Miracle-dz/UniFAR-FTUs huggingface dataset:
KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods

Link Prediction is the task of predicting missing relations between knowledge graph entities (KG). Recent work in link prediction mainly attempted to adapt a model to increase link prediction accuracy by using more layers in neural network architecture, which heavily rely on computational resources. This paper proposes the refinement of knowledge graphs to perform link prediction operations more accurately using relatively fast translational models. Translational link prediction models have significantly less complexity than deep learning approaches; this motivated us to improve their accuracy. Our method uses the ontologies of knowledge graphs to add information as auxiliary nodes to the graph. Then, these auxiliary nodes are connected to ordinary nodes of the KG that contain auxiliary information in their hierarchy. Our experiments show that our method can significantly increase the performance of translational link prediction methods in Hit@10, Mean Rank, and Mean Reciprocal Rank.
Enhancing the generalization for Intent Classification and Out-of-Domain Detection in SLU

Intent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to detect unsupported out-of-domain (OOD) utterances has a critical effect in practical use. Recent works have shown that using extra data and labels can improve the OOD detection performance, yet it could be costly to collect such data. This paper proposes to train a model with only IND data while supporting both IND intent classification and OOD detection. Our method designs a novel domain-regularized module (DRM) to reduce the overconfident phenomenon of a vanilla classifier, achieving a better generalization in both cases. Besides, DRM can be used as a drop-in replacement for the last layer in any neural network-based intent classifier, providing a low-cost strategy for a significant improvement. The evaluation on four datasets shows that our method built on BERT and RoBERTa models achieves state-of-the-art performance against existing approaches and the strong baselines we created for the comparisons.
AniVis: Generating Animated Transitions Between Statistical Charts with a Tree Model

Animated transitions help viewers understand changes between related visualizations. To clearly present the underlying relations between statistical charts, animation authors need to have a high level of expertise and a considerable amount of time to describe the relations with reasonable animation stages. We present AniVis, an automated approach for generating animated transitions to demonstrate the changes between two statistical charts. AniVis models each statistical chart into a tree-based structure. Given an input chart pair, the differences of data and visual properties of the chart pair are formalized as tree edit operations. The edit operations can be mapped to atomic transition units. Through this approach, the animated transition between two charts can be expressed as a set of transition units. Then, we conduct a formative study to understand people's preferences for animation sequences. Based on the study, we propose a set of principles and a sequence composition algorithm to compose the transition units into a meaningful animation sequence. Finally, we synthesize these units together to deliver a smooth and intuitive animated transition between charts. To test our approach, we present a prototype system and its generated results to illustrate the usage of our framework. We perform a comparative study to assess the transition sequence derived from the tree model. We further collect qualitative feedback to evaluate the effectiveness and usefulness of our method.
DONet: Learning Category-Level 6D Object Pose and Size Estimation from Depth Observation

We propose a method of Category-level 6D Object Pose and Size Estimation (COPSE) from a single depth image, without external pose-annotated real-world training data. While previous works [43, 39, 4] exploit visual cues in RGB(D) images, our method makes inferences based on the rich geometric information of the object in the depth channel alone. Essentially, our framework explores such geometric information by learning the uniﬁed 3D Orientation-Consistent Representations (3D-OCR) module, and further enforced by the property of Geometry-constrained Reﬂec-tion Symmetry (GeoReS) module. The magnitude information of object size and the center point is ﬁnally estimated by Mirror-Paired Dimensional Estimation (MPDE) module. Extensive experiments on the category-level NOCS benchmark demonstrate that our framework competes with state-of-the-art approaches that require labeled real-world images. We also deploy our approach to a physical Baxter robot to perform manipulation tasks on unseen but category-known instances, and the results further validate the efﬁcacy of our proposed model.
Exploring Spatial Indexing for Accelerated Feature Retrieval in HPC

Despite the critical role that range queries play in analysis and visualization for HPC applications, there has been no comprehensive analysis of indices that are designed to accelerate range queries and the extent to which they are viable in HPC. In this paper we present the first such evaluation, examining 20 open-source C and C++ libraries that support range queries. Contributions of this paper include answering the following questions: which of the implementations are viable in HPC, how do these libraries compare in terms of build time, query time, memory usage, and scalability, what are other trade-offs between these implementations, is there a single overall best solution, and when does a brute force solution offer the best performance? We also share key insights learned during this process that can assist both HPC application scientists and spatial index developers. While we find that there is no single best solution, three libraries, Boost, CGAL and R-tree, offer some of the best performance, scalability, memory overheads, and support for different mesh types. We find several areas where the spatial indices could be substantially improved: better performance when there are a large number of query matches, reduced memory overheads, and better support for GPUs or other accelerators.
Persian Causality Corpus (PerCause) and the Causality Detection Benchmark

