Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-attention based end-to-end Hindi-English Neural Machine Translation

Machine Translation (MT) is a zone of concentrate in Natural Language processing which manages the programmed interpretation of human language, starting with one language then onto the next by the PC. Having a rich research history spreading over about three decades, Machine interpretation is a standout amongst the most looked for after region of research in the computational linguistics network. As a piece of this current ace's proposal, the fundamental center examines the Deep-learning based strategies that have gained critical ground as of late and turning into the de facto strategy in MT. We would like to point out the recent advances that have been put forward in the field of Neural Translation models, different domains under which NMT has replaced conventional SMT models and would also like to mention future avenues in the field. Consequently, we propose an end-to-end self-attention transformer network for Neural Machine Translation, trained on Hindi-English parallel corpus and compare the model's efficiency with other state of art models like encoder-decoder and attention-based encoder-decoder neural models on the basis of BLEU. We conclude this paper with a comparative analysis of the three proposed models.
Plasmon-exciton polaritonics shed light on quantum dot dark-state dynamics

The strong coupling of quantum emitters to plasmonic cavities has emerged as an exciting frontier in quantum plasmonics and optics. Here we report an extensive set of measurements of plasmonic cavities hosting one to a few semiconductor quantum dots (QDs). Scattering spectra demonstrate that these devices are at or close to the strong coupling regime. Using Hanbury Brown and Twiss (HBT) interferometry, we demonstrate non-classical emission from the QDs, allowing us to directly determine their number in each device. Surprisingly, PL spectra measured from QDs coupled to the plasmonic devices are narrower than scattering spectra and show smaller values of the apparent Rabi splitting. Using extended Jaynes-Cummings model simulations, we find that the involvement of a dark state of the QDs explains these experimental findings. Indeed, the coupling of the dark state to the plasmonic cavity makes its emission bright enough to appear as a strong separate peak in the PL spectrum. The calculations also show that a slow decay component in the HBT correlation curves can be attributed to the relaxation of the dark state. The coupling of quantum emitters to plasmonic cavities thus emerges as a means to probe and manipulate excited-state dynamics in an unconventional manner and expose complex relaxation pathways.
Quantum theory of dissipative topological systems

Topological phases of matter are protected from local perturbations and therefore have been thought to be robust against decoherence. However, it has not been systematically explored whether and how topological states are dynamically robust against the environment-induced decoherence. In this Letter, we develop a theory for topological systems that incorporate dissipations, noises and thermal effects. We derive novelly the exact master equation and the transient quantum transport for the study of dissipative topological systems, mainly focusing on noninteracting topological insulators and topological superconductors. The resulting exact master equation and the transient transport current are also applicable for the systems initially entangled with environments. We apply the theory to the topological Haldane model (Chern insulator) and the quantized Majorana conductance to explore topological phases of matter that incorporate dissipations, noises and thermal effects, and demonstrate the dissipative dynamics of topological states.
Dependency-based Text Graphs for Keyphrase and Summary Extraction with Applications to Interactive Content Retrieval

We build a bridge between neural network-based machine learning and graph-based natural language processing and introduce a unified approach to keyphrase, summary and relation extraction by aggregating dependency graphs from links provided by a deep-learning based dependency parser. We reorganize dependency graphs to focus on the most relevant content elements of a sentence, integrate sentence identifiers as graph nodes and after ranking the graph, we extract our keyphrases and summaries from its largest strongly-connected component. We take advantage of the implicit structural information that dependency links bring to extract subject-verb-object, is-a and part-of relations. We put it all together into a proof-of-concept dialog engine that specializes the text graph with respect to a query and reveals interactively the document's most relevant content elements. The open-source code of the integrated system is available at this https URL . Keywords: graph-based natural language processing, dependency graphs, keyphrase, summary and relation extraction, query-driven salient sentence extraction, logic-based dialog engine, synergies between neural and symbolic processing.
Model Reference Adaptive Control Allocation for Constrained Systems with Guaranteed Closed Loop Stability

This paper proposes an adaptive control allocation approach for uncertain over-actuated systems with actuator saturation. The proposed method does not require uncertainty estimation or a persistent excitation assumption. Using the element-wise non-symmetric projection algorithm, the adaptive parameters are restricted to satisfy certain optimality conditions leading to overall closed loop system stability. Furthermore, a sliding mode controller with a time-varying sliding surface, working in tandem with the adaptive control allocation, is proposed to guarantee the outer loop stability and reference tracking in the presence of control allocation errors and disturbances. Simulation results are provided, where the Aerodata Model in Research Environment is used as an over-actuated system with actuator saturation, to demonstrate the effectiveness of the proposed method.
Whole-Body Geometric Retargeting for Humanoid Robots

