Miracle-dz/UniFAR-FTUs huggingface dataset:
Depth-AGMNet: an Atrous Granular Multiscale Stereo Network Based on Depth Edge Auxiliary Task

Recently, end-to-end convolutional neural networks have achieved remarkable success in stereo estimation tasks. It is still a valuable but hard problem to find the correct correspondence for the ill-posed regions, such as texture-less areas, edge details, and small objects. Based on the above questions, this paper proposes Dedge-AGMNet to alleviate it in different methods. First, we present the multi-task network composes of a depth-edge auxiliary network and disparity estimation this http URL fuse the information of the ancillary system effectively, we design Dedge-SPP to embed the depth edge contours. Besides, On the supervised and unsupervised tasks of the depth edge, the corresponding loss function is constructed for depth edge constraints. Second, we design the AGM module, which utilized the fusion of internal fine-grained feature and stacking convolution layers within a single layer, combining with the dilated convolution and parallel structure reasonably. It is beneficial to provide dense -scale receptive fields and potential correspondence. The results show that for the unsupervised task of depth edge, the network can optimize the edge effectively. Our proposed approach shows accurate disparity estimates and achieves state-of-the-art performances on Sceneflow, KITTI2012, and KITTI2015 benchmarks datasets.
Exploring Radiation Response in Two Head and Neck Squamous Carcinoma Cell Lines Through Metabolic Profiling

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common form of cancer worldwide. Radiotherapy, with or without surgery, represents the major approach to curative treatment. However, not all tumors are equally sensitive to irradiation. It is therefore of interest to apply newer system biology approaches (e.g., metabolic profiling) in squamous cancer cells with different radiosensitivities in order to provide new insights on the mechanisms of radiation response. In this study, two cultured HNSCC cell lines from the same donor, UM-SCC-74A and UM-SCC-74B, were first genotyped using Short Tandem Repeat (STR), and assessed for radiation response by the means of clonogenic survival and growth inhibition assays. Thereafter, cells were cultured, irradiated and collected for subsequent metabolic profiling analyses using liquid chromatography-mass spectrometry (LC-MS). STR verified the similarity of UM-SCC-74A and UM-SCC-74B cells, and three independent assays proved UM-SCC-74B to be clearly more radioresistant than UM-SCC-74A. The LC-MS metabolic profiling demonstrated significant differences in the intracellular metabolome of the two cell lines before irradiation, as well as significant alterations after irradiation. The most important differences between the two cell lines before irradiation were connected to nicotinic acid and nicotinamide metabolism and purine metabolism. In the more radiosensitive UM-SCC-74A cells, the most significant alterations after irradiation were linked to tryptophan metabolism. In the more radioresistant UM-SCC-74B cells, the major alterations after irradiation were connected to nicotinic acid and nicotinamide metabolism, purine metabolism, the methionine cycle as well as the serine, and glycine metabolism. The data suggest that the more radioresistant cell line UM-SCC-74B altered the metabolism to control redox-status, manage DNA-repair, and change DNA methylation after irradiation. This provides new insights on the mechanisms of radiation response, which may aid future identification of biomarkers associated with radioresistance of cancer cells.
Active Visitor: Augmenting Libraries into Social Spaces

Apart from their definitive role in the preservation of the treasures of human thought, libraries offer scientific, educative and entertainment services to their visitors. With the competition rising every day from various different sources, traditional or not, like the Internet, libraries should elevate their services in order to retain their position in an everyday changing world. Our contribution to the field of digital systems for libraries is Active Visitor, a system that offers both AR and social services to the visitors of a library. The system was designed taking into account the user requirements of library personnel and visitors. Active Visitor aims at the engagement of a library visitor through advanced AR services that permit book annotation through a mobile app and the sharing of such information at broader communities through specific social services. The system implementation is based on current AR technologies in order to offer services to library visitors through their smartphones. However, it can support modern wearable technologies like AR glasses which is the next step to the human computer interaction technologies. In order to stress the strength of the proposed system towards users’ satisfaction a quantitative and qualitative evaluation methodology is performed. Evaluation results suggest that the proposed system is an efficient attempt towards a system that transforms libraries into active digital social environments, while it offers a new avenue for additional revenue for libraries.
Transductive Data-Selection Algorithms for Fine-Tuning Neural Machine Translation

Machine Translation models are trained to translate a variety of documents from one language into another. However, models specifically trained for a particular characteristics of the documents tend to perform better. Fine-tuning is a technique for adapting an NMT model to some domain. In this work, we want to use this technique to adapt the model to a given test set. In particular, we are using transductive data selection algorithms which take advantage the information of the test set to retrieve sentences from a larger parallel set. In cases where the model is available at translation time (when the test set is provided), it can be adapted with a small subset of data, thereby achieving better performance than a generic model or a domain-adapted model.
Simulating the Activity of Archaeological Excavation in the Immersive Virtual Reality

