Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Unified Semantic Discourse Structure for High-quality Headline Generation

Headline generation aims to summarize a long document with a short, catchy title that reflects the main idea. This requires accurately capturing the core document semantics, which is challenging due to the lengthy and background information-rich na ture of the texts. In this work, We propose using a unified semantic discourse structure (S3) to represent document semantics, achieved by combining document-level rhetorical structure theory (RST) trees with sentence-level abstract meaning representation (AMR) graphs to construct S3 graphs. The hierarchical composition of sentence, clause, and word intrinsically characterizes the semantic meaning of the overall document. We then develop a headline generation framework, in which the S3 graphs are encoded as contextual features. To consolidate the efficacy of S3 graphs, we further devise a hierarchical structure pruning mechanism to dynamically screen the redundant and nonessential nodes within the graph. Experimental results on two headline generation datasets demonstrate that our method outperforms existing state-of-art methods consistently. Our work can be instructive for a broad range of document modeling tasks, more than headline or summarization generation.
DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving

End-to-end driving has made significant progress in recent years, demonstrating benefits such as system simplicity and competitive driving performance under both open-loop and closed-loop settings. Nevertheless, the lack of interpretability and controllability in its driving decisions hinders real-world deployment for end-to-end driving systems. In this paper, we collect a comprehensive end-to-end driving dataset named DriveCoT, leveraging the CARLA simulator. It contains sensor data, control decisions, and chain-of-thought labels to indicate the reasoning process. We utilize the challenging driving scenarios from the CARLA leaderboard 2.0, which involve high-speed driving and lane-changing, and propose a rule-based expert policy to control the vehicle and generate ground truth labels for its reasoning process across different driving aspects and the final decisions. This dataset can serve as an open-loop end-to-end driving benchmark, enabling the evaluation of accuracy in various chain-of-thought aspects and the final decision. In addition, we propose a baseline model called DriveCoT-Agent, trained on our dataset, to generate chain-of-thought predictions and final decisions. The trained model exhibits strong performance in both open-loop and closed-loop evaluations, demonstrating the effectiveness of our proposed dataset.
Raiders of the Lost Mud: the geology behind drilling incidents within the Balder Formation around the Corona Ridge, West of Shetland

The Faroe–Shetland Basin, NE Atlantic continental margin, hosts a number of important hydrocarbon fields; although deep water and narrow weather windows mean that drilling costs are considerably higher than for other parts of the UK Continental Shelf. Any additional drilling complications are therefore important to predict and negate as such issues can result in avoidable multi-million pound cost implications. This study focuses on the Corona Ridge, an intrabasinal high which contains the Rosebank Field, where a plethora of drilling issues, of enigmatic origin, are common within a key stratigraphic marker known as the Balder Formation. Drilling fluid loss, bit balling, wellbore breakouts and wellbore ‘ballooning’, where lost drilling fluid returns to the wellbore, are all recognized within the Balder Formation along the Corona Ridge. We find that many of the drilling incidents can be traced back to both the lithological character of the Balder Formation and the mid-Miocene tectonic inversion of the Corona Ridge. Moreover, we find that this geological explanation has wider implications for exploration in the region, including the mitigation of drilling incidents in future wells through drill-bit selection. Supplementary material: A table detailing the drilling acronyms and terminology used in this study (adapted from Mark et al. 2018) is available at https://doi.org/10.6084/m9.figshare. c.4290602
SAT Encoding of Partial Ordering Models for Graph Coloring Problems

In this paper, we suggest new SAT encodings of the partial-ordering based ILP model for the graph coloring problem (GCP) and the bandwidth coloring problem (BCP). The GCP asks for the minimum number of colors that can be assigned to the vertices of a given graph such that each two adjacent vertices get different colors. The BCP is a generalization, where each edge has a weight that enforces a minimal"distance"between the assigned colors, and the goal is to minimize the"largest"color used. For the widely studied GCP, we experimentally compare our new SAT encoding to the state-of-the-art approaches on the DIMACS benchmark set. Our evaluation confirms that this SAT encoding is effective for sparse graphs and even outperforms the state-of-the-art on some DIMACS instances. For the BCP, our theoretical analysis shows that the partial-ordering based SAT and ILP formulations have an asymptotically smaller size than that of the classical assignment-based model. Our practical evaluation confirms not only a dominance compared to the assignment-based encodings but also to the state-of-the-art approaches on a set of benchmark instances. Up to our knowledge, we have solved several open instances of the BCP from the literature for the first time.
What Happens to a Dataset Transformed by a Projection-based Concept Removal Method?

