pwc-archive/papers-with-abstracts huggingface dataset:
Polar Shapelets
Authors: Richard Massey, Alexandre Refregier
Date: 2004-08-24 00:00:00
Tasks: Image Manipulation

The shapelets method for image analysis is based upon the decomposition of localised objects into a series of orthogonal components with convenient mathematical properties. We extend the "Cartesian shapelet" formalism from earlier work, and construct "polar shapelet" basis functions that separate an image into components with explicit rotational symmetries. These frequently provide a more compact parameterisation, and can be interpreted in an intuitive way. Image manipulation in shapelet space is simplified by the concise expressions for linear coordinate transformations; and shape measures (including object photometry, astrometry and galaxy morphology estimators) take a naturally elegant form. Particular attention is paid to the analysis of astronomical survey images, and we test shapelet techniques upon real data from the Hubble Space Telescope. We present a practical method to automatically optimise the quality of an arbitrary shapelet decomposition in the presence of observational noise, pixellisation and a Point-Spread Function. A central component of this procedure is the adaptive choice of the shapelet expansion's scale size and truncation order. A complete software package to perform shapelet image analysis is made available on the world-wide web at http://www.astro.caltech.edu/~rjm/shapelets/ .
Measuring Intelligence through Games
Authors: Tom Schaul, Julian Togelius, Jürgen Schmidhuber
Date: 2011-09-06 00:00:00
Tasks: Motion Planning

Artificial general intelligence (AGI) refers to research aimed at tackling the full problem of artificial intelligence, that is, create truly intelligent agents. This sets it apart from most AI research which aims at solving relatively narrow domains, such as character recognition, motion planning, or increasing player satisfaction in games. But how do we know when an agent is truly intelligent? A common point of reference in the AGI community is Legg and Hutter's formal definition of universal intelligence, which has the appeal of simplicity and generality but is unfortunately incomputable. Games of various kinds are commonly used as benchmarks for "narrow" AI research, as they are considered to have many important properties. We argue that many of these properties carry over to the testing of general intelligence as well. We then sketch how such testing could practically be carried out. The central part of this sketch is an extension of universal intelligence to deal with finite time, and the use of sampling of the space of games expressed in a suitably biased game description language.
Highly Efficient Architecture of NewHope-NIST on FPGA using Low-Complexity NTT/INTT
Authors: Neng Zhang, Bohan Yang, Chen Chen, Shouyi Yin, Shaojun Wei and Leibo Liu*
Date: 2020-02-01 00:00:00
Proceeding: IACR Transactions on Cryptographic Hardware and Embedded Systems 2020 2

NewHope-NIST is a promising ring learning with errors (RLWE)-based post- quantum cryptography (PQC) for key encapsulation mechanisms. The performance on the field-programmable gate array (FPGA) affects the applicability of NewHope- NIST. In RLWE-based PQC algorithms, the number theoretic transform (NTT) is one of the most time-consuming operations. In this paper, low-complexity NTT and inverse NTT (INTT) are used to implement highly efficient NewHope-NIST on FPGA. First, both the pre-processing of NTT and the post-processing of INTT are merged into the fast Fourier transform (FFT) algorithm, which reduces N and 2N modular multiplications for N-point NTT and INTT, respectively. Second, a compact butterfly unit and an efficient modular reduction on the modulus 12289 are proposed for the low-complexity NTT/INTT architecture, which achieves an improvement of approximately 3× in the area time product (ATP) compared with the results of the state-of-the-art designs. Finally, a highly efficient architecture with doubled bandwidth and timing hiding for NewHope-NIST is presented. The implementation results on an FPGA show that our design is at least 2.5× faster and has 4.9× smaller ATP compared with the results of the state-of-the-art designs of NewHope-NIST on similar platforms.
The Technology Research in Decomposition and Reconstruction of Image Based on Two-dimensional Wavelet Transform
Authors: Zhang xin-yang Dept. College of Mathematics and computer science Guizhou Normal University Guiyang, China
Date: 2012-05-01 00:00:00
Proceeding: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery 2012 5

