Miracle-dz/UniFAR-FTUs huggingface dataset:
Two radio pulsars in the globular cluster NGC 6624

We report the discovery and accurate timing measurements of two single radio pulsars in the globular cluster NGC 6624. PSR 1820−30A is a 5-ms pulsar that is located close to the low-mass X-ray binary 4U 1820−30. It seems that the recently reported radio detection of 4U 1820−30 with the VLA is, in fact, that of the pulsar. The observed period and period-derivative of PSR 1820−30A make it disturbingly young, and also conflict with the accretion spin-up theory unless acceleration in the cluster potential is largely responsible for the observed period-derivative. This requires that the cluster core lies much closer to the pulsar than has been believed hitherto
MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization

State-of-the-art summarization systems can generate highly fluent summaries. These summaries, however, may contain factual inconsistencies and/or information not present in the source. Hence, an important component of assessing the quality of summaries is to determine whether there is information consistency between the source and the summary. Existing approaches are typically based on lexical matching or representation-based methods. In this work, we introduce an alternative scheme based on standard information-theoretic measures in which the information present in the source and summary is directly compared. We propose a Multiple-choice Question Answering and Generation framework, MQAG, which approximates the information consistency by computing the expected statistical distance between summary and source answer distributions over automatically generated multiple-choice questions. This approach exploits multiple-choice answer probabilities, as predicted answer distributions can be compared. We conduct experiments on four summary evaluation datasets: QAG-CNNDM/XSum, XSum-Hallucination, Podcast Assessment, and SummEval. Experiments show that MQAG, using models trained on SQuAD or RACE, outperforms existing evaluation methods on the majority of tasks.
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex

Semantic parsing is a technique aimed at constructing a structured representation of the meaning of a natural-language question. Recent advances in language models trained on code have shown superior performance in generating these representations compared to language models trained solely on natural language text. The existing fine-tuned neural semantic parsers are vulnerable to adversarial attacks on natural-language inputs. While it has been established that the robustness of smaller semantic parsers can be enhanced through adversarial training, this approach is not feasible for large language models in real-world scenarios, as it requires both substantial computational resources and expensive human annotation on in-domain semantic parsing data. This paper presents the first empirical study on the adversarial robustness of a prompt-based semantic parser based on CODEX, a stateof-the-art (SOTA) language model trained on code. Our results demonstrate that the large language model of code is vulnerable to carefully crafted adversarial examples. To overcome this challenge, we propose methods for enhancing robustness without requiring substantial amounts of labelled data or intensive computational resources.
Intrinsic Bayesian Optimisation on Complex Constrained Domain

Motivated by the success of Bayesian optimisation algorithms in the Euclidean space, we propose a novel approach to construct Intrinsic Bayesian optimisation (In-BO) on manifolds with a primary focus on complex constrained domains or irregular-shaped spaces arising as submanifolds of R2, R3 and beyond. Data may be collected in a spatial domain but restricted to a complex or intricately structured region corresponding to a geographic feature, such as lakes. Traditional Bayesian Optimisation (Tra-BO) defined with a Radial basis function (RBF) kernel cannot accommodate these complex constrained conditions. The In-BO uses the Sparse Intrinsic Gaussian Processes (SIn-GP) surrogate model to take into account the geometric structure of the manifold. SInGPs are constructed using the heat kernel of the manifold which is estimated as the transition density of the Brownian Motion on manifolds. The efficiency of In-BO is demonstrated through simulation studies on a U-shaped domain, a Bitten torus, and a real dataset from the Aral sea. Its performance is compared to that of traditional BO, which is defined in Euclidean space.
Possible depth-resolved reconstruction of shear moduli in the cornea following collagen crosslinking (CXL) with optical coherence tomography and elastography

Corneal collagen crosslinking (CXL) is commonly used to prevent or treat keratoconus. Although changes in corneal stiffness induced by CXL surgery can be monitored with non-contact dynamic optical coherence elastography (OCE) by tracking mechanical wave propagation, depth dependent changes are still unclear if the cornea is not crosslinked through the whole depth. Here, phase-decorrelation measurements on optical coherence tomography (OCT) structural images are combined with acoustic micro-tapping (AμT) OCE to explore possible reconstruction of depth-dependent stiffness within crosslinked corneas in an ex vivo human cornea sample. Experimental OCT images are analyzed to define the penetration depth of CXL into the cornea. In a representative ex vivo human cornea sample, crosslinking depth varied from ~ 100 μm in the periphery to ~ 150 μm in the cornea center and exhibited a sharp in-depth transition between crosslinked and untreated areas. This information was used in an analytical two-layer guided wave propagation model to quantify the stiffness of the treated layer. We also discuss how the elastic moduli of partially CXL-treated cornea layers reflect the effective engineering stiffness of the entire cornea to properly quantify corneal deformation.
High-speed optical FSK demodulator using plasmonic nanorods