The popularization of virtual reality brings together educational fields such as science, art, and culture. However, most current cultural education regarding archaeological civilization is based on excavated cultural relics. In this research, we script the process of the Taiwan First Dog being unearthed. Based on this story, we develop a virtual reality application which guides players to experience the process of excavating relics in a virtual world with reconstructed archaeological scenes and various pieces of apparatus. In addition, we also provide non-contact haptic feedback devices and well-arranged guidance to improve the immersive experience.
Precise system-wide concatic malware unpacking

Run time packing is a common approach malware use to obfuscate their payloads, and automatic unpacking is, therefore, highly relevant. The problem has received much attention, and so far, solutions based on dynamic analysis have been the most successful. Nevertheless, existing solutions lack in several areas, both conceptually and architecturally, because they focus on a limited part of the unpacking problem. These limitations significantly impact their applicability, and current unpackers have, therefore, experienced limited adoption. In this paper, we introduce a new tool, called Minerva, for effective automatic unpacking of malware samples. Minerva introduces a unified approach to precisely uncover execution waves in a packed malware sample and produce PE files that are well-suited for follow-up static analysis. At the core, Minerva deploys a novel information flow model of system-wide dynamically generated code, precise collection of API calls and a new approach for merging execution waves and API calls. Together, these novelties amplify the generality and precision of automatic unpacking and make the output of Minerva highly usable. We extensively evaluate Minerva against synthetic and real-world malware samples and show that our techniques significantly improve on several aspects compared to previous work.
The First International Conference On Intelligent Computing in Data Sciences F uzzy logic approach applied to cred

Microfinance institutions in Morocco target clients who are excluded from guarantees, exposing them to the risks of default and therefore bankruptcy. information asymmetry which makes it difficult to apply classical theory given the low d the behavioral history of bad customers. The aim of this research is to use fuzzy logic approach dynamic treatment of the customers by integrating the opinion of the experts in order loan default and ensure the viability and the sustainability of the microfinance institutions. The research used three composed Fuzzy Input variables with their trapezoidal membership function, an output variable and seventy eight fuzzy rules in the development of an evaluation model
Promoting collaboration skills on reflection concept through multimedia-based integrated instruction

Multimedia-Based Integrated Instructions (MBI2) has been developed to promote the collaboration skills on reflection concepts turn into more real and meaningful learning. The initial design of MBI2 in the form of a multimedia computer that allows users to explore the concept of the overall reflectance of the light through the conceptual and practical aspects that have been developed. MBI2has been developed to promoteone of the skills that the 21st-century skills to students’junior high school that is collaboration skill in order to compete in the future life. The ability to collaborate is divided into five aspects, namely contributions, time management, problem-solving, working with others and research techniques. Research methods utiliseed in this study is an exploration and instructional development 4D model (define, design, develop and disseminate). Based on data analysis, it can be concluded that the development of integrated multimedia-based instruction (MBI2) on the concept of reflection through the 4D developing model was effectively to enhance collaboration skills of students’junior high school.Multimedia-Based Integrated Instructions (MBI2) has been developed to promote the collaboration skills on reflection concepts turn into more real and meaningful learning. The initial design of MBI2 in the form of a multimedia computer that allows users to explore the concept of the overall reflectance of the light through the conceptual and practical aspects that have been developed. MBI2has been developed to promoteone of the skills that the 21st-century skills to students’junior high school that is collaboration skill in order to compete in the future life. The ability to collaborate is divided into five aspects, namely contributions, time management, problem-solving, working with others and research techniques. Research methods utiliseed in this study is an exploration and instructional development 4D model (define, design, develop and disseminate). Based on data analysis, it can be concluded that the development of integrated multimedia-based instruction (MBI2) on the concept of reflection through the.
A Joint Model for Aspect-Category Sentiment Analysis with Contextualized Aspect Embedding

Aspect-category sentiment analysis (ACSA) aims to identify all the aspect categories mentioned in the text and their corresponding sentiment polarities. Some joint models have been proposed to address this task. However, these joint models do not solve the following two problems well: mismatching between the aspect categories and the sentiment words, and data deficiency of some aspect categories. To solve them, we propose a novel joint model which contains a contextualized aspect embedding layer and a shared sentiment prediction layer. The contextualized aspect embedding layer extracts the aspect category related information, which is used to generate aspect-specific representations for sentiment classification like traditional context-independent aspect embedding (CIAE) and is therefore called contextualized aspect embedding (CAE). The CAE can mitigate the mismatching problem because it is semantically more related to sentiment words than CIAE. The shared sentiment prediction layer transfers sentiment knowledge between aspect categories and alleviates the problem caused by data deficiency. Experiments conducted on SemEval 2016 Datasets show that our proposed model achieves state-of-the-art performance.
Pushing down the lateral dimension of single and coupled magnetic dots to the nanometric scale: Characteristics and evolution of the spin-wave eigenmodes

