pwc-archive/papers-with-abstracts huggingface dataset:
Five-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Likelihoods and Parameters from the WMAP data
Authors: J. Dunkley, E. Komatsu, M. R. Nolta, D. N. Spergel, D. Larson, G. Hinshaw, L. Page, C. L. Bennett, B. Gold, N. Jarosik, J. L. Weiland, M. Halpern, R. S. Hill, A. Kogut, M. Limon, S. S. Meyer, G. S. Tucker, E. Wollack, E. L. Wright
Date: 2008-03-05 00:00:00

This paper focuses on cosmological constraints derived from analysis of WMAP data alone. A simple LCDM cosmological model fits the five-year WMAP temperature and polarization data. The basic parameters of the model are consistent with the three-year data and now better constrained: Omega_b h^2 = 0.02273+-0.00062, Omega_c h^2 = 0.1099+-0.0062, Omega_L = 0.742+-0.030, n_s = 0.963+0.014- 0.015, tau = 0.087+-0.017, sigma_8 = 0.796+-0.036. With five years of polarization data, we have measured the optical depth to reionization, tau>0, at 5 sigma significance. The redshift of an instantaneous reionization is constrained to be z_reion = 11.0+-1.4 with 68% confidence. This excludes a sudden reionization of the universe at z=6 at more than 3.5 sigma significance, suggesting that reionization was an extended process. Using two methods for polarized foreground cleaning we get consistent estimates for the optical depth, indicating an error due to foreground treatment of tau~0.01. This cosmological model also fits small-scale CMB data, and a range of astronomical data measuring the expansion rate and clustering of matter in the universe. We find evidence for the first time in the CMB power spectrum for a non-zero cosmic neutrino background, or a background of relativistic species, with the standard three light neutrino species preferred over the best-fit LCDM model with N_eff=0 at >99.5% confidence, and N_eff > 2.3 (95% CL) when varied. The five-year WMAP data improve the upper limit on the tensor-to-scalar ratio to r < 0.43 (95% CL), for power-law models. With longer integration we find no evidence for a running spectral index, with dn_s/dlnk = -0.037+-0.028.
Tangent-based manifold approximation with locally linear models
Authors: Sofia Karygianni, Pascal Frossard
Date: 2012-11-06 00:00:00

In this paper, we consider the problem of manifold approximation with affine subspaces. Our objective is to discover a set of low dimensional affine subspaces that represents manifold data accurately while preserving the manifold's structure. For this purpose, we employ a greedy technique that partitions manifold samples into groups that can be each approximated by a low dimensional subspace. We start by considering each manifold sample as a different group and we use the difference of tangents to determine appropriate group mergings. We repeat this procedure until we reach the desired number of sample groups. The best low dimensional affine subspaces corresponding to the final groups constitute our approximate manifold representation. Our experiments verify the effectiveness of the proposed scheme and show its superior performance compared to state-of-the-art methods for manifold approximation.
Hierarchical Control in Islanded DC Microgrids with Flexible Structures
Authors: Pulkit Nahata, Alessio La Bella, Riccardo Scattolini, Giancarlo Ferrari-Trecate
Date: 2019-10-11 00:00:00
Tasks: energy management, Management

Hierarchical architectures stacking primary, secondary, and tertiary layers are widely employed for the operation and control of islanded DC microgrids (DCmGs), composed of Distribution Generation Units (DGUs), loads, and power lines. However, a comprehensive analysis of all the layers put together is often missing. In this work, we remedy this limitation by setting out a top-to-bottom hierarchical control architecture. Decentralized voltage controllers attached to DGUs form our primary layer. Governed by an MPC--based Energy Management System (EMS), our tertiary layer generates optimal power references and decision variables for DGUs. In particular, decision variables can turn DGUs ON/OFF and select their operation modes. An intermediary secondary layer translates EMS power references into appropriate voltage signals required by the primary layer. More specifically, to provide a voltage solution, the secondary layer solves an optimization problem embedding power-flow equations shown to be always solvable. Since load voltages are not directly enforced, their uniqueness is necessary for DGUs to produce reference powers handed down by the EMS. To this aim, we deduce a novel uniqueness condition based only on local load parameters. Our control framework, besides being applicable for generic DCmG topologies, can accommodate topological changes caused by EMS commands. Its functioning is validated via simulations on a modified 16-bus DC system.
Deep Amortized Relational Model with Group-Wise Hierarchical Generative Process
Authors: Huafeng Liu, Tong Zhou, Jiaqi Wang, Liping Jing
Date: 2022-06-28 00:00:00
Proceeding: AAAI 2022 6
Tasks: Community Detection, Link Prediction

