Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual AGNs: Precursors of Binary Supermassive Black Hole Formation and Mergers

The presence of dual active galactic nuclei (AGN) on scales of a few tens of kpc can be used to study merger-induced accretion on supermassive black holes (SMBHs) and offer insights about SMBH mergers, using dual AGNs as merger precursors. In this study, we use the Romulus25 cosmological simulation to investigate the properties and evolution of dual AGNs. We first analyze the properties of AGNs ($L_{bol}>10^{43} \rm erg/s$) and their neighboring SMBHs (any SMBHs closer than 30 pkpc to an AGN) at $z \leq 2$. This is our underlying population. Subsequently, we applied the luminosity threshold of $L_{bol}>10^{43} erg/s$ to the neighboring SMBHs thereby identifying dual and multiple AGNs in our simulation. We examined the properties and statistics of dual AGNs in comparison to single AGNs. Our findings indicate an increase in the number of both single and dual AGNs from lower to higher redshifts. All dual AGNs in our sample resulted from major mergers. Compared with the single AGN population, duals are characterized by a lower black hole to halo mass ratio. We found that the properties of dual AGN host halos, including halo mass, stellar mass, star formation rate (SFR), and gas mass, are generally consistent with those of single AGN halos, albeit tending towards the higher end of their respective property ranges. Our analysis uncovered a diverse array of evolutionary patterns among dual AGNs, including rapidly evolving systems, slower ones, and instances where SMBH mergers are ineffective.
Mechanisms of cell migration in the nervous system

Many neurons resemble other cells in developing embryos in migrating long distances before they differentiate. However, despite shared basic machinery, neurons differ from other migrating cells. Most dramatically, migrating neurons have a long and dynamic leading process, and may extend an axon from the rear while they migrate. Neurons must coordinate the extension and branching of their leading processes, cell movement with axon specification and extension, switching between actin and microtubule motors, and attachment and recycling of diverse adhesion proteins. New research is needed to fully understand how migration of such morphologically complicated cells is coordinated over space and time.
BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat

Creating new air combat tactics and discovering novel maneuvers can require numerous hours of expert pilots' time. Additionally, for each different combat scenario, the same strategies may not work since small changes in equipment performance may drastically change the air combat outcome. For this reason, we created a reinforcement learning environment to help investigate potential air combat tactics in the field of beyond-visual-range (BVR) air combat: the BVR Gym. This type of air combat is important since long-range missiles are often the first weapon to be used in aerial combat. Some existing environments provide high-fidelity simulations but are either not open source or are not adapted to the BVR air combat domain. Other environments are open source but use less accurate simulation models. Our work provides a high-fidelity environment based on the open-source flight dynamics simulator JSBSim and is adapted to the BVR air combat domain. This article describes the building blocks of the environment and some use cases.
Risk factors for stage IV breast cancer at the time of presentation in Turkey.

BACKGROUND Breast cancer (BC) is the one of the most common cancers in women. It is also a leading cause of death. Unfortunately, some patients initially present with distant metastases and are diagnosed with stage IV disease that is nearly always, by then, incurable. This retrospective analysis investigated the risk factors for stage IV BC that may underlie such late presentation. MATERIALS AND METHODS In all, 916 patients with BC who visited the medical oncology polyclinic of eight different centres in Turkeybetween December 2011 and January 2013 were analysed. RESULTS A total of 115 patients (12.6%) presented with stage IV disease. In univariate analysis; to comparing these with patients at other stages, no statistical difference was found for median diagnosis age or age at menarche (p=0.611 and p=0.820), whereas age at menopause and age at first live birth were significant (p=0.018 and p=0.003). No difference was detected in terms of accompanying diseases, use of oral contraceptives and hormone replacement therapy, smoking, alcohol consumption and the rate of family history of BC between the patients (p=0.655, p=0.389, p=0.762, p=0.813, p=0.229, p=0.737). However, screening methods were employed less often, the rate of illiteracy was higher, and the rate of other cancers was higher in patients with stage IV BC (p=0.022, p=0.022, p=0.018). No statistical difference was observed between the patients in terms of tumour histopathology, and status of oestrogen receptor, progesterone receptor, or human epidermal growth factor-2 receptor (p=0.389, p=0.326, p=0.949, p=0.326). Grade 3 tumours were more frequent in patients with stage IV disease (p<0.001). On multivariate analysis, risk factors for stage IV breast cancer at the time of presentation were found to be age at first live birth and educational level (p=0.003 and p=0.047). CONCLUSIONS Efforts should be made to perform mammography scans, in particular, at regular intervals through national training programs for all women, particularly those with family histories of breast and other types of cancer, and to establish early diagnosis of BC long before it proceeds to stage IV. Additionally, women's education had better be upgraded. In order to make women aware of BC, national education-programmes must be organised.
Kodaira-Spencer Anomalies with Stora-Zumino Method

