Miracle-dz/UniFAR-FTUs huggingface dataset:
On Using Admissible Bounds for Learning Forward Search Heuristics

In recent years, there has been growing interest in utilizing modern machine learning techniques to learn heuristic functions for forward search algorithms. Despite this, there has been little theoretical understanding of \emph{what} they should learn, \emph{how} to train them, and \emph{why} we do so. This lack of understanding has resulted in the adoption of diverse training targets (suboptimal vs optimal costs vs admissible heuristics) and loss functions (e.g., square vs absolute errors) in the literature. In this work, we focus on how to effectively utilize the information provided by admissible heuristics in heuristic learning. We argue that learning from poly-time admissible heuristics by minimizing mean square errors (MSE) is not the correct approach, since its result is merely a noisy, inadmissible copy of an efficiently computable heuristic. Instead, we propose to model the learned heuristic as a truncated gaussian, where admissible heuristics are used not as training targets but as lower bounds of this distribution. This results in a different loss function from the MSE commonly employed in the literature, which implicitly models the learned heuristic as a gaussian distribution. We conduct experiments where both MSE and our novel loss function are applied to learning a heuristic from optimal plan costs. Results show that our proposed method converges faster during training and yields better heuristics, with 40% lower MSE on average.
Cached Operator Reordering: A Unified View for Fast GNN Training

Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering. However, the sparse nature of GNN computation poses new challenges for performance optimization compared to traditional deep neural networks. We address these challenges by providing a unified view of GNN computation, I/O, and memory. By analyzing the computational graphs of the Graph Convolutional Network (GCN) and Graph Attention (GAT) layers -- two widely used GNN layers -- we propose alternative computation strategies. We present adaptive operator reordering with caching, which achieves a speedup of up to 2.43x for GCN compared to the current state-of-the-art. Furthermore, an exploration of different caching schemes for GAT yields a speedup of up to 1.94x. The proposed optimizations save memory, are easily implemented across various hardware platforms, and have the potential to alleviate performance bottlenecks in training large-scale GNN models.
The Challenges of Machine Learning for Trust and Safety: A Case Study on Misinformation Detection

We examine the disconnect between scholarship and practice in applying machine learning to trust and safety problems, using misinformation detection as a case study. We systematize literature on automated detection of misinformation across a corpus of 270 well-cited papers in the field. We then examine subsets of papers for data and code availability, design missteps, reproducibility, and generalizability. Our paper corpus includes published work in security, natural language processing, and computational social science. Across these disparate disciplines, we identify common errors in dataset and method design. In general, detection tasks are often meaningfully distinct from the challenges that online services actually face. Datasets and model evaluation are often non-representative of real-world contexts, and evaluation frequently is not independent of model training. Data and code availability is poor. We demonstrate the limitations of current detection methods in a series of three replication studies. Based on the results of these analyses and our literature survey, we offer recommendations for evaluating applications of machine learning to trust and safety problems in general. Our aim is for future work to avoid the pitfalls that we identify.
Cyclic Orderings of Paving Matroids

A matroid M is cyclically orderable if there is a cyclic permutation of the elements of M such that any r consecutive elements form a basis in M. An old conjecture of Kajitani, Miyano, and Ueno states that a matroid M is cyclically orderable if and only if for all nonempty subsets X in E(M), |X|/r(M) is less than or equal to |E(M)|/r(M). In this paper, we verify this conjecture for all paving matroids.
Mitigating the effect of population model uncertainty on strong lensing Bayes factor using nonparametric methods

Strong lensing of gravitational waves can produce several detectable images as repeated events in the upcoming observing runs, which can be detected with the posterior overlap analysis (Bayes factor). The choice of the binary black hole population plays an important role in the analysis as two gravitational-wave events could be similar either because of lensing or astrophysical coincidence. In this study, we investigate the biases induced by different population models on the Bayes factor. We build up a mock catalogue of gravitational-wave events following a benchmark population and reconstruct it using both non-parametric and parametric methods. Using these reconstructions, we compute the Bayes factor for lensed pair events by utilizing both models and compare the results with a benchmark model. We show that the use of a non-parametric population model gives a smaller bias than parametric population models. Therefore, our study demonstrates the importance of choosing a sufficiently agnostic population model for strong lensing analyses.
An extensible point-based method for data chart value detection

We present an extensible method for identifying semantic points to reverse engineer (i.e. extract the values of) data charts, particularly those in scientific articles. Our method uses a point proposal network (akin to region proposal networks for object detection) to directly predict the position of points of interest in a chart, and it is readily extensible to multiple chart types and chart elements. We focus on complex bar charts in the scientific literature, on which our model is able to detect salient points with an accuracy of 0.8705 F1 (@1.5-cell max deviation); it achieves 0.9810 F1 on synthetically-generated charts similar to those used in prior works. We also explore training exclusively on synthetic data with novel augmentations, reaching surprisingly competent performance in this way (0.6621 F1) on real charts with widely varying appearance, and we further demonstrate our unchanged method applied directly to synthetic pie charts (0.8343 F1). Datasets, trained models, and evaluation code are available at https://github.com/BNLNLP/PPN_model.
Black holes in degenerate Einstein Gauss-Bonnet gravity: Can QNMs distinguish them from GR?

