Miracle-dz/UniFAR-FTUs huggingface dataset:
TimeBERT: Enhancing Pre-Trained Language Representations with Temporal Information

Time is an important aspect of text documents, which has been widely exploited in natural language processing and has strong influence, for example, in temporal information retrieval, where the temporal information of queries or documents need to be identified for relevance estimation. Event-related tasks like event ordering, which aims to order events by their occurrence time, also need to determine the temporal information of events. In this work, we investigate methods for incorporating temporal information during pre-training, to further improve the performance on time-related tasks. Compared with BERT which utilizes synchronic document collections (BooksCorpus and English Wikipedia) as the training corpora, we use long-span temporal news collection for building word representations, since temporal information constitutes one of the most significant features of news articles. We then introduce TimeBERT, a novel language representation model trained on a temporal collection of news articles via two new pre-training tasks, which harness two distinct temporal signals to construct time-aware language representation. The experimental results show that TimeBERT consistently outperforms BERT and other existing pre-trained models, with substantial gains on different downstream NLP tasks or applications for which time is of importance 1 .
Global existence versus blow-up for multi-d hyperbolized compressible Navier-Stokes equations

We consider the non-isentropic compressible Navier-Stokes equations in two or three space dimensions for which the heat conduction of Fourier’s law is replaced by Cattaneo’s law and the classical Newtonian ﬂow is replaced by a revised Maxwell ﬂow. We show that a physical entropy exists for this new model. For two special cases, we show the global well-posedness of solutions with small initial data and the blow-up of solutions in ﬁnite time for a class of large initial data. Moreover, for vanishing relaxation parameters, the solutions (if it exists) are shown to converge to solutions of the classical system.
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion

Prior work on integrating text corpora with knowledge graphs (KGs) to improve Knowledge Graph Embedding (KGE) have obtained good performance for entities that co-occur in sentences in text corpora. Such sentences (textual mentions of entity-pairs) are represented as Lexicalised Dependency Paths (LDPs) between two entities. However, it is not possible to represent relations between entities that do not co-occur in a single sentence using LDPs. In this paper, we propose and evaluate several methods to address this problem, where we borrow LDPs from the entity pairs that co-occur in sentences in the corpus (i.e. with mentions entity pairs) to represent entity pairs that do not co-occur in any sentence in the corpus (i.e. without mention entity pairs). We propose a supervised borrowing method, SuperBorrow, that learns to score the suitability of an LDP to represent a without-mentions entity pair using pre-trained entity embeddings and contextualised LDP representations. Experimental results show that SuperBorrow improves the link prediction performance of multiple widely-used prior KGE methods such as TransE, DistMult, ComplEx and RotatE.
Towards Exploring the Code Reuse from Stack Overflow during Software Development

As one of the most well-known programmer Q&A websites, Stack Overflow (i.e., SO) is serving tens of thousands of developers ev-ery day. Previous work has shown that many developers reuse the code snippets on SO when they find an answer (from SO) that functionally matches the programming problem they encounter in their development activities. To study how programmers reuse code on SO during project development, we conduct a comprehensive empirical study. First, to capture the development activities of pro-grammers, we collect 342,148 modified code snippets in commits from 793 open-source Java projects, and these modified code can reflect the programming problems encountered during development. We also collect the code snippets from 1,355,617 posts on SO. Then, we employ CCFinder to detect the code clone between the modified code from commits and the code from SO, and further analyze the code reuse when programmer solves a programming problem during development. We count the code reuse ratios of the modified code snippets in the commits of each project in different years, the results show that the average code reuse ratio is 6.32%, and the maximum is 8.38%. The code reuse ratio in project commits has increased year by year, and the proportion of code reuse in the newly established project is higher than that of old projects. We also find that some projects reuse the code snippets from many years ago. Additionally, we find that experienced developers seem to be more likely to reuse the knowledge on SO. Moreover, we find that the code reuse ratio in bug-related commits (6.67%) is slightly higher than that of in non-bug-related commits (6.59%). Furthermore, we also find that the code reuse ratio (14.44%) in Java class files that have undergone multiple modifications is more than double the overall code reuse ratio (6.32%).
Coupled Iterative Refinement for 6D Multi-Object Pose Estimation

