Miracle-dz/UniFAR-FTUs huggingface dataset:
Energized Rigid Body Fracture

Compelling animation of fracture is a vital challenge for computer graphics. Methods based on continuum mechanics are physically accurate, but computationally expensive since they require computing elastic deformation. In many applications, this elastic deformation is imperceptible, so simulation methods based on rigid body dynamic with breakable constraints are popular in practice. Simply deleting constraints when thresholds on force or displacement are reached ignores the elastic energy that is stored just before fracture, which is captured by continuum mechanics based methods. Our approach computes the energy stored in these constraints when they are broken, and reintroduces it to the system as kinetic energy. As a result, our method is able to animate energetic fracture scenarios with results comparable to continuum mechanics approaches, but with the computational efficiency of rigid body simulation.
Sense-Aware Neural Models for Pun Location in Texts

A homographic pun is a form of wordplay in which one signifier (usually a word) suggests two or more meanings by exploiting polysemy for an intended humorous or rhetorical effect. In this paper, we focus on the task of pun location, which aims to identify the pun word in a given short text. We propose a sense-aware neural model to address this challenging task. Our model first obtains several WSD results for the text, and then leverages a bidirectional LSTM network to model each sequence of word senses. The outputs at each time step for different LSTM networks are then concatenated for prediction. Evaluation results on the benchmark SemEval 2017 dataset demonstrate the efficacy of our proposed model.
Context-Aware Multi-Criteria Handover with Fuzzy Inference in Software Defined 5G HetNets

With the explosive growth of mobile devices and subsequent traffic volume, densified deployment of Heterogeneous Network (HetNet) coexisting with a variety of cells with overlay coverage has emerged as a viable solution for future 5G networks. Despite many advantages, this new architecture also introduces numerous new network management issues, such as frequent handovers. Although a number of handover mechanisms have been proposed, these methods generally depend on one or more objective attributes from the perspective of the users and network, and do not consider the subjective demands of personalized users and specific applications that demand differentiated network services. In this paper, we first develop an software defined networking (SDN)-driven handover architecture that is capable of perceiving global network statements and requirements from all perspectives, including the physical layer, users, and applications. Then, a context-aware multi-criteria handover mechanism is developed in the SDN controller to provide differentiated services. Considering the many complicated factors, the handover decision is made based on a hierarchical fuzzy inference system to process diverse attributes and fuzzy information described in natural language. The evaluation results demonstrate that our scheme outperforms the baseline Received Signal Strength Indicator (RSSI)-based handover scheme, more efficiently providing differentiated services with respect to throughput, bandwidth cost, and application satisfaction.
Pre- and In-Parsing Models for Neural Empty Category Detection

Motivated by the positive impact of empty category on syntactic parsing, we study neural models for pre- and in-parsing detection of empty category, which has not previously been investigated. We find several non-obvious facts: (a) BiLSTM can capture non-local contextual information which is essential for detecting empty categories, (b) even with a BiLSTM, syntactic information is still able to enhance the detection, and (c) automatic detection of empty categories improves parsing quality for overt words. Our neural ECD models outperform the prior state-of-the-art by significant margins.
Learning-based Composite Metrics for Improved Caption Evaluation

The evaluation of image caption quality is a challenging task, which requires the assessment of two main aspects in a caption: adequacy and fluency. These quality aspects can be judged using a combination of several linguistic features. However, most of the current image captioning metrics focus only on specific linguistic facets, such as the lexical or semantic, and fail to meet a satisfactory level of correlation with human judgements at the sentence-level. We propose a learning-based framework to incorporate the scores of a set of lexical and semantic metrics as features, to capture the adequacy and fluency of captions at different linguistic levels. Our experimental results demonstrate that composite metrics draw upon the strengths of stand-alone measures to yield improved correlation and accuracy.
Enhancement of Encoder and Attention Using Target Monolingual Corpora in Neural Machine Translation

A large-scale parallel corpus is required to train encoder-decoder neural machine translation. The method of using synthetic parallel texts, in which target monolingual corpora are automatically translated into source sentences, is effective in improving the decoder, but is unreliable for enhancing the encoder. In this paper, we propose a method that enhances the encoder and attention using target monolingual corpora by generating multiple source sentences via sampling. By using multiple source sentences, diversity close to that of humans is achieved. Our experimental results show that the translation quality is improved by increasing the number of synthetic source sentences for each given target sentence, and quality close to that using a manually created parallel corpus was achieved.
HATP: Hierarchical Agent-based Task Planner