We investigate the behavior of methods that use linear projections to remove information about a concept from a language representation, and we consider the question of what happens to a dataset transformed by such a method. A theoretical analysis and experiments on real-world and synthetic data show that these methods inject strong statistical dependencies into the transformed datasets. After applying such a method, the representation space is highly structured: in the transformed space, an instance tends to be located near instances of the opposite label. As a consequence, the original labeling can in some cases be reconstructed by applying an anti-clustering method.
ChatPattern: Layout Pattern Customization via Natural Language

Existing works focus on fixed-size layout pattern generation, while the more practical free-size pattern generation receives limited attention. In this paper, we propose ChatPattern, a novel Large-Language-Model (LLM) powered framework for flexible pattern customization. ChatPattern utilizes a two-part system featuring an expert LLM agent and a highly controllable layout pattern generator. The LLM agent can interpret natural language requirements and operate design tools to meet specified needs, while the generator excels in conditional layout generation, pattern modification, and memory-friendly patterns extension. Experiments on challenging pattern generation setting shows the ability of ChatPattern to synthesize high-quality large-scale patterns.
Rapid microfluidic prototyping of sophisticated protein analysis platforms using grayscale photopatterning

We present a technique that utilizes hydrogel photopatterning via laser-printed grayscale masks to controllably define non-uniform pore-size distributions from a single <;60s UV exposure and acrylamide precursor solution. This method enables facile and rapid fabrication of spatially complex chromatography tools that are impossible to realize using traditional methods. To highlight its utility we present two workhorse analytical electrophoresis platforms: (1) a 24-plex electrophoresis screening assay in adhesive microfluidics and (2) a 96-plex gradient gel-based protein sizing assay in a free-standing polyacrylamide gel platform.
Synapse: Learning Preferential Concepts from Visual Demonstrations

This paper addresses the problem of preference learning, which aims to learn user-specific preferences (e.g.,"good parking spot","convenient drop-off location") from visual input. Despite its similarity to learning factual concepts (e.g.,"red cube"), preference learning is a fundamentally harder problem due to its subjective nature and the paucity of person-specific training data. We address this problem using a new framework called Synapse, which is a neuro-symbolic approach designed to efficiently learn preferential concepts from limited demonstrations. Synapse represents preferences as neuro-symbolic programs in a domain-specific language (DSL) that operates over images, and leverages a novel combination of visual parsing, large language models, and program synthesis to learn programs representing individual preferences. We evaluate Synapse through extensive experimentation including a user case study focusing on mobility-related concepts in mobile robotics and autonomous driving. Our evaluation demonstrates that Synapse significantly outperforms existing baselines as well as its own ablations. The code and other details can be found on the project website https://amrl.cs. utexas.edu/synapse .
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud Segmentation

Curb detection is an important function in intelligent driving and can be used to determine drivable areas of the road. However, curbs are difficult to detect due to the complex road environment. This paper introduces CurbNet, a novel framework for curb detection, leveraging point cloud segmentation. Addressing the dearth of comprehensive curb datasets and the absence of 3D annotations, we have developed the 3D-Curb dataset, encompassing 7,100 frames, which represents the largest and most categorically diverse collection of curb point clouds currently available. Recognizing that curbs are primarily characterized by height variations, our approach harnesses spatially-rich 3D point clouds for training. To tackle the challenges presented by the uneven distribution of curb features on the xy-plane and their reliance on z-axis high-frequency features, we introduce the multi-scale and channel attention (MSCA) module, a bespoke solution designed to optimize detection performance. Moreover, we propose an adaptive weighted loss function group, specifically formulated to counteract the imbalance in the distribution of curb point clouds relative to other categories. Our extensive experimentation on 2 major datasets has yielded results that surpass existing benchmarks set by leading curb detection and point cloud segmentation models. By integrating multi-clustering and curve fitting techniques in our post-processing stage, we have substantially reduced noise in curb detection, thereby enhancing precision to 0.8744. Notably, CurbNet has achieved an exceptional average metrics of over 0.95 at a tolerance of just 0.15m, thereby establishing a new benchmark. Furthermore, corroborative real-world experiments and dataset analyzes mutually validate each other, solidifying CurbNet's superior detection proficiency and its robust generalizability.
Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning

