Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency-resolved photon correlations in cavity optomechanics

Frequency-resolved photon correlations have proven to be a useful resource to unveil nonlinearities hidden in standard observables such as the spectrum or the standard (color-blind) photon correlations. In this manuscript, we analyze the frequency-resolved correlations of the photons being emitted from an optomechanical system where light is nonlinearly coupled to the quantized motion of a mechanical mode of a resonator, but where the quantum nonlinear response is typically hard to evidence. We present and unravel a rich landscape of frequency-resolved correlations, and discuss how the time-delayed correlations can reveal information about the dynamics of the system. We also study the dependence of correlations on relevant parameters such as the single-photon coupling strength, the filtering linewidth, or the thermal noise in the environment. This enriched understanding of the system can trigger new experiments to probe nonlinear phenomena in optomechanics, and provide insights into dynamics of generic nonlinear systems.
Extracting Optimal Solution Manifolds using Constrained Neural Optimization

Constrained Optimization solution algorithms are restricted to point based solutions. In practice, single or multiple objectives must be satisfied, wherein both the objective function and constraints can be non-convex resulting in multiple optimal solutions. Real world scenarios include intersecting surfaces as Implicit Functions, Hyperspectral Unmixing and Pareto Optimal fronts. Local or global convexification is a common workaround when faced with non-convex forms. However, such an approach is often restricted to a strict class of functions, deviation from which results in sub-optimal solution to the original problem. We present neural solutions for extracting optimal sets as approximate manifolds, where unmodified, non-convex objectives and constraints are defined as modeler guided, domain-informed $L_2$ loss function. This promotes interpretability since modelers can confirm the results against known analytical forms in their specific domains. We present synthetic and realistic cases to validate our approach and compare against known solvers for bench-marking in terms of accuracy and computational efficiency.
Solving Math Word Problems by Scoring Equations with Recursive Neural Networks

Solving math word problems is a cornerstone task in assessing language understanding and reasoning capabilities in NLP systems. Recent works use automatic extraction and ranking of candidate solution equations providing the answer to math word problems. In this work, we explore novel approaches to score such candidate solution equations using tree-structured recursive neural network (Tree-RNN) configurations. The advantage of this Tree-RNN approach over using more established sequential representations, is that it can naturally capture the structure of the equations. Our proposed method consists in transforming the mathematical expression of the equation into an expression tree. Further, we encode this tree into a Tree-RNN by using different Tree-LSTM architectures. Experimental results show that our proposed method (i) improves overall performance with more than 3% accuracy points compared to previous state-of-the-art, and with over 18% points on a subset of problems that require more complex reasoning, and (ii) outperforms sequential LSTMs by 4% accuracy points on such more complex problems.
Development of a Dataset and a Deep Learning Baseline Named Entity Recognizer for Three Low Resource Languages: Bhojpuri, Maithili and Magahi

In Natural Language Processing (NLP) pipelines, Named Entity Recognition (NER) is one of the preliminary problems, which marks proper nouns and other named entities such as Location, Person, Organization, Disease etc. Such entities, without an NER module, adversely affect the performance of a machine translation system. NER helps in overcoming this problem by recognising and handling such entities separately, although it can be useful in Information Extraction systems also. Bhojpuri, Maithili and Magahi are low resource languages, usually known as Purvanchal languages. This paper focuses on the development of an NER benchmark dataset for Machine Translation systems developed to translate from these languages to Hindi by annotating parts of the available corpora with named entities. Bhojpuri, Maithili and Magahi corpora of sizes 228373, 157468 and 56190 tokens, respectively, were annotated using 22 entity labels. The annotation considers coarse-grained annotation labels followed by the tagset used in one of the Hindi NER datasets. We also report a Deep Learning baseline that uses an LSTM-CNNs-CRF model. The lower baseline F1-scores from the NER tool obtained by using Conditional Random Fields models are 70.56% for Bhojpuri, 73.19% for Maithili and 84.18% for Magahi. The Deep Learning-based technique (LSTM-CNNs-CRF) achieved 61.41% for Bhojpuri, 71.38% for Maithili and 86.39% for Magahi. As the results show, LSTM-CNNs-CRF fails to outperform the lower baseline in the case of Bhojpuri and Maithili, which have more data in terms of the number of tokens, but not in terms of the number of named entities. However, the cross-lingual model training of LSTM-CNNs-CRF for Bhojpuri and Maithili performed better than the CRF.
Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network