Recognizing causal elements and causal relations in text is one of the challenging issues in natural language processing; specifically, in low resource languages such as Persian. In this research we prepare a causality human annotated corpus for the Persian language which consists of 4446 sentences and 5128 causal relations and three labels of cause, effect and causal mark if possibleare specified for each relation. We have used this corpus to train a system for detecting causal elements’ boundaries. Also, we present a causality detection benchmark for three machine learning methods and two deep learning systems based on this corpus. Performance evaluations indicate that our best total result is obtained through CRF classifier which has F-measure of 0.76 and the best accuracy obtained through BiLSTM-CRF deep learning method with Accuracy equal to %91.4.
Classification of Contract-Amendment Relationships

In Contract Life-cycle Management (CLM), managing and tracking the master agreements and their associated amendments is essential, in order to be kept informed with different due dates and obligations. An automatic solution can facilitate the daily jobs and improve the efficiency of legal practitioners. In this paper, we propose an approach based on machine learning (ML) and Natural Language Processing (NLP) to detect the amendment relationship between two documents. The algorithm takes two PDF documents preprocessed by OCR (Optical Character Recognition) and NER (Named Entity Recognition) as input, and then it builds the features of each document pair and classifies the relationship. We experimented with different configurations on a dataset consisting of 1124 pairs of contract-amendment documents in English and French. The best result obtained a F1-score of 91%, which outperformed 23% compared to a heuristic-based baseline.
Making Images Real Again: A Comprehensive Survey on Deep Image Composition

As a common image editing operation, image composition aims to combine the foreground from one image and another background image, resulting in a composite image. However, there are many issues that could make the composite images unrealistic. These issues can be summarized as the inconsistency between foreground and background, which includes appearance inconsistency (e.g., incompatible illumination), geometry inconsistency (e.g., unreasonable size), and semantic inconsistency (e.g., mismatched semantic context). Image composition task could be decomposed into multiple sub-tasks, in which each sub-task targets at one or more issues. Specifically, object placement aims to find reasonable scale, location, and shape for the foreground. Image blending aims to address the unnatural boundary between foreground and background. Image harmonization aims to adjust the illumination statistics of foreground. Shadow generation aims to generate plausible shadow for the foreground. These sub-tasks can be executed sequentially or parallelly to acquire realistic composite images. To the best of our knowledge, there is no previous survey on image composition. In this paper, we conduct comprehensive survey over the sub-tasks and combinatorial task of image composition. For each one, we summarize the existing methods, available datasets, and common evaluation metrics. Datasets and codes for image composition are summarized at https://github.com/bcmi/Awesome-Image-Composition. We have also contributed the first image composition toolbox: libcom https://github.com/bcmi/libcom, which assembles 10+ image composition related functions (e.g., image blending, image harmonization, object placement, shadow generation, generative composition). The ultimate goal of this toolbox is solving all the problems related to image composition with simple `import libcom'.
Stochastic Parameterization Using Compressed Sensing: Application to the Lorenz-96 Atmospheric Model

Growing set of optimization and regression techniques, based upon sparse representations of signals, to build models from data sets has received widespread attention recently with the advent of compressed sensing. This paper deals with the parameterization of the Lorenz-96 model with two time-scales that mimics mid-latitude atmospheric dynamics with microscopic convective processes. Compressed sensing is used to build models (vector fields) to emulate the behavior of the fine-scale process, so that explicit simulations become an online benchmark for parameterization. We apply compressed sensing, where the sparse recovery is achieved by constructing a sensing/dictionary matrix from ergodic samples generated by the Lorenz-96 atmospheric model, to parameterize the unresolved variables in terms of resolved variables. Stochastic parameterization is achieved by auto-regressive modelling of noise. We utilize the ensemble Kalman filter for data assimilation, where observations (direct measurements) are assimilated in the low-dimensional stochastic parameterized model to provide predictions. Finally, we compare the predictions of compressed sensing and Wilk's polynomial regression to demonstrate the potential effectiveness of the proposed methodology.
Implementation of a time-dependent multiconfiguration self-consistent-field method for coupled electron-nuclear dynamics in diatomic molecules driven by intense laser pulses

Yang Li, ∗ Takeshi Sato, 2, 3, † and Kenichi L. Ishikawa 2, 3, ‡ Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan Photon Science Center, School of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan Research Institute for Photon Science and Laser Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Model-Advantage Optimization for Model-Based Reinforcement Learning