In this study, for the first time, we analyse the quasinormal modes of black holes occurring within the framework of degenerate gravity. We investigate the properties of the asymptotically flat spacetimes introduced recently in [JCAP 02(2022)02] that satisfy degenerate Einstein Gauss-Bonnet(dEGB) action and belong to a much larger class of solutions which include cosmological constant. This solution has two distinct branches akin to Einstein Gauss-Bonnet(EBG) gravity. However, unlike the EBG solutions, both the branches of dEGB are well-defined asymptotically. The negative branches from both theories can be identified for the asymptotically flat case. We observe black holes for specific ranges of the Gauss-Bonnet coupling parameter and perform a stability analysis by calculating the quasinormal modes (QNMs) under scalar wave propagation. Finally, the ringdown spectrums of our black holes are compared with their GR counterparts.
Geo-Sketcher: Rapid 3D Geological Modeling using Geological and Topographic Map Sketches

The construction of 3D geological models is an essential task in oil/gas exploration, development and production. However, it is a cumbersome, time-consuming and error-prone task mainly because of the model's geometric and topological complexity. The models construction is usually separated into interpretation and 3D modeling, performed by different highly specialized individuals, which leads to inconsistencies and intensifies the challenges. In addition, the creation of models following geological rules is paramount for properly depicting static and dynamic properties of oil/gas reservoirs. In this work, we propose a sketch-based approach to expedite the creation of valid 3D geological models by mimicking how domain experts interpret geological structures, allowing creating models directly from interpretation sketches. Our sketch-based modeler (Geo-Sketcher) is based on sketches of standard 2D topographic and geological maps, comprised of lines, symbols and annotations. We developed a graph-based representation to enable (1) the automatic computation of the relative ages of rock series and layers, and (2) the embedding of specific geological rules directly in the sketching. We introduce the use of Hermite-Birkhoff Radial Basis Functions to interpolate the geological map constraints, and demonstrate the capabilities of our approach with a variety of results with different levels of complexity.
Prospects of detecting gamma-ray signal of dark matter interaction with the MACE telescope

The MACE (Major Atmospheric Cherenkov Experiment) telescope has started its regular gamma-ray observations at Hanle in India. Located at an altitude of $\sim$ 4.3 km above sea level and equipped with a 21 m diameter large quasi-parabolic reflector, it has the capability to explore the gamma-ray sky in the energy range above 20 GeV with very high sensitivity. In this work, we present the results from the feasibility studies for searching high-energy gamma-ray signals from dark matter interaction in potential astrophysical environments. We study the impact of MACE response function and other instrumental characteristics to probe the velocity average interaction cross-section ($<\sigma v>$) of the weakly interacting massive particles (WIMPs), expected from the thermal dark matter freeze-out during the decoupling era. We consider the presence of dark matter in the form of pure WIMPs in the mass range 200 GeV - 10 TeV to produce distinctive gamma-ray spectra through its self-annihilation into standard model particles using the Pythia simulation package. The convolution of gamma-ray spectra corresponding to different standard model channels with the MACE response function is used to estimate the upper limit on $<\sigma v>$ for 100 hours of expected MACE observation of Segue1 (a dwarf spheroidal galaxy) which is a potential site of dark matter.
Boosting Diffusion Models with an Adaptive Momentum Sampler

Diffusion probabilistic models (DPMs) have been shown to generate high-quality images without the need for delicate adversarial training. However, the current sampling process in DPMs is prone to violent shaking. In this paper, we present a novel reverse sampler for DPMs inspired by the widely-used Adam optimizer. Our proposed sampler can be readily applied to a pre-trained diffusion model, utilizing momentum mechanisms and adaptive updating to smooth the reverse sampling process and ensure stable generation, resulting in outputs of enhanced quality. By implicitly reusing update directions from early steps, our proposed sampler achieves a better balance between high-level semantics and low-level details. Additionally, this sampler is flexible and can be easily integrated into pre-trained DPMs regardless of the sampler used during training. Our experimental results on multiple benchmarks demonstrate that our proposed reverse sampler yields remarkable improvements over different baselines. We will make the source code available.
Multi-object Detection, Tracking and Prediction in Rugged Dynamic Environments

