of bases and pivots for the more general family of network hypergraphs. Our work provides the first proof of polynomial-time convergence of Scarf's algorithm on hypergraphic stable matching problems, giving hope to the possibility of polynomial-time convergence of Scarf's algorithm for other families of polytope.
Skel3D: Skeleton Guided Novel View Synthesis
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we present an approach for monocular open-set novel view synthesis (NVS) that leverages object skeletons to guide the underlying diffusion model. Building upon a baseline that utilizes a pre-trained 2D image generator, our method takes advantage of the Objaverse dataset, which includes animated objects with bone structures. By introducing a skeleton guide layer following the existing ray conditioning normalization (RCN) layer, our approach enhances pose accuracy and multi-view consistency. The skeleton guide layer provides detailed structural information for the generative model, improving the quality of synthesized views. Experimental results demonstrate that our skeleton-guided method significantly enhances consistency and accuracy across diverse object categories within the Objaverse dataset. Our method outperforms existing state-of-the-art NVS techniques both quantitatively and qualitatively, without relying on explicit 3D representations.
Gesture Classification in Artworks Using Contextual Image Features
Venue: arXiv cs.CV 2024
Year: 2024

Recognizing gestures in artworks can add a valuable dimension to art understanding and help to acknowledge the role of the sense of smell in cultural heritage. We propose a method to recognize smell gestures in historical artworks. We show that combining local features with global image context improves classification performance notably on different backbones.
YT-30M: A multi-lingual multi-category dataset of YouTube comments
Venue: arXiv cs.SI 2024
Year: 2024

This paper introduces two large-scale multilingual comment datasets, YT-30M (and YT-100K) from YouTube. The analysis in this paper is performed on a smaller sample (YT-100K) of YT-30M. Both the datasets: YT-30M (full) and YT-100K (randomly selected 100K sample from YT-30M) are publicly released for further research. YT-30M (YT-100K) contains 32236173 (108694) comments posted by YouTube channel that belong to YouTube categories. Each comment is associated with a video ID, comment ID, commentor name, commentor channel ID, comment text, upvotes, original channel ID and category of the YouTube channel (e.g., 'News & Politics', 'Science & Technology', etc.).
Cluster Specific Representation Learning
Venue: arXiv cs.LG 2024
Year: 2024

Representation learning aims to extract meaningful lower-dimensional embeddings from data, known as representations. Despite its widespread application, there is no established definition of a ``good'' representation. Typically, the representation quality is evaluated based on its performance in downstream tasks such as clustering, de-noising, etc. However, this task-specific approach has a limitation where a representation that performs well for one task may not necessarily be effective for another. This highlights the need for a more agnostic formulation, which is the focus of our work. We propose a downstream-agnostic formulation: when inherent clusters exist in the data, the representations should be specific to each cluster. Under this idea, we develop a meta-algorithm that jointly learns cluster-specific representations and cluster assignments. As our approach is easy to integrate with any representation learning framework, we demonstrate its effectiveness in various setups, including Autoencoders, Variational Autoencoders, Contrastive learning models, and Restricted Boltzmann Machines. We qualitatively compare our cluster-specific embeddings to standard embeddings and downstream tasks such as de-noising and clustering. While our method slightly increases runtime and parameters compared to the standard model, the experiments clearly show that it extracts the inherent cluster structures in the data, resulting in improved performance in relevant applications.