We address the task of 6D multi-object pose: given a set of known 3D objects and an RGB or RGB-D input image, we detect and estimate the 6D pose of each object. We propose a new approach to 6D object pose estimation which consists of an end-to-end differentiable architecture that makes use of geometric knowledge. Our approach iteratively refines both pose and correspondence in a tightly coupled manner, allowing us to dynamically remove outliers to improve accuracy. We use a novel differentiable layer to perform pose refinement by solving an optimization problem we refer to as Bidirectional Depth-Augmented Perspective-N-Point (BD-PnP). Our method achieves state-of-the-art accuracy on standard 6D Object Pose benchmarks. Code is available at https://github.com/princeton-vl/Coupled-Iterative-Refinement.
Network classification-based structural analysis of real networks and their model-generated counterparts

Abstract Data-driven analysis of complex networks has been in the focus of research for decades. An important area of research is to study how well real networks can be described with a small selection of metrics, furthermore how well network models can capture the relations between graph metrics observed in real networks. In this paper, we apply machine-learning techniques to investigate the aforementioned problems. We study 500 real-world networks along with 2000 synthetic networks generated by four frequently used network models with previously calibrated parameters to make the generated graphs as similar to the real networks as possible. This paper unifies several branches of data-driven complex network analysis, such as the study of graph metrics and their pair-wise relationships, network similarity estimation, model calibration, and graph classification. We find that the correlation profiles of the structural measures significantly differ across network domains and the domain can be efficiently determined using a small selection of graph metrics. The structural properties of the network models with fixed parameters are robust enough to perform parameter calibration. The goodness-of-fit of the network models highly depends on the network domain. By solving classification problems, we find that the models lack the capability of generating a graph with a high clustering coefficient and relatively large diameter simultaneously. On the other hand, models are able to capture exactly the degree-distribution-related metrics.
Maximum Entropy Dueling Network Architecture

In recent years, there have been many deep structures for Reinforcement Learning, mainly for value function estimation and representations. These methods achieved great success in Atari 2600 domain. In this paper, we propose an improved architecture based upon Dueling Networks, in this architecture, there are two separate estimators, one approximate the state value function and the other, state advantage function. This improvement based on Maximum Entropy, shows better policy evaluation compared to the original network and other value-based architectures in Atari domain.
The Competition of ARB Constructions in Polish

Abstract:Polish has three quasi-synonymous impersonal constructions: the -no/-to construction, the reflexive impersonal, and the 3pl impersonal. This raises the question of what the differences between them are and how one of them is selected. This paper presents the results of an acceptability judgment test, which is informed by an explorative corpus study and examines the following factors: colloquial vs. neutral register; perfective vs. imperfective aspect; present tense vs. preterite; and generic vs. specific reading. The main findings are that the 3pl impersonal turned out to be better in colloquial discourse and the reflexive impersonal is much more acceptable with the imperfective than with the perfective aspect. Furthermore, the corpus data and the acceptability judgments show numerically that both reflexive and 3pl impersonal have a certain tendency towards present tense and generic reading, and that the -no/-to construction is more typical with the perfective than the imperfective aspect.
Canonical Workflow for Machine Learning Tasks

Abstract There is a huge gap between (1) the state of workflow technology on the one hand and the practices in the many labs working with data driven methods on the other and (2) the awareness of the FAIR principles and the lack of changes in practices during the last 5 years. The CWFR concept has been defined which is meant to combine these two intentions, increasing the use of workflow technology and improving FAIR compliance. In the study described in this paper we indicate how this could be applied to machine learning which is now used by almost all research disciplines with the well-known effects of a huge lack of repeatability and reproducibility. Researchers will only change practices if they can work efficiently and are not loaded with additional tasks. A comprehensive CWFR framework would be an umbrella for all steps that need to be carried out to do machine learning on selected data collections and immediately create a comprehensive and FAIR compliant documentation. The researcher is guided by such a framework and information once entered can easily be shared and reused. The many iterations normally required in machine learning can be dealt with efficiently using CWFR methods. Libraries of components that can be easily orchestrated using FAIR Digital Objects as a common entity to document all actions and to exchange information between steps without the researcher needing to understand anything about PIDs and FDO details is probably the way to increase efficiency in repeating research workflows. As the Galaxy project indicates, the availability of supporting tools will be important to let researchers use these methods. Other as the Galaxy framework suggests, however, it would be necessary to include all steps necessary for doing a machine learning task including those that require human interaction and to document all phases with the help of structured FDOs.
Enhanced Open-Mindedness and Problem Finding Among Gifted Female Students Involved in Future Robotics Design

