Miracle-dz/UniFAR-FTUs huggingface dataset:
Relativity and Aberration

The established way of looking at special relativity is based on Einstein postulates: the principle of relativity and the constancy of the velocity of light. In the most general geometric approach to the theory of special relativity, the principle of relativity, in contrast to Einstein formulation, is only a consequence of the (pseudo-Euclidean) geometry of space-time. The space-time geometric approach deals with all possible choices of coordinates (clock synchronization conventions) of the chosen reference frames. In previous papers, we pointed out the very important role that the space-time geometric approach plays in accelerator engineering. The purpose of this paper is to provide a novel insight into the problem of aberration of light based on the space-time geometric approach. We will investigate the case of a plane-polarized light wave reflected from mirrors moving tangentially to its surface. It is generally believed that there is no aberration (deviation of the energy transport) for light reflected from mirrors moving transversely (also for light transmitted through a hole in the moving opaque screen or, consequently, through a moving open end of the telescope barrel). We show that this typical textbook statement is incorrect. The aberration of starlight seems to be one of the simplest phenomena of astronomical observations. The lack of symmetry, between the cases when either the source or detector is moving is shown clearly on the basis of the separation of binary stars. Such aberration is not observed. We have shown that the fact that we do not see myriads of widely separated binaries in wild gyration does not require any fundamental change of outlook, but it does require that aberration of "distant" stars should be treated in the framework of space-time geometric approach.
Production of 
X(3872)
 and a photon in 
e+e−
 annihilation

If the $X(3872)$ is a weakly bound charm-meson molecule, it can be produced in $e^+ e^-$ annihilation by the creation of $D^{*0} \bar D^{*0}$ from a virtual photon followed by the rescattering of the P-wave charm-meson pair into the $X$ and a photon. A triangle singularity produces a narrow peak in the cross section for $e^+ e^- \to X \gamma$ 2.2 MeV above the $D^{*0} \bar{D}^{*0}$ threshold. We predict the normalized cross section in the region of the peak. We show that the absorptive contribution to the cross section for $e^+ e^- \to D^{*0} \bar D^{*0} \to X \gamma$, which was calculated previously by Dubynskiy and Voloshin, does not give a good approximation to the peak from the triangle singularity. We also derive an approximation for the cross section at energies well above the $D^{*0} \bar{D}^{*0}$ threshold in terms of the wavefunction of the $X$ at the origin.
DeepHealth: Deep Learning for Health Informatics

Machine learning and deep learning have provided us with an exploration of a whole new research era. As more data and better computational power become available, they have been implemented in various fields. The demand for artificial intelligence in the field of health informatics is also increasing and we can expect to see the potential benefits of artificial intelligence applications in healthcare. Deep learning can help clinicians diagnose disease, identify cancer sites, identify drug effects for each patient, understand the relationship between genotypes and phenotypes, explore new phenotypes, and predict infectious disease outbreaks with high accuracy. In contrast to traditional models, its approach does not require domain-specific data pre-process, and it is expected that it will ultimately change human life a lot in the future. Despite its notable advantages, there are some challenges on data (high dimensionality, heterogeneity, time dependency, sparsity, irregularity, lack of label) and model (reliability, interpretability, feasibility, security, scalability) for practical use. This article presents a comprehensive review of research applying deep learning in health informatics with a focus on the last five years in the fields of medical imaging, electronic health records, genomics, sensing, and online communication health, as well as challenges and promising directions for future research. We highlight ongoing popular approaches' research and identify several challenges in building deep learning models.
Small-scale reconstruction in three-dimensional Kolmogorov flows using four-dimensional variational data assimilation

Significant insights in computational fluid dynamics have been obtained in recent years by adopting the data assimilation methods developed in the meteorology community. We apply the four-dimensional variational method to reconstruct the small scales of three-dimensional turbulent velocity fields with a moderate Reynolds number, given a time sequence of measurement data on a coarse set of grid points. The problem presents new challenges because the evolution of the flow is dominated by the nonlinear vortex stretching and the energy cascade process, which are absent from two-dimensional flows. The results show that, reconstruction is successful when the resolution of the measurement data, given in terms of the wavenumber, is of the order of the threshold value $k_{c}=0.2\unicode[STIX]{x1D702}_{K}^{-1}$, where $\unicode[STIX]{x1D702}_{K}$ is the Kolmogorov length scale of the flow. When the data are available over a period of one large eddy turnover time scale, the filtered enstrophy and other small-scale quantities are reconstructed with a 30 % or smaller normalized pointwise error, and a 90 % pointwise correlation. The spectral correlation between the reconstructed and target fields is higher than 80 % for all wavenumbers. Minimum volume enclosing ellipsoids (MVEEs) and MVEE trees are introduced to quantitatively compare the geometry of non-local structures. Results show that, for the majority samples, errors in the locations and the sizes of the reconstructed structures are within 15 %, and those in the orientations are within $15^{\circ }$. Overall, for this flow, satisfactory reconstruction of the scales two or more octaves smaller is possible if data at large scales are available for at least one large eddy turnover time. In comparison, a direct substitution scheme results in three times bigger pointwise discrepancy in enstrophy. The spectral difference between the reconstructed and target velocity fields is more than ten times higher than what is obtained with the four-dimensional variational method. The results show that further investigation is warranted to verify the efficacy of the method in flows with higher Reynolds numbers.
PRECLINICAL AND CLINICAL DEVELOPMENT OF THE LIPOPHILIC CAMPTOTHECIN ANALOGUE AR-67