Accurate demand forecasting of different public transport modes (e.g., buses and light rails) is essential for public service operation. However, the development level of various modes often varies significantly, which makes it hard to predict the demand of the modes with insufficient knowledge and sparse station distribution (i.e., station-sparse mode). Intuitively, different public transit modes may exhibit shared demand patterns temporally and spatially in a city. As such, we propose to enhance the demand prediction of station-sparse modes with the data from station-intensive mode and design a Memory-Augmented Multi-task Re current Network (MATURE) to derive the transferable demand patterns from each mode and boost the prediction of station-sparse modes through adapting the relevant patterns from the station-intensive mode. Specifically, MATURE comprises three components: 1) a memory-augmented recurrent network for strengthening the ability to capture the long-short term information and storing temporal knowledge of each transit mode; 2) a knowledge adaption module to adapt the relevant knowledge from a station-intensive source to station-sparse sources; 3) a multi-task learning framework to incorporate all the information and forecast the demand of multiple modes jointly. The experimental results on a real-world dataset covering four public transport modes demonstrate that our model can promote the demand forecasting performance for the station-sparse modes.
SPARQL with XQuery-based Filtering

Linked Open Data (LOD) has been proliferated over various domains, however, there are still lots of open data in various format other than RDF, a standard data description framework in LOD. These open data can also be connected to entities in LOD when they are associated with URIs. Document-centric XML data are such open data that are connected with entities in LOD as supplemental documents for these entities, and to convert these XML data into RDF requires various techniques such as information extraction, ontology design and ontology mapping with human prior knowledge. To utilize document-centric XML data linked from entities in LOD, in this paper, a SPARQL-based seamless access method on RDF and XML data is proposed. In particular, an extension to SPARQL, XQueryFILTER, which enables XQuery as a filter in SPARQL is proposed. For efficient query processing of the combination of SPARQL and XQuery, a database theory-based query optimization is proposed. Real-world scenario-based experiments in this paper showcase that effectiveness of XQueryFILTER and efficiency of the optimization.
Contextuality of quantum fluctuations characterized by conditional weak values of entangled states

The quantum fluctuations of a physical property can be observed in the measurement statistics of any measurement that is at least partially sensitive to that physical property. Quantum theory indicates that the effective distribution of values taken by the physical property depends on the specific measurement context based on which these values are determined and weak values have been identified as the contextual values describing this dependence of quantum fluctuations on the measurement context. Here, the relation between classical statistics and quantum contextuality is explored by considering systems entangled with a quantum reference. The quantum fluctuations of the system can then be steered by precise projective measurements of the reference, resulting in different contextual values of the quantum fluctuations depending on the effective state preparation context determined by the measurement of the reference. The results show that mixed state statistics are consistent with a wide range of potential contexts, indicating that the precise definition of a context requires maximal quantum coherence in both state preparation and measurement.
Label-Free Segmentation of COVID-19 Lesions in Lung CT

Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via voxel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans. Our modeling is inspired by the observation that the parts of tracheae and vessels, which lay in the high-intensity range where lesions belong to, exhibit strong patterns. To facilitate the learning of such patterns at a voxel level, we synthesize ‘lesions’ using a set of simple operations and insert the synthesized ‘lesions’ into normal CT lung scans to form training pairs, from which we learn a normalcy-recognizing network (NormNet) that recognizes normal tissues and separate them from possible COVID-19 lesions. Our experiments on three different public datasets validate the effectiveness of NormNet, which conspicuously outperforms a variety of unsupervised anomaly detection (UAD) methods.
An Atlas of Cultural Commonsense for Machine Reasoning

Existing commonsense reasoning datasets for AI and NLP tasks fail to address an important aspect of human life: cultural differences. In this work, we introduce an approach that extends prior work on crowdsourcing commonsense knowledge by incorporating differences in knowledge that are attributable to cultural or national groups. We demonstrate the technique by collecting commonsense knowledge that surrounds three fairly universal rituals---coming-of-age, marriage, and funerals---across three different national groups: the United States, India, and the Philippines. Our pilot study expands the different types of relationships identified by existing work in the field of commonsense reasoning for commonplace events, and uses these new types to gather information that distinguishes the knowledge of the different groups. It also moves us a step closer towards building a machine that doesn't assume a rigid framework of universal (and likely Western-biased) commonsense knowledge, but rather has the ability to reason in a contextually and culturally sensitive way. Our hope is that cultural knowledge of this sort will lead to more human-like performance in NLP tasks such as question answering (QA) and text understanding and generation.
Manifold attack

Machine Learning in general and Deep Learning in particular has gained much interest in the recent decade and has shown significant performance improvements for many Computer Vision or Natural Language Processing tasks. In order to deal with databases which have just a small amount of training samples or to deal with models which have large amount of parameters, the regularization is indispensable. In this paper, we enforce the manifold preservation (manifold learning) from the original data into latent presentation by using "manifold attack". The later is inspired in a fashion of adversarial learning : finding virtual points that distort mostly the manifold preservation then using these points as supplementary samples to train the model. We show that our approach of regularization provides improvements for the accuracy rate and for the robustness to adversarial examples.
At Your Command! An Empirical Study on How Laypersons Teach Robots New Functions

