Miracle-dz/UniFAR-FTUs huggingface dataset:
BoA-PTA, An Bayesian Optimization Accelerated PTA Solver for SPICE Simulation

Simulation WEI W. XING, School of Integrated Circuit Science and Engineering, Beihang University; Beihang Hangzhou Innovation Institute Yuhang, China XIANG JIN, School of Integrated Circuit Science and Engineering, Beihang University, China TIAN FENG, Department of Computer Science and Technology, University of Petroleum-Beijing, China DAN NIU, School of Automation, Southeast University, China WEISHEN ZHAO, School of Integrated Circuit Science and Engineering, Beihang University, China ZHOU JIN, Department of Computer Science and Technology, University of Petroleum-Beijing, China
Efficient Trust Based Information Sharing Schemes over Distributed Collaborative Networks

In distributed collaborative networks such as peer-to-peer systems, privacy preserving information sharing and dissemination heavily relies on effective trust management. Trust based encryption (TBE) has been proposed to be a solution to enabling privacy preserving information sharing and dissemination for such networks. Unfortunately, the previously proposed schemes are not efficient in terms of communications overhead, and require a constantly online trust authority. In this paper, we propose two trust based encryption schemes with significantly improved efficiency. In the first scheme, we develop a generic transformation approach based on the recently proposed identity based broadcast encryption (IBBE) technique, which can significantly reduce both memory space and communication overhead when static reputation is considered. For the dynamic reputation scenarios, we present a trust based encryption scheme which is based on a recently proposed revocable identity based encryption technique, resulting in significantly reduced communication overhead at the central trust authority.
Variational Point Encoding Deformation for Dental Modeling

Digital dentistry has made significant advancements in recent years, yet numerous challenges remain to be addressed. In this study, we release a new extensive dataset of tooth meshes to encourage further research. Additionally, we propose Variational FoldingNet (VF-Net) , which extends FoldingNet to enable probabilistic learning of point cloud representations. A key challenge in existing latent variable models for point clouds is the lack of a 1-to-1 mapping between input points and output points. Instead, they must rely on optimizing Chamfer distances, a metric that does not have a normalized distributional counterpart, preventing its usage in probabilistic models. We demonstrate that explicit minimization of Chamfer distances can be replaced by a suitable encoder, which allows us to increase computational efficiency while simplifying the probabilistic extension. Our experimental findings present empirical evidence demonstrating the superior performance of VF-Net over existing models in terms of dental scan reconstruction and extrapolation. Additionally, our investigation highlights the robustness of VF-Net’s latent representations. These results underscore the promising prospects of VF-Net as an effective and reliable method for point cloud reconstruction and analysis 1 .
Height Filtrations and Base Loci on Flag Bundles over a Curve

Let $k$ be an algebraically closed field of characteristic zero. Let $C/k$ be a projective smooth curve with function field $K=k(C)$ and $G/k$ be a connected reductive group. Let $F$ be a principal $G$-bundle on $C$. Let $P \subseteq G$ be a parabolic subgroup and $\lambda: P \longrightarrow G$ be a strictly anti-dominant character. Then $F/P \longrightarrow C$ is a flag bundle and $\mathcal{L}_\lambda=F \times_P k_\lambda$ on $F/P$ is a relatively ample line bundle. Let $h_{\mathcal{L}_\lambda}$ be the height function on $X=(F/P)_K$ induced by $\mathcal{L}_\lambda$. We compute its height filtration and successive minima: the height filtration is given by a Bruhat decomposition and successive minima are given by slopes. An interesting application is that, in this case, Zhang's inequality on successive minima can be enhanced into an equality. This is proved from the convex geometry point of view. Let $f \in N^1(F/P)$ be the numerical class of a vertical fiber. We also compute the augmented base loci $\mathrm{B}_+(\mathcal{L}_\lambda-tf)$ for any $t \in \mathbb{R}$ and it turns out that they are almost the same as the height filtration, which accords with the philosophy in Arakelov geometry that the positivity of the line bundle $\mathcal{L}_\lambda$ is equivalent to the positivity of its induced height function $h_{\mathcal{L}_\lambda}$. As a corollary, we compute the $k$-th movable cones of flag bundles over curves for all $k$.
Bring Event into RGB and LiDAR: Hierarchical Visual-Motion Fusion for Scene Flow