An optical frequency-shift keying (FSK) demodulator with ultrasmall plasmonic nanorods that can filter the coherent optical frequency is developed. Since the filtering efficiency depends strongly on the position and number of the plasmonic nanorods in the array, binary teaching–learning-based optimization (BTLBO) algorithm is proposed to design an array of the plasmonic nanorods in order to achieve maximum absorption coefficient spectrum. In BTLBO, a group of learner consists of a matrix with binary entries; control the presence (‘1’) or the absence (‘0’) of the nanoparticles in the array.
On the relaxed greedy randomized Kaczmarz methods with momentum acceleration for solving matrix equation AXB=C

With the growth of data, it is more important than ever to develop an efﬁcient and robust method for solving the consistent matrix equation AX B = C . The randomized Kaczmarz (RK) method has received a lot of attention because of its computational efﬁciency and low memory footprint. A recently proposed approach is the matrix equation relaxed greedy RK (ME-RGRK) method, which greedily uses the loss of the index pair as a threshold to detect and avoid projecting the working rows onto that are too far from the current iterate. In this work, we utilize the Polyak’s and Nesterov’s momentums to further speed up the convergence rate of the ME-RGRK method. The resulting methods are shown to converge linearly to a least-squares solution with minimum Frobenius norm. Finally, some numerical experiments are provided to illustrate the feasibility and effectiveness of our proposed methods. In addition, a real-world application, i.e., tensor product surface ﬁtting in computer-aided geometry design, has also been presented for explanatory purpose.
Deep Learning for Human Parsing: A Survey

—Human parsing is a key topic in image processing with many applications, such as surveillance analysis, human-robot interaction, person search, and clothing category classiﬁ- cation, among many others. Recently, due to the success of deep learning in computer vision, there are a number of works aimed at developing human parsing algorithms using deep learning models. As methods have been proposed, a comprehensive survey of this topic is of great importance. In this survey, we provide an analysis of state-of-the-art human parsing methods, covering a broad spectrum of pioneering works for semantic human parsing. We introduce ﬁve insightful categories: (1) structure- driven architectures exploit the relationship of different human parts and the inherent hierarchical structure of a human body, (2) graph-based networks capture the global information to achieve an efﬁcient and complete human body analysis, (3) context-aware networks explore useful contexts across all pixel to characterize a pixel of the corresponding class, (4) LSTM-based methods can combine short-distance and long-distance spatial dependencies to better exploit abundant local and global contexts, and (5) combined auxiliary information approaches use related tasks or supervision to improve network performance. We also discuss the advantages/disadvantages of the methods in each category and the relationships between methods in different categories, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.
Should Users Trust Their Android Devices? A Scoring System for Assessing Security and Privacy Risks of Pre-Installed Applications

Android devices are equipped with many pre-installed applications which have the capability of tracking and monitoring users. Although applications coming pre-installed pose a great danger to user security and privacy, they have received little attention so far among researchers in the field. In this study, we collect a dataset comprising such applications and make it publicly available. Using this dataset, we analyze tracker SDKs, manifest files and the use of cloud services and report our results. We also conduct a user survey to understand concerns and perceptions of users. Last but not least, we present a risk scoring system which assigns scores for smart phones consolidating our findings based on carefully weighted criteria. With this scoring system, users could give their own trust decisions based on the available concise information about the security and privacy impacts of applications pre-installed on their Android devices.
Theory of intrinsic and H-passivated screw dislocations in GaN

The relative stabilities of specific atomic models of the b=c[0001] screw dislocation in GaN are calculated using density-functional theory. The calculations show that the dislocation core structure and electrical activity changes as the chemical potential of Ga varies across the thermodynamically allowed range. In Ga-rich conditions, the core region is filled with Ga atoms and these atoms form a pair of helical chains. In N-rich conditions, a core structure containing both Ga and N atoms is most stable. Both types of dislocations give rise to bands of electronic states deep inside the gap. The conditions required for H to passivate the screw dislocation are determined.
ClusterFuG: Clustering Fully connected Graphs by Multicut

We propose a graph clustering formulation based on multicut (a.k.a. weighted correlation clustering) on the complete graph. Our formulation does not need specification of the graph topology as in the original sparse formulation of multicut, making our approach simpler and potentially better performing. In contrast to unweighted correlation clustering we allow for a more expressive weighted cost structure. In dense multicut, the clustering objective is given in a factorized form as inner products of node feature vectors. This allows for an efficient formulation and inference in contrast to multicut/weighted correlation clustering, which has at least quadratic representation and computation complexity when working on the complete graph. We show how to rewrite classical greedy algorithms for multicut in our dense setting and how to modify them for greater efficiency and solution quality. In particular, our algorithms scale to graphs with tens of thousands of nodes. Empirical evidence on instance segmentation on Cityscapes and clustering of ImageNet datasets shows the merits of our approach.
Maximal co-occurrence nonoverlapping sequential rule mining

