Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral Features from Ultrarelativistic Ions in Gamma-Ray Bursts?

Gamma-ray burst outflows may entrain small blobs or filaments of dense, highly ionized metal-rich material. Such inhomogeneities, accelerated by the flow to Lorentz factors in the range 10-100, could have a significant coverage factor and give rise to broad features, especially because of Fe K edges, which influence the spectrum below the MeV range, leading to a progressively decreasing hardness ratio.
Element Availability Importance in Generalized k-out-of-r-from-n Systems

An algorithm for evaluating the overall system availability and the availability importance of elements in a generalized consecutive k-out-of-r-from-n:F system is presented. This system consists of n linearly ordered nonidentical elements with different reliability characteristics and performance rates. Each element can have two states: (i) complete failure with a performance rate of zero; and (ii) perfect functioning with a nominal performance rate. The system fails if the sum of the performance rates of any r consecutive elements is lower than a demand W. The suggested algorithm, based on an extended universal moment generating function, is convenient for numerical implementation. Analytical and numerical examples of evaluating availability importance indices are presented.
$ M$-strongly convex subsets and their generating sets

For subsets of a Banach space the notions of a generating set and an -strongly convex set are introduced. The latter can be represented as the intersection of sets of the form , which are translates of the generating set . A generating set must satisfy a condition that ensures a special support principle, as shown in the paper. Using this support principle a new area of convex analysis is constructed enabling one to strengthen classical results of the type of the Caratheodory and Krein-Milman theorems. Various classes of generating sets are described and the properties of -strongly convex sets are studied.
Resistance of MMTV-NeuT/ATTAC mice to anti-PD-1 immune checkpoint therapy is associated with macrophage infiltration and Wnt pathway expression

One of the central challenges for cancer therapy is the identification of factors in the tumor microenvironment that increase tumor progression and immune tolerance. In breast cancer, fibrosis is a histopathologic criterion for invasive cancer and poor survival that results from inflammatory factors and remodeling of the extracellular matrix to produce an immune tolerant microenvironment. To determine whether tolerance is associated with the immune checkpoint, Programmed Cell Death 1 (PD-1), NeuT/ATTAC mice, a conditional model of mammary fibrosis that we recently developed, were administered a murine-specific anti-PD-1 mAb related to pembrolizumab, and drug response was monitored by tumor development, imaging mass cytometry, immunohistochemistry and tumor gene expression by RNAseq. Tumor progression in NeuT/ATTAC mice was unaffected by weekly injection of anti-PD-1 over four months. Insensitivity to anti-PD-1 was associated with several processes, including increased tumor-associated macrophages (TAM), epithelial to mesenchymal transition (EMT), fibroblast proliferation, an enhanced extracellular matrix and the Wnt signaling pathway, including increased expression of Fzd5, Wnt5a, Vimentin, Mmp3, Col2a1 and Tgfβ1. These results suggest potential therapeutic avenues that may enhance PD-1 immune checkpoint sensitivity, including the use of tumor microenvironment targeted agents and Wnt pathway inhibitors.
Predictive factors of vaccination status, knowledge, attitudes, and practice towards prevention of hepatitis B infection among Bangladeshi people: A cross‐sectional study

Infection with the hepatitis B virus is a serious public health problem that is growing all over the world. Therefore, in this context, there is no exception to public participation in disease burden reduction. Consequently, for the first time in Bangladesh, the current study aims to assess the level of vaccination status, knowledge, attitude, and practice of hepatitis B infection among general people.
Adaptive mean shift and particle filter tracking method based on joint feature

In the object tracking area, both particle filter and mean shift algorithm have proven successful approaches. However, both of them have notable weakness. In this paper, we present a new algorithm which combined the two algorithms to track the target. First, the mean shift algorithm is employed to search an object candidate near the target state. Then, if the candidate is good enough, it will be used to adapt the particle filter parameters, including the number of particle filter, and etc. Finally, the particle filter will estimate the target state based on these new parameters. Further, the paper introduces the color-texture combined feature instead of color feature.
New pyrazolone derivatives, synthesis, characterization, and neuroprotective effect against PTZ- induced neuroinflammation in mice