In this paper, we propose Deep amortized Relational Model (DaRM) with group-wise hierarchical generative process for community discovery and link prediction on relational data (e.g., graph, network). It provides an efficient neural relational model architecture by grouping nodes in a group-wise view rather than node-wise or edge-wise view. DaRM simultaneously learns what makes a group, how to divide nodes into groups, and how to adaptively control the number of groups. The dedicated group generative process is able to sufficiently exploit pair-wise or higher-order interactions between data points in both inter-group and intra-group, which is useful to sufficiently mine the hidden structure among data. A series of experiments have been conducted on both synthetic and real-world datasets. The experimental results demonstrated that DaRM can obtain high performance on both community detection and link prediction tasks.
Moment Analysis Applied to LMC Star Clusters
Authors: Timothy Banks, R. J. Dodd, D. J. Sullivan
Date: 1994-09-29 00:00:00
Tasks: Position

Statistical moment-based ellipse fitting was performed on observations of Large Magellanic Cloud clusters, confirming that trends are evident in their position angles and ellipticities, as had been reported in the literature. Artificial cluster images with known parameters were generated, and subjected to the same analysis techniques, revealing apparent trends caused by stochastic processes. Caution should therefore be exercised in the interpretation of observational trends in young LMC clusters.
Learning Over Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems (Extended Version)
Authors: Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester
Date: 2023-04-12 00:00:00

This paper presents a policy parameterization for learning-based control on nonlinear, partially-observed dynamical systems. The parameterization is based on a nonlinear version of the Youla parameterization and the recently proposed Recurrent Equilibrium Network (REN) class of models. We prove that the resulting Youla-REN parameterization automatically satisfies stability (contraction) and user-tunable robustness (Lipschitz) conditions on the closed-loop system. This means it can be used for safe learning-based control with no additional constraints or projections required to enforce stability or robustness. We test the new policy class in simulation on two reinforcement learning tasks: 1) magnetic suspension, and 2) inverting a rotary-arm pendulum. We find that the Youla-REN performs similarly to existing learning-based and optimal control methods while also ensuring stability and exhibiting improved robustness to adversarial disturbances.
An Adaptive Logical Rule Embedding Model for Inductive Reasoning over Temporal Knowledge Graphs
Authors: Xin Mei, Libin Yang, Zuowei Jiang, Xiaoyan Cai
Date: 2022-12-07 00:00:00
Proceeding: Empirical Methods in Natural Language Processing 2022 12
Tasks: Knowledge Graphs, Relation

Temporal knowledge graphs (TKGs) extrapolation reasoning predicts future events based on historical information, which has great research significance and broad application value. Existing methods can be divided into embeddingbased methods and logical rule-based methods. Embedding-based methods rely on learned entity and relation embeddings to make predictions and thus lack interpretability. Logical rule-based methods bring scalability problems due to being limited by the learned logical rules. We combine the two methods to capture deep causal logic by learning rule embeddings, and propose an interpretable model for temporal knowledge graph reasoning called adaptive logical rule embedding model for inductive reasoning (ALRE-IR). ALRE-IR can adaptively extract and assess reasons contained in historical events, and make predictions based on causal logic. Furthermore, we propose a one-class augmented matching loss for optimization. When evaluated on ICEWS14, ICEWS0515 and ICEWS18 datasets, the performance of ALRE-IR outperforms other stateof-the-art baselines. The results also demonstrate that ALRE-IR still shows outstanding performance when transferred to related dataset with common relation vocabulary, indicating our proposed model has good zero-shot reasoning ability.1
Open-TransMind: A New Baseline and Benchmark for 1st Foundation Model Challenge of Intelligent Transportation
Authors: Yifeng Shi, Feng Lv, Xinliang Wang, Chunlong Xia, Shaojie Li, Shujie Yang, Teng Xi, Gang Zhang
Date: 2023-04-12 00:00:00
Tasks: 2D Object Detection, Image Retrieval, Retrieval