The aim of sequential pattern mining (SPM) is to discover potentially useful information from a given se-quence. Although various SPM methods have been investigated, most of these focus on mining all of the patterns. However, users sometimes want to mine patterns with the same specific prefix pattern, called co-occurrence pattern. Since sequential rule mining can make better use of the results of SPM, and obtain better recommendation performance, this paper addresses the issue of maximal co-occurrence nonoverlapping sequential rule (MCoR) mining and proposes the MCoR-Miner algo-rithm. To improve the efficiency of support calculation, MCoR-Miner employs depth-first search and backtracking strategies equipped with an indexing mechanism to avoid the use of sequential searching. To obviate useless support calculations for some sequences, MCoR-Miner adopts a filtering strategy to prune the sequences without the prefix pattern. To reduce the number of candidate patterns, MCoR-Miner applies the frequent item and binomial enumeration tree strategies. To avoid searching for the maximal rules through brute force, MCoR-Miner uses a screening strategy. To validate the per-formance of MCoR-Miner, eleven competitive algorithms were conducted on eight sequences. Our experimental results showed that MCoR-Miner outperformed other competitive algorithms, and yielded better recommendation performance than frequent co-occurrence pattern mining. All algorithms and datasets can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/MCoR-Miner.
MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring

Electrocardiogram (ECG) is a widely used technique for diagnosing cardiovascular disease. The widespread emergence of smart ECG devices has sparked the demand for intelligent single-lead ECG-based diagnostic systems. However, it is challenging to develop a single-lead-based ECG interpretation model for multiple disease diagnosis due to the lack of some key disease information. We aim to improve the diagnostic capabilities of single-lead ECG for multi-label disease classification in a new teacher-student manner, where the teacher trained by multi-lead ECG educates a student who observes only single-lead ECG We present a new disease-aware Contrastive Lead-information Transferring (CLT) to improve the mutual disease information between the single-lead-based ECG interpretation model and multi-lead-based ECG interpretation model. Moreover, We modify the traditional Knowledge Distillation into Multi-label disease Knowledge Distillation (MKD) to make it applicable for multi-label disease diagnosis. The whole knowledge transferring process is inter-lead Multi-View Knowledge Transferring of ECG (MVKT-ECG). By employing the training strategy, we can effectively transfer comprehensive disease knowledge from various views of ECG, such as the 12-lead ECG, to a single-lead-based ECG interpretation model. This enables the model to extract intricate details from single-lead ECG signals and enhances the model's capability of diagnosing and identifying single-lead signals. Extensive experiments on two commonly used public multi-label datasets, ICBEB2018 and PTB-XL demonstrate that our MVKT-ECG yields exceptional diagnostic performance improvements for single-lead ECG. The student outperforms its baseline observably on the PTB-XL dataset (1.3 % on PTB.super, and 1.4 % on PTB.sub), and on ICBEB2018 dataset (3.2 %).
The Leafed Induced Subtree in chordal and bounded treewidth graphs

In the Fully Leafed Induced Subtrees, one is given a graph $G$ and two integers $a$ and $b$ and the question is to find an induced subtree of $G$ with $a$ vertices and at least $b$ leaves. This problem is known to be NP-complete even when the input graph is $4$-regular. Polynomial algorithms are known when the input graph is restricted to be a tree or series-parallel. In this paper we generalize these results by providing an FPT algorithm parameterized by treewidth. We also provide a polynomial algorithm when the input graph is restricted to be a chordal graph.
A Comprehensive Investigation of Metals in the Circumgalactic Medium of Nearby Dwarf Galaxies