Objective(s): The study was aimed at synthesis of the new derivatives of the pyrazolone nucleus, and their spectroscopic and pharmacological analysis and evaluation. Materials and Methods: Three series of compounds, with 2-picolinic acid (I a-d), 3-picolinic acid (II a-d), and 4-picolinic acid (III a-d) were synthesized and characterized by FT-IR, 1HNMR, 13C NMR, elemental, and melting points. The new compounds were then evaluated for their anti-oxidant, anti-inflammatory, and anti-epileptic potential. The hind paw edema model was used to screen anti-inflammatory potential, while the anticonvulsant effect was evaluated by employing the acute model of anti-epileptic activity. The in vivo anti-oxidant potential was determined through glutathione (GSH), glutathione-S-transferase (GST), catalase, and lipid peroxidase enzyme (LPO) assays. The expression of key biomarkers involved in inflammation and neuroprotection, including tumor necrotic factors alpha (TNF-α) and phosphorylated nuclear factor kappa B (NF-κB), was detected through enzyme-linked immunosorbent assay (ELISA) and Western blot analysis. Results: The tested compounds showed anti-oxidant potential. The selected compounds exhibited good anti-inflammatory potential. The PTZ-induced elevation of these inflammatory mediators and oxidative stress were ameliorated significantly by the selected compound Ic. Results of molecular analysis (ELISA and Western blot analysis) for potent compound Ic showed a prominent inhibitory effect against neuroinflammatory mediators, including TNF-α and NF-κB. Conclusion: It is concluded that the derivative Ic ameliorated PTZ-induced seizures, oxidative stress, and inflammatory cascades by regulating the NF-κB/ TNF-α/ROS pathway.
Broadband and high‐efficiency conversion from guided waves to spoof surface plasmon polaritons

The conversion from spatial propagating waves to surface plasmon polaritons (SPPs) has been well studied, and shown to be very efficient by using gradient‐index metasurfaces. However, feeding energies into and extracting signals from functional plasmonic devices or circuits through transmission lines require the efficient conversion between SPPs and guided waves, which has not been reported, to the best of our knowledge. In this paper, a smooth bridge between the conventional coplanar waveguide (CPW) with 50 Ω impedance and plasmonic waveguide (e.g., an ultrathin corrugated metallic strip) has been proposed in the microwave frequency, which converts the guided waves to spoof SPPs with high efficiency in broadband. A matching transition has been proposed and designed, which is constructed by gradient corrugations and flaring ground, to match both the momentum and impedance of CPW and the plasmonic waveguide. Simulated and measured results on the transmission coefficients and near‐filed distributions show excellent transmission efficiency from CPW to a plasmonic waveguide to CPW in a wide frequency band. The high‐efficiency and broadband conversion between SPPs and guided waves opens up a new avenue for advanced conventional plasmonic integrated functional devices and circuits.
Semi-Supervised Learning of Monocular Depth Estimation via Consistency Regularization with K-way Disjoint Masking

Semi-Supervised Learning (SSL) has recently accom-plished successful achievements in various ﬁelds such as image classiﬁcation, object detection, and semantic segmentation, which typically require a lot of labour to con-struct ground-truth. Especially in the depth estimation task, annotating training data is very costly and time-consuming, and thus recent SSL regime seems an attractive solution. In this paper, for the ﬁrst time, we introduce a novel framework for semi-supervised learning of monocular depth estimation networks, using consistency regularization to mitigate the reliance on large ground-truth depth data. We propose a novel data augmentation approach, called K - way disjoint masking, which allows the network for learning how to reconstruct invisible regions so that the model not only becomes robust to perturbations but also gener-ates globally consistent output depth maps. Experiments on the KITTI and NYU-Depth-v2 datasets demonstrate the effectiveness of each component in our pipeline, robustness to the use of fewer and fewer annotated images, and superior results compared to other state-of-the-art, semi-supervised methods for monocular depth estimation. Our code is available at https://github.com/KU-CVLAB/MaskingDepth .
Generating music with sentiment using Transformer-GANs