Hierarchical Task Network (HTN) planning is a proven approach to solving complex, real world planning problems more efficiently than planning from first principles when 'standard operating procedures' (or `recipes') can be supplied by the user. By planning for tasks in the same order that they are later executed, total-order HTN planners always know the complete state of the world at each planning step. This enables writing more expressive planning domains than what is possible in partial-order HTN planning, such as preconditions with calls to external procedures. Such features have facilitated the use of total-order HTN planners in agent systems and seen them excel in AI games. This paper describes the Hierarchical Agent-based Task Planner (HATP), a total-order HTN planner. Since its first implementation, HATP has had various extensions and integrations over the years, such as support for splitting a solution into multiple streams and assigning them to the agents in the domain; modelling their beliefs as distinct world states; allowing 'social rules' to be included by the user to define what kind of agent behaviour is appropriate; allowing tasks to be planned by taking the human's safety and comfort into account; and to interleave HTN and geometric planning. Since many of these implementations have remained prototypes, we have significantly enhanced them as well as HATP itself, and integrated them into a stand-alone distribution, which is now available as open source software (under a BSD 2-Clause License). This paper presents some of our recent improvements to HATP, and gives an overview of its user-friendly language, which treats agents as distinct entities; its mechanisms for effective control over decomposition; and its integration into our larger robotics framework.
Building a Hand and Upper Extremity Transplantation Program: Lessons Learned From the First 20 Years of Vascularized Composite Allotransplantation

Background: Upper extremity transplantation is a quality-of-life enhancing treatment for select patients with upper extremity loss. This article reviews the preoperative, intraoperative, and postoperative challenges in the upper extremity transplantation process and the lessons learned from the first 2 decades of hand transplantation. Methods: Key components of the author’s hand transplantation protocol including patient selection, donor screening, surgical rehearsal, donor procurement, transplantation, immunosuppression, and patient outcome reporting/follow-up are reported to assist other teams who wish to establish a hand transplantation program. Results: There have been many advancements in the first 20 years of hand transplantation including better patient selection criteria, the recent addition of pediatric patients, improved surgical techniques such as the use of virtual surgical planning, and improved immunosuppression protocols. Improvement has also taken place in the tracking and reporting of hand transplant outcomes, but more work is clearly needed to fully define the benefits of transplantation, especially for pediatric patients. Conclusions: Over the past 20 years, significant progress has been made in upper extremity transplantation although a number of challenges remain including how to best document and share outcome measures, optimize immunosuppression, and diagnose/treat rejection. The authors encourage upper extremity transplant programs to report their experience and protocols to advance hand transplantation as standard of care for properly selected individuals.
Generalizing Diffuse Interface Methods on Graphs: Nonsmooth Potentials and Hypergraphs

Diffuse interface methods have recently been introduced for the task of semisupervised learning. The underlying model is well known in materials science but was extended to graphs using a Ginzburg--Landau functional and the graph Laplacian. We here generalize the previously proposed model by a nonsmooth potential function. Additionally, we show that the diffuse interface method can be used for the segmentation of data coming from hypergraphs. For this we show that the graph Laplacian in almost all cases is derived from hypergraph information. Additionally, we show that the formerly introduced hypergraph Laplacian coming from a relaxed optimization problem is well suited to be used within the diffuse interface method. We present computational experiments for graph and hypergraph Laplacians.
Non-rigid 3D Model Classification Using 3D Hahn Moment Convolutional Neural Networks

In this paper, we propose a new architecture of 3D deep neural network called 3D Hahn Moments Convolutional Neural Network (3D HMCNN) to enhance the classification accuracy and reduce the computational complexity of a 3D pattern recognition system. The proposed architecture is derived by combining the concepts of image Hahn moments and convolutional neural network (CNN), frequently utilized in pattern recognition applications. Indeed, the advantages of the moments concerning their global information coding mechanism even in lower orders, along with the high effectiveness of the CNN, are combined to make up the proposed robust network. The aim of this work is to investigate the classification capabilities of 3D HMCNN on small 3D datasets. The experiment simulations with 3D HMCNN have been performed on the articulated parts of McGill 3D shape Benchmark database and SHREC 2011 database. The obtained results show the significantly high performance in the classification rates of the proposed model and its ability to decrease the computational cost by training low number of features generated by the first 3D moments layer.
Towards Opinion Summarization of Customer Reviews

