OpenRaiser/Intern-Atlas huggingface dataset:
SDPL: Shifting-Dense Partition Learning for UAV-View Geo-Localization
Venue: arXiv cs.CV 2024
Year: 2024

Cross-view geo-localization aims to match images of the same target from different platforms, e.g., drone and satellite. It is a challenging task due to the changing appearance of targets and environmental content from different views. Most methods focus on obtaining more comprehensive information through feature map segmentation, while inevitably destroying the image structure, and are sensitive to the shifting and scale of the target in the query. To address the above issues, we introduce simple yet effective part-based representation learning, shifting-dense partition learning (SDPL). We propose a dense partition strategy (DPS), dividing the image into multiple parts to explore contextual information while explicitly maintaining the global structure. To handle scenarios with non-centered targets, we further propose the shifting-fusion strategy, which generates multiple sets of parts in parallel based on various segmentation centers, and then adaptively fuses all features to integrate their anti-offset ability. Extensive experiments show that SDPL is robust to position shifting, and performs com-petitively on two prevailing benchmarks, University-1652 and SUES-200. In addition, SDPL shows satisfactory compatibility with a variety of backbone networks (e.g., ResNet and Swin). https://github.com/C-water/SDPL release.
Image Coding for Machines with Edge Information Learning Using Segment Anything
Venue: arXiv cs.CV 2024
Year: 2024

Image Coding for Machines (ICM) is an image compression technique for image recognition. This technique is essential due to the growing demand for image recognition AI. In this paper, we propose a method for ICM that focuses on encoding and decoding only the edge information of object parts in an image, which we call SA-ICM. This is an Learned Image Compression (LIC) model trained using edge information created by Segment Anything. Our method can be used for image recognition models with various tasks. SA-ICM is also robust to changes in input data, making it effective for a variety of use cases. Additionally, our method provides benefits from a privacy point of view, as it removes human facial information on the encoder's side, thus protecting one's privacy. Furthermore, this LIC model training method can be used to train Neural Representations for Videos (NeRV), which is a video compression model. By training NeRV using edge information created by Segment Anything, it is possible to create a NeRV that is effective for image recognition (SA-NeRV). Experimental results confirm the advantages of SA-ICM, presenting the best performance in image compression for image recognition. We also show that SA-NeRV is superior to ordinary NeRV in video compression for machines. Code is available at https://github.com/final-0/SA-ICM.
Regularized DeepIV with Model Selection
Venue: arXiv cs.LG 2024
Year: 2024

In this paper, we study nonparametric estimation of instrumental variable (IV) regressions. While recent advancements in machine learning have introduced flexible methods for IV estimation, they often encounter one or more of the following limitations: (1) restricting the IV regression to be uniquely identified; (2) requiring minimax computation oracle, which is highly unstable in practice; (3) absence of model selection procedure. In this paper, we present the first method and analysis that can avoid all three limitations, while still enabling general function approximation. Specifically, we propose a minimax-oracle-free method called Regularized DeepIV (RDIV) regression that can converge to the least-norm IV solution. Our method consists of two stages: first, we learn the conditional distribution of covariates, and by utilizing the learned distribution, we learn the estimator by minimizing a Tikhonov-regularized loss function. We further show that our method allows model selection procedures that can achieve the oracle rates in the misspecified regime. When extended to an iterative estimator, our method matches the current state-of-the-art convergence rate. Our method is a Tikhonov regularized variant of the popular DeepIV method with a non-parametric MLE first-stage estimator, and our results provide the first rigorous guarantees for this empirically used method, showcasing the importance of regularization which was absent from the original work.
Conflict and Fairness in Resource Allocation
Venue: arXiv cs.GT 2024
Year: 2024

In the standard model of fair allocation of resources to agents, every agent has some utility for every resource, and the goal is to assign resources to agents so that the agents' welfare is maximized. Motivated by job scheduling, interest in this problem dates back to the work of Deuermeyer et al. [SIAM J. on Algebraic Discrete Methods'82]. Recent works consider the compatibility between resources and assign only mutually compatible resources to an agent. We study a fair allocation problem in which we are given a set of agents, a set of resources, a utility function for every agent over a set of resources, and a {\it conflict graph} on the set of resources (where an edge denotes incompatibility). The goal is to assign resources to the agents such that $(i)$ the set of resources allocated to an agent are compatible with each other, and $(ii)$ the minimum satisfaction of an agent is maximized, where the satisfaction of an agent is the sum of the utility of the assigned resources. Chiarelli et al. [Algorithmica'22] explore this problem from the classical complexity perspective to draw the boundary between the cases that are polynomial-time solvable and those that are \NP-hard. In this article, we study the parameterized complexity of the problem (and its variants) by considering several natural and structural parameters.
GenML: A Python Library to Generate the Mittag-Leffler Correlated Noise
Venue: arXiv cs.MS 2024
Year: 2024