Dwarf galaxies are found to have lost most of their metals via feedback processes; however, there still lacks consistent assessment on the retention rate of metals in their circumgalactic medium (CGM). Here we investigate the metal content in the CGM of 45 isolated dwarf galaxies with M * = 106.5–9.5 M ⊙ (M 200m = 1010.0–11.5 M ⊙) using the Hubble Space Telescope/Cosmic Origins Spectrograph. While H i (Lyα) is ubiquitously detected (89%) within the CGM, we find low detection rates (≈5%–22%) in C ii, C iv, Si ii, Si iii, and Si iv, largely consistent with literature values. Assuming these ions form in the cool (T ≈ 104 K) CGM with photoionization equilibrium, the observed H i and metal column density profiles can be best explained by an empirical model with low gas density and high volume filling factor. For a typical galaxy with M 200m = 1010.9 M ⊙ (median of the sample), our model predicts a cool gas mass of M CGM,cool ∼ 108.4 M ⊙, corresponding to ∼2% of the galaxy’s baryonic budget. Assuming a metallicity of 0.3 Z ⊙, we estimate that the dwarf galaxy’s cool CGM likely harbors ∼10% of the metals ever produced, with the rest either in more ionized states in the CGM or transported to the intergalactic medium. We further examine the EAGLE simulation and show that H i and low ions may arise from a dense cool medium, while C iv arises from a diffuse warmer medium. Our work provides the community with a uniform data set on dwarf galaxies’ CGM that combines our recent observations, additional archival data and literature compilation, which can be used to test various theoretical models of dwarf galaxies.
Fast Matching of Regular Patterns with Synchronizing Counting (Technical Report)

Fast matching of regular expressions with bounded repetition, aka counting, such as (ab){50,100}, i.e., matching linear in the length of the text and independent of the repetition bounds, has been an open problem for at least two decades. We show that, for a wide class of regular expressions with counting, which we call synchronizing, fast matching is possible. We empirically show that the class covers nearly all counting used in usual applications of regex matching. This complexity result is based on an improvement and analysis of a recent matching algorithm that compiles regexes to deterministic counting-set automata (automata with registers that hold sets of numbers).
Interactive Log Parsing via Light-weight User Feedback

Template mining is one of the foundational tasks to support log analysis, which supports the diagnosis and troubleshooting of large scale Web applications. This paper develops a human-in-the-loop template mining framework to support interactive log analysis, which is highly desirable in real-world diagnosis or troubleshooting of Web applications but yet previous template mining algorithms fail to support it. We formulate three types of light-weight user feedback and based on them we design three atomic human-in-the-loop template mining algorithms. We derive mild conditions under which the outputs of our proposed algorithms are provably correct. We also derive upper bounds on the computational complexity and query complexity of each algorithm. We demonstrate the versatility of our proposed algorithms by combining them to improve the template mining accuracy of five representative algorithms over sixteen widely used benchmark datasets.
Demystifying Privacy Policy of Third-Party Libraries in Mobile Apps

The privacy of personal information has received significant attention in mobile software. Although researchers have designed methods to identify the conflict between app behavior and privacy policies, little is known about the privacy compliance issues relevant to third-party libraries (TPLs). The regulators enacted articles to regulate the usage of personal information for TPLs (e.g., the CCPA requires businesses clearly notify consumers if they share consumers' data with third parties or not). However, it remains challenging to investigate the privacy compliance issues of TPLs due to three reasons: 1) Difficulties in collecting TPLs' privacy policies. In contrast to Android apps, which are distributed through markets like Google Play and must provide privacy policies, there is no unique platform for collecting privacy policies of TPLs. 2) Difficulties in analyzing TPL's user privacy access behaviors. TPLs are mainly provided in binary files, such as jar or aar, and their whole functionalities usually cannot be executed independently without host apps. 3) Difficulties in identifying consistency between TPL's functionalities and privacy policies, and host app's privacy policy and data sharing with TPLs. This requires analyzing not only the privacy policies of TPLs and host apps but also their functionalities. In this paper, we propose an automated system named ATPChecker to analyze whether Android TPLs comply with the privacy-related regulations. We construct a data set that contains a list of 458 TPLs, 247 TPL's privacy policies, 187 TPL's binary files and 641 host apps and their privacy policies. Then, we analyze the bytecode of TPLs and host apps, design natural language processing systems to analyze privacy policies, and implement an expert system to identify TPL usage-related regulation compliance. The experimental results show that 23% TPLs violate regulation requirements for providing privacy policies. Over 47% TPLs miss disclosing data usage in their privacy policies. Over 65% host apps share user data with TPLs while 65% of them miss disclosing interactions with TPLs. Our findings remind developers to be mindful of TPL usage when developing apps or writing privacy policies to avoid violating regulations,
Composing Task Knowledge with Modular Successor Feature Approximators

Recently, the Successor Features and Generalized Policy Improvement (SF&GPI) framework has been proposed as a method for learning, composing, and transferring predictive knowledge and behavior. SF&GPI works by having an agent learn predictive representations (SFs) that can be combined for transfer to new tasks with GPI. However, to be effective this approach requires state features that are useful to predict, and these state-features are typically hand-designed. In this work, we present a novel neural network architecture,"Modular Successor Feature Approximators"(MSFA), where modules both discover what is useful to predict, and learn their own predictive representations. We show that MSFA is able to better generalize compared to baseline architectures for learning SFs and modular architectures