In recent years, the number of texts has grown rapidly. For example, most review-based portals, like Yelp or Amazon, contain thousands of user-generated reviews. It is impossible for any human reader to process even the most relevant of these documents. The most promising tool to solve this task is a text summarization. Most existing approaches, however, work on small, homogeneous, English datasets, and do not account to multi-linguality, opinion shift, and domain effects. In this paper, we introduce our research plan to use neural networks on user-generated travel reviews to generate summaries that take into account shifting opinions over time. We outline future directions in summarization to address all of these issues. By resolving the existing problems, we will make it easier for users of review-sites to make more informed decisions.
SDR Virtualization in Future Mobile Networks: Enabling Multi-Programmable Air-Interfaces

The fifth generation of mobile networks is envisioned to provide connectivity services to a multitude of devices with vastly different requirements. Current mobile systems rely on inflexible hardware- based RF front- end that provide a "one-size-fits-all" air-interface. Instead, future mobile networks should be flexible, providing different air-interfaces for particular users and applications. In this paper, we present HyDRA, a software-defined radio virtualization layer that enables the execution of multiple programmable air-interfaces on top of one RF front-end. Our solution multiplexes digitized IQ signal samples of multiple virtual radios into a single stream. We have implemented HyDRA and experimentally evaluate its performance in a scenario that considers a base station executing LTE and NB-IoT VRs. Results obtained show that HyDRA is able to efficiently multiplex these two technologies, while the computational analysis shows that HyDRA is not CPU-intensive and can run in standard, commodity computers. HyDRA is a promising framework to enable RRH slicing, multi-radio access networks, and flexible multi-tenant networks.
Modeling Consecutive Task Learning with Task Graph Agendas

Recent advances in transfer, multi-task, and lifelong learning have demonstrated that agents can efficiently learn a challenging target task through a curriculum of simpler-to-harder tasks [15]. Yet relatively little work examines how learning can be self-directed, especially when there can be multiple underspecified targets, or when the environment combines their rewards, creating ambiguity. Transitioning between tasks works best when they are similar [1] and can be done efficiently if easier (but related) tasks are learned first [2, 12]. The task graph, introduced for classification and regression problems by Eaton et al. [6] and applied to reinforcement learning (RL) by Svetlik et al. [19], is a general model of inter-task transferability. Task graphs make the relations between different tasks explicit, and provide methods for reasoning over the entire domain. But computing a task graph can be costly. Instead of measuring transferability directly we use task descriptors, which provide an efficient and clearly defined mechanism to reason about transfer [3, 5, 9, 16, 19]. While task descriptors have been used to model transfer, they have primarily been used to transfer to a single target task, although a single target (and often a single source) may not always be appropriate. For example, goal reasoning agents may be tasked with many simultaneous goals [20]. We show that task descriptors can also be used to efficiently construct and navigate a task graph. Agents may not always be provided a well-defined target task or may be asked to switch between multiple tasks in dynamic or otherwise demanding environments. For example, an agent may be guided to select related tasks to promote generalization [4] or to search through task space to identify a niche where it is most useful. We extend a transfer learning agent with an agenda, which is a general mechanism that can handle multiple guiding principles for how the agent should move through the space of tasks.
Clustering Prominent People and Organizations in Topic-Specific Text Corpora

Named entities in text documents are the names of people, organization, location or other types of objects in the documents that exist in the real world. A persisting research challenge is to use computational techniques to identify such entities in text documents. Once identified, several text mining tools and algorithms can be utilized to leverage these discovered named entities and improve NLP applications. In this paper, a method that clusters prominent names of people and organizations based on their semantic similarity in a text corpus is proposed. The method relies on common named entity recognition techniques and on recent word embeddings models. The semantic similarity scores generated using the word embeddings models for the named entities are used to cluster similar entities of the people and organizations types. Two human judges evaluated ten variations of the method after it was run on a corpus that consists of 4,821 articles on a specific topic. The performance of the method was measured using three quantitative measures. The results of these three metrics demonstrate that the method is effective in clustering semantically similar named entities.
Numerical solution of functional differential equations: a Green's function-based iterative approach

ABSTRACT In this article, a recently introduced iterative method based on Green's functions and fixed-point iteration schemes is presented for the approximate solutions of delay and functional differential equations. The approach is especially suited for handling boundary value problems (BVPs). The algorithm is illustrated through a number of examples that confirm the high accuracy and efficiency of the strategy. The results of the test examples show excellent agreement with exact solutions and outperforms other existing numerical iterative schemes.
Sbsd: Detecting the Sequence Attack through Sensor Data in ICSs