OF DISSERTATION PRECLINICAL AND CLINICAL DEVELOPMENT OF THE LIPOPHILIC CAMPTOTHECIN ANALOGUE AR-67 AR-67 is a lipophilic third generation camptothecin analogue, currently under early stage clinical trials. It acts by targeting Topoisomerase 1 (Top1), a nuclear enzyme essential for DNA replication and transcription and is present in two forms, the pharmacologically active lipophilic lactone and the charged carboxylate. In oncology patients participating in a phase I clinical trial, AR-67 lactone was the predominant species in plasma. Similarly to other camptothecins, the identified dose-limiting toxicities for AR-67 were neutropenia, thrombocytopenia and fatigue. In addition, in vitro metabolism studies indicated AR-67 lactone as a substrate for CYP3A4/5 as well as the UGT1A7 and UGT1A8 enzymes localizing in the liver and the gut. Numerous studies have demonstrated the over-expression of transporters in certain tumor types. Here, the effect of interactions between AR-67 and efflux or uptake transporters on the antitumor efficacy of AR-67 in vitro was studied. We showed that BCRP and MDR1 overexpression confers resistance to AR-67. Moreover, we demonstrated the therapeutic superiority of protracted dosing over more intense dosing regimens of AR-67 using xenografts models. Our studies indicated the schedule-dependent expression of Top1 and the preferential partitioning of AR-67 in the tumor tissue. We reason that these are factors that need to be taken into consideration when designing dosing schedules aiming to maximize efficacy. As most cytotoxic drugs, AR-67 has a narrow therapeutic window. Thus, it is essential to identify the variables influencing exposure to this camptothecin analogue. A thorough compartmental pharmacokinetic analysis was performed on the patient data obtained in a phase 1 clinical trial on AR-67. Moreover, sources of intersubject variability associated with obtaining pharmacokinetic parameter estimates were identified and a population covariate pharmacokinetic model was developed. In conclusion, the drug development of AR-67 is a work in process. Findings presented above provide an insight on the factors contributing to its efficacy and toxicity when given to cancer patients.
An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction

The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, consensus is lacking on experimental configurations, namely, choosing how the pseudo data should be generated or used. In this study, these choices are investigated through extensive experiments, and state-of-the-art performance is achieved on the CoNLL-2014 test set (F0.5=65.0) and the official test set of the BEA-2019 shared task (F0.5=70.2) without making any modifications to the model architecture.
Drag reduction in boiling Taylor–Couette turbulence

We create a highly controlled laboratory environment – accessible to both global and local monitoring – to analyse turbulent boiling flows and in particular their shear stress in a statistically stationary state. By precisely monitoring the drag of strongly turbulent Taylor–Couette flow (the flow in between two coaxially rotating cylinders, Reynolds number $Re\approx 10^{6}$ ) during its transition from non-boiling to boiling, we show that the intuitive expectation, namely that a few volume per cent of vapour bubbles would correspondingly change the global drag by a few per cent, is wrong. Rather, we find that for these conditions a dramatic global drag reduction of up to 45 % occurs. We connect this global result to our local observations, showing that for major drag reduction the vapour bubble deformability is crucial, corresponding to Weber numbers larger than one. We compare our findings with those for turbulent flows with gas bubbles, which obey very different physics from those of vapour bubbles. Nonetheless, we find remarkable similarities and explain these.
Revisiting the orbital tracking problem

Consider a space object in an orbit about the earth. An uncertain initial state can be represented as a point cloud which can be propagated to later times by the laws of Newtonian motion. If the state of the object is represented in Cartesian earth centered inertial (Cartesian-ECI) coordinates, then even if initial uncertainty is Gaussian in this coordinate system, the distribution quickly becomes non-Gaussian as the propagation time increases. Similar problems arise in other standard fixed coordinate systems in astrodynamics, e.g. Keplerian and to some extent equinoctial. To address these problems, a local "Adapted STructural (AST)'' coordinate system has been developed in which uncertainty is represented in terms of deviations from a "central state". Given a sequence of angles-only measurements, the iterated nonlinear extended (IEKF) and unscented (IUKF) Kalman filters are often the most appropriate variants to use. In particular, they can be much more accurate than the more commonly used non-iterated versions, the extended (EKF) and unscented (UKF) Kalman filters, especially under high eccentricity. In addition, iterated Kalman filters can often be well-approximated by two new closed form filters, the observation-centered extended (OCEKF) and unscented (OCUKF) Kalman filters.
Multi-time measurements in Hawking radiation: information at higher-order correlations