Multi-object tracking (MOT) has important applications in monitoring, logistics, and other fields. This paper develops a real-time multi-object tracking and prediction system in rugged environments. A 3D object detection algorithm based on Lidar-camera fusion is designed to detect the target objects. Based on the Hungarian algorithm, this paper designs a 3D multi-object tracking algorithm with an adaptive threshold to realize the stable matching and tracking of the objects. We combine Memory Augmented Neural Networks (MANN) and Kalman filter to achieve 3D trajectory prediction on rugged terrains. Besides, we realize a new dynamic SLAM by using the results of multi-object tracking to remove dynamic points for better SLAM performance and static map. To verify the effectiveness of the proposed multi-object tracking and prediction system, several simulations and physical experiments are conducted. The results show that the proposed system can track dynamic objects and provide future trajectory and a more clean static map in real-time.
Patient Clustering via Integrated Profiling of Clinical and Digital Data

We introduce a novel profile-based patient clustering model designed for healthcare clinical data. By utilizing a method grounded on constrained low-rank approximation, our model takes advantage of patients' clinical data and digital interaction data, including browsing and search, to construct patient profiles. As a result of the method, nonnegative embedding vectors are generated, serving as a low-dimensional representation of the patients. Our model was assessed using real-world patient data from a healthcare web portal, with a comprehensive evaluation approach which considered clustering and recommendation capabilities. In comparison to other baselines, our approach demonstrated superior performance in terms of clustering coherence and recommendation accuracy.
Knowledge-injected Prompt Learning for Chinese Biomedical Entity Normalization

The Biomedical Entity Normalization (BEN) task aims to align raw, unstructured medical entities to standard entities, thus promoting data coherence and facilitating better downstream medical applications. Recently, prompt learning methods have shown promising results in this task. However, existing research falls short in tackling the more complex Chinese BEN task, especially in the few-shot scenario with limited medical data, and the vast potential of the external medical knowledge base has yet to be fully harnessed. To address these challenges, we propose a novel Knowledge-injected Prompt Learning (PL-Knowledge) method. Specifically, our approach consists of five stages: candidate entity matching, knowledge extraction, knowledge encoding, knowledge injection, and prediction output. By effectively encoding the knowledge items contained in medical entities and incorporating them into our tailor-made knowledge-injected templates, the additional knowledge enhances the model's ability to capture latent relationships between medical entities, thus achieving a better match with the standard entities. We extensively evaluate our model on a benchmark dataset in both few-shot and full-scale scenarios. Our method outperforms existing baselines, with an average accuracy boost of 12.96\% in few-shot and 0.94\% in full-data cases, showcasing its excellence in the BEN task.
Combinatorial insight of Riemann Boundary value problem in lattice walk problems

Enumeration of quarter-plane lattice walks with small steps is a classical problem in combinatorics. An effective approach is the kernel method and the solution is derived by positive term extraction. Another approach is to reduce the lattice walk problem to a Carleman type Riemann boundary value problem (RBVP) and solve it by complex analysis. There are two parameter characterizing the solutions of a Carleman type Riemann Boundary value problem, the index $\chi$ and the conformal gluing function $w(x)$. In this paper, we propose a combinatorial insight to the RBVP approach. We show that the index can be treated as the canonical factorization in the kernel method and the conformal gluing function can be turned into a conformal mapping such that after this mapping, the positive degree terms and the negative degree terms can be naturally separated. The combinatorial insight of RBVP provide a connection between the kernel method, the RBVP approach and the Tutte's invariant method.
CED: Consistent ensemble distillation for audio tagging

Augmentation and knowledge distillation (KD) are well-established techniques employed in audio classification tasks, aimed at enhancing performance and reducing model sizes on the widely recognized Audioset (AS) benchmark. Although both techniques are effective individually, their combined use, called consistent teaching, hasn't been explored before. This paper proposes CED, a simple training framework that distils student models from large teacher ensembles with consistent teaching. To achieve this, CED efficiently stores logits as well as the augmentation methods on disk, making it scalable to large-scale datasets. Central to CED's efficacy is its label-free nature, meaning that only the stored logits are used for the optimization of a student model only requiring 0.3\% additional disk space for AS. The study trains various transformer-based models, including a 10M parameter model achieving a 49.0 mean average precision (mAP) on AS. Pretrained models and code are available at https://github.com/RicherMans/CED.
Dynamics of K$_2$Ni$_2$(SO$_4$)$_3$ governed by proximity to a 3D spin liquid model