Even though intelligent systems such as Siri or Google Assistant are enjoyable (and useful) dialog partners, users can only access predefined functionality. Enabling end-users to extend the functionality of intelligent systems will be the next big thing. To promote research in this area we carried out an empirical study on how laypersons teach robots new functions by means of natural language instructions. The result is a labeled corpus consisting of 3168 submissions given by 870 subjects.The analysis of the dataset revealed that many participants used certain wordings to express their wish to teach new functionality; two corresponding trigrams are among the most frequent. On the contrary, more than one third (37%) did not verbalize the teaching intent at all. We labeled the semantic constituents in the utterances: declaration (including the name of the function) and intermediate steps. The full corpus is publicly available: http://dx.doi. org/10.21227/zecn-6c61
Persistent And Scalable JADE: A Cloud based InMemory Multi-agent Framework

Multi-agent systems are often limited in terms of persistenceand scalability. This issue is more prevalent for applications inwhich agent states changes frequently. This makes the existingmethods less usable as they increase the agent's complexityand are less scalable. This research study has presented anovel in-memory agent persistence framework. Two prototypeshave been implemented, one using the proposed solution andthe other using an established agent persistency environment.Experimental results confirm that the proposed framework ismore scalable than existing approaches whilst providing asimilar level of persistency. These findings will help futurereal-time multiagent systems to become scalable and persistentin a dynamic cloud environment.
Simplex: Repurposing Intel Memory Protection Extensions for Information Hiding

With the rapid increase in software exploits, the last few decades have seen several hardware-level features to enhance security (e.g., Intel MPX, ARM TrustZone, Intel SGX, Intel CET). Due to security, performance and/or usability issues these features have attracted steady criticism. One such feature is the Intel Memory Protection Extensions (MPX), an instruction set architecture extension promising spatial memory safety at a lower performance cost due to hardware-accelerated bounds checking. However, recent investigations into MPX have found that is neither as performant, accurate, nor precise as cutting-edge software-based spatial memory safety. As a direct consequence, compiler and operating system support for MPX is dying, and Intel has begun to manufacture desktop CPUs without MPX. Nonetheless, given how ubiquitous MPX is, it provides an excellent yet under-utilized hardware resource that can be aptly salvaged for security purposes. In this paper, we propose Simplex, a library framework that re-purposes MPX registers as general purpose registers. Using Simplex, we demonstrate how MPX registers can be used to store sensitive information (e.g., encryption keys) directly on the hardware. We evaluate Simplex for performance and find that its overhead is small enough to permit its deployment in all but the most performance-intensive code. We refactored the string.h buffer manipulation functions and found a geometric mean 0.9% performance overhead. We also modified the deepsjeng and lbm SPEC CPU2017 benchmarks to use Simplex and found a 1% and 0.98% performance overhead respectively. Finally, we investigate the behavior of the MPX context with regards to multi-process and multi-thread programs.
Molecular cloning and expression analysis of a phytochelatin synthase gene, PcPCS1, from Pyrus calleryana Dcne.

Pear rootstocks bean pear(Pyrus calleryana Dcne.)was used as an experimental materials for cloning phytochelatin synthase gene and researching its expression characterization via rapid amplification of cDNA ends(RACE)and fluorescent quantitative reverse transcription polymerase chain reaction(RT-PCR)methods.PcPCS1 cDNA sequence length is 1 970 bp,which contains an open reading frame that would encode a 54.77 kD protein of 497 amino acids.Two typical phytochelatin subfamily domains constitute PcPCS1 protein,which includes three adjacent Cys-Cys elements(331-332,351-352 and 369-370 location amino acid)in the C-terminal region and all characteristics sites(Cys-56,Cys-90/91 and Cys-109)of phytochelatin synthase protein.PcPCS1 and other PCS1 proteins from leguminous plants belong to the same branch in PCS1 phylogenetic tree.Moreover,PcPCS1 had the highest homology(84%) with Lotus corniculatus LjPCS1.Fluorescence quantitative RT-PCR results showed that PcPCS1 gene was constitutively expression and its expressions were different in various organs.Furthermore,PcPCS1 transcription level was quickly climbed up after 20 μmol · L-1 CdSO4 treatment 24 hours,which was higher in leaf than in root.It was very significant that three heavy-metal ions had different abilities to up-regulated PcPCS1 expression as the sequence Cd2+ Zn2+ Cu2+. It is need to point out that PcPCS1 expression was significantly inhibited by 200 μmol · L-1 L-Buthionine sulfoximine(BSO) which was one of γ-glutamyl cysteine synthetase inhibitors.However,its expression returned to the normal level of single heavy-metal treatment or exceeded it after adding 200 μmol · L-1 L-Glutathionereduced(GSH)to the nutrient solution.In summary,the bean pear phytochelatins synthesis used glutathione as substrate,which was produced through γ-glutamyl cysteine synthetase pathway.
Revisiting Factorizing Aggregated Posterior in Learning Disentangled Representations

