Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of drug-resistance mechanism of Pseudomonas aeruginosa developing from a sensitive strain to a persister during carbapenem treatment.

Abstract We explored the mechanism of the development from sensitivity to resistance to carbapenem in Pseudomonas aeruginosa. Two P. aeruginosa strains were collected during treatment with carbapenem. Strain homology was investigated using pulsed-field gel electrophoresis. Porin oprD2 expression was analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The minimum inhibitory concentrations (MICs) of imipenem and meropenem with or without MC207110 were determined using the agar dilution method. The expression level of efflux pump mRNA was tested using real-time polymerase chain reaction. Metallo-lactamases (MBLs) were screened using the EDTA-disk synergy test. Genes encoding MBLs were amplified and then analyzed by DNA sequencing. The two treated strains belonged to the same pulsed-field gel electrophoresis type. The SDS-PAGE profile of the P. aeruginosa strains revealed that the 46-kDa membrane protein OprD2 of IMP(R)MEM(R) type strains was lost, whereas OprD2 of 1 IMP(S)MEM(S) strain was normal. With or without MC207110 treatment, the MIC of carbapenem-resistant P. aeruginosa decreased by 4-fold, while the MIC of carbapenem-sensitive P. aeruginosa did not. Compared with the carbapenem-sensitive strain, MexX mRNA expression in the carbapenem-resistant strain increased by 102.5-fold, while the mRNA expression of other efflux pumps did not markedly increase. Neither carbapenem-resistant nor carbapenem-sensitive P. aeruginosa produced MBL. The mechanism of development from sensitivity to resistance of P. aeruginosa to carbapenem during carbapenem treatment is due to porin oprD2 loss and an increased expression level of MexXY-OprM.
Sequential Production Chains : A General-Equilibrium Analysis

In this paper, we develop a two-sector general equilibrium trade model which includes offshoring, sequential production and endogenous market structures. We analyze how relative factor endowments and various forms of globalization and technological change affect incomes, production patterns and market structures. We show that, against common belief, a reduction in trade costs may lower the range of tasks offshored even though the total volume of offshoring increases. Also, we show that more fluctuation in offshoring production costs reduces firms’ average number of production stages, which might explain recent empirical observations.
Sedimentological and paleoenvironmental characterisation of transgressive sediments on the Guadiana Shelf ( Northern Gulf of Cadiz , SW Iberia )

During the transgression following the Last Glacial Maximum, four backstepping parasequences were deposited on the northern Gulf of C! adiz shelf (SW Iberia). In contrast to other areas on the shelf, these transgressive deposits are at, or very close to, the surface to the southeast of the Guadiana Estuary mouth. This is particularly true for transgressive parasequence TC, possibly associated with the Younger Dryas event, and the youngest parasequence TD, probably linked to a slow-down in sea-level rise at around 8.2 ka BP, both of which form a sandy transgressive bulge on the upper middle shelf. The older and more distal parasequence TC is characterised on seismic records by convex low-angle sigmoidal clinoforms, and contains high amounts of quartz and bioclasts. In contrast, the younger and more proximal parasequence TD shows relatively steep concave prograding clinoforms, and contains high amounts of quartz and other terrigenous components, but comparatively low amounts of bioclasts. Furthermore, the difference in age between both parasequences is clearly marked by a difference in the content of glauconite. The results indicate that sediments found in parasequence TC are associated with deposition related to storm events, with frequent reworking of components, and the more proximal and younger sediments in TD are the result of rapid sediment accumulation related to floods in the Guadiana River basin, possibly during the transition from a dry cold period to a warmer more humid period, when vegetation cover was lowest and flood frequency increased. Additionally, accumulations of terrigenous components directly associated with the Guadiana River basin found on the outer shelf indicate that, at least during the beginning of the transgression, overflow channels active during large-scale flood events may have spilled material to an area located immediately to the south of the Guadiana Estuary mouth. r 2004 Elsevier Ltd and INQUA. All rights reserved.
Digital Predistortion of Millimeter-Wave Phased Antenna Arrays