Single RGB or LiDAR is the mainstream sensor for the challenging scene flow, which relies heavily on visual features to match motion features. Compared with single modality, existing methods adopt a fusion strategy to directly fuse the cross-modal complementary knowledge in motion space. However, these direct fusion methods may suffer the modality gap due to the visual intrinsic heterogeneous nature between RGB and LiDAR, thus deteriorating motion features. We discover that event has the homogeneous nature with RGB and LiDAR in both visual and motion spaces. In this work, we bring the event as a bridge between RGB and LiDAR, and propose a novel hierarchical visual-motion fusion framework for scene flow, which explores a homogeneous space to fuse the cross-modal complementary knowledge for physical interpretation. In visual fusion, we discover that event has a complementarity (relative v.s. absolute) in luminance space with RGB for high dynamic imaging, and has a complementarity (local boundary v.s. global shape) in scene structure space with LiDAR for structure integrity. In motion fusion, we figure out that RGB, event and LiDAR are complementary (spatial-dense, temporal-dense v.s. spatiotemporal-sparse) to each other in correlation space, which motivates us to fuse their motion correlations for motion continuity. The proposed hierarchical fusion can explicitly fuse the multimodal knowledge to progressively improve scene flow from visual space to motion space. Extensive experiments have been performed to verify the superiority of the proposed method.
Time-Efficient Light-Field Acquisition Using Coded Aperture and Events

We propose a computational imaging method for time-efficient light-field acquisition that combines a coded aperture with an event-based camera. Different from the conventional coded-aperture imaging method, our method applies a sequence of coding patterns during a single exposure for an image frame. The parallax information, which is related to the differences in coding patterns, is recorded as events. The image frame and events, all of which are measured in a single exposure, are jointly used to computationally reconstruct a light field. We also designed an algorithm pipeline for our method that is end-to-end trainable on the basis of deep optics and compatible with real camera hardware. We experimentally showed that our method can achieve more accurate reconstruction than several other imaging methods with a single exposure. We also developed a hardware prototype with the potential to complete the measurement on the camera within 22 msec and demonstrated that light fields from real 3-D scenes can be obtained with convincing visual quality. Our software and supplementary video are available from our project website.
Whiteness-based bilevel learning of regularization parameters in imaging

We consider an unsupervised bilevel optimization strategy for learning regularization parameters in the context of imaging inverse problems in the presence of additive white Gaussian noise. Compared to supervised and semi-supervised metrics relying either on the prior knowledge of reference data and/or on some (partial) knowledge on the noise statistics, the proposed approach optimizes the whiteness of the residual between the observed data and the observation model with no need of ground-truth data.We validate the approach on standard Total Variation-regularized image deconvolution problems which show that the proposed quality metric provides estimates close to the mean-square error oracle and to discrepancy-based principles.
Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs

Event commonsense reasoning requires the ability to reason about the relationship between events, as well as infer implicit context underlying that relationship. However, data scarcity makes it challenging for language models to learn to generate commonsense inferences for contexts and questions involving interactions between complex events. To address this demand, we present COM2 (COMplex COMmonsense), a new dataset created by sampling multi-hop logical queries (e.g., the joint effect or cause of both event A and B, or the effect of the effect of event C) from an existing commonsense knowledge graph (CSKG), and verbalizing them using handcrafted rules and large language models into multiple-choice and text generation questions. Our experiments show that language models trained on COM2 exhibit significant improvements in complex reasoning ability, resulting in enhanced zero-shot performance in both in-domain and out-of-domain tasks for question answering and generative commonsense reasoning, without expensive human annotations.
MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki

NLP in the age of monolithic large language models is approaching its limits in terms of size and information that can be handled. The trend goes to modularization, a necessary step into the direction of designing smaller sub-networks and components with specialized functionality. In this paper, we present the MAMMOTH toolkit: a framework designed for training massively multilingual modular machine translation systems at scale, initially derived from OpenNMT-py and then adapted to ensure efficient training across computation clusters.We showcase its efficiency across clusters of A100 and V100 NVIDIA GPUs, and discuss our design philosophy and plans for future information.The toolkit is publicly available online at https://github.com/Helsinki-NLP/mammoth.
Electrically Programmable Pixelated Graphene-Integrated Plasmonic Metasurfaces for Coherent Mid-Infrared Emission

Active metasurfaces have recently emerged as compact, lightweight, and efficient platforms for dynamic control of electromagnetic fields and optical responses. However, the complexities associated with their post-fabrication tunability significantly hinder their widespread applications, especially for the mid-infrared range due to material scarcity and design intricacy. Here, we experimentally demonstrate highly dynamic, pixelated modulations of coherent mid-infrared emission based on an electrically programmable plasmonic metasurface integrated with graphene field effect transistors (Gr-FETs). The ultrabroad infrared transparency of graphene allows for free-form control over plasmonic meta-atoms, thus achieving coherent mid-infrared states across a broad range of wavelengths and polarizations. The spatial temperature modulation generated by Gr-FETs is effectively synergized with the emissivity control by the localized surface plasmon polaritons from gold nanoantennas. This integrated temperature-emissivity modulation of metasurfaces is systematically extended to form a pixelated 2D array, envisioning new approaches toward scalable 2D electrical wiring for densely packed, independently controlled pixels.
Gauge Symmetries and Conserved Currents in AdS/BCFT