It is believed that no information can be stored in Hawking radiation, because correlations between quanta of different field modes vanish. However, such correlations have been defined only with reference to a single moment of time. In this article, we develop a method for the evaluation of multi-time correlations. We find that these correlations are highly non-trivial: for a scalar field in the Schwarzschild black hole, multi-time correlations have an explicit dependence on angular variables and on the scattering history of Hawking quanta. This result leads us to the conjecture that some pre-collapse information can be stored in multi-time correlations after backreaction effects have been incorporated in the physical description.
Attention-based Pairwise Multi-Perspective Convolutional Neural Network for Answer Selection in Question Answering

Over the past few years, question answering and information retrieval systems have become widely used. These systems attempt to find the answer of the asked questions from raw text sources. A component of these systems is Answer Selection which selects the most relevant answer from candidate answers. Syntactic similarities were mostly used to compute the similarity, but in recent works, deep neural networks have been used which have made a significant improvement in this field. In this research, a model is proposed to select the most relevant answers to the factoid question from the candidate answers. The proposed model ranks the candidate answers in terms of semantic and syntactic similarity to the question, using convolutional neural networks. In this research, Attention mechanism and Sparse feature vector use the context-sensitive interactions between questions and answer sentence. Wide convolution increases the importance of the interrogative word. Pairwise ranking is used to learn differentiable representations to distinguish positive and negative answers. Our model indicates strong performance on the TrecQA beating previous state-of-the-art systems by 2.62% in MAP and 2.13% in MRR while using the benefits of no additional syntactic parsers and external tools. The results show that using context-sensitive interactions between question and answer sentences can help to find the correct answer more accurately.
Composing Knowledge Graph Embeddings via Word Embeddings

Learning knowledge graph embedding from an existing knowledge graph is very important to knowledge graph completion. For a fact $(h,r,t)$ with the head entity $h$ having a relation $r$ with the tail entity $t$, the current approaches aim to learn low dimensional representations $(\mathbf{h},\mathbf{r},\mathbf{t})$, each of which corresponds to the elements in $(h, r, t)$, respectively. As $(\mathbf{h},\mathbf{r},\mathbf{t})$ is learned from the existing facts within a knowledge graph, these representations can not be used to detect unknown facts (if the entities or relations never occur in the knowledge graph). This paper proposes a new approach called TransW, aiming to go beyond the current work by composing knowledge graph embeddings using word embeddings. Given the fact that an entity or a relation contains one or more words (quite often), it is sensible to learn a mapping function from word embedding spaces to knowledge embedding spaces, which shows how entities are constructed using human words. More importantly, composing knowledge embeddings using word embeddings makes it possible to deal with the emerging new facts (either new entities or relations). Experimental results using three public datasets show the consistency and outperformance of the proposed TransW.
Security in Process: Detecting Attacks in Industrial Process Data

Due to the fourth industrial revolution, industrial applications make use of the progress in communication and embedded devices. This allows industrial users to increase efficiency and manageability while reducing cost and effort. Furthermore, the fourth industrial revolution, creating the so-called Industry 4.0, opens a variety of novel use and business cases in the industrial environment. However, this progress comes at the cost of an enlarged attack surface of industrial companies. Operational networks that have previously been phyiscally separated from public networks are now connected in order to make use of new communication capabilites. This motivates the need for industrial intrusion detection solutions that are compatible to the long-term operation machines in industry as well as the heterogeneous and fast-changing networks. In this work, process data is analysed. The data is created and monitored on real-world hardware. After a set up phase, attacks are introduced into the systems that influence the process behaviour. A time series-based anomaly detection approach, the Matrix Profiles, are adapted to the specific needs and applied to the intrusion detection. The results indicate an applicability of these methods to detect attacks in the process behaviour. Furthermore, they are easily integrated into existing process environments. Additionally, one-class classifiers One-Class Support Vector Machines and Isolation Forest are applied to the data without a notion of timing. While Matrix Profiles perform well in terms of creating and visualising results, the one-class classifiers perform poorly.
Extraction of Dzyaloshinskii-Moriya interaction from propagating spin waves