Wavelet is the last decade developed and quickly applied to a mathematical tool in many areas of image processing and speech analysis is a new breakthrough following Fourier (Joseph Fourier) analysis.Wavelet transform analysis of the local characteristics of the digital signal can depend on the scale stretching and displacement transformation,and then show the essential characteristics of digital signals within the.Firstly,the principle of the wavelet transform algorithm,and then use the two-dimensional discrete wavelet transform image decomposition and reconstruction,and experiments carried out by using MATLAB software environment.The desired results of expe-riments on wavelet transform image processing capability has been fully affirmed.
Joint Beamforming Coordination and User Selection for CoMP Enabled NR-U Networks
Authors: Qimei Chen, Kexin Yang, Hao Jiang, and Meikang Qiu
Date: 2022-08-15 00:00:00
Proceeding: IEEE Internet Things J. 2022 8

The sixth generation (6G) era is expected to provide even higher levels of massive connectivity/Internet of Things (IoT), tremendous data rate and low latency than the 5G communication. Therefore, the future 6G wireless networks would confront with much more severe spectrum scarcity problems, which makes the new radio in unlicensed spectrum (NR-U) technology attractive. Meanwhile, the high densely deployed massive IoT devices lead to fearful inter- and intra-cell interference issues. To address these challenges, a coordinated multi-point (CoMP) technology can be adopted. In this work, we introduce a CoMP based NR-U network, particularly for 6G enabled massive IoT scenarios. Under the proposed network, we introduce a spatial listen-before-talk (LBT) scheme to control mobile network operators (MNOs) to coexist with the incumbent WiFi devices orthogonally, which can significantly improve the spectrum efficiency. To suppress the strong co-channel interference among multiple MNOs, we investigate a joint beamforming coordination and user selection problem, which is NP-hard. To deal with this problem, we first adopt a fractional programming and an integer replacement method to transform the objective problem into a tractable one. Then, we introduce a semi-distributed alternating direction method of multipliers and block coordinate update (ADMM-BCU) algorithm to find a suboptimal solution, which only requires limited information exchange via a CoMP server and can help reduce energy consumption. Theoretical analysis and numerical results demonstrate the effectiveness of the proposed algorithm for large-scale 6G IoT scenarios.
Dense Pixel-to-Pixel Harmonization via Continuous Image Representation
Authors: Jianqi Chen, Yilan Zhang, Zhengxia Zou, Keyan Chen, Zhenwei Shi
Date: 2023-03-03 00:00:00
Tasks: Image Generation, Image Harmonization

High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and image editing. However, due to the high memory costs, existing dense pixel-to-pixel harmonization methods are mainly focusing on processing low-resolution (LR) images. Some recent works resort to combining with color-to-color transformations but are either limited to certain resolutions or heavily depend on hand-crafted image filters. In this work, we explore leveraging the implicit neural representation (INR) and propose a novel image Harmonization method based on Implicit neural Networks (HINet), which to the best of our knowledge, is the first dense pixel-to-pixel method applicable to HR images without any hand-crafted filter design. Inspired by the Retinex theory, we decouple the MLPs into two parts to respectively capture the content and environment of composite images. A Low-Resolution Image Prior (LRIP) network is designed to alleviate the Boundary Inconsistency problem, and we also propose new designs for the training and inference process. Extensive experiments have demonstrated the effectiveness of our method compared with state-of-the-art methods. Furthermore, some interesting and practical applications of the proposed method are explored. Our code is available at https://github.com/WindVChen/INR-Harmonization.
Ancient Chinese Word Segmentation and Part-of-Speech Tagging Using Distant Supervision
Authors: Shuo Feng, Piji Li
Date: 2023-03-03 00:00:00
Tasks: Chinese Word Segmentation, Memorization, Part-Of-Speech Tagging, POS, POS Tagging

Ancient Chinese word segmentation (WSG) and part-of-speech tagging (POS) are important to study ancient Chinese, but the amount of ancient Chinese WSG and POS tagging data is still rare. In this paper, we propose a novel augmentation method of ancient Chinese WSG and POS tagging data using distant supervision over parallel corpus. However, there are still mislabeled and unlabeled ancient Chinese words inevitably in distant supervision. To address this problem, we take advantage of the memorization effects of deep neural networks and a small amount of annotated data to get a model with much knowledge and a little noise, and then we use this model to relabel the ancient Chinese sentences in parallel corpus. Experiments show that the model trained over the relabeled data outperforms the model trained over the data generated from distant supervision and the annotated data. Our code is available at https://github.com/farlit/ACDS.
TRR360D: A dataset for 360 degree rotated rectangular box table detection
Authors: Wenxing Hu, Minglei Tong
Date: 2023-03-03 00:00:00
Tasks: 2D Object Detection, Table Detection