Object reconstruction is relevant for many autonomous robotic tasks that require interaction with the environment. A key challenge in such scenarios is planning view configurations to collect informative measurements for reconstructing an initially unknown object. One-shot view planning enables efficient data collection by predicting view configurations and planning the globally shortest path connecting all views at once. However, geometric priors about the object are required to conduct one-shot view planning. In this work, we propose a novel one-shot view planning approach that utilizes the powerful 3D generation capabilities of diffusion models as priors. By incorporating such geometric priors into our pipeline, we achieve effective one-shot view planning starting with only a single RGB image of the object to be reconstructed. Our planning experiments in simulation and real-world setups indicate that our approach balances well between object reconstruction quality and movement cost.
A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation

This paper explores techniques that focus on understanding and resolving ambiguity in language within the field of natural language processing (NLP), highlighting the complexity of linguistic phenomena such as polysemy and homonymy and their implications for computational models. Focusing extensively on Word Sense Disambiguation (WSD), it outlines diverse approaches ranging from deep learning techniques to leveraging lexical resources and knowledge graphs like WordNet. The paper introduces cutting-edge methodologies like word sense extension (WSE) and neuromyotonic approaches, enhancing disambiguation accuracy by predicting new word senses. It examines specific applications in biomedical disambiguation and language specific optimisation and discusses the significance of cognitive metaphors in discourse analysis. The research identifies persistent challenges in the field, such as the scarcity of sense annotated corpora and the complexity of informal clinical texts. It concludes by suggesting future directions, including using large language models, visual WSD, and multilingual WSD systems, emphasising the ongoing evolution in addressing lexical complexities in NLP. This thinking perspective highlights the advancement in this field to enable computers to understand language more accurately.
A quadraxial feed for ultra-wide bandwidth quadruple-ridged flared horn antennas

A quadraxial feed, excited by two orthogonal differential modes, is proposed for ultra wideband quadruple-ridged flared horn (QRFH) antennas that obviates the need for a balun. It is shown that in this configuration the fundamental TE11 mode is most strongly excited over the entire frequency band, while the higher-order modes are significantly suppressed, as compared to the conventional excitation using the ridge-to-coax balun transition. These properties lead to several advantages for QRFH antenna applications which require frequency-invariant beam characteristics, high port isolation and low cross-polarisation level, such as e.g. reflector antenna feeds for future radio telescopes.
ORGANIZING TEXTURE IN A PERCEPTUAL SPACE

Several features can be extracted from a texture to represent it as a point in a high dimensional space. In this paper we advocate the use of perceptually relevant features in order to obtain a space that appeals to the intuition of a human observer. To show the feasibility of our proposal in this paper we detail the construction of a two-dimensional perceptual space based on local directionality and local contrast. The results obtained testing the validity of the proposed classification against some benchmark texture database are also reported.
BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval

The burgeoning integration of 3D medical imaging into healthcare has led to a substantial increase in the workload of medical professionals. To assist clinicians in their diagnostic processes and alleviate their workload, the development of a robust system for retrieving similar case studies presents a viable solution. While the concept holds great promise, the field of 3D medical text-image retrieval is currently limited by the absence of robust evaluation benchmarks and curated datasets. To remedy this, our study presents a groundbreaking dataset, BIMCV-R (This dataset will be released upon acceptance.), which includes an extensive collection of 8,069 3D CT volumes, encompassing over 2 million slices, paired with their respective radiological reports. Expanding upon the foundational work of our dataset, we craft a retrieval strategy, MedFinder. This approach employs a dual-stream network architecture, harnessing the potential of large language models to advance the field of medical image retrieval beyond existing text-image retrieval solutions. It marks our preliminary step towards developing a system capable of facilitating text-to-image, image-to-text, and keyword-based retrieval tasks.
From Raw Data to Safety: Reducing Conservatism by Set Expansion

In response to safety concerns associated with learning-based algorithms, safety filters have been proposed as a modular technique. Generally, these filters heavily rely on the system's model, which is contradictory if they are intended to enhance a data-driven or end-to-end learning solution. This paper extends our previous work, a purely Data-Driven Safety Filter (DDSF) based on Willems' lemma, to an extremely short-sighted and non-conservative solution. Specifically, we propose online and offline sample-based methods to expand the safe set of DDSF and reduce its conservatism. Since this method is defined in an input-output framework, it can systematically handle both unknown and time-delay LTI systems using only one single batch of data. To evaluate its performance, we apply the proposed method to a time-delay system under various settings. The simulation results validate the effectiveness of the set expansion algorithm in generating a notably large input-output safe set, resulting in safety filters that are not conservative, even with an extremely short prediction horizon.
FineWAVE: Fine-Grained Warning Verification of Bugs for Automated Static Analysis Tools