The interfacial Dzyaloshinksii-Moriya interaction (iDMI) is of great interest in thin-film magnetism because of its ability to stabilize chiral spin textures. It can be quantified by investigating the frequency non-reciprocity of oppositely propagating spin waves. However, as the iDMI is an interface interaction the relative effect reduces when the films become thicker making quantification more difficult. Here, we utilize all-electrical Propagating Spin Wave Spectroscopy (PSWS) to disentangle multiple contributions to spin wave frequency non-reciprocity to determine the iDMI. This is done by investigating non-reciprocities across a wide range of magnetic layer thicknesses (from 4 to 26 nm) in Pt/Co/Ir, Pt/Co/Pt, and Ir/Co/Pt stacks. We find the expected sign change in the iDMI when inverting the stack order, and a negligible iDMI for the symmetric Pt/Co/Pt. We additionally extract a difference in surface anisotropies and find a large contribution due to the formation of different crystalline phases of the Co, which is corroborated using nuclear magnetic resonance and high-resolution transmission-electron-microscopy measurements. These insights will open up new avenues to investigate, quantify and disentangle the fundamental mechanisms governing the iDMI, and pave a way towards engineered large spin-wave non-reciprocities for magnonic applications.
Matrix calculations for moments of Markov processes

Abstract Matryoshka dolls, the traditional Russian nesting figurines, are known worldwide for each doll’s encapsulation of a sequence of smaller dolls. In this paper, we exploit the structure of a new sequence of nested matrices we call matryoshkan matrices in order to compute the moments of the one-dimensional polynomial processes, a large class of Markov processes. We characterize the salient properties of matryoshkan matrices that allow us to compute these moments in closed form at a specific time without computing the entire path of the process. This simplifies the computation of the polynomial process moments significantly. Through our method, we derive explicit expressions for both transient and steady-state moments of this class of Markov processes. We demonstrate the applicability of this method through explicit examples such as shot noise processes, growth–collapse processes, ephemerally self-exciting processes, and affine stochastic differential equations from the finance literature. We also show that we can derive explicit expressions for the self-exciting Hawkes process, for which finding closed-form moment expressions has been an open problem since their introduction in 1971. In general, our techniques can be used for any Markov process for which the infinitesimal generator of an arbitrary polynomial is itself a polynomial of equal or lower order.
An Adaptive Stochastic Nesterov Accelerated Quasi Newton Method for Training RNNs

A common problem in training neural networks is the vanishing and/or exploding gradient problem which is more prominently seen in training of Recurrent Neural Networks (RNNs). Thus several algorithms have been proposed for training RNNs. This paper proposes a novel adaptive stochastic Nesterov accelerated quasiNewton (aSNAQ) method for training RNNs. The proposed method aSNAQ is an accelerated method that uses the Nesterov's gradient term along with second order curvature information. The performance of the proposed method is evaluated in Tensorflow on benchmark sequence modeling problems. The results show an improved performance while maintaining a low per-iteration cost and thus can be effectively used to train RNNs.
Indirect detection imprint of leptophilic dark matter

In this paper we revisit constraints on the leptophilic dark matter (DM) arising from the DM indirect detection experiments. Interactions between the charged leptons and the scalar-type, Dirac-type or vector-type DM are written in terms of effective operators. After classifying all interactions that may give non-zero signals in indirect detections, we study constraints on the parameter space of these effective interactions from the latest results of AMS-02, Planck, Fermi-LAT and H.E. S.S. , as well as the observed relic abundance. Main results are summarized in the Table. I. It shows that the $\tau$-flavored DM is almost excluded by the DM indirect detection results in some scenario.
Renormalization-group running induced cosmic inflation

As a contribution to a viable candidate for a standard model of cosmology, we here show that pre-inflationary quantum fluctuations can provide a scenario for the long-sought initial conditions for the inflaton field. Our proposal is based on the assumption that at very high energies (higher than the energy scale of inflation) the vacuum-expectation value (VeV) of the field is trapped in a false vacuum and then, due to renormalization-group (RG) running, the potential starts to flatten out toward low energy, eventually tending to a convex one which allows the field to roll down to the true vacuum. We argue that the proposed mechanism should apply to large classes of inflationary potentials with multiple concave regions. Our findings favor a particle physics origin of chaotic, large-field inflationary models as we eliminate the need for large field fluctuations at the GUT scale. In our analysis, we provide a specific example of such an inflationary potential, whose parameters can be tuned to reproduce the existing cosmological data with good accuracy.
What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis

Building named entity recognition (NER) models for languages that do not have much training data is a challenging task. While recent work has shown promising results on cross-lingual transfer from high-resource languages, it is unclear what knowledge is transferred. In this paper, we first propose a simple and efficient neural architecture for cross-lingual NER. Experiments show that our model achieves competitive performance with the state-of-the-art. We further explore how transfer learning works for cross-lingual NER on two transferable factors: sequential order and multilingual embedding. Our results shed light on future research for improving cross-lingual NER.