Mittag-Leffler correlated noise (M-L noise) plays a crucial role in the dynamics of complex systems, yet the scientific community has lacked tools for its direct generation. Addressing this gap, our work introduces GenML, a Python library specifically designed for generating M-L noise. We detail the architecture and functionalities of GenML and its underlying algorithmic approach, which enables the precise simulation of M-L noise. The effectiveness of GenML is validated through quantitative analyses of autocorrelation functions and diffusion behaviors, showcasing its capability to accurately replicate theoretical noise properties. Our contribution with GenML enables the effective application of M-L noise data in numerical simulation and data-driven methods for describing complex systems, moving beyond mere theoretical modeling.
A challenge in A(G)I, cybernetics revived in the Ouroboros Model as one algorithm for all thinking
Venue: arXiv cs.AI 2024
Year: 2024

A topical challenge for algorithms in general and for automatic image categorization and generation in particular is presented in the form of a drawing for AI to understand. In a second vein, AI is challenged to produce something similar from verbal description. The aim of the paper is to highlight strengths and deficiencies of current Artificial Intelligence approaches while coarsely sketching a way forward. A general lack of encompassing symbol-embedding and (not only) -grounding in some bodily basis is made responsible for current deficiencies. A concomitant dearth of hierarchical organization of concepts follows suite. As a remedy for these shortcomings, it is proposed to take a wide step back and to newly incorporate aspects of cybernetics and analog control processes. It is claimed that a promising overarching perspective is provided by the Ouroboros Model with a valid and versatile algorithmic backbone for general cognition at all accessible levels of abstraction and capabilities. Reality, rules, truth, and Free Will are all useful abstractions according to the Ouroboros Model. Logic deduction as well as intuitive guesses are claimed as produced on the basis of one compartmentalized memory for schemata and a pattern-matching, i.e., monitoring process termed consumption analysis. The latter directs attention on short (attention proper) and also on long times scales (emotional biases). In this cybernetic approach, discrepancies between expectations and actual activations (e.g., sensory precepts) drive the general process of cognition and at the same time steer the storage of new and adapted memory entries. Dedicated structures in the human brain work in concert according to this scheme.
Understanding how social discussion platforms like Reddit are influencing financial behavior
Venue: arXiv cs.SI 2024
Year: 2024

This study proposes content and interaction analysis techniques for a large repository created from social media content. Though we have presented our study for a large platform dedicated to discussions around financial topics, the proposed methods are generic and applicable to all platforms. Along with an extension of topic extraction method using Latent Dirichlet Allocation, we propose a few measures to assess user participation, influence and topic affinities specifically. Our study also maps user-generated content to components of behavioral finance. While these types of information are usually gathered through surveys, it is obvious that large scale data analysis from social media can reveal many potentially unknown or rare insights. Characterising users based on their platform behavior to provide critical insights about how communities are formed and trust is established in these platforms using graphical analysis is also studied.
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series
Venue: arXiv cs.LG 2024
Year: 2024

This paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models. The study involves a comprehensive evaluation across three different datasets: MSL, SMAP, and SWaT. Each dataset poses unique challenges, allowing for a robust assessment of the models' capabilities in varied contexts. The dimensionality reduction techniques examined include PCA, UMAP, Random Projection, and t-SNE, each offering distinct advantages in simplifying high-dimensional data. Our findings reveal that dimensionality reduction not only aids in reducing computational complexity but also significantly enhances anomaly detection performance in certain scenarios. Moreover, a remarkable reduction in training times was observed, with reductions by approximately 300\% and 650\% when dimensionality was halved and minimized to the lowest dimensions, respectively. This efficiency gain underscores the dual benefit of dimensionality reduction in both performance enhancement and operational efficiency. The MUTANT model exhibits notable adaptability, especially with UMAP reduction, while the Anomaly-Transformer demonstrates versatility across various reduction techniques. These insights provide a deeper understanding of the synergistic effects of dimensionality reduction and anomaly detection, contributing valuable perspectives to the field of time series analysis. The study underscores the importance of selecting appropriate dimensionality reduction strategies based on specific model requirements and dataset characteristics, paving the way for more efficient, accurate, and scalable solutions in anomaly detection.
RoboKube: Establishing a New Foundation for the Cloud Native Evolution in Robotics
Venue: arXiv cs.RO 2024
Year: 2024