In the problem of learning disentangled representations, one of the promising methods is to factorize aggregated posterior by penalizing the total correlation of sampled latent variables. However, this well-motivated strategy has a blind spot: there is a disparity between the sampled latent representation and its corresponding mean representation. In this paper, we provide a theoretical explanation that low total correlation of sampled representation cannot guarantee low total correlation of the mean representation. Indeed, we prove that for the multivariate normal distributions, the mean representation with arbitrarily high total correlation can have a corresponding sampled representation with bounded total correlation. We also propose a method to eliminate this disparity. Experiments show that our model can learn a mean representation with much lower total correlation, hence a factorized mean representation. Moreover, we offer a detailed explanation of the limitations of factorizing aggregated posterior -- factor disintegration. Our work indicates a potential direction for future research of disentangled learning.
Objective early identification of kinematic instabilities in shear flows

A kinematic approach for the identification of flow instabilities is proposed. By defining a flow instability in the Lagrangian frame as the increased folding of lines of fluid particles, subtle perturbations and unstable growth thereof are detected early based solely on the curvature change of material lines over finite time. The material line curvature is objective, parametrization independent, and can be applied to flows of general complexity without knowledge of the base flow. An analytic connection between the growth of Eulerian velocity modes perturbing a general shear flow and the induced flow map and Lagrangian curvature change is derived. The approach is verified to capture instabilities promptly in a temporally developing jet flow, an unstable separated shear flow over a cambered airfoil, and in the onset of a wake instability behind a circular cylinder.
Electron internal energy and internal motion (Zitterbewegung) as consequence of local U(1) gauge invariance in two-spinor language

Starting with the results obtained in a previous paper in which classical local U(1) gauge invariance in terms of the electromagnetic field strenghts instead of the usual formulation mediated by the four potential was introduced it is shown that using the gauge freedom associated with the third component of the magnetic field, previously obtained spinor equations of motion describe the internal dynamics of a free 1/2 spin particle suggesting a kinematic origin of its rest mass and helicity. The controversial Zitterbewegung (trembling motion) appears in a natural way as internal motion with the velocity of light. Such an interpretation is in contrast with the usual quantum mechanical explanation of transitions between positive and negative energy states.
Sensitivity Assisted Alternating Directions Method of Multipliers for Distributed Optimization and Statistical Learning

This paper considers the problem of distributed model fitting using the alternating directions method of multipliers (ADMM). ADMM splits the learning problem into several smaller subproblems, usually by partitioning the data samples. The different subproblems can be solved in parallel by a set of worker computing nodes coordinated by a master node, and the subproblems are repeatedly solved until convergence. At each iteration, the worker nodes must solve a convex optimization problem whose difficulty increases with the size of the problem. In this paper, we propose a sensitivity-assisted ADMM algorithm that leverages the parametric sensitivities such that the subproblems solutions can be approximated using a tangential predictor, thus easing the computational burden to computing one linear solve. We study the convergence properties of the proposed sensitivity-assisted ADMM algorithm. The numerical performance of the algorithm is illustrated on a nonlinear parameter estimation problem, and a multilayer perceptron learning problem.
Performance Evaluation of Linear Regression Algorithm in Cluster Environment

Cluster computing was introduced to replace the superiority of super computers. Cluster computing is able to overcome the problems that cannot be effectively dealt with supercomputers. In this paper, we are going to evaluate the performance of cluster computing by executing one of data mining techniques in the cluster environment. The experiment will attempt to predict the flight delay by using linear regression algorithm with apache spark as a framework for cluster computing. The result shows that, by involving 5 PCs in cluster environment with equal specifications can increase the performance of computation up to 39.76% compared to the standalone one. Attaching more nodes to the cluster can make the process become faster significantly.
ASSESSING THE HIERARCHICAL HAMILTONIAN SPLITTING INTEGRATOR FOR COLLISIONLESS N -BODY SIMULATIONS

The large dynamic range in some astrophysical N -body problems led to the use of adaptive multi-time-steps; however, the search for optimal strategies is still challenging. We numerically quantify the performance of the hierarchical Hamiltonian Splitting (HHS) integrator for collisionless simulations using a direct summation code. We compare HHS with the constant global time-step leapfrog integrator, and with the adaptive one (AKDK). We find that HHS is approximately reversible, whereas AKDK is not. Therefore, it is possible to find a combination of parameters such that the energy drift is considerably milder for HHS, resulting in a better performance. We conclude that HHS is an attractive alternative to AKDK, and it is certainly advantageous for direct summation and P3M codes. Also, we find advantages with GADGET4 (Tree/FMM) HHS implementation that are worth exploring further.