The field of Automatic Music Generation has seen significant progress thanks to the advent of Deep Learning. However, most of these results have been produced by unconditional models, which lack the ability to interact with their users, not allowing them to guide the generative process in meaningful and practical ways. Moreover, synthesizing music that remains coherent across longer timescales while still capturing the local aspects that make it sound ``realistic'' or ``human-like'' is still challenging. This is due to the large computational requirements needed to work with long sequences of data, and also to limitations imposed by the training schemes that are often employed. In this paper, we propose a generative model of symbolic music conditioned by data retrieved from human sentiment. The model is a Transformer-GAN trained with labels that correspond to different configurations of the valence and arousal dimensions that quantitatively represent human affective states. We try to tackle both of the problems above by employing an efficient linear version of Attention and using a Discriminator both as a tool to improve the overall quality of the generated music and its ability to follow the conditioning signals.
Attend to the Right Context: A Plug-and-Play Module for Content-Controllable Summarization

Content-Controllable Summarization generates summaries focused on the given controlling signals. Due to the lack of large-scale training corpora for the task, we propose a plug-and-play module RelAttn to adapt any general summarizers to the content-controllable summarization task. RelAttn first identifies the relevant content in the source documents, and then makes the model attend to the right context by directly steering the attention weight. We further apply an unsupervised online adaptive parameter searching algorithm to determine the degree of control in the zero-shot setting, while such parameters are learned in the few-shot setting. By applying the module to three backbone summarization models, experiments show that our method effectively improves all the summarizers, and outperforms the prefix-based method and a widely used plug-and-play model in both zero- and few-shot settings. Tellingly, more benefit is observed in the scenarios when more control is needed.
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Constructing Moral Discussions

Morality in dialogue systems has raised great attention in research recently. A moral dialogue system could better connect users and enhance conversation engagement by gaining users’ trust. In this paper, we propose a framework, M ORAL D IAL to train and evaluate moral dialogue systems. In our framework, we ﬁrst explore the communication mechanisms of morality and resolve expressed morality into four sub-modules. The sub-modules indicate the roadmap for building a moral dialogue system. Based on that, we design a simple yet effective method: constructing moral discussions from Rules of Thumb (RoTs) be-tween simulated speciﬁc users and the dialogue system. The constructed discussion consists of expressing, explaining, and revising the moral views in dialogue exchanges, which makes conversational models learn morality well in a natural manner. Furthermore, we pro-pose a novel evaluation method in the framework. We evaluate the multiple aspects of morality by judging the relation between dialogue responses and RoTs in discussions, where the multifaceted nature of morality is particularly considered. Automatic and manual experiments demonstrate that our framework is promising to train and evaluate moral dialogue systems.
Optimal cycles enclosing all the nodes of a $k$-dimensional hypercube

We solve the general problem of visiting all the $2^k$ nodes of a $k$-dimensional hypercube by using a polygonal chain that has minimum link-length, and we show that this optimal value is given by $h(2,k):=3 \cdot 2^{k-2}$ if and only if $k \in \mathbb{N}-\{0,1\}$. Furthermore, for any $k$ above one, we constructively prove that it is possible to visit once and only once all the aforementioned nodes, $H(2,k):=\{\{0,1\} \times \{0,1\} \times \dots \times \{0,1\}\} \subset \mathbb{R}^k$, with a cycle (i.e., a closed path) having only $3 \cdot 2^{k-2}$ links.
High-resolution electric field and temperature distributions in positive streamers

In this work, we aim to take a detailed experimental picture of the positive streamer. We apply optical emission spectroscopy to the first negative system (FNS, B 2 Σ u + → X 2 Σ g + ) of N 2 + and the second positive system (SPS, C3Πu → B3Πg) of N2. Large, centimeter wide, and highly reproducible streamers are created in pure nitrogen and synthetic air, at pressures ranging from 33 to 266 mbar. Direct time resolved spectral imaging of the space charge layer resulted in spatiotemporal maps of the calculated reduced electric field strength (E/N) and rovibrational temperature in sub-nanosecond and sub-millimetre resolution. The E/N peaks at approximately 540 and 480 Td, directly in front of the space charge layer, for synthetic air and pure nitrogen respectively, as determined by using the intensity ratio method of FNS and SPS. A global model for pure nitrogen in PLASIMO uses the experimentally determined E/N distribution to draw a picture of the gas kinetics around the space charge layer passage. In addition, the results of the global model serve as a reference to interpret the rotational and vibrational temperatures obtained from experimental FNS and SPS emissions.
Reaction diffusion systems and extensions of quantum stochastic processes