Quantum spin liquids (QSLs) have become a key area of research in magnetism due to their remarkable properties, such as long-range entanglement, fractional excitations, pinch-point singularities, and topologically protected phenomena. In recent years, the search for QSLs has expanded into the three-dimensional world, where promising features have been found in materials that form pyrochlore and hyper-kagome lattices, despite the suppression of quantum fluctuations due to high dimensionality. One such material is the $S = 1$ K$_2$Ni$_2$(SO$_4$)$_3$ compound, which belongs to the langbeinite family consisting of two interconnected trillium lattices. Although magnetically ordered, K$_2$Ni$_2$(SO$_4$)$_3$ has been found to exhibit a highly dynamical and correlated state which can be driven into a pure quantum spin liquid under magnetic fields of only $B \simeq 4$~T. In this article, we combine inelastic neutron scattering measurements with pseudo-fermion functional renormalization group (PFFRG) and classical Monte Carlo (cMC) calculations to study the magnetic properties of K$_2$Ni$_2$(SO$_4$)$_3$, revealing a high level of agreement between the experiment and theory. We further reveal the origin of the dynamical state in K$_2$Ni$_2$(SO$_4$)$_3$ by studying a larger set of exchange parameters, uncovering an `island of liquidity' around a focal point given by a magnetic network composed of tetrahedra on a trillium lattice.
Enhanced Residual SwinV2 Transformer for Learned Image Compression

Recently, the deep learning technology has been successfully applied in the field of image compression, leading to superior rate-distortion performance. However, a challenge of many learning-based approaches is that they often achieve better performance via sacrificing complexity, which making practical deployment difficult. To alleviate this issue, in this paper, we propose an effective and efficient learned image compression framework based on an enhanced residual Swinv2 transformer. To enhance the nonlinear representation of images in our framework, we use a feature enhancement module that consists of three consecutive convolutional layers. In the subsequent coding and hyper coding steps, we utilize a SwinV2 transformer-based attention mechanism to process the input image. The SwinV2 model can help to reduce model complexity while maintaining high performance. Experimental results show that the proposed method achieves comparable performance compared to some recent learned image compression methods on Kodak and Tecnick datasets, and outperforms some traditional codecs including VVC. In particular, our method achieves comparable results while reducing model complexity by 56% compared to these recent methods.
Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics

Selecting the right resources for big data analytics jobs is hard because of the wide variety of configuration options like machine type and cluster size. As poor choices can have a significant impact on resource efficiency, cost, and energy usage, automated approaches are gaining popularity. Most existing methods rely on profiling recurring workloads to find near-optimal solutions over time. Due to the cold-start problem, this often leads to lengthy and costly profiling phases. However, big data analytics jobs across users can share many common properties: they often operate on similar infrastructure, using similar algorithms implemented in similar frameworks. The potential in sharing aggregated profiling runs to collaboratively address the cold start problem is largely unexplored. We present Karasu, an approach to more efficient resource configuration profiling that promotes data sharing among users working with similar infrastructures, frameworks, algorithms, or datasets. Karasu trains lightweight performance models using aggregated runtime information of collaborators and combines them into an ensemble method to exploit inherent knowledge of the configuration search space. Moreover, Karasu allows the optimization of multiple objectives simultaneously. Our evaluation is based on performance data from diverse workload executions in a public cloud environment. We show that Karasu is able to significantly boost existing methods in terms of performance, search time, and cost, even when few comparable profiling runs are available that share only partial common characteristics with the target job.
Tau Prolog: A Prolog interpreter for the Web

Tau Prolog is a client-side Prolog interpreter fully implemented in JavaScript, which aims at implementing the ISO Prolog Standard. Tau Prolog has been developed to be used with either Node.js or a browser seamlessly, and therefore, it has been developed following a non-blocking, callback-based approach to avoid blocking web browsers. Taking the best from JavaScript and Prolog, Tau Prolog allows the programmer to handle browser events and manipulate the Document Object Model (DOM) of a web using Prolog predicates. In this paper we describe the architecture of Tau Prolog and its main packages for interacting with the Web, and we present its programming environment.
Frequency-Space Prediction Filtering for Phase Aberration Correction in Plane-Wave Ultrasound

Ultrasound imaging often suffers from image degradation stemming from phase aberration, which represents a significant contributing factor to the overall image degradation in ultrasound imaging. Frequency-space prediction filtering or FXPF is a technique that has been applied within focused ultrasound imaging to alleviate the phase aberration effect. It presupposes the existence of an autoregressive (AR) model across the signals received at the transducer elements and removes any components that do not conform to the established model. In this study, we illustrate the challenge of applying this technique to plane-wave imaging, where, at shallower depths, signals from more distant elements lose relevance, and a fewer number of elements contribute to image reconstruction. While the number of contributing signals varies, adopting a fixed-order AR model across all depths, results in suboptimal performance. To address this challenge, we propose an AR model with an adaptive order and quantify its effectiveness using contrast and generalized contrast-to-noise ratio metrics.