With the continuous improvement of computing power and deep learning algorithms in recent years, the foundation model has grown in popularity. Because of its powerful capabilities and excellent performance, this technology is being adopted and applied by an increasing number of industries. In the intelligent transportation industry, artificial intelligence faces the following typical challenges: few shots, poor generalization, and a lack of multi-modal techniques. Foundation model technology can significantly alleviate the aforementioned issues. To address these, we designed the 1st Foundation Model Challenge, with the goal of increasing the popularity of foundation model technology in traffic scenarios and promoting the rapid development of the intelligent transportation industry. The challenge is divided into two tracks: all-in-one and cross-modal image retrieval. Furthermore, we provide a new baseline and benchmark for the two tracks, called Open-TransMind. According to our knowledge, Open-TransMind is the first open-source transportation foundation model with multi-task and multi-modal capabilities. Simultaneously, Open-TransMind can achieve state-of-the-art performance on detection, classification, and segmentation datasets of traffic scenarios. Our source code is available at https://github.com/Traffic-X/Open-TransMind.
PLN: Parasitic-Like Network for Barely Supervised Medical Image Segmentation
Authors: Shumeng Li, Heng Cai; Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao
Date: 2022-09-30 00:00:00
Proceeding: IEEE Transactions on Medical Imaging 2022 9
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

It is known that annotations for 3D medical image segmentation tasks are laborious, time-consuming and expensive. Considering the similarities existing in inter-slice and inter-volume, we believe that the delineation way and the model architecture should be tightly coupled. In this paper, by introducing an extremely sparse annotation way of labeling only one slice per 3D image, we investigate a novel barely-supervised segmentation setting with only a few sparsely-labeled images along with a large amount of unlabeled images. To achieve this goal, we present a new parasitic-like network including a registration module (as host) and a semi-supervised segmentation module (as parasite) to deal with inter-slice label propagation and inter-volume segmentation prediction, respectively. Specifically, our parasitism mechanism effectively achieves the collaboration of these two modules through three stages of infection, development and eclosion, providing accurate pseudo-labels for training. Extensive results demonstrate that our framework is capable of achieving high performance on extremely sparse annotation tasks, e.g., we achieve Dice of 84.83% on LA dataset with only 16 labeled slices. The code is available at https://github.com/ShumengLI/PLN .
Cross-View Hierarchy Network for Stereo Image Super-Resolution
Authors: Wenbin Zou, Hongxia Gao, Liang Chen, Yunchen Zhang, Mingchao Jiang, Zhongxin Yu, Ming Tan
Date: 2023-04-13 00:00:00
Tasks: Image Super-Resolution, Stereo Image Super-Resolution, Super-Resolution

Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views. To attain superior performance, many methods have prioritized designing complex modules to fuse similar information across views, yet overlooking the importance of intra-view information for high-resolution reconstruction. It also leads to problems of wrong texture in recovered images. To address this issue, we explore the interdependencies between various hierarchies from intra-view and propose a novel method, named Cross-View-Hierarchy Network for Stereo Image Super-Resolution (CVHSSR). Specifically, we design a cross-hierarchy information mining block (CHIMB) that leverages channel attention and large kernel convolution attention to extract both global and local features from the intra-view, enabling the efficient restoration of accurate texture details. Additionally, a cross-view interaction module (CVIM) is proposed to fuse similar features from different views by utilizing cross-view attention mechanisms, effectively adapting to the binocular scene. Extensive experiments demonstrate the effectiveness of our method. CVHSSR achieves the best stereo image super-resolution performance than other state-of-the-art methods while using fewer parameters. The source code and pre-trained models are available at https://github.com/AlexZou14/CVHSSR.
Verbs in Action: Improving verb understanding in video-language models
Authors: Liliane Momeni, Mathilde Caron, Arsha Nagrani, Andrew Zisserman, Cordelia Schmid
Date: 2023-04-13 00:00:00
Proceeding: ICCV 2023 1
Tasks: Contrastive Learning, Question Answering, Text Matching, Video Classification, Video Question Answering, Zero-Shot Video Question Answer

Understanding verbs is crucial to modelling how people and objects interact with each other and the environment through space and time. Recently, state-of-the-art video-language models based on CLIP have been shown to have limited verb understanding and to rely extensively on nouns, restricting their performance in real-world video applications that require action and temporal understanding. In this work, we improve verb understanding for CLIP-based video-language models by proposing a new Verb-Focused Contrastive (VFC) framework. This consists of two main components: (1) leveraging pretrained large language models (LLMs) to create hard negatives for cross-modal contrastive learning, together with a calibration strategy to balance the occurrence of concepts in positive and negative pairs; and (2) enforcing a fine-grained, verb phrase alignment loss. Our method achieves state-of-the-art results for zero-shot performance on three downstream tasks that focus on verb understanding: video-text matching, video question-answering and video classification. To the best of our knowledge, this is the first work which proposes a method to alleviate the verb understanding problem, and does not simply highlight it.
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images
Authors: Carlos Rodriguez-Pardo, Melania Prieto-Martin, Dan Casas, Elena Garces
Date: 2023-04-13 00:00:00
Tasks: Data Augmentation, Material Recognition, Perceptual Distance, Transfer Learning

We propose a method to estimate the mechanical parameters of fabrics using a casual capture setup with a depth camera. Our approach enables to create mechanically-correct digital representations of real-world textile materials, which is a fundamental step for many interactive design and engineering applications. As opposed to existing capture methods, which typically require expensive setups, video sequences, or manual intervention, our solution can capture at scale, is agnostic to the optical appearance of the textile, and facilitates fabric arrangement by non-expert operators. To this end, we propose a sim-to-real strategy to train a learning-based framework that can take as input one or multiple images and outputs a full set of mechanical parameters. Thanks to carefully designed data augmentation and transfer learning protocols, our solution generalizes to real images despite being trained only on synthetic data, hence successfully closing the sim-to-real loop.Key in our work is to demonstrate that evaluating the regression accuracy based on the similarity at parameter space leads to an inaccurate distances that do not match the human perception. To overcome this, we propose a novel metric for fabric drape similarity that operates on the image domain instead on the parameter space, allowing us to evaluate our estimation within the context of a similarity rank. We show that out metric correlates with human judgments about the perception of drape similarity, and that our model predictions produce perceptually accurate results compared to the ground truth parameters.
Learning Personalized Decision Support Policies
Authors: Umang Bhatt, Valerie Chen, Katherine M. Collins, Parameswaran Kamalaruban, Emma Kallina, Adrian Weller, Ameet Talwalkar
Date: 2023-04-13 00:00:00
Tasks: Language Modelling, Large Language Model, Multi-Armed Bandits