Cloud native technologies have been observed to expand into the realm of Internet of Things (IoT) and Cyber-physical Systems, of which an important application domain is robotics. In this paper, we review the cloudification practice in the robotics domain from both literature and industrial perspectives. We propose RoboKube, an adaptive framework that is based on the Kubernetes (K8s) ecosystem to set up a common platform across the device-cloud continuum for the deployment of cloudified Robotic Operating System (ROS) powered applications, to facilitate the cloud native evolution in robotics. We examine the process of modernizing ROS applications using cloud-native technologies, focusing on both the platform and application perspectives. In addition, we address the challenges of networking setups for heterogeneous environments. This paper intends to serves as a guide for developers and researchers, offering insights into containerization strategies, ROS node distribution and clustering, and deployment options. To demonstrate the feasibility of our approach, we present a case study involving the cloudification of a teleoperation testbed.
Effect of turbulent diffusion in modeling anaerobic digestion
Venue: arXiv cs.CE 2024
Year: 2024

In this study, the impact of turbulent diffusion on mixing of biochemical reaction models is explored by implementing and validating different models. An original codebase called CHAD (Coupled Hydrodynamics and Anaerobic Digestion) is extended to incorporate turbulent diffusion and validate it against results from OpenFOAM with 2D Rayleigh-Taylor Instability and lid-driven cavity simulations. The models are then tested for the applications with Anaerobic Digestion - a widely used wastewater treatment method. The findings demonstrate that the implemented models accurately capture turbulent diffusion when provided with an accurate flow field. Specifically, a minor effect of chemical turbulent diffusion on biochemical reactions within the anaerobic digestion tank is observed, while thermal turbulent diffusion significantly influences mixing. By successfully implementing turbulent diffusion models in CHAD, its capabilities for more accurate anaerobic digestion simulations are enhanced, aiding in optimizing the design and operation of anaerobic digestion reactors in real-world wastewater treatment applications.
Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset
Venue: arXiv cs.CL 2024
Year: 2024

Conversational recommender system is an emerging area that has garnered an increasing interest in the community, especially with the advancements in large language models (LLMs) that enable diverse reasoning over conversational input. Despite the progress, the field has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such challenges, we present a novel conversational recommendation dataset named PEARL, synthesized with persona- and knowledge-augmented LLM simulators. We obtain detailed persona and knowledge from real-world reviews and construct a large-scale dataset with over 57k dialogues. Our experimental results demonstrate that utterances in PEARL include more specific user preferences, show expertise in the target domain, and provide recommendations more relevant to the dialogue context than those in prior datasets.
VLSI Architectures of Forward Kinematic Processor for Robotics Applications
Venue: arXiv cs.RO 2024
Year: 2024

This paper aims to get a comprehensive review of current-day robotic computation technologies at VLSI architecture level. We studied several repots in the domain of robotic processor architecture. In this work, we focused on the forward kinematics architectures which consider CORDIC algorithms, VLSI circuits of WE DSP16 chip, parallel processing and pipelined architecture, and lookup table formula and FPGA processor. This study gives us an understanding of different implementation methods for forward kinematics. Our goal is to develop a forward kinematics processor with FPGA for real-time applications, requires a fast response time and low latency of these devices, useful for industrial automation where the processing speed plays a great role.
Architectural Blueprint For Heterogeneity-Resilient Federated Learning
Venue: arXiv cs.LG 2024
Year: 2024

This paper proposes a novel three tier architecture for federated learning to optimize edge computing environments. The proposed architecture addresses the challenges associated with client data heterogeneity and computational constraints. It introduces a scalable, privacy preserving framework that enhances the efficiency of distributed machine learning. Through experimentation, the paper demonstrates the architecture capability to manage non IID data sets more effectively than traditional federated learning models. Additionally, the paper highlights the potential of this innovative approach to significantly improve model accuracy, reduce communication overhead, and facilitate broader adoption of federated learning technologies.
ShuffleBench: A Benchmark for Large-Scale Data Shuffling Operations with Distributed Stream Processing Frameworks
Venue: arXiv cs.SE 2024
Year: 2024