Model-based Reinforcement Learning (MBRL) algorithms have been traditionally designed with the goal of learning accurate dynamics of the environment. This introduces a mismatch between the objectives of model-learning and the overall learning problem of ﬁnding an optimal policy. Value-aware model learning, an alternative model-learning paradigm to maximum likelihood, proposes to inform model-learning through the value function of the learnt policy. While this paradigm is theoretically sound, it does not scale beyond toy settings. In this work, we propose a novel value-aware objective that is an upper bound on the absolute performance difference of a policy across two models. Further, we propose a general purpose algorithm that modiﬁes the standard MBRL pipeline – enabling learning with value aware objectives. Our proposed objective, in conjunction with this algorithm, is the ﬁrst successful instantiation of value-aware MBRL on challenging continuous control environments, outperforming previous value-aware objectives and with competitive performance w.r. t. MLE-based MBRL approaches.
Using relative weight analysis with residualization to detect relevant nonlinear interaction effects in ordinary and logistic regressions

Relative weight analysis is a classic tool for detecting whether one variable or interaction in a model is relevant. In this study, we focus on the construction of relative weights for non-linear interactions using restricted cubic splines. Our aim is to provide an accessible method to analyze a multivariate model and identify one subset with the most representative set of variables. Furthermore, we developed a procedure for treating control, fixed, free and interaction terms simultaneously in the residual weight analysis. The interactions are residualized properly against their main effects to maintain their true effects in the model. We tested this method using two simulated examples.
Unitarity and Page Curve for Evaporation of 2D AdS Black Holes

We explore the Hawking evaporation of two-dimensional anti-de Sitter (AdS2), dilatonic black hole coupled with conformal matter, and derive the Page curve for the entanglement entropy of radiation. We first work in a semiclassical approximation with backreaction. We show that the end-point of the evaporation process is AdS2 with a vanishing dilaton, i.e., a regular, singularity-free, zero-entropy state. We explicitly compute the entanglement entropies of the black hole and the radiation as functions of the horizon radius, using the conformal field theory (CFT) dual to AdS2 gravity. We use a simplified toy model, in which evaporation is described by the forming and growing of a negative mass configuration in the positive-mass black hole interior. This is similar to the “islands” proposal, recently put forward to explain the Page curve for evaporating black holes. The resulting Page curve for AdS2 black holes is in agreement with unitary evolution. The entanglement entropy of the radiation initially grows, closely following a thermal behavior, reaches a maximum at half-way of the evaporation process, and then goes down to zero, following the Bekenstein–Hawking entropy of the black hole. Consistency of our simplified model requires a non-trivial identification of the central charge of the CFT describing AdS2 gravity with the number of species of fields describing Hawking radiation.
Extracting the astrophysics of reionization from the Lyα forest power spectrum: a first forecast

The impact of cosmic reionization on the Lyα forest power spectrum has recently been shown to be significant even at low redshifts (z ∼ 2). This memory of reionization survives cosmological time-scales because high-entropy mean-density gas is heated to ∼3 × 104 K by reionization, which is inhomogeneous, and subsequent shocks from denser regions. In the near future, the first measurements of the Lyα forest 3D power spectrum will be very likely achieved by upcoming observational efforts such as the Dark Energy Spectroscopic Instrument (DESI). In addition to abundant cosmological information, these observations have the potential to extract the astrophysics of reionization from the Lyα forest. We forecast, for the first time, the accuracy with which the measurements of Lyα forest 3D power spectrum can place constraints on the reionization parameters with DESI. Specifically, we demonstrate that the constraints on the ionization efficiency, ζ, and the threshold mass for haloes that host ionizing sources, mturn, will have the 1σ error at the level of ζ = 25.0 ± 11.6 and $\log _{10} (m_{\rm turn}/{\rm M}_\odot) = 8.7^{+0.36}_{-0.70}$, respectively. The Lyα forest 3D power spectrum will thus provide an independent probe of reionization, probably even earlier in detection with DESI, with a sensitivity only slightly worse than the upcoming 21-cm power spectrum measurement with the Hydrogen Epoch of Reionization Array (HERA), i.e. σDESI/σHERA ≈ 1.5 for ζ and σDESI/σHERA ≈ 2.0 for log10(mturn/M⊙). Nevertheless, the Lyα forest constraint will be improved about three times tighter than the current constraint from reionization observations with high-z galaxy priors, i.e. σDESI/σcurrent ≈ 1/3 for ζ.
Approximate Maximum Halfspace Discrepancy