Reaction diffusion systems describe the behaviour of dynamic, interacting, particulate systems. Quantum stochastic processes generalise Brownian motion and Poisson processes, having operator valued Itô calculus machinery. Here it is shown that the three standard noises of quantum stochastic processes can be extended to model reaction diffusion systems, the methods being exemplified with spatial birth–death processes. The usual approach for these systems are master equations, or Doi-Peliti path integration techniques. The machinery described here provide efficient analyses for many systems of interest, and offer an alternative set of tools to investigate such problems.
PIM: Video Coding using Perceptual Importance Maps

Human perception is at the core of lossy video compression, with numerous approaches developed for perceptual quality assessment and improvement over the past two decades. In the determination of perceptual quality, different spatio-temporal regions of the video differ in their relative importance to the human viewer. However, since it is challenging to infer or even collect such fine-grained information, it is often not used during compression beyond low-level heuristics. We present a framework which facilitates research into fine-grained subjective importance in compressed videos, which we then utilize to improve the rate-distortion performance of an existing video codec (x264). The contributions of this work are threefold: (1) we introduce a web-tool which allows scalable collection of fine-grained perceptual importance, by having users interactively paint spatio-temporal maps over encoded videos; (2) we use this tool to collect a dataset with 178 videos with a total of 14443 frames of human annotated spatio-temporal importance maps over the videos; and (3) we use our curated dataset to train a lightweight machine learning model which can predict these spatio-temporal importance regions. We demonstrate via a subjective study that encoding the videos in our dataset while taking into account the importance maps leads to higher perceptual quality at the same bitrate, with the videos encoded with importance maps preferred $1.8 \times$ over the baseline videos. Similarly, we show that for the 18 videos in test set, the importance maps predicted by our model lead to higher perceptual quality videos, $2 \times$ preferred over the baseline at the same bitrate.
On the regularity of matrix refinable functions

It is shown that the transition operator $\T$ associated with the matrix refinement mask $\p(\go )=2^{-d}\Sigma_{\alpha \in [0,N]^d} \p_{\alpha}\hbox{exp}(-i\alpha \go )$ is equivalent to the matrix $(2^{-d}\a _{2i-j})_{i, j}$ with $\a _j=\Sigma_{\gk \in [0, N]^d}\p _{\gk -j}\otimes \p_{\gk}$ and $\p _{\gk -j}\otimes \p_{\gk}$ denoting the Kronecker product of matrices $\p _{\gk -j}$, $\p_{\gk}$. Some spectral properties of T are studied and a complete characterization of the matrix refinable functions in the Sobolev space $W^n(\RR^d)$ for nonnegative integers n is provided. The Sobolev regularity estimate of the matrix refinable function is given in terms of the spectral radius of a restricted transition operator. These estimates are analyzed in some examples.
Distributed Quantum Computing: a Survey

Nowadays, quantum computing has reached the engineering phase, with fully-functional quantum processors integrating hundred of noisy qubits available. Yet – to fully unveil the potential of quantum computing out of the labs and into business reality – the challenge ahead is to substantially scale the qubit number, reaching orders of magnitude exceeding the thousands (if not millions) of noise-free qubits. To this aim, there exists a broad consensus among both academic and industry communities about considering the distributed computing paradigm as the key solution for achieving such a scaling, by envision multiple moderate-to-small-scale quantum processors communicating and cooperating to execute computational tasks exceeding the computational resources available within a single processing device. The aim of this survey is to provide the reader with an overview about the main challenges and open problems arising with distributed quantum computing, and with an easy access and guide towards the relevant literature and the prominent results from a computer/communications engineering perspective.
Robust nonlinear inversion of wave-tilt data