The Industrial Control System (ICS) refers to the national critical infrastructure, such as Energy and Water facility, which is significant for the national security. Sequence attack is a unique attack type in ICS, and many detection approaches have been proposed. A common and unrealistic hypothesis of these approaches is that they have gained the command sequences. In the real world, we can only obtain the observations from sensors. The single observation detection technique is a common approach to find anomalies by the observations. However, the highly skilled attacker can compromise some Programmable Logic Controllers (PLCs) in ICS and fake their sensor measurements. Under this circumstance, this detection approach becomes invalid and increases the false-negative rate. In this paper, we first analyze the sequence attack by their attack capability in ICS. Then we propose a State-Based Sequence Detection approach (SBSD). The SBSD uses the equipment's observation information, belonging to many PLCs, to create Hidden Markov Models (HMMs) for detecting the sequence attack. The experiment results in an ICS testbed have shown the effectiveness of SBSD.
Engineering Intelligent Environments: Preliminary Findings of a Systematic Review

Intelligent environments are complex systems that may require a diverse set of hardware devices, software libraries, networking and human computer interactions. New tools and techniques that can facilitate the engineering of such systems are thus critical. However, given the size and heterogeneity of the literature and in the light of, to our knowledge, there being only informal surveys restricted to specific issues have been conducted, we have seen the need to organise and synthesise the existent research corpus to obtain a clear idea on the main approaches that have been utilised for the development of IEs. To address this research gap, this systematic literature review was carried out. This paper presents the review’s preliminary findings that are expected to provide avenues for further research in this area. We find that there are different approaches for developing IEs and the development cycle consists of several phases. However, not all phases have received equal consideration. An evaluation framework which could offer guidance on the choice of the most suitable techniques per phase should also be the target of research efforts.
A Novel Bvg-Repressed Promoter Causes vrg-Like Transcription of fim3 but Does Not Result in the Production of Serotype 3 Fimbriae in Bvg− Mode Bordetella pertussis

In Bordetella pertussis, the promoter Pfim3-15C for the major fimbrial subunit gene fim3 is activated by the two-component system BvgAS in the Bvg+ mode but not in the Bvg− mode. However, many transcriptional profiling studies have shown that fim3 is transcribed in the Bvg− mode even when Pfim3 is in a nonpermissive state (Pfim3-13C), suggesting the presence of a reciprocally regulated element upstream of Pfim3. Here, we provide evidence that BRP is the cause of this anomalous behavior of fim3. Although BRP effects vrg-like transcription of fim3 in the Bvg− mode, it does not lead to stable production of FIM3 fimbriae, because expression of the chaperone and usher proteins FimB and FimC occurs only in the Bvg+ mode. ABSTRACT In Bordetella pertussis, two serologically distinct fimbriae, FIM2 and FIM3, undergo on/off phase variation independently of each other via variation in the lengths of C stretches in the promoters for their major subunit genes, fim2 and fim3. These two promoters are also part of the BvgAS virulence regulon and therefore, if in an on configuration, are activated by phosporylated BvgA (BvgA~P) under normal growth conditions (Bvg+ mode) but not in the Bvg− mode, inducible by growth in medium containing MgSO4 or other compounds, termed modulators. In the B. pertussis Tohama I strain (FIM2+ FIM3−), the fim3 promoter is in the off state. However, a high level of transcription of the fim3 gene is observed in the Bvg− mode. In this study, we provide an explanation for this anomalous behavior by defining a Bvg-repressed promoter (BRP), located approximately 400 bp upstream of the Pfim3 transcriptional start. Although transcription of the fim3 gene in the Bvg− mode resulted in Fim3 translation, as measured by LacZ translational fusions, no accumulation of Fim3 protein was detectable. We propose that Fim3 protein resulting from translation of mRNA driven by BRP in the Bvg− mode is unstable due to a lack of the fimbrial assembly apparatus encoded by the fimBC genes, located within the fha operon, and therefore is not expressed in the Bvg− mode. IMPORTANCE In Bordetella pertussis, the promoter Pfim3-15C for the major fimbrial subunit gene fim3 is activated by the two-component system BvgAS in the Bvg+ mode but not in the Bvg− mode. However, many transcriptional profiling studies have shown that fim3 is transcribed in the Bvg− mode even when Pfim3 is in a nonpermissive state (Pfim3-13C), suggesting the presence of a reciprocally regulated element upstream of Pfim3. Here, we provide evidence that BRP is the cause of this anomalous behavior of fim3. Although BRP effects vrg-like transcription of fim3 in the Bvg− mode, it does not lead to stable production of FIM3 fimbriae, because expression of the chaperone and usher proteins FimB and FimC occurs only in the Bvg+ mode.