Humanoid robot teleoperation allows humans to integrate their cognitive capabilities with the apparatus to perform tasks that need high strength, manoeuvrability and dexterity. This paper presents a framework for teleoperation of humanoid robots using a novel approach for motion retargeting through inverse kinematics over the robot model. The proposed method enhances scalability for retargeting, i.e., it allows teleop-erating different robots by different human users with minimal changes to the proposed system. Our framework enables an intuitive and natural interaction between the human operator and the humanoid robot at the configuration space level. We validate our approach by demonstrating whole-body retargeting with multiple robot models. Furthermore, we present experimental validation through teleoperation experiments using two state-of-the-art whole-body controllers for humanoid robots.
Incremental Fairness in Two-Sided Market Platforms: On Updating Recommendations Fairly

Major online platforms today can be thought of as two-sided markets with producers and customers of goods and services. There have been concerns that over-emphasis on customer satisfaction by the platforms may affect the well-being of the producers. To counter such issues, few recent works have attempted to incorporate fairness for the producers. However, these studies have overlooked an important issue in such platforms -- to supposedly improve customer utility, the underlying algorithms are frequently updated, causing abrupt changes in the exposure of producers. In this work, we focus on the fairness issues arising out of such frequent updates, and argue for incremental updates of the platform algorithms so that the producers have enough time to adjust (both logistically and mentally) to the change. However, naive incremental updates may become unfair to the customers. Thus focusing on recommendations deployed on two-sided platforms, we formulate an ILP based online optimization to deploy changes incrementally in $\eta$ steps, where we can ensure smooth transition of the exposure of items while guaranteeing a minimum utility for every customer. Evaluations over multiple real world datasets show that our proposed mechanism for platform updates can be efficient and fair to both the producers and the customers in two-sided platforms.
Robot Navigation in Crowds by Graph Convolutional Networks With Attention Learned From Human Gaze

Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task. Previous work has shown the power of deep reinforcement learning frameworks to train efficient policies. However, their performance deteriorates when the crowd size grows. We suggest that this can be addressed by enabling the network to identify and pay attention to the humans in the crowd that are most critical to navigation. We propose a novel network utilizing a graph representation to learn the policy. We first train a graph convolutional network based on human gaze data that accurately predicts human attention to different agents in the crowd as they perform a navigation task based on a top down view of the environment. We incorporate the learned attention into a graph-based reinforcement learning architecture. The proposed attention mechanism enables the assignment of meaningful weightings to the neighbors of the robot, and has the additional benefit of interpretability. Experiments on real-world dense pedestrian datasets with various crowd sizes demonstrate that our model outperforms state-of-art methods, increasing task completion rate by 18.4% and decreasing navigation time by 16.4%.
Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions

In order to achieve a better performance for point cloud analysis, many researchers apply deep neural networks using stacked Multi-Layer-Perceptron (MLP) convolutions over an irregular point cloud. However, applying these dense MLP convolutions over a large amount of points (e.g., autonomous driving application) leads to limitations due to the computation and memory capabilities. To achieve higher performances but decrease the computational complexity, we propose a deep-wide neural network, named ShufflePointNet, which can exploit fine-grained local features, but also reduce redundancies using group convolution and channel shuffle operation. Unlike conventional operations that directly apply MLPs on the high-dimensional features of a point cloud, our model goes “wider” by splitting features into groups with smaller depth in advance, having the respective MLP computations applied only to a single group, which can significantly reduce complexity and computation. At the same time, we allow communication between groups by shuffling the feature channel to capture fine-grained features. We further discuss the multi-branch method for wider neural networks being also beneficial to feature extraction for point clouds. We present extensive experiments for shape classification tasks on a ModelNet40 dataset and semantic segmentation task on large scale datasets ShapeNet part, S3DIS and KITTI. Finally, we carry out an ablation study and compare our model to other state-of-the-art algorithms to show its efficiency in terms of complexity and accuracy.
Decentralized Markov Chain Gradient Descent