To address the problem of scarcity and high annotation costs of rotated image table detection datasets, this paper proposes a method for building a rotated image table detection dataset. Based on the ICDAR2019MTD modern table detection dataset, we refer to the annotation format of the DOTA dataset to create the TRR360D rotated table detection dataset. The training set contains 600 rotated images and 977 annotated instances, and the test set contains 240 rotated images and 499 annotated instances. The AP50(T<90) evaluation metric is defined, and this dataset is available for future researchers to study rotated table detection algorithms and promote the development of table detection technology. The TRR360D rotated table detection dataset was created by constraining the starting point and annotation direction, and is publicly available at https://github.com/vansin/TRR360D.
A Deep Learning Framework for Optimization of MISO Downlink Beamforming
Date: 2020-03-01 00:00:00
Tasks: Deep Learning

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-singleoutput (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for realtime implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.
A Complete Recipe for Diffusion Generative Models
Authors: Kushagra Pandey, Stephan Mandt
Date: 2023-03-03 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Generation

Score-based Generative Models (SGMs) have demonstrated exceptional synthesis outcomes across various tasks. However, the current design landscape of the forward diffusion process remains largely untapped and often relies on physical heuristics or simplifying assumptions. Utilizing insights from the development of scalable Bayesian posterior samplers, we present a complete recipe for formulating forward processes in SGMs, ensuring convergence to the desired target distribution. Our approach reveals that several existing SGMs can be seen as specific manifestations of our framework. Building upon this method, we introduce Phase Space Langevin Diffusion (PSLD), which relies on score-based modeling within an augmented space enriched by auxiliary variables akin to physical phase space. Empirical results exhibit the superior sample quality and improved speed-quality trade-off of PSLD compared to various competing approaches on established image synthesis benchmarks. Remarkably, PSLD achieves sample quality akin to state-of-the-art SGMs (FID: 2.10 for unconditional CIFAR-10 generation). Lastly, we demonstrate the applicability of PSLD in conditional synthesis using pre-trained score networks, offering an appealing alternative as an SGM backbone for future advancements. Code and model checkpoints can be accessed at \url{https://github.com/mandt-lab/PSLD}.
Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees
Authors: Moran Barenboim, Idan Lev-Yehudi, Vadim Indelman
Date: 2023-03-03 00:00:00
Tasks: Computational Efficiency

Autonomous agents that operate in the real world must often deal with partial observability, which is commonly modeled as partially observable Markov decision processes (POMDPs). However, traditional POMDP models rely on the assumption of complete knowledge of the observation source, known as fully observable data association. To address this limitation, we propose a planning algorithm that maintains multiple data association hypotheses, represented as a belief mixture, where each component corresponds to a different data association hypothesis. However, this method can lead to an exponential growth in the number of hypotheses, resulting in significant computational overhead. To overcome this challenge, we introduce a pruning-based approach for planning with ambiguous data associations. Our key contribution is to derive bounds between the value function based on the complete set of hypotheses and the value function based on a pruned-subset of the hypotheses, enabling us to establish a trade-off between computational efficiency and performance. We demonstrate how these bounds can both be used to certify any pruning heuristic in retrospect and propose a novel approach to determine which hypotheses to prune in order to ensure a predefined limit on the loss. We evaluate our approach in simulated environments and demonstrate its efficacy in handling multi-modal belief hypotheses with ambiguous data associations.
Eventual Discounting Temporal Logic Counterfactual Experience Replay
Authors: Cameron Voloshin, Abhinav Verma, Yisong Yue
Date: 2023-03-03 00:00:00
Tasks: counterfactual, Counterfactual Reasoning

Linear temporal logic (LTL) offers a simplified way of specifying tasks for policy optimization that may otherwise be difficult to describe with scalar reward functions. However, the standard RL framework can be too myopic to find maximally LTL satisfying policies. This paper makes two contributions. First, we develop a new value-function based proxy, using a technique we call eventual discounting, under which one can find policies that satisfy the LTL specification with highest achievable probability. Second, we develop a new experience replay method for generating off-policy data from on-policy rollouts via counterfactual reasoning on different ways of satisfying the LTL specification. Our experiments, conducted in both discrete and continuous state-action spaces, confirm the effectiveness of our counterfactual experience replay approach.
Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs
Authors: Muhammad Shaheer, Jose Andres Millan-Romera, Hriday Bavle, Jose Luis Sanchez-Lopez, Javier Civera, Holger Voos
Date: 2023-03-03 00:00:00
Tasks: Graph Matching