Distributed stream processing frameworks help building scalable and reliable applications that perform transformations and aggregations on continuous data streams. This paper introduces ShuffleBench, a novel benchmark to evaluate the performance of modern stream processing frameworks. In contrast to other benchmarks, it focuses on use cases where stream processing frameworks are mainly employed for shuffling (i.e., re-distributing) data records to perform state-local aggregations, while the actual aggregation logic is considered as black-box software components. ShuffleBench is inspired by requirements for near real-time analytics of a large cloud observability platform and takes up benchmarking metrics and methods for latency, throughput, and scalability established in the performance engineering research community. Although inspired by a real-world observability use case, it is highly configurable to allow domain-independent evaluations. ShuffleBench comes as a ready-to-use open-source software utilizing existing Kubernetes tooling and providing implementations for four state-of-the-art frameworks. Therefore, we expect ShuffleBench to be a valuable contribution to both industrial practitioners building stream processing applications and researchers working on new stream processing approaches. We complement this paper with an experimental performance evaluation that employs ShuffleBench with various configurations on Flink, Hazelcast, Kafka Streams, and Spark in a cloud-native environment. Our results show that Flink achieves the highest throughput while Hazelcast processes data streams with the lowest latency.
Machine learning and information theory concepts towards an AI Mathematician
Venue: arXiv cs.AI 2024
Year: 2024

The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning. What could be missing? Can we learn something useful about that gap from how the brains of mathematicians go about their craft? This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities -- which correspond to our intuition and habitual behaviors -- but still lacks something important regarding system 2 abilities -- which include reasoning and robust uncertainty estimation. It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement, which could guide future work in crafting an AI mathematician. The focus is not on proving a given theorem but on discovering new and interesting conjectures. The central hypothesis is that a desirable body of theorems better summarizes the set of all provable statements, for example by having a small description length while at the same time being close (in terms of number of derivation steps) to many provable statements.
Rectangular Rotational Invariant Estimator for High-Rank Matrix Estimation
Venue: arXiv cs.IT 2024
Year: 2024

We consider estimating a matrix from noisy observations coming from an arbitrary additive bi-rotational invariant perturbation. We propose an estimator which is optimal among the class of rectangular rotational invariant estimators and can be applied irrespective of the prior on the signal. For the particular case of Gaussian noise, we prove the optimality of the proposed estimator, and we find an explicit expression for the MMSE in terms of the limiting singular value distribution of the observation matrix. Moreover, we prove a formula linking the asymptotic mutual information and the limit of a log-spherical integral of rectangular matrices. We also provide numerical checks for our results for general bi-rotational invariant noise, as well as Gaussian noise, which match our theoretical predictions.
Modeling reputation-based behavioral biases in school choice
Venue: arXiv cs.GT 2024
Year: 2024

A fundamental component in the theoretical school choice literature is the problem a student faces in deciding which schools to apply to. Recent models have considered a set of schools of different selectiveness and a student who is unsure of their strength and can apply to at most $k$ schools. Such models assume that the student cares solely about maximizing the quality of the school that they attend, but experience suggests that students' decisions are also influenced by a set of behavioral biases based on reputational effects: a subjective reputational benefit when admitted to a selective school, whether or not they attend; and a subjective loss based on disappointment when rejected. Guided by these observations, and inspired by recent behavioral economics work on loss aversion relative to expectations, we propose a behavioral model by which a student chooses schools to balance these behavioral effects with the quality of the school they attend. Our main results show that a student's choices change in dramatic ways when these reputation-based behavioral biases are taken into account. In particular, where a rational applicant spreads their applications evenly, a biased student applies very sparsely to highly selective schools, such that above a certain threshold they apply to only an absolute constant number of schools even as their budget of applications grows to infinity. Consequently, a biased student underperforms a rational student even when the rational student is restricted to a sufficiently large upper bound on applications and the biased student can apply to arbitrarily many. Our analysis shows that the reputation-based model is rich enough to cover a range of different ways that biased students cope with fear of rejection, including not just targeting less selective schools, but also occasionally applying to schools that are too selective, compared to rational students.
Lightweight Cross-Modal Representation Learning
Venue: arXiv cs.LG 2024
Year: 2024

Low-cost cross-modal representation learning is crucial for deriving semantic representations across diverse modalities such as text, audio, images, and video. Traditional approaches typically depend on large specialized models trained from scratch, requiring extensive datasets and resulting in high resource and time costs. To overcome these challenges, we introduce a novel approach named Lightweight Cross-Modal Representation Learning (LightCRL). This method uses a single neural network titled Deep Fusion Encoder (DFE), which projects data from multiple modalities into a shared latent representation space. This reduces the overall parameter count while still delivering robust performance comparable to more complex systems.