Decentralized stochastic gradient method emerges as a promising solution for solving large-scale machine learning problems. This paper studies the decentralized Markov chain gradient descent (DMGD) algorithm - a variant of the decentralized stochastic gradient methods where the random samples are taken along the trajectory of a Markov chain. This setting is well-motivated when obtaining independent samples is costly or impossible, which excludes the use of the traditional stochastic gradient algorithms. Specifically, we consider the first- and zeroth-order versions of decentralized Markov chain gradient descent over a connected network, where each node only communicates with its neighbors about intermediate results. The nonergodic convergence and the ergodic convergence rate of the proposed algorithms have been rigorously established, and their critical dependences on the network topology and the mixing time of Markov chain have been highlighted. The numerical tests further validate the sample efficiency of our algorithm.
Multikernel Regression with Sparsity Constraint

In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised learning in a continuous-domain hybrid search space with gTV regularization. We show that the solution admits a multi-kernel expansion with adaptive positions. In this representation, the number of active kernels is upper-bounded by the number of data points while the gTV regularization imposes an $\ell_1$ penalty on the kernel coefficients. Finally, we illustrate numerically the outcome of our theory.
A hybrid probabilistic seismic hazard analysis of a low and moderate seismic region : Sri Lanka – a case study

Probabilistic seismic hazard assessment (PSHA) is a well-known approach adopted by researchers towards estimating seismic design loads. However the application of such approaches to regions with paucity of data is fraught with difficulties and uncertainties. Regions like Sri Lanka pose further challenges due to the nature of seismic activities and the distribution of seismic sources. Colombo the capital city has been subjected to a few earthquakes historically (Mw >5) but the rest of inland region have been subjected to mild seismicity within the continental crust. In recent times there are reports of seismic effects experienced from long distant earthquakes that originated from the oceanic crust outside the landmass. The first and second authors have presented attenuation models for the region in earlier publications. In this paper, the authors have presented hazard estimates from conventional PSHA approach from earlier works and compared some salient results with seismic hazard averaged from global seismicity data. Results show that seismic hazard of Sri Lanka is less than world average. Using notional PGA values and Colombo as a hot spot region, a design response spectrum that can be easily adopted by practising engineers is recommended as a guide for estimating seismic loads.
Two Level Key Frame Extraction for Action Recognition Using Content Based Adaptive Threshold

Extracting keyframes for action recognition purposes is a challenging task, as compression is to be achieved without losing the impression of the action. Insufficient or wrong keyframes can lead to confusion in the type of action. Proposed two-level key frame extraction algorithm uses an adaptive threshold technique to identify most dissimilar frames as keyframes. At the first level, global features based on intensity histogram and at second level local features computed from wavelet decomposition are used as similarity measures. A new performance parameter, Compression Ratio-Normalized Fidelity (CRNF) is introduced for evaluating the keyframe extraction algorithm. Average CRNF of 0.81 is achieved by the proposed algorithm, as compared 0.37 achieved by existing histogram-based method for action recognition dataset. CRNF value of 0.95 is achieved for Open Video project dataset, which is more than existing methods. Qualitative results further prove the effectiveness of the proposed algorithm.
Leadership importance in construction productivity improvement

PhD from Faculty of Social Sciences, University of Stavanger. He received his BEng in Civil Engineering at University of Alexandria, Egypt in 1996 followed by MSc in Construction Engineering and Project Management in 2006. He is working as the Head of Construction and Maintenance Department in Port Training Institute – Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt. His long-term research goal is on construction productivity management. Email: emad_elmagraby@hotmail.com
CONTROL OF DYNAMIC VOLTAGE RESTORER FOR VOLTAGE SAG MITIGATION

A dynamic voltage restorer (DVR) is power electronics equipment that can protect a sensitive load from the effect of voltage sag. In this paper present the comparative performance of dynamic voltage restorer using PI, fuzzy logic and neural network control. Design of controller to control the dynamic voltage restorer (DVR) that can result a quick and precise respond should be present in this paper.
Variance-aware multiple importance sampling

Many existing Monte Carlo methods rely on multiple importance sampling (MIS) to achieve robustness and versatility. Typically, the balance or power heuristics are used, mostly thanks to the seemingly strong guarantees on their variance. We show that these MIS heuristics are oblivious to the effect of certain variance reduction techniques like stratification. This shortcoming is particularly pronounced when unstratified and stratified techniques are combined (e.g., in a bidirectional path tracer). We propose to enhance the balance heuristic by injecting variance estimates of individual techniques, to reduce the variance of the combined estimator in such cases. Our method is simple to implement and introduces little overhead.
Protecting Implicit Sensitive Information from Inference Attacks