In this paper, we study massless/massive vector and $p$-form field perturbations in AdS spacetime with an end-of-the-world brane. By imposing $U(1)$ preserving Neumann boundary condition on the end-of-the-world brane, we study their spectrum and discuss their implications for dual BCFT operators. When the perturbation is massless, the dual BCFT operator is a conserved current and we show that such an operator indeed satisfies the $U(1)$ preserving conformal boundary condition. On the other hand, when the perturbation is massive, in general there exists non-vanishing perpendicular components of the dual BCFT operator, even in the massless limit. We explain this difference between massless and massive perturbations from the point of view of the bulk gauge symmetry, or equivalently from different structure of equations of motion. We also find several brane-tension-independent modes in massless perturbations, and these are understood as boundary-condition-independent modes from the dual BCFT point of view.
Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning

The rapid proliferation of digital content and the ever-growing need for precise object recognition and segmentation have driven the advancement of cutting-edge techniques in the field of object classification and segmentation. This paper introduces"Learn and Search", a novel approach for object lookup that leverages the power of contrastive learning to enhance the efficiency and effectiveness of retrieval systems. In this study, we present an elegant and innovative methodology that integrates deep learning principles and contrastive learning to tackle the challenges of object search. Our extensive experimentation reveals compelling results, with"Learn and Search"achieving superior Similarity Grid Accuracy, showcasing its efficacy in discerning regions of utmost similarity within an image relative to a cropped image. The seamless fusion of deep learning and contrastive learning to address the intricacies of object identification not only promises transformative applications in image recognition, recommendation systems, and content tagging but also revolutionizes content-based search and retrieval. The amalgamation of these techniques, as exemplified by"Learn and Search,"represents a significant stride in the ongoing evolution of methodologies in the dynamic realm of object classification and segmentation.
Community Movement in Applying Mosquito Net on House Ventilations: An Initial Support for Green Architecture to Decrease Dengue Disease in Bandung Indonesia

Green architecture still has risk to dengue disease when trees cover house roofs’ gutter. This study was aimed to continue a geographical information system (GIS) and remote sensing (RS) study on roofs factor association with dengue disease by initiating community movement in applyingmosquito net on house ventilations to cut the disease transmission and mosquito breeding sites inside house. Our methods was an operational research in which improvement of interventions, policies and regulations towards dengue disease prevention is our intended endpoint. Several steps were conducted such as: (1) research problems formulation from GIS-RS analysis from previous phase research in Bandung city, (2) informal and formal approach to community leaders and primary healthcare centre (Puskesmas), (3) Video education and focus group discussion (FGD), (4) initial application of mosquito nets on house in communities; and (5) advocacy to Mayor of Bandung city (was on progress). Our study resulted several supports: one of sub-city leaders (Camat) in the city, village leaders (Lurah), and sub-village leaders (Ketua RW) of 5 villages (kelurahan), one kelurahan which mainly comprised formal settlements needed more efforts, which was experts on dengue disease from university to directly explain the mosquito nets application to its community. Informal leaders in all kelurahan’s community suggested only mothers movement was not enough, thus, youths in community was mentioned to help the community movement on the mosquito nets application.
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives

This work addresses the Brain Magnetic Resonance Image Synthesis for Tumor Segmentation (BraSyn) challenge, which was hosted as part of the Brain Tumor Segmentation (BraTS) challenge in 2023. In this challenge, researchers are invited to synthesize a missing magnetic resonance image sequence, given other available sequences, to facilitate tumor segmentation pipelines trained on complete sets of image sequences. This problem can be tackled using deep learning within the framework of paired image-to-image translation. In this study, we propose investigating the effectiveness of a commonly used deep learning framework, such as Pix2Pix, trained under the supervision of different image-quality loss functions. Our results indicate that the use of different loss functions significantly affects the synthesis quality. We systematically study the impact of various loss functions in the multi-sequence MR image synthesis setting of the BraSyn challenge. Furthermore, we demonstrate how image synthesis performance can be optimized by combining different learning objectives beneficially.
Explainable Learning with Gaussian Processes

The field of explainable artificial intelligence (XAI) attempts to develop methods that provide insight into how complicated machine learning methods make predictions. Many methods of explanation have focused on the concept of feature attribution, a decomposition of the model's prediction into individual contributions corresponding to each input feature. In this work, we explore the problem of feature attribution in the context of Gaussian process regression (GPR). We take a principled approach to defining attributions under model uncertainty, extending the existing literature. We show that although GPR is a highly flexible and non-parametric approach, we can derive interpretable, closed-form expressions for the feature attributions. When using integrated gradients as an attribution method, we show that the attributions of a GPR model also follow a Gaussian process distribution, which quantifies the uncertainty in attribution arising from uncertainty in the model. We demonstrate, both through theory and experimentation, the versatility and robustness of this approach. We also show that, when applicable, the exact expressions for GPR attributions are both more accurate and less computationally expensive than the approximations currently used in practice. The source code for this project is freely available under MIT license at https://github.com/KurtButler/2024_attributions_paper.
SFVInt: Simple, Fast and Generic Variable-Length Integer Decoding using Bit Manipulation Instructions