Holomorphic diffeomorphism anomalies of $2\,n$ dimensional gravitational theories in Beltrami parametrisation (Kodaira-Spencer anomalies) are computed in the BRST framework, using an extension of the Stora-Zumino method. This method, which allows to compute anomalies in a very concise way, makes manifest the topological origin of anomalies. They have a clear geometric interpretation, since they are expressed in terms of Chern polynomials and Pontryagin invariants. The key ingredient is the formulation of the BRST transformations in terms of polyforms, whose total degree is the sum of the form degree and of the ghost number. This approach simplifies significantly the analysis available in literature and it allows to compute many other solutions. Namely, an anomaly, which was computed using different methods, is proved to be a consistent BRST anomaly, disproving a conclusion in a previous analysis.
Dynamic data aggregation protocol based on multiple objective tree in Wireless Sensor Networks

Data aggregation has been widely applied as efficient techniques in order to reduce the data redundancy and the communication load in Wireless Sensor Networks (WSNs). However, for dynamic scenarios, structured protocols may incur high overhead in the construction and the maintenance of the static structure. Without the explicit downstream and upstream relationship of nodes, it is also difficult to obtain high aggregation efficiency by using structure-free protocols. In order to address these aspects, we propose a semi-structured protocol based on the multi-objective tree. The routing scheme can explore the optimal structure by using the Ant Colony Optimization (ACO). Moreover, by using the prediction model for the arriving packets based on the sliding window, the adaptive timing policy can reduce the transmission delay and enhance the aggregation probability. Therefore, the packet transmission converges from both spatial and temporal points of view for the data aggregation procedure. Finally, simulation results validate the feasibility and the high efficiency of the novel protocol when compared with other existing approaches.
Feasibility study- novel optical soft tactile array sensing for minimally invasive surgery

The absence of touch of sense is a widely known drawback of robotic minimally invasive surgery (MIS). This paper proposes a design of optic soft tactile arrays which is promising to be adapted for MIS. The proposed design consists of multiple soft material channels. Each channel is designed using the Bernoulli pipe structure to amplify the sensor's sensitivity through input and output diameter difference. A multi-core optic fiber cable and a camera are used to capture the change of light intensity caused by the contact forces applied onto the individual soft material channels. The proposed sensor has the following advantages: 1) making use of 3D printing and soft material casting, it is suitable for designing sensors with high density of tactile elements; 2) it also allows the sensor to be designed in an arbitrary shape to fit various MIS applications; 3) compared to other light-intensity based tactile sensor, it is easy to fabricate and miniaturize; it avoids the complexity of attaching reflectors to individual sensing elements; 4) it is immune to electromagnetic interference. In this paper, a prototype which has 3×3 tactile elements in an area of 9.5 × 11 mm2 has been developed and test for feasibility study. Also, a noise-filtering algorithm is developed to reduce the imaging noise. Validation experiments were carried out and results show that the average measurable force range for a single tactile element is 0 to 1.622N with an average accuracy of 97%. The sensor has low crosstalk-to-signal ratio, 1.8% on average, and has no signal drift over time.
Association Between Diffuse Myocardial Fibrosis by Cardiac Magnetic Resonance Contrast-Enhanced T1 Mapping and Subclinical Myocardial Dysfunction in Diabetic Patients: A Pilot Study

Background— Diabetic patients have increased interstitial myocardial fibrosis on histological examination. Magnetic resonance imaging (MRI) T1 mapping is a previously validated imaging technique that can quantify the burden of global and regional interstitial fibrosis. However, the association between MRI T1 mapping and subtle left ventricular (LV) dysfunction in diabetic patients is unknown. Methods and Results— Fifty diabetic patients with normal LV ejection fraction (EF) and no underlying coronary artery disease or regional macroscopic scar on MRI delayed enhancement were prospectively recruited. Diabetic patients were compared with 19 healthy controls who were frequency matched in age, sex and body mass index. There were no significant differences in mean LV end-diastolic volume index, end-systolic volume index and LVEF between diabetic patients and healthy controls. Diabetic patients had significantly shorter global contrast-enhanced myocardial T1 time (425±72 ms vs. 504±34 ms, P<0.001). There was no correlation between global contrast-enhanced myocardial T1 time and LVEF (r=0.14, P=0.32) in the diabetic patients. However, there was good correlation between global contrast-enhanced myocardial T1 time and global longitudinal strain (r=−0.73, P<0.001). Global contrast-enhanced myocardial T1 time was the strongest independent determinant of global longitudinal strain on multivariate analysis (standardized &bgr;=−0.626, P<0.001). Similarly, there was good correlation between global contrast-enhanced myocardial T1 time and septal E′ (r=0.54, P<0.001). Global contrast-enhanced myocardial T1 time was also the strongest independent determinant of septal E′ (standardized &bgr;=0.432, P<0.001). Conclusions— A shorter global contrast-enhanced myocardial T1 time was associated with more impaired longitudinal myocardial systolic and diastolic function in diabetic patients.
A Globally Convergent Gradient Method with Momentum