ABSTRACT This article investigated the impact of a robotic-based enrichment program on problem finding (PF) and active-openminded thinking skills (AOT) in 60 gifted female students (eighth and ninth graders) from the Eastern region of Saudi Arabia. The participants were randomly selected from several cohorts of gifted students who participated in an enrichment summer program. The enrichment program lasted for 4 weeks. The study instruments included the Problem Generation (PG) test and the Active-Openminded Thinking (AOT) scale, which were administered to the participants at the beginning of the program and at the end of the program. The change in the measured program outcomes after attending the robotics intervention program was assessed by a regression model, where posttest scores were regressed on pretest scores. The results revealed differences for the three subscales of AOT: Belief Identification (BI), Flexible Thinking (FT), and Dogmatic Thinking (DT) as well as the total score of the AOT in favor of posttest condition. Moreover, the results indicated that ninth graders benefited more from the enrichment program than eighth graders did. As for PF, fluency scores showed no significant differences between pretest and posttest, while originality scores were significantly higher for the posttest scores when compared to the pretest scores.
Disruptions at the edges: Ecotone crossing with Black and Indigenous creative pedagogues

Abstract In this article, I lean toward the ecological site of ecotone and the act of crossing to think about the pedagogical decisions I made as a scholar and practitioner teaching Black studies and English education classes. Within the classroom, I suggest centering Black and Indigenous women’s poetry to help students think about interdependence, ecological precarity, and ethical engagements. Black and Indigenous poets invite us to move beyond our disciplines into a cultivated ecotone, or space where we can unearth anti-Blackness in environmental education and invite intellectual curiosity and self-reflection. What is documented here is how I used my location between two disciplines, and intellectual curiosity in other fields/disciplines, to activate ecotonal crossings with my students that can move us toward more transformative ways of teaching, learning, and knowing.
Genomic Island-Mediated Horizontal Transfer of the Erythromycin Resistance Gene erm(X) among Bifidobacteria

Bifidobacteria are a very important group of gut microbiota, and the presence of these bacteria has many beneficial effects for the host. Thus, bifidobacteria have attracted growing interest owing to their potential probiotic properties. ABSTRACT Antibiotic resistance is a serious medical issue driven by antibiotic misuse. Bifidobacteria may serve as a reservoir for antibiotic resistance genes (ARGs) that have the potential risk of transfer to pathogens. The erythromycin resistance gene erm(X) is an ARG with high abundance in bifidobacteria, especially in Bifidobacterium longum species. However, the characteristics of the spread and integration of the gene erm(X) into the bifidobacteria genome are poorly understood. In this study, 10 tetW-positive bifidobacterial strains and 1 erm(X)-positive bifidobacterial strain were used to investigate the transfer of ARGs. Conjugation assays found that the erm(X) gene could transfer to five other bifidobacterial strains. Dimethyl sulfoxide (DMSO) and vorinostat significantly promoted the transfer of the erm(X) from strain Bifidobacterium catenulatum subsp. kashiwanohense DSM 21854 to Bifidobacterium longum subsp. suis DSM 20211. Whole-genome sequencing and comparative genomic analysis revealed that the erm(X) gene was located on the genomic island BKGI1 and that BKGI1 was conjugally mobile and transferable. To our knowledge, this is the first report that a genomic island-mediated gene erm(X) transfer in bifidobacteria. Additionally, BKGI1 is very unstable in B. catenulatum subsp. kashiwanohense DSM 21854 and transconjugant D2TC and is highly excisable and has an intermediate circular formation. In silico analysis showed that the BKGI1 homologs were also present in other bifidobacterial strains and were especially abundant in B. longum strains. Thus, our results confirmed that genomic island BKGI1 was one of the vehicles for erm(X) spread. These findings suggest that genomic islands play an important role in the dissemination of the gene erm(X) among Bifidobacterium species. IMPORTANCE Bifidobacteria are a very important group of gut microbiota, and the presence of these bacteria has many beneficial effects for the host. Thus, bifidobacteria have attracted growing interest owing to their potential probiotic properties. Bifidobacteria have been widely exploited by the food industry as probiotic microorganisms, and some species have a long history of safe use in food and feed production. However, the presence of antibiotic resistance raises the risk of its application. In this study, we analyzed the transfer of the erythromycin resistance gene erm(X) and revealed that the molecular mechanism behind the spread of the gene erm(X) was mediated by genomic island BKGI1. To the best of our knowledge this is the first report to describe the transfer of the gene erm(X) via genomic islands among bifidobacteria. This may be an important way to disseminate the gene erm(X) among bifidobacteria.
Towards a Truly Integrated Vector Processing Unit for Memory-bound Applications Based on a Cost-competitive Computational SRAM Design Solution