The ubiquity of variable-length integers in data storage and communication necessitates efficient decoding techniques. In this paper, we present SFVInt, a simple and fast approach to decode the prevalent Little Endian Base-128 (LEB128) varints. Our approach, distilled into a mere 500 lines of code, effectively utilizes the Bit Manipulation Instruction Set 2 (BMI2) in modern Intel and AMD processors, achieving significant performance improvement while maintaining simplicity and avoiding overengineering. SFVInt, with its generic design, effectively processes both 32-bit and 64-bit unsigned integers using a unified code template, marking a significant leap forward in varint decoding efficiency. We thoroughly evaluate SFVInt's performance across various datasets and scenarios, demonstrating that it achieves up to a 2x increase in decoding speed when compared to varint decoding methods used in established frameworks like Facebook Folly and Google Protobuf.
Supporting Annotators with Affordances for Efficiently Labeling Conversational Data

Without well-labeled ground truth data, machine learning-based systems would not be as ubiquitous as they are today, but these systems rely on substantial amounts of correctly labeled data. Unfortunately, crowdsourced labeling is time consuming and expensive. To address the concerns of effort and tedium, we designed CAL, a novel interface to aid in data labeling. We made several key design decisions for CAL, which include preventing inapt labels from being selected, guiding users in selecting an appropriate label when they need assistance, incorporating labeling documentation into the interface, and providing an efficient means to view previous labels. We implemented a production-quality implementation of CAL and report a user-study evaluation that compares CAL to a standard spreadsheet. Key findings of our study include users using CAL reported lower cognitive load, did not increase task time, users rated CAL to be easier to use, and users preferred CAL over the spreadsheet.
On Uniqueness of Participation Factors

In modal analysis and control of a nonlinear dynamical system, participation factors of state variables with respect to a mode of interest serve as pivotal tools for stability studies. Linear participation factors are uniquely determined by the mode's shape and composition, which are defined by the right and left eigenvectors of the linearized model. For nonlinear participation factors as well as five other variants of participation factors, this paper finds the sufficient conditions for them to be unique against scaling factors on the shape and composition of a mode. Besides, the similarity between the scaling factor and perturbation amplitude is also discussed.
Imagine a dragon made of seaweed: How images enhance learning in Wikipedia

Though images are ubiquitous across Wikipedia, it is not obvious that the image choices optimally support learning. When well selected, images can enhance learning by dual coding, complementing, or supporting articles. When chosen poorly, images can mislead, distract, and confuse. We developed a large dataset containing 470 questions&answers to 94 Wikipedia articles with images on a wide range of topics. Through an online experiment (n=704), we determined whether the images displayed alongside the text of the article are effective in helping readers understand and learn. For certain tasks, such as learning to identify targets visually (e.g.,"which of these pictures is a gujia? "), article images significantly improve accuracy. Images did not significantly improve general knowledge questions (e.g.,"where are gujia from? "). Most interestingly, only some images helped with visual knowledge questions (e.g.,"what shape is a gujia? "). Using our findings, we reflect on the implications for editors and tools to support image selection.
Anderson acceleration for iteratively reweighted $\ell_1$ algorithm

Iteratively reweighted L1 (IRL1) algorithm is a common algorithm for solving sparse optimization problems with nonconvex and nonsmooth regularization. The development of its acceleration algorithm, often employing Nesterov acceleration, has sparked significant interest. Nevertheless, the convergence and complexity analysis of these acceleration algorithms consistently poses substantial challenges. Recently, Anderson acceleration has gained prominence owing to its exceptional performance for speeding up fixed-point iteration, with numerous recent studies applying it to gradient-based algorithms. Motivated by the powerful impact of Anderson acceleration, we propose an Anderson-accelerated IRL1 algorithm and establish its local linear convergence rate. We extend this convergence result, typically observed in smooth settings, to a nonsmooth scenario. Importantly, our theoretical results do not depend on the Kurdyka-Lojasiewicz condition, a necessary condition in existing Nesterov acceleration-based algorithms. Furthermore, to ensure global convergence, we introduce a globally convergent Anderson accelerated IRL1 algorithm by incorporating a classical nonmonotone line search condition. Experimental results indicate that our algorithm outperforms existing Nesterov acceleration-based algorithms.