In this paper, we propose a solution for legged robot localization using architectural plans. Our specific contributions towards this goal are several. Firstly, we develop a method for converting the plan of a building into what we denote as an architectural graph (A-Graph). When the robot starts moving in an environment, we assume it has no knowledge about it, and it estimates an online situational graph representation (S-Graph) of its surroundings. We develop a novel graph-to-graph matching method, in order to relate the S-Graph estimated online from the robot sensors and the A-Graph extracted from the building plans. Note the challenge in this, as the S-Graph may show a partial view of the full A-Graph, their nodes are heterogeneous and their reference frames are different. After the matching, both graphs are aligned and merged, resulting in what we denote as an informed Situational Graph (iS-Graph), with which we achieve global robot localization and exploitation of prior knowledge from the building plans. Our experiments show that our pipeline shows a higher robustness and a significantly lower pose error than several LiDAR localization baselines.
Uncertainty Estimation by Fisher Information-based Evidential Deep Learning
Authors: Danruo Deng, Guangyong Chen, Yang Yu, Furui Liu, Pheng-Ann Heng
Date: 2023-03-03 00:00:00
Tasks: Deep Learning, Informativeness, Representation Learning

Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. Recently proposed evidential neural networks explicitly account for different uncertainties by treating the network's outputs as evidence to parameterize the Dirichlet distribution, and achieve impressive performance in uncertainty estimation. However, for high data uncertainty samples but annotated with the one-hot label, the evidence-learning process for those mislabeled classes is over-penalized and remains hindered. To address this problem, we propose a novel method, Fisher Information-based Evidential Deep Learning ($\mathcal{I}$-EDL). In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes. The generalization ability of our network is further improved by optimizing the PAC-Bayesian bound. As demonstrated empirically, our proposed method consistently outperforms traditional EDL-related algorithms in multiple uncertainty estimation tasks, especially in the more challenging few-shot classification settings.
Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies
Authors: Hannah Pinson, Joeri Lenaerts, Vincent Ginis
Date: 2023-03-03 00:00:00

We here present a stepping stone towards a deeper understanding of convolutional neural networks (CNNs) in the form of a theory of learning in linear CNNs. Through analyzing the gradient descent equations, we discover that the evolution of the network during training is determined by the interplay between the dataset structure and the convolutional network structure. We show that linear CNNs discover the statistical structure of the dataset with non-linear, ordered, stage-like transitions, and that the speed of discovery changes depending on the relationship between the dataset and the convolutional network structure. Moreover, we find that this interplay lies at the heart of what we call the ``dominant frequency bias'', where linear CNNs arrive at these discoveries using only the dominant frequencies of the different structural parts present in the dataset. We furthermore provide experiments that show how our theory relates to deep, non-linear CNNs used in practice. Our findings shed new light on the inner working of CNNs, and can help explain their shortcut learning and their tendency to rely on texture instead of shape.
Topic Modeling Based on Two-Step Flow Theory: Application to Tweets about Bitcoin
Authors: Aos Mulahuwaish, Matthew Loucks, Basheer Qolomany, Ala Al-Fuqaha
Date: 2023-03-03 00:00:00

Digital cryptocurrencies such as Bitcoin have exploded in recent years in both popularity and value. By their novelty, cryptocurrencies tend to be both volatile and highly speculative. The capricious nature of these coins is helped facilitated by social media networks such as Twitter. However, not everyone's opinion matters equally, with most posts garnering little to no attention. Additionally, the majority of tweets are retweeted from popular posts. We must determine whose opinion matters and the difference between influential and non-influential users. This study separates these two groups and analyzes the differences between them. It uses Hypertext-induced Topic Selection (HITS) algorithm, which segregates the dataset based on influence. Topic modeling is then employed to uncover differences in each group's speech types and what group may best represent the entire community. We found differences in language and interest between these two groups regarding Bitcoin and that the opinion leaders of Twitter are not aligned with the majority of users. There were 2559 opinion leaders (0.72% of users) who accounted for 80% of the authority and the majority (99.28%) users for the remaining 20% out of a total of 355,139 users.