Collecting as much data as possible is commonly seen as a necessary method for effective machine learning algorithms. However, privacy concerns rise if the data contains personal and possibly sensitive user information. While there are regulations, like the GDPR, to keep the sensitive information of the collected data protected, it is still possible to extract implicit user-demographic information such as a user’s gender. A study by Weinsberg et al. (2012) has addressed this issue by removing implicit gender information from the user-item matrix in a movie ratings dataset; however their algorithm fails to take into account that simple data visualization can reveal that data obfuscation has been applied. Building on the work of Weinsberg et al. (2012), we thus present two improved binary gender obfuscation algorithms to counter this problem. This approach enables the resulting obfuscated datasets to be robust to gender inference attacks in such a way that an attacker can hardly determine if the dataset is obfuscated. Our findings provide important implications for and contribute toward methods to protect user privacy and facilitate secure data sharing in a multi-stakeholder environment.
Computer simulations show that the bacteriophage packaging motor induces a chiral organization of the genome

Molecular motors that translocate DNA are ubiquitous in nature. In double stranded (ds) DNA bacteriophages a molecular motor drives the viral genome inside its protein capsid during phage morphogenesis. Several models have been proposed for the three-dimensional geometry of the packaged genome, however, very little is known of the signature of the molecular packing motor on the ﬁnal structure of the genome. Furthermore, current models of the packing reaction are not faithful to biophysical experiments and have reached contradictory conclusions when determining the role of the torsion of the DNA by the molecular motor. In this study we compare the geometrical signatures imposed by diﬀerent mechanisms proposed for bacteriophage packing motors. These mechanisms include: rotation, revolution, and rotation and revolution. All investigated models were implemented in a deterministic and stochastic frameworks. We ﬁnd that both deterministic and stochastic models give consistent results. Molecular motors that incorporate torsion induce a more ordered layered structure of the genome than those that do not incorporate rotation. We also ﬁnd that rotation is necessary for the accumulation of DNA writhe and for the chiral organization of the genome. By extending the simulation time over current models, we observe that while in the initial steps of the packing reaction the torsional strain of the genome is released by rotation of the molecule, in the later stages it is released by the accumulation of writhe. We suggest that the molecular motor plays a key role in determining the ﬁnal structure of the encapsidated genome in bacteriophages.
Representing lexical ambiguity in prototype models of lexical semantics

We show, contrary to some recent claims in the literature, that prototype distributional semantic models (DSMs) are capable of representing multiple senses of ambiguous words, including infrequent meanings. We propose that word2vec contains a natural, model-internal way of operationalizing the disambiguation process by leveraging the two sets of representations word2vec learns, instead of just one as most work on this model does. We evaluate our approach on artificial language simulations where other prototype DSMs have been shown to fail. We furthermore assess whether these results scale to the disambiguation of naturalistic corpus examples. We do so by replacing all instances of sampled pairs of words in a corpus with pseudo-homonym tokens, and testing whether models, after being trained on one half of the corpus, were able to disambiguate pseudo-homonyms on the basis of their linguistic contexts in the second half of the corpus. We observe that word2vec well surpasses the baseline of always guessing the most frequent meaning to be the right one. Moreover, it degrades gracefully: As words are more unbalanced, the baseline is higher, and it is harder to surpass it; nonetheless, Word2vec succeeds at surpassing the baseline, even for pseudo-homonyms whose most frequent meaning is much more frequent than the other.
Modeling of Nematic Electrolytes and Nonlinear Electroosmosis | SIAM Journal on Applied Mathematics | Vol. 76, No. 6 | Society for Industrial and Applied Mathematics

We derive a mathematical model of a nematic electrolyte based on the Ericksen– Leslie theory of liquid crystal flow. Our goal is to investigate the nonlinear electrokinetic effects that occur because the nematic matrix is anisotropic, in particular, transport of ions in a direction perpendicular to the electric field as well as quadratic dependence of the induced flow velocity on the electric field. The latter effect makes it possible to generate sustained flows in the nematic electrolyte that do not reverse their direction when the polarity of the applied electric field is reversed. From a practical perspective, this enables the design of AC-driven electrophoretic and electroosmotic devices. Our study of a special flow in a thin nematic film shows good qualitative agreement with laboratory experiments.