Planar magnetic nanoelements, either single- or multilayered, are exploited in a variety of current or forthcoming spintronic and/or ICT devices, such as read heads, magnetic memory cells, spin-torque nano-oscillators, nanomagnetic logic circuits, magnonic crystals, and artificial spin-ices. The lateral dimensions of the elemental magnetic components have been squeezed down during the last decade to a few tens of nanometers, but they are still an order of magnitude larger than the exchange correlation length of the constituent materials. This means that the spectrum of spin-wave eigenmodes, occurring in the gigahertz range, is relatively complex and cannot be described within a simple macrospin approximation. On the other hand, a detailed knowledge of the dynamical spectrum is needed to understand or to predict crucial characteristics of the devices. With this focused review, we aim at the analysis and the rationalization of the characteristics of the eigenmode spectrum of magnetic nanodots, paying special attention to the following key points: (i) Consider and compare the case of in-plane and out of-plane orientation of the magnetization, as well as of single- and multilayered dots, putting in evidence similarities and diversities, and proposing a unifying nomenclature and labeling scheme; (ii) Underline the evolution of the spectrum when the lateral size of magnetic dots is squeezed down from hundreds to tens of nanometers, as in current devices, with emphasis given to the occurrence of soft modes and to the change of spatial localization of the fundamental mode for in-plane magnetized dots; (iii) Extend the analysis from isolated elements to twins of dots, as well as to dense arrays of dipolarly interacting dots, showing how the discretized eigenmodes distinctive of the single element transform in finite-width frequency bands of spin waves propagating through the array.Planar magnetic nanoelements, either single- or multilayered, are exploited in a variety of current or forthcoming spintronic and/or ICT devices, such as read heads, magnetic memory cells, spin-torque nano-oscillators, nanomagnetic logic circuits, magnonic crystals, and artificial spin-ices. The lateral dimensions of the elemental magnetic components have been squeezed down during the last decade to a few tens of nanometers, but they are still an order of magnitude larger than the exchange correlation length of the constituent materials. This means that the spectrum of spin-wave eigenmodes, occurring in the gigahertz range, is relatively complex and cannot be described within a simple macrospin approximation. On the other hand, a detailed knowledge of the dynamical spectrum is needed to understand or to predict crucial characteristics of the devices. With this focused review, we aim at the analysis and the rationalization of the characteristics of the eigenmode spectrum of magnetic nanodots, paying specia.
Bayesian Stacked Parametric Survival with Frailty Components and Interval‐Censored Failure Times: An Application to Food Allergy Risk

To better understand the risk of exposure to food allergens, food challenge studies are designed to slowly increase the dose of an allergen delivered to allergic individuals until an objective reaction occurs. These dose‐to‐failure studies are used to determine acceptable intake levels and are analyzed using parametric failure time models. Though these models can provide estimates of the survival curve and risk, their parametric form may misrepresent the survival function for doses of interest. Different models that describe the data similarly may produce different dose‐to‐failure estimates. Motivated by predictive inference, we developed a Bayesian approach to combine survival estimates based on posterior predictive stacking, where the weights are formed to maximize posterior predictive accuracy. The approach defines a model space that is much larger than traditional parametric failure time modeling approaches. In our case, we use the approach to include random effects accounting for frailty components. The methodology is investigated in simulation, and is used to estimate allergic population eliciting doses for multiple food allergens.
A Comparison of Control Design Methods for Philips Glass Furnace using Experimental Data

— This paper describes a comparison of control system design to Philips glass furnace. The first stage considers the application of system identification techniques to obtain models using experimental data. Several models were identified, wherein the one picked up for control design has the largest level of fitness. In the second stage a set of improved control methods are implemented to design controllers based on non-adaptive (including LQR, LQGR, H 2 , H oo ) and adaptive (including MRAC, L1 ) methods. Simulation studies were performed and evaluated .
Using Constraint Grammar for Treebank Retokenization

This paper presents a Constraint Grammar-based method for changing the tokenization of existing annotated data, establishing standard space-based tokenization for corpora otherwise using MWE fusion and contraction splitting for syntactic transparency or semantic reasons. Our method preserves ingoing and outgoing dependency arcs and allows the addition of internal tags and structure for MWEs. We discuss rule examples and evaluate the method against both a Portuguese treebank and live news text annotation.
Multi-level representation for the control design of a super capacitor storage system for a microgrid connected application

