Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlled Hawking process by quantum energy teleportation

In this paper, a new quantum mechanical method to extract energy from black holes with contracting horizons is proposed. The method is based on a gedanken experiment on quantum energy teleportation, which has been recently proposed in quantum information theory. We consider this quantum energy teleportation protocol for N massless fields in near-horizon regions of large-mass black holes with near-horizon geometry described by the Minkowski metric. For each field, a two-level spin is strongly coupled with the local quantum fluctuation outside the horizon during a short time period. After the measurement of N fields, N-bit information is obtained. During the measurement, positive-energy wave packets of the fields form and then fall into the black hole. The amount of excitation energy is independent of the measurement result. After absorption of the wave packets and increase of the black-hole mass, a measurement-result-dependent local operation of the N fields is performed outside the horizon. Then, accompanying the extraction of positive energy from the quantum fluctuation by the operation, negative-energy wave packets of the fields form and then fall into the black hole, decreasing the black-hole mass. This implies that a part of the absorbed positive energy emitted from the measurement devices is effectivelymore » retrieved from the black hole via the measurement results. « less
The Relationship Between Business Strategy and Customer Satisfaction: A study of South African business performance

The purpose of this paper is to investigate the inter-connected relationships of business strategy, customer satisfaction and business performance. Data was collected from senior executives in small, medium, large and corporate businesses in South Africa, and supplemented with existing available national customer satisfaction data. A total sample of 1,069 business leaders was realised of which 64 businesses had available customer satisfaction data. Findings revealed a relation between a clear business strategy and high levels of customer satisfaction. More importantly, both business strategy and customer satisfaction differentiate businesses on overall performance/success. This single survey study could not capture business-performance drivers within a value chain of decisions, actions and outcomes. The association between business strategy and customer satisfaction cannot be ignored by practitioners who aspire to improve business performance. Thus, if customer satisfaction aligns with a clear business strategy it can provide superior business performance in comparison to industry competitors. These results show conclusively that the clarity of a business’s purpose, through consistently prioritising one of Porter’s generic strategies, succeeds in satisfying customers. While previous research analysed the relationship between both business strategy and customer satisfaction with business performance, the originality of this study lies in linking business strategy with customer satisfaction. Furthermore, the research adapted existing business measurements to bring it in line with Porter’s generic strategies, and then adjusted the strategy classification to be consistent with how businesses prioritise strategies.
A note on global regularity results for 2D Boussinesq equations with fractional dissipation

In this paper we study the Cauchy problem for the two-dimensional (2D) incompressible Boussinesq equations with fractional Laplacian dissipation and thermal diffusion. Invoking the energy method and several commutator estimates, we get the global regularity result of the 2D Boussinesq equations as long as $1-\alpha<\beta< \min\Big\{\frac{\alpha}{2},\,\, \frac{3\alpha-2}{2\alpha^{2}-6\alpha+5}, \,\,\frac{2-2\alpha}{4\alpha-3}\Big\}$ with $0.77963\thickapprox\alpha_{0}<\alpha<1$. As a result, this result is a further improvement of the previous two works \cite{MX,YXX}.
NUCLEOSYNTHESIS CONSTRAINTS ON THE NEUTRON STAR–BLACK HOLE MERGER RATE

We derive constraints on the time-averaged event rate of neutron star–black hole (NS–BH) mergers by using estimates of the population-integrated production of heavy rapid neutron-capture (r-process) elements with nuclear mass numbers A > 140 by such events in comparison to the Galactic repository of these chemical species. Our estimates are based on relativistic hydrodynamical simulations convolved with theoretical predictions of the binary population. This allows us to determine a strict upper limit of the average NS–BH merger rate of ∼6× 10−5 per year. We quantify the uncertainties of this estimate to be within factors of a few mostly because of the unknown BH spin distribution of such systems, the uncertain equation of state of NS matter, and possible errors in the Galactic content of r-process material. Our approach implies a correlation between the merger rates of NS–BH binaries and of double NS systems. Predictions of the detection rate of gravitational-wave signals from such compact object binaries by Advanced LIGO and Advanced Virgo on the optimistic side are incompatible with the constraints set by our analysis.
Some chirality-related properties of the 4D massive Dirac propagator and determinant in an arbitrary gauge field

For a 4D massive Dirac field in the background of arbitrary gauge fields, we show that the Dirac propagator and functional determinant are completely determined by knowledge of the corresponding quantities for just one of the chirality sectors of the second-order Dirac operator. This generalizes the related, previously known, statements in (anti)self-dual background gauge fields. The logarithms of the (renormalized) functional determinants from the two chirality sectors are shown to be different only by a term reflecting the integrated chiral anomaly.
TURNING BACK THE CLOCK: INFERRING THE HISTORY OF THE EIGHT O'CLOCK ARC

We present the results from an optical and near-infrared (NIR) spectroscopic study of the ultraviolet-luminous z = 2.73 galaxy, the 8 o'clock arc. Due to gravitational lensing, this galaxy is magnified by a factor of μ > 10, allowing in-depth measurements which are usually unfeasible at such redshifts. In the optical spectra, we measured the systemic redshift of the galaxy, z = 2.7322± 0.0012, using stellar photospheric lines. This differs from the redshift of absorption lines in the interstellar medium, z = 2.7302 ± 0.0006, implying gas outflows on the order of 160 km s−1. With H- and K-band NIR spectra, we have measured nebular emission lines of Hα, Hβ, Hγ, [N ii], and [O iii], which have a redshift z = 2.7333 ± 0.0001, consistent with the derived systemic redshift. From the Balmer decrement, we measured the dust extinction in this galaxy to be A5500 = 1.17 ± 36 mag. Correcting the Hα line flux for dust extinction as well as the assumed lensing factor, we measure a star formation rate (SFR) of ∼270 M☉ yr−1, which is higher than ∼85% of star-forming galaxies at z ∼ 2–3. Using combinations of all detected emission lines, we find that the 8 o'clock arc has a gas-phase metallicity of ∼0.8 Z☉, showing that enrichment at high redshift is not rare, even in blue, star-forming galaxies. Studying spectra from two of the arc components separately, we find that one component dominates both the dust extinction and SFR, although the metallicities between the two components are similar. We derive the mass via stellar population modeling, and find that the arc has a total stellar mass of ∼4.2 × 1011 M☉, which falls on the mass–metallicity relation at z ∼ 2. Finally, we estimate the total gas mass, and find it to be only ∼12% of the stellar mass, implying that the 8 o'clock arc is likely nearing the end of a starburst.
Numerical analysis of variation among accessions of West African okra Abelmoschuscaillei (a. chev) stevel)

Nineteen quantitative characters were observed on 14 accessions of West African Okra. Principal Component Analysis (PCA) and Complete Linkage Cluster Analysis (CLCA) were employed to analyses the variation pattern in these accessions. The first three principal components accounted for 68.47% of the total variation. Using character variation, complete linkage cluster analysis summarized the position of accessions into a dendrogram at interval of 5% level of similarity, while the accessions were sorted into five distinct groups.
A model-based approach to the forward and inverse problems in spirometry

The respiratory system is one of the most essential systems sustaining human life