A robust method for characterizing homogeneous or layered geophysical media is proposed. It determines the electrical constitutive parameters and the thicknesses of the layers that make up the probed medium by inverting magnetic wave-tilt data collected on or above the ground surface. An algorithm is presented for establishing whether the medium should be considered homogeneous or stratified. An exact analytical solution to the inverse problem of homogeneous media is deduced. The inverse problem of stratified media is solved by a three-layer feedforward neural network. An algorithm is proposed for speeding up the training of the neural network. Compared to the standard back-propagation algorithm, the new algorithm is faster, more robust, and easier to implement both in hardware form and in software form. Also, the new algorithm requires less memory space. Such an algorithm stems from applying a new gradient-based method to the problem of adjusting the free parameters of a feedforward neural network. A versatile network-pruning technique is proposed. It explores the fact that the free parameters of a trained neural network tend to take on values close to their mean. The characteristics of magnetic wave-tilt data are analysed in detail. The insight provided by this analysis helps to properly interpret the results of the inversions.
Quench induced chaotic dynamics of Anderson localized interacting Bose-Einstein condensates in one dimension

We study the effect of atomic interaction on the localization and the associated dynamics of Bose-Einstein condensates in a one-dimensional quasiperiodic optical lattice and random Gaussian disordered potentials. When the interactions are absent, the condensates exhibit localization, which weakens as we increase the interaction strength beyond a threshold value for both potential types. We inspect the localized and delocalized states by perturbing the system via quenching the interaction strength instantaneously to zero and studying the dynamics of the condensate, which we further corroborate using the out-of-time-order correlator. The temporal behaviour of the time correlator displays regular dynamics for the localized state, while it shows temporal chaos for the delocalized state. We confirm this dynamical behaviour by analyzing the power spectral density of the time correlator. We further identify that the condensate admits a quasiperiodic route to chaotic dynamics for both potentials. Finally, we present the variation of the maximal Lyapunov exponents for different nonlinearity and disorder strengths that have a positive value in the regime where the time correlator function shows chaotic behaviour. Through this, we establish the strong connection between the spatially delocalized state of the condensate and its temporal chaos.
Soft X-ray observations of the early B giants $\beta$ and $\epsilon$ CMa

Observations made with the PSPC on board the Rontgensatellit (ROSAT) of the two nearby giant stars β CMa (B1 II-III) and e CMa (B2 II) are presented. These stars are of special interest because the exceptional lack of any significant absorbing interstellar column towards them affords us an unhindered view of their soft X-ray emission. The PSPC data on both stars are examined for variability. Upper limits in the region of ±10 per cent can be set on the variation of the 0.1-0.4 keV and 0.5-2.0 keV fluxes from β CMa. More limited data on e CMa set upper limits of around ±20 per cent. The best model fit to the soft X-ray spectrum of both stars is a two-temperature optically thin plasma in which the lower ∼2×10 6 -K temperature component provides about three-quarters of the total emission measure
Relaxation of Wegner's Planar Graph Conjecture for maximum degree 4

The famous Wegner's Planar Graph Conjecture asserts tight upper bounds on the chromatic number of the square $G^2$ of a planar graph $G$, depending on the maximum degree $\Delta(G)$ of $G$. The only case that the conjecture is resolved is when $\Delta(G)=3$, which was proven to be true by Thomassen, and independently by Hartke, Jahanbekam, and Thomas. For $\Delta(G)=4$, Wegner's Planar Graph Conjecture states that the chromatic number of $G^2$ is at most 9; even this case is still widely open, and very recently Bousquet, de Meyer, Deschamps, and Pierron claimed an upper bound of 12. We take a completely different approach, and show that a relaxation of properly coloring the square of a planar graph $G$ with $\Delta(G)=4$ can be achieved with 9 colors. Instead of requiring every color in the neighborhood of a vertex to be unique, which is equivalent to a proper coloring of $G^2$, we seek a proper coloring of $G$ such that at most one color is allowed to be repeated in the neighborhood of a vertex of degree 4, but nowhere else.