Individual human decision-makers may benefit from different forms of support to improve decision outcomes, but when each form of support will yield better outcomes? In this work, we posit that personalizing access to decision support tools can be an effective mechanism for instantiating the appropriate use of AI assistance. Specifically, we propose the general problem of learning a decision support policy that, for a given input, chooses which form of support to provide to decision-makers for whom we initially have no prior information. We develop $\texttt{Modiste}$, an interactive tool to learn personalized decision support policies. $\texttt{Modiste}$ leverages stochastic contextual bandit techniques to personalize a decision support policy for each decision-maker and supports extensions to the multi-objective setting to account for auxiliary objectives like the cost of support. We find that personalized policies outperform offline policies, and, in the cost-aware setting, reduce the incurred cost with minimal degradation to performance. Our experiments include various realistic forms of support (e.g., expert consensus and predictions from a large language model) on vision and language tasks. Our human subject experiments validate our computational experiments, demonstrating that personalization can yield benefits in practice for real users, who interact with $\texttt{Modiste}$.
LSFSL: Leveraging Shape Information in Few-shot Learning
Authors: Deepan Chakravarthi Padmanabhan, Shruthi Gowda, Elahe Arani, Bahram Zonooz
Date: 2023-04-13 00:00:00
Tasks: Few-Shot Learning

Few-shot learning (FSL) techniques seek to learn the underlying patterns in data using fewer samples, analogous to how humans learn from limited experience. In this limited-data scenario, the challenges associated with deep neural networks, such as shortcut learning and texture bias behaviors, are further exacerbated. Moreover, the significance of addressing shortcut learning is not yet fully explored in the few-shot setup. To address these issues, we propose LSFSL, which enforces the model to learn more generalizable features utilizing the implicit prior information present in the data. Through comprehensive analyses, we demonstrate that LSFSL-trained models are less vulnerable to alteration in color schemes, statistical correlations, and adversarial perturbations leveraging the global semantics in the data. Our findings highlight the potential of incorporating relevant priors in few-shot approaches to increase robustness and generalization.
Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
Authors: Jaemin Cho, Linjie Li, Zhengyuan Yang, Zhe Gan, Lijuan Wang, Mohit Bansal
Date: 2023-04-13 00:00:00
Tasks: Diagnostic, Image Generation, Layout-to-Image Generation

Spatial control is a core capability in controllable image generation. Advancements in layout-guided image generation have shown promising results on in-distribution (ID) datasets with similar spatial configurations. However, it is unclear how these models perform when facing out-of-distribution (OOD) samples with arbitrary, unseen layouts. In this paper, we propose LayoutBench, a diagnostic benchmark for layout-guided image generation that examines four categories of spatial control skills: number, position, size, and shape. We benchmark two recent representative layout-guided image generation methods and observe that the good ID layout control may not generalize well to arbitrary layouts in the wild (e.g., objects at the boundary). Next, we propose IterInpaint, a new baseline that generates foreground and background regions step-by-step via inpainting, demonstrating stronger generalizability than existing models on OOD layouts in LayoutBench. We perform quantitative and qualitative evaluation and fine-grained analysis on the four LayoutBench skills to pinpoint the weaknesses of existing models. We show comprehensive ablation studies on IterInpaint, including training task ratio, crop&paste vs. repaint, and generation order. Lastly, we evaluate the zero-shot performance of different pretrained layout-guided image generation models on LayoutBench-COCO, our new benchmark for OOD layouts with real objects, where our IterInpaint consistently outperforms SOTA baselines in all four splits. Project website: https://layoutbench.github.io
Do deep neural networks have an inbuilt Occam's razor?
Authors: Chris Mingard, Henry Rees, Guillermo Valle-Pérez, Ard A. Louis
Date: 2023-04-13 00:00:00
Tasks: Inductive Bias

The remarkable performance of overparameterized deep neural networks (DNNs) must arise from an interplay between network architecture, training algorithms, and structure in the data. To disentangle these three components, we apply a Bayesian picture, based on the functions expressed by a DNN, to supervised learning. The prior over functions is determined by the network, and is varied by exploiting a transition between ordered and chaotic regimes. For Boolean function classification, we approximate the likelihood using the error spectrum of functions on data. When combined with the prior, this accurately predicts the posterior, measured for DNNs trained with stochastic gradient descent. This analysis reveals that structured data, combined with an intrinsic Occam's razor-like inductive bias towards (Kolmogorov) simple functions that is strong enough to counteract the exponential growth of the number of functions with complexity, is a key to the success of DNNs.