The cost of deployment of reliable, high-throughput, fifth-generation (5G) millimeterwave (mm-wave) base stations will depend significantly on the maximum equivalent isotropically radiated power (EIRP) that the base stations can transmit. High EIRP can be generated using active beamforming antenna arrays with large apertures and driven by an array of power amplifiers (PAs). However, given the tight half-wavelength lattice that the arrays must retain to ensure a wide beam steering range, the achievable EIRP quickly becomes thermally-limited. Efficient power amplification is thus imperative to low-cost and reliable beamforming antenna array design. This work considers the application of Digital Predistortion (DPD) as an efficiency-enhancement technique for mm-wave beamforming antenna arrays. Two RF beamforming configurations were considered and corresponding DPD schemes were investigated. First, a single-input single-output (SISO) DPD is proposed that can linearize a singleuser RF beamforming array in the presence of non-idealities such as PA load modulation and variation of phase shifter gain with phase. The SISO DPD relies on a feedback signal which reflects a coherent summation of the PA outputs. The SISO DPD is then validated by measurement of a 4-element and 64-element array at 28 GHz with 800 MHz modulated signals using a single over-the-air feedback signal. The SISO DPD uses different sets of coefficients to cope with changes in both linear and non-linear distortions as the beam is steered. Second, the SISO DPD formulation is extended to multi-user RF beamforming to linearize multiple sub-arrays. In this configuration, non-negligible inter-user interference can affect the DPD training. To address the interference, a linear estimate of the interference is calculated and canceled for each user before extracting the SISO DPD coefficients in each sub-array. The SISO DPD with interference cancellation is validated by measurement of a 2-user 2x64-element subarray hybrid at 28 GHz with 800 MHz modulated signals across different combinations of steering angles for the two users.
Efficient Deep Learning in Network Compression and Acceleration

While deep learning delivers state-of-the-art accuracy on many artificial intelligence tasks, it comes at the cost of high computational complexity due to large parameters. It is important to design or develop efficient methods to support deep learning toward enabling its scalable deployment, particularly for embedded devices such as mobile, Internet of things (IOT), and drones. In this chapter, I will present a comprehensive sur - vey of several advanced approaches for efficient deep learning in network compression and acceleration. I will describe the central ideas behind each approach and explore the similarities and differences between different methods. Finally, I will present some future directions in this field.
TerraSAR-X technologies and first results

TerraSAR-X is a satellite that is scheduled to be launched in October 2006 and is currently being built in the framework of a public-private partnership between the German Aerospace Centre (DLR) and EADS Astrium GmbH Germany and will carry an X-band synthetic aperture radar (SAR) instrument equipped with an active phased array antenna. Its operational flexibility will allow the use of the instrument for scientific and commercial applications. High amplitude and phase stability of the radar instrument is achieved by a carbon fibre reinforced plastic slotted waveguide radiator and a high precision transmit/receive module. Additionally, internal calibration hardware will allow for determination of the residual drifts. Precise central electronics controls the radar instrument, provides an arbitrary transmit chirp and receives the radar echo with selectable bandwidth and raw data compression ratio. The ground station's multimode SAR processor is supported by a novel satellite steering law to reduce the attitude-dependent mean Doppler shift. This article summarises the EUSAR 2004 TerraSAR-X contributions.
Radiomics in Cancer Radiotherapy: a Review

Radiomics is a nascent field in quantitative imaging that uses advanced algorithms and considerable computing power to describe tumor phenotypes, monitor treatment response, and assess normal tissue toxicity quantifiably. Remarkable interest has been drawn to the field due to its noninvasive nature and potential for diagnosing and predicting patient prognosis. This review will attempt to comprehensively and critically discuss the various aspects of radiomics including its workflow, applications to different modalities, potential applications in cancer radiotherapy, and limitations.
Minimum "Norm" Neural Networks are Splines

We develop a general framework based on splines to understand the interpolation properties of overparameterized neural networks. We prove that minimum "norm" two-layer neural networks (with appropriately chosen activation functions) that interpolate scattered data are minimal knot splines. Our results follow from understanding key relationships between notions of neural network "norms", linear operators, and continuous-domain linear inverse problems.
Scalable Object-Oriented Sequential Generative Models

The main limitation of previous approaches to unsupervised sequential object-oriented representation learning is in scalability. Most of the previous models have been shown to work only on scenes with a few objects. In this paper, we propose SCALOR, a generative model for SCALable sequential Object-oriented Representation. With the proposed spatially-parallel attention and proposal-rejection mechanism, SCALOR can deal with orders of magnitude more number of objects compared to the current state-of-the-art models. Besides, we introduce the background model so that SCALOR can model complex background along with many foreground objects. We demonstrate that SCALOR can deal with crowded scenes containing nearly a hundred objects while modeling complex background as well. Importantly, SCALOR is the first unsupervised model demonstrating its working in natural scenes containing several tens of moving objects.
A theorem of Kalman and minimal state-space realization of Vector Autoregressive Models