In this work, we consider smooth unconstrained optimization problems and we deal with the class of gradient methods with momentum, i.e., descent algorithms where the search direction is defined as a linear combination of the current gradient and the preceding search direction. This family of algorithms includes nonlinear conjugate gradient methods and Polyak's heavy-ball approach, and is thus of high practical and theoretical interest in large-scale nonlinear optimization. We propose a general framework where the scalars of the linear combination defining the search direction are computed simultaneously by minimizing the approximate quadratic model in the 2 dimensional subspace. This strategy allows us to define a class of gradient methods with momentum enjoying global convergence guarantees and an optimal worst-case complexity bound in the nonconvex setting. Differently than all related works in the literature, the convergence conditions are stated in terms of the Hessian matrix of the bi-dimensional quadratic model. To the best of our knowledge, these results are novel to the literature. Moreover, extensive computational experiments show that the gradient methods with momentum here presented outperform classical conjugate gradient methods and are (at least) competitive with the state-of-art method for unconstrained optimization, i.e, L-BFGS method.
AgentStudio: A Toolkit for Building General Virtual Agents

Creating autonomous virtual agents capable of using arbitrary software on any digital device remains a major challenge for artificial intelligence. Two key obstacles hinder progress: insufficient infrastructure for building virtual agents in real-world environments, and the need for in-the-wild evaluation of fundamental agent abilities. To address this, we introduce AgentStudio, an online, realistic, and multimodal toolkit that covers the entire lifecycle of agent development. This includes environment setups, data collection, agent evaluation, and visualization. The observation and action spaces are highly generic, supporting both function calling and human-computer interfaces. This versatility is further enhanced by AgentStudio's graphical user interfaces, which allow efficient development of datasets and benchmarks in real-world settings. To illustrate, we introduce a visual grounding dataset and a real-world benchmark suite, both created with our graphical interfaces. Furthermore, we present several actionable insights derived from AgentStudio, e.g., general visual grounding, open-ended tool creation, learning from videos, etc. We have open-sourced the environments, datasets, benchmarks, and interfaces to promote research towards developing general virtual agents for the future.
Guarded-Based Disjunctive Tuple-Generating Dependencies

We perform an in-depth complexity analysis of query answering under guarded-based classes of disjunctive tuple-generating dependencies (DTGDs), focusing on (unions of) conjunctive queries ((U)CQs). We show that the problem under investigation is very hard, namely 2ExpTime-complete, even for fixed sets of dependencies of a very restricted form. This is a surprising lower bound that demonstrates the enormous impact of disjunction on query answering under guarded-based tuple-generating dependencies, and also reveals the source of complexity for expressive logics such as the guarded fragment of first-order logic. We then proceed to investigate whether prominent subclasses of (U)CQs (i.e., queries of bounded treewidth and hypertree-width, and acyclic queries) have a positive impact on the complexity of the problem under consideration. We show that queries of bounded treewidth and bounded hypertree-width do not reduce the complexity of our problem, even if we focus on predicates of bounded arity or on fixed sets of DTGDs. Regarding acyclic queries, although the problem remains 2ExpTime-complete in general, in some relevant settings the complexity reduces to ExpTime-complete. Finally, with the aim of identifying tractable cases, we focus our attention on atomic queries. We show that atomic queries do not make the query answering problem easier under classes of guarded-based DTGDs that allow more than one atom to occur in the body of the dependencies. However, the complexity significantly decreases in the case of dependencies that can have only one atom in the body. In particular, we obtain a Ptime-completeness if we focus on predicates of bounded arity, and AC0-membership when the set of dependencies and the query are fixed. Interestingly, our results can be used as a generic tool for establishing complexity results for query answering under various description logics.
The CIDOC CRM, an Ontological Approach to Schema Heterogeneity