Consider the geometric range space $(X, \mathcal{H}_d)$ where $X \subset \mathbb{R}^d$ and $\mathcal{H}_d$ is the set of ranges defined by $d$-dimensional halfspaces. In this setting we consider that $X$ is the disjoint union of a red and blue set. For each halfspace $h \in \mathcal{H}_d$ define a function $\Phi(h)$ that measures the"difference"between the fraction of red and fraction of blue points which fall in the range $h$. In this context the maximum discrepancy problem is to find the $h^* = \arg \max_{h \in (X, \mathcal{H}_d)} \Phi(h)$. We aim to instead find an $\hat{h}$ such that $\Phi(h^*) - \Phi(\hat{h}) \le \varepsilon$. This is the central problem in linear classification for machine learning, in spatial scan statistics for spatial anomaly detection, and shows up in many other areas. We provide a solution for this problem in $O(|X| + (1/\varepsilon^d) \log^4 (1/\varepsilon))$ time, which improves polynomially over the previous best solutions. For $d=2$ we show that this is nearly tight through conditional lower bounds. For different classes of $\Phi$ we can either provide a $\Omega(|X|^{3/2 - o(1)})$ time lower bound for the exact solution with a reduction to APSP, or an $\Omega(|X| + 1/\varepsilon^{2-o(1)})$ lower bound for the approximate solution with a reduction to 3SUM. A key technical result is a $\varepsilon$-approximate halfspace range counting data structure of size $O(1/\varepsilon^d)$ with $O(\log (1/\varepsilon))$ query time, which we can build in $O(|X| + (1/\varepsilon^d) \log^4 (1/\varepsilon))$ time.
A Training-free and Reference-free Summarization Evaluation Metric via Centrality-weighted Relevance and Self-referenced Redundancy

In recent years, reference-based and supervised summarization evaluation metrics have been widely explored. However, collecting human-annotated references and ratings are costly and time-consuming. To avoid these limitations, we propose a training-free and reference-free summarization evaluation metric. Our metric consists of a centrality-weighted relevance score and a self-referenced redundancy score. The relevance score is computed between the pseudo reference built from the source document and the given summary, where the pseudo reference content is weighted by the sentence centrality to provide importance guidance. Besides an F_1-based relevance score, we also design an F_\beta-based variant that pays more attention to the recall score. As for the redundancy score of the summary, we compute a self-masked similarity score with the summary itself to evaluate the redundant information in the summary. Finally, we combine the relevance and redundancy scores to produce the final evaluation score of the given summary. Extensive experiments show that our methods can significantly outperform existing methods on both multi-document and single-document summarization evaluation. The source code is released at https://github.com/Chen-Wang-CUHK/Training-Free-and-Ref-Free-Summ-Evaluation.
Training Massive Deep Neural Networks in a Smart Contract: A New Hope

Deep neural networks (DNNs) could be very useful in blockchain applications such as DeFi and NFT trading. However, training / running large-scale DNNs as part of a smart contract is infeasible on today's blockchain platforms, due to two fundamental design issues of these platforms. First, blockchains nowadays typically require that each node maintain the complete world state at any time, meaning that the node must execute all transactions in every block. This is prohibitively expensive for computationally intensive smart contracts involving DNNs. Second, existing blockchain platforms expect smart contract transactions to have deterministic, reproducible results and effects. In contrast, DNNs are usually trained / run lock-free on massively parallel computing devices such as GPUs, TPUs and / or computing clusters, which often do not yield deterministic results. This paper proposes novel platform designs, collectively called A New Hope (ANH), that address the above issues. The main ideas are (i) computing-intensive smart contract transactions are only executed by nodes who need their results, or by specialized serviced providers, and (ii) a non-deterministic smart contract transaction leads to uncertain results, which can still be validated, though at a relatively high cost; specifically for DNNs, the validation cost can often be reduced by verifying properties of the results instead of their exact values. In addition, we discuss various implications of ANH, including its effects on token fungibility, sharding, private transactions, and the fundamental meaning of a smart contract.
A general framework to test gravity using galaxy clusters IV: Cluster and halo properties in DGP gravity

We study and model the properties of galaxy clusters in the normal-branch Dvali-Gabadadze-Porrati (nDGP) model of gravity, which is representative of a wide class of theories which exhibit the Vainshtein screening mechanism. Using the first cosmological simulations which incorporate both full baryonic physics and nDGP, we find that, despite being efficiently screened within clusters, the fifth force can raise the temperature of the intra-cluster gas, affecting the scaling relations between the cluster mass and three observable mass proxies: the gas temperature, the Compton $Y$-parameter of the Sunyaev-Zel'dovich effect and the X-ray analogue of the $Y$-parameter. Therefore, unless properly accounted for, this could lead to biased measurements of the cluster mass in tests that make use of cluster observations, such as cluster number counts, to probe gravity. Using a suite of dark-matter-only simulations, which span a wide range of box sizes and resolutions, and which feature very different strengths of the fifth force, we also calibrate general fitting formulae which can reproduce the nDGP halo concentration at percent accuracy for $0\leq z\leq1$, and halo mass function with $\lesssim3\%$ accuracy at $0\leq z\leq1$ (increasing to $\lesssim5\%$ for $1\leq z\leq 2$), over a halo mass range spanning four orders of magnitude. Our model for the concentration can be used for converting between halo mass overdensities and predicting statistics such as the nonlinear matter power spectrum. The results of this work will form part of a framework for unbiased constraints of gravity using the data from ongoing and upcoming cluster surveys.