We introduce a concept of $autoregressive$ (AR)state-space realization that could be applied to all transfer functions $\boldsymbol{T}(L)$ with $\boldsymbol{T}(0)$ invertible. We show that a theorem of Kalman implies each Vector Autoregressive model (with exogenous variables) has a minimal $AR$-state-space realization of form $\boldsymbol{y}_t = \sum_{i=1}^p\boldsymbol{H}\boldsymbol{F}^{i-1}\boldsymbol{G}\boldsymbol{x}_{t-i}+\boldsymbol{\epsilon}_t$ where $\boldsymbol{F}$ is a nilpotent Jordan matrix and $\boldsymbol{H}, \boldsymbol{G}$ satisfy certain rank conditions. The case $VARX(1)$ corresponds to reduced-rank regression. Similar to that case, for a fixed Jordan form $\boldsymbol{F}$, $\boldsymbol{H}$ could be estimated by least square as a function of $\boldsymbol{G}$. The likelihood function is a determinant ratio generalizing the Rayleigh quotient. It is unchanged if $\boldsymbol{G}$ is replaced by $\boldsymbol{S}\boldsymbol{G}$ for an invertible matrix $\boldsymbol{S}$ commuting with $\boldsymbol{F}$. Using this invariant property, the search space for maximum likelihood estimate could be constrained to equivalent classes of matrices satisfying a number of orthogonal relations, extending the results in reduced-rank analysis. Our results could be considered a multi-lag canonical-correlation-analysis. The method considered here provides a solution in the general case to the polynomial product regression model of Velu et. al. We provide estimation examples. We also explore how the estimates vary with different Jordan matrix configurations and discuss methods to select a configuration. Our approach could provide an important dimensional reduction technique with potential applications in time series analysis and linear system identification. In the appendix, we link the reduced configuration space of $\boldsymbol{G}$ with a geometric object called a vector bundle.
Differential Privacy-enabled Federated Learning for Sensitive Health Data

Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource constraints for transferring and integrating data from multiple sites, and risk of a single point of failure. In this paper, we introduce a federated learning framework that can learn a global model from distributed health data held locally at different sites. The framework offers two levels of privacy protection. First, it does not move or share raw data across sites or with a centralized server during the model training process. Second, it uses a differential privacy mechanism to further protect the model from potential privacy attacks. We perform a comprehensive evaluation of our approach on two healthcare applications, using real-world electronic health data of 1 million patients. We demonstrate the feasibility and effectiveness of the federated learning framework in offering an elevated level of privacy and maintaining utility of the global model.
On Rational Invariant Summation of p-Adic Power Series with Binomial Coefficient

In the work we have considered p-adic functional series with binomial coeﬃcients and discussed its p-adic convergence. Then we have derived a recurrence relation following with a summation formula which is invariant for rational argument. More precisely, we have investigated a suﬃcient condition under which the p-adic power series converges to a rational invariant sum 0. Finally we have shown application of the invariant summation formula to get some intersting relations involving Bernoulli numbers and Bernoulli polynomials.
On the Existence of Block-Diagonal Solutions to Lyapunov and ${\mathcal {H}_\infty }$ Riccati Inequalities

In this note, we describe sufficient conditions when block-diagonal solutions to Lyapunov and <inline-formula><tex-math notation="LaTeX">${\mathcal {H}_\infty }$</tex-math></inline-formula> Riccati inequalities exist. In order to derive our results, we define a new type of comparison systems, which are positive and are computed using the state-space matrices of the original (possibly nonpositive) systems. Computing the comparison system involves only the calculation of <inline-formula><tex-math notation="LaTeX">${\mathcal {H}_\infty }$</tex-math></inline-formula> norms of its subsystems. We show that the stability of this comparison system implies the existence of block-diagonal solutions to Lyapunov and Riccati inequalities. Furthermore, our proof is constructive, and the overall framework allows the computation of block-diagonal solutions to these matrix inequalities with linear algebra and linear programming. Numerical examples illustrate our theoretical results.
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion

Matrix completion constantly receives tremendous attention from many research fields. It is commonly applied for recommender systems such as movie ratings, computer vision such as image reconstruction or completion, multi-task learning such as collaboratively modeling time-series trends of multiple sensors, and many other applications. Matrix completion techniques are usually computationally exhaustive and/or fail to capture the heterogeneity in the data. For example, images usually contain a heterogeneous set of objects, and thus it is a challenging task to reconstruct images with high levels of missing data. In this paper, we propose the sparse reverse of principal component analysis for matrix completion. The proposed approach maintains smoothness across the matrix, produces accurate estimates of the missing data, converges iteratively, and it is computationally tractable with a controllable upper bound on the number of iterations until convergence. The accuracy of the proposed technique is validated on natural images, movie ratings, and multisensor data. It is also compared with common benchmark methods used for matrix completion.
Conditions for jet breakout in neutron stars’ mergers