The creation of the World Wide Web has had a profound impact on the ease with which information can be distributed and presented. Now with more and more information becoming available, there is an increasing demand for targeted global search, comparative studies, data transfer and data migration between heterogeneous sources of cultural and scholarly contents. This requires interoperability not only at the encoding level - a task solved well by XML for instance - but also at the more complex semantics level, where lie the characteristics of the domain. In the meanwhile, the reality of semantic interoperability is getting frustrating. In the cultural area alone, dozens of “standard” and hundreds of proprietary metadata and data structures exist, as well as hundreds of terminology systems. Core systems like the Dublin Core represent a common denominator by far too small to fulfil advanced requirements. Overstretching its already limited semantics in order to capture complex contents leads to further loss of meaning[1]. The CIDOC Conceptual Reference Model (CRM) [2], [3] is a core ontology that aims at enabling information exchange and integration between heterogeneous sources of cultural heritage information, archives and libraries. It provides semantic definitions and clarifications needed to transform disparate, heterogeneous information sources into a coherent global resource, be it within a larger institution, in intranets or on the Internet. Its use comprises: intellectual guidance for conceptual modelling; common upper level for application ontology development; reference ontology for data translation and global schema in federated systems [4], [5], [6]. The CIDOC CRM was developed over the past 8 years by an interdisciplinary working group of the International Committee for Documentation of the International Council of Museums (CIDOC/ICOM) under the scientific lead of ICS-FORTH. It is on vote as Draft ISO standard (ISO/DIS 21127) until February 2005 [7]. The latest version 4.0, which was released on March 12th, 2004, consists of 80 classes and 132 properties. It is developed in the knowledge representation language TELOS, and available in RDFS and other formats.
Scalable fault localization for SystemC TLM designs

SystemC and Transaction Level Modeling (TLM) have become the de-facto standard for Electronic System Level (ESL) design. For the costly task of verification at ESL, simulation is the most widely used and scalable approach. Besides the Design Under Test (DUT), the TLM verification environment typically consists of stimuli generators and checkers where the latter are responsible for detecting errors. However, in case of an error, the subsequent debugging process is still very timeconsuming.
ExpressEdit: Video Editing with Natural Language and Sketching

Informational videos serve as a crucial source for explaining conceptual and procedural knowledge to novices and experts alike. When producing informational videos, editors edit videos by overlaying text/images or trimming footage to enhance the video quality and make it more engaging. However, video editing can be difficult and time-consuming, especially for novice video editors who often struggle with expressing and implementing their editing ideas. To address this challenge, we first explored how multimodality$-$natural language (NL) and sketching, which are natural modalities humans use for expression$-$can be utilized to support video editors in expressing video editing ideas. We gathered 176 multimodal expressions of editing commands from 10 video editors, which revealed the patterns of use of NL and sketching in describing edit intents. Based on the findings, we present ExpressEdit, a system that enables editing videos via NL text and sketching on the video frame. Powered by LLM and vision models, the system interprets (1) temporal, (2) spatial, and (3) operational references in an NL command and spatial references from sketching. The system implements the interpreted edits, which then the user can iterate on. An observational study (N=10) showed that ExpressEdit enhanced the ability of novice video editors to express and implement their edit ideas. The system allowed participants to perform edits more efficiently and generate more ideas by generating edits based on user's multimodal edit commands and supporting iterations on the editing commands. This work offers insights into the design of future multimodal interfaces and AI-based pipelines for video editing.
Co-Occurring of Object Detection and Identification towards unlabeled object discovery

In this paper, we propose a novel deep learning based approach for identifying co-occurring objects in conjunction with base objects in multilabel object categories. Nowadays, with the advancement in computer vision based techniques we need to know about co-occurring objects with respect to base object for various purposes. The pipeline of the proposed work is composed of two stages: in the first stage of the proposed model we detect all the bounding boxes present in the image and their corresponding labels, then in the second stage we perform co-occurrence matrix analysis. In co-occurrence matrix analysis, we set base classes based on the maximum occurrences of the labels and build association rules and generate frequent patterns. These frequent patterns will show base classes and their corresponding co-occurring classes. We performed our experiments on two publicly available datasets: Pascal VOC and MS-COCO. The experimental results on public benchmark dataset is reported in Sec 4. Further we extend this work by considering all frequently objects as unlabeled and what if they are occluded as well.
Semi-Supervised Text Classification Using EM

For several decades, statisticians have advocated using a combination of labeled and unlabeled data to train classifiers by estimating parameters of a generative model through iterative Expectation-Maximization (EM) techniques. This chapter explores the effectiveness of this approach when applied to the domain of text classification. Text documents are represented here with a bag-of-words model, which leads to a generative classification model based on a mixture of multinomials. This model is an extremely simplistic representation of the complexities of written text. This chapter explains and illustrates three key points about semi-supervised learning for text classification with generative models. First, despite the simplistic representation, some text domains have a high positive correlation between generative model probability and classification accuracy. In these domains, a straightforward application of EM with the naive Bayes text model works well. Second, some text domains do not have this correlation. Here we can adopt a more expressive and appropriate generative model that does have a positive correlation. In these domains, semi-supervised learning again improves classification accuracy. Finally, EM suffers from the problem of local maxima, especially in high dimension domains such as text classification. We demonstrate that deterministic annealing, a variant of EM, can help overcome the problem of local maxima and increase classification accuracy further when the generative model is appropriate.