The continual expansion of software size and complexity has led to an increased focus on reducing defects and bugs during development. Although Automated Static Analysis Tools (ASATs) offer help, in practice, the significant number of false positives can impede developers' productivity and confidence in the tools. Therefore, previous research efforts have explored learning-based methods to validate the reported warnings. Nevertheless, there are still some limitations. (1) The granularity of prior research is coarse, as it focuses on identifying either actionable warnings throughout extensive development histories or potential true warnings at the function level. These approaches lack specificity regarding individual bugs and warnings. (2) Machine learning-based approaches need much manual effort for feature engineering while existing deep learning-based approaches ignore key semantics between source code and warnings. (3) The small number of selected projects hinders the comprehensive evaluation of these approaches. In this paper, we proposed a fine-grained warning verification approach that is sensitive to bugs for improving the results of ASATs, namely \ourtool. Specifically, we design a novel LSTM-based model that captures both fine-grained semantics of source code and warnings from ASATs and highlights their correlations with cross-attention. To tackle the data scarcity of training and evaluation, we collected a large-scale dataset of 280,273 warnings, namely FineWA. It is ten times larger than the existing largest dataset. Then, we conducted extensive experiments on the dataset to evaluate FineWAVE. The experimental results demonstrate the effectiveness of our approach, with an F1-score of 97.79% for reducing false alarms and 67.06% for confirming actual warnings, which also significantly outperforms all baselines.
Quantum bumpless pipe dreams

Schubert polynomials give polynomial representatives of Schubert classes in the cohomology of the complete flag variety and have a combinatorial formulation in terms of bumpless pipe dreams. Quantum double Schubert polynomials are polynomial representatives of Schubert classes in the torus-equivariant quantum cohomology of the complete flag variety, but they have no analogous combinatorial formulation. We introduce a generalization of the bumpless pipe dreams called quantum bumpless pipe dreams, giving a novel combinatorial formula for quantum double Schubert polynomials as a sum of binomial weights of quantum bumpless pipe dreams. We give a bijective proof for this formula by showing that the sum of binomial weights satisfies a defining recursion that is a special case of Monk's rule.
Preliminary Study Contamination of Organochlorine Pesticide (Heptachlor) and Heavy Metal (Arsenic) in Shallow Groundwater Aquifer of Semarang Coastal Areas

The present study was conducted to assess the level of pesticide and heavy metal contamination in shallow aquifer of Semarang coastal areas. Results indicated that Heptachlor and Arsenic were detected in the water samples in the range 0.023-0.055 μg L-1 and 0,03-1,63 μg L-1, respectively. Compared to the standard limits of the organochlorine contents in the water sample by World Health Organization (WHO) limits and Indonesian Drinking and Domestic Water Quality Standard for Ground Water (IWQS), groundwater of Semarang Coastal Areas was contaminated with pesticide and heavy metal. This study has proven the presence of organochlorine and heavy metal contamination of some shallow aquifer supplies in the coastal areas of Semarang.
Efficient Fair Secure Two-Party Computation

Yao first introduced a constant-round protocol for secure two-party computation (2PC) withstanding semi-honest adversaries by using a tool called "garbled circuit". Later, many protocols based on garbled circuit approach have been presented, most of which discussed malicious adversaries and efficiency about 2PC. However, there only have a few protocols dealing with the fundamental property of fairness for Yao's garbled circuit approach, in which one involved a trusted third party and the others are very expensive. In the paper, we propose' an efficient Yao's garbled circuit protocol for fair secure 2PC based on ElGamal encryption, Pedersen commitment, Cachin et al.'s verifiable oblivious transfer and Ou-Ruan et al.'s gradual release homomorphic timed commitment. The protocol achieves two advantages: it doesn't need the third party and it is more efficient than other fair secure Yao's protocols.
Unbiased Extremum Seeking Based on Lie Bracket Averaging

Extremum seeking is an online, model-free optimization algorithm traditionally known for its practical stability. This paper introduces an extremum seeking algorithm designed for unbiased convergence to the extremum asymptotically, allowing users to define the convergence rate. Unlike conventional extremum seeking approaches utilizing constant gains, our algorithms employ time-varying parameters. These parameters reduce perturbation amplitudes towards zero in an asymptotic manner, while incorporating asymptotically growing controller gains. The stability analysis is based on state transformation, achieved through the multiplication of the input state by asymptotic growth function, and Lie bracket averaging applied to the transformed system. The averaging ensures the practical stability of the transformed system, which, in turn, leads to the asymptotic stability of the original system. Moreover, for strongly convex maps, we achieve exponentially fast convergence. The numerical simulations validate the feasibility of the introduced designs.