We consider conditions for jet breakout through ejecta following mergers of neutron stars and provide simple relations for the breakout conditions. We demonstrate that: (i) break-out requires that the isotropic-equivalent jet energy Ej exceeds the ejecta energy Eej by Ej ≥ Eej/βej, where βej = Vej/c, Vej is the maximum velocity of the ejecta. If the central engine terminates before the breakout, the shock approaches the edge of the ejecta slowly ∝ 1/t; late breakout occurs only if at the termination moment the head of the jet was relatively close to the edge. (ii) If there is a substantial delay between the ejecta’s and the jet’s launching, the requirement on the jet power increases. (iii) The forward shock driven by the jet is mildly strong, with Mach number M ≈ 5/4 (increasing with time delay td); (iii) the delay time td between the ejecta and the jet’s launching is important for $t_\mathrm{ d} \gt t_0= ({3 }/{16}) {c M_{\mathrm{ ej}} V_{\mathrm{ ej}}}/{L_\mathrm{ j}} = 1.01 {\rm \mathrm{ s}} M_{\mathrm{ ej}, -2} L_{\mathrm{ j}, 51} ^{-1} \left({\beta _{\mathrm{ ej}}} /{0.3} \right)$, where Mej is ejecta mass, Lj is the jet luminosity (isotropic equivalent). For small delays, t0 is also an estimate of the break-out time.
Well-posedness of backward stochastic partial differential equations with Lyapunov condition

Abstract In this paper we show the existence and uniqueness of strong solutions for a large class of backward SPDEs, where the coefficients satisfy a specific type Lyapunov condition instead of the classical coercivity condition. Moreover, based on the generalized variational framework, we also use the local monotonicity condition to replace the standard monotonicity condition, which is applicable to various quasilinear and semilinear BSPDE models.
Deep Kernel Learning via Random Fourier Features

Kernel learning methods are among the most effective learning methods and have been vigorously studied in the past decades. However, when tackling with complicated tasks, classical kernel methods are not flexible or "rich" enough to describe the data and hence could not yield satisfactory performance. In this paper, via Random Fourier Features (RFF), we successfully incorporate the deep architecture into kernel learning, which significantly boosts the flexibility and richness of kernel machines while keeps kernels' advantage of pairwise handling small data. With RFF, we could establish a deep structure and make every kernel in RFF layers could be trained end-to-end. Since RFF with different distributions could represent different kernels, our model has the capability of finding suitable kernels for each layer, which is much more flexible than traditional kernel-based methods where the kernel is pre-selected. This fact also helps yield a more sophisticated kernel cascade connection in the architecture. On small datasets (less than 1000 samples), for which deep learning is generally not suitable due to overfitting, our method achieves superior performance compared to advanced kernel methods. On large-scale datasets, including non-image and image classification tasks, our method also has competitive performance.
How Transformer Revitalizes Character-based Neural Machine Translation: An Investigation on Japanese-Vietnamese Translation Systems

While translating between East Asian languages, many works have discovered clear advantages of using characters as the translation unit. Unfortunately, traditional recurrent neural machine translation systems hinder the practical usage of those character-based systems due to their architectural limitations. They are unfavorable in handling extremely long sequences as well as highly restricted in parallelizing the computations. In this paper, we demonstrate that the new transformer architecture can perform character-based trans- lation better than the recurrent one. We conduct experiments on a low-resource language pair: Japanese-Vietnamese. Our models considerably outperform the state-of-the-art systems which employ word-based recurrent architectures.
Sub-kelvin Andreev reflection spectroscopy of superconducting gaps in FeSe

Point contact Andreev reflection studies have been conducted on FeSe single crystals by lowering the temperatures down to 0.5 K. The point contact Andreev reflection spectra were analyzed in the framework of the two-band model. As a result, the presence of two anisotropic superconducting gaps in FeSe were certainly established and their BCS-like temperature dependencies were obtained. The weights of each gap have been determined and the anisotropy parameter has been calculated. It is shown, that sub-kelvin temperatures are necessary to ascertain details of the superconducting gap structure, especially for multiband materials when Andreev reflection spectroscopy is applied.
Conductor-Discriminant Inequality for Hyperelliptic Curves in Odd Residue Characteristic

We prove an inequality between the conductor and the discriminant for all hyperelliptic curves defined over discretely valued fields $K$ with perfect residue field of characteristic not $2$. Specifically, if such a curve is given by $y^{2} = f(x)$ with $f(x) \in \mathcal{O}_{K}[x]$, and if $\mathcal{X}$ is its minimal regular model over $\mathcal{O}_{K}$, then the negative of the Artin conductor of $\mathcal{X}$ (and thus also the number of irreducible components of the special fiber of $\mathcal{X}$) is bounded above by the valuation of $\operatorname{disc}(f)$. There are no restrictions on genus of the curve or on the ramification of the splitting field of $f$. This generalizes earlier work of Ogg, Saito, Liu, and the second author.