This article presents Computational SRAM (C-SRAM) solution combining In- and Near-Memory Computing approaches. It allows performing arithmetic, logic, and complex memory operations inside or next to the memory without transferring data over the system bus, leading to significant energy reduction. Operations are performed on large vectors of data occupying the entire physical row of C-SRAM array, leading to high performance gains. We introduce the C-SRAM solution in this article as an integrated vector processing unit to be used by a scalar processor as an energy-efficient and high performing co-processor. We detail the C-SRAM system design on different levels: (i) circuit design and silicon proof of concept, (ii) system interface and instruction set architecture, and (iii) high-level software programming and simulation. Experimental results on two complete memory-bound applications, AES and MobileNetV2, show that the C-SRAM implementation achieves up to 70× timing speedup and 37× energy reduction compared to scalar architecture, and up to 17× timing speedup and 5× energy reduction compared to SIMD architecture.
Early Design Space Exploration Framework for Memristive Crossbar Arrays

For memristive crossbar arrays, currently, no high-level design validation and early space exploration tools exist in the literature. Such tools are essential to quickly verify the design functionality as well as compare design alternatives in terms of power and performance. In this work, we propose a VHDL-based framework that enables us to quickly perform behavioral simulation as well as estimate dynamic energy consumption and speed of any large memristive crossbar array. We propose a high-level (VHDL) model of a memristor based on which crossbar architectures can be modeled. The individual memristor model is embedded with power and delay numbers obtained from a detailed memristor model. We demonstrate the framework for MAGIC-style memristive crossbars. We validate the framework against detailed Verilog-A based model on fifteen combinational benchmarks. For the single row model, we obtained 153x simulation speedup over HSPICE, average estimation errors of 6.64% and 0% for dynamic energy consumption and cycle-time, respectively. For the transpose model, we obtained average estimation errors of 5.51% and 10.90% for dynamic energy consumption and cycle-time, respectively. We also extend our framework to support another prominent logic style and validate through a case study. The proposed framework can be easily extended to other emerging technologies.
Longitudinal PET studies of mGluR5 in FXS using an FMR1 knockout mouse model

Abstract Fragile X syndrome (FXS) is a monogenic disorder characterized by intellectual disability and behavioral challenges. It is caused by aberrant methylation of the fragile X mental retardation 1 (FMR1) gene. Given the failure of clinical trials in FXS and growing evidence of a role of metabotropic glutamate subtype 5 receptors (mGluR5) in the pathophysiology of the disorder, we investigated mGluR5 function in FMR1 Knockout (FMR1-KO) mice and age- and sex-matched control mice using longitudinal positron emission tomography (PET) imaging to better understand the disorder. The studies were repeated at four time points to examine age- and disease-induced changes in mGluR5 availability using 3-fluoro-[18F]5-(2-pyridinylethynyl)benzonitrile ([18F]FPEB). We found that the binding potential (BP) of [18F]FPEB was significantly lower in the KO mice in mGluR5-implicated brain areas including striatum, cortex, hippocampus, thalamus, and olfactory bulb. The BP also changed with age, regardless of disorder status, increasing in early adulthood in male but not in female mice before decreasing later in both sexes. The difference in mGluR5 availability between the FMR1-KO and control mice and the change in BP in the KO mice as a function of age and sex illustrate the nature of the disorder and its progression, providing mechanistic insights for treatment design.
Remotely piloted aircraft system with optimum avoidance maneuvers

In order for remotely piloted aircraft systems (RPAS) to operate in unsegregated airspace, one of the most important risks to mitigate is that of the mid-air collision. The RPAS must be capable of detecting the potential for a mid-air collision and performing avoidance maneuvers at normal flight conditions and under contingency or emergency conditions. In particular, when the link is severed the RPAS will become “uncontrolled” by the remote pilot, but the loss of link does not imply the loss of the remotely piloted aircraft. A D&A system is required for safe operations of RPAS in airspace shared with other aircraft, including manned aircraft. The onboard system of an unmanned aircraft has to be equipped with an efficient detection and early risk analysis system, as well as a system assisting the pilot in making a decision or an autonomous control system able to perform a maneuver to avoid collision. Collision avoidance maneuvers should be planned by taking into account the operational specificities of the RPAS. The resolution advisory’s strength and direction could be adapted to the capabilities of the RPAS. For example, horizontal resolution advisories could be introduced for RPAS unable of reaching the vertical acceleration/speeds required for vertical resolution advisories. The selection of types of sensors, their sensitivity and range can be made by taking into account RPAS maneuverability. The traffic advisory and resolution advisory regions could be defined by the same circumstances, among others. The aim of this work is to determine an optimal trajectory of horizontal avoidance maneuver of a mini remotely piloted aircraft, minimizing the time of performing a maneuver when avoiding a manned aircraft flying at a much higher speed, guaranteeing a minimally required distance between the passing objects.