In this paper an average multi-level representation of power electronic converters is used to design a dynamic model of a Super Capacitor Bank for a microgrid application. A dedicated multi-level modeling is designed to quantify energy variations in storage elements (super capacitors, DC bus) and various losses. We demonstrate that this model is sufficient to design the control system of the entire power storage system, meanwhile the super capacitors, the chopper and the grid connected converter. The proposed global model is simulated with the help of Matlab-Simulink by considering two units of 48V 144F super capacitor modules.
Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease

Alzheimer's disease is the most common neurodegenerative disease. The aim of this study is to infer structural changes in brain connectivity resulting from disease progression using cortical thickness measurements from a cohort of participants who were either healthy control, or with mild cognitive impairment, or Alzheimer's disease patients. For this purpose, we develop a novel approach for inference of multiple networks with related edge values across groups. Specifically, we infer a Gaussian graphical model for each group within a joint framework, where we rely on Bayesian hierarchical priors to link the precision matrix entries across groups. Our proposal differs from existing approaches in that it flexibly learns which groups have the most similar edge values, and accounts for the strength of connection (rather than only edge presence or absence) when sharing information across groups. Our results identify key alterations in structural connectivity that may reflect disruptions to the healthy brain, such as decreased connectivity within the occipital lobe with increasing disease severity. We also illustrate the proposed method through simulations, where we demonstrate its performance in structure learning and precision matrix estimation with respect to alternative approaches.
Determining surface phase diagrams including anharmonic effects

We introduce a massively parallel replica-exchange grand-canonical sampling algorithm to simulate materials at realistic conditions, in particular surfaces and clusters in reactive atmospheres. Its purpose is to determine in an automated fashion equilibrium phase diagrams for a given potential-energy surface (PES) and for any observable sampled in the grand-canonical ensemble. The approach enables an unbiased sampling of the phase space and is embarrassingly parallel. It is demonstrated for a model of Lennard-Jones system describing a surface in contact with a gas phase. Furthermore, the algorithm is applied to Si$_M$ clusters ($M=2, 4$) in contact with an H$_{2}$ atmosphere, with all interactions described at the \textit{ab initio} level, i.e., via density-functional theory, with the PBE gradient-corrected exchange-correlation functional. We identify the most thermodynamically stable phases at finite $T, p$(H$_{2}$) conditions.
Localization Crossover Near Metal-Insulator Transition in Two-Dimension Limit of CaCu3Ru4O12

Artificial confinement of electrons by tailoring the layer thickness has turned out to be a powerful tool to harness control over competing phases in complex oxides. We investigate the effect of dimensionality and strain on the electronic band structure and transport properties of d-electron based heavy-fermion metal CaCu3Ru4O12. Transport behavior evolves from metallic to localized regime upon reducing thickness and a metal insulator transition is observed below 3 nm for which sheet resistance crosses $h/e^{2} \approx 25 K{\Omega}$, the quantum resistance in 2D. A strong interplay between inelastic and spin-orbit scattering lengths close to metal insulator transition is observed which results in a weak antilocalization to weak localization crossover in magnetoconducatance upon reducing film thickness. The cause of metal-insulator transition and magnetoconductance crossover are explained using band structure calculation and 2D magnetotransport theory.
Defending Against Misclassification Attacks in Transfer Learning

Transfer learning accelerates the development of new models (Student Models). It applies relevant knowledge from a pre-trained model (Teacher Model) to the new ones with a small amount of training data, yet without affecting the model accuracy. However, these Teacher Models are normally open in order to facilitate sharing and reuse, which creates an attack plane in transfer learning systems. Among others, recent emerging attacks demonstrate that adversarial inputs can be built with negligible perturbations to the normal inputs. Such inputs can mimic the internal features of the student models directly based on the knowledge of the Teacher Models and cause misclassification in final predictions. In this paper, we propose an effective defence against the above misclassification attacks in transfer learning. First, we propose a distilled differentiator that can address the targeted attacks, where adversarial inputs are misclassified to a specific class. Specifically, this dedicated differentiator is designed with network activation pruning and retraining in a fine-tuned manner, so as to reach high defence rates and high model accuracy. To address the non-targeted attacks that misclassify adversarial inputs to randomly selected classes, we further employ an ensemble structure from the differentiators to cover all possible misclassification. Our evaluations over common image recognition tasks confirm that the student models applying our defence can reject most of the adversarial inputs with a marginal accuracy loss. We also show that our defence outperforms prior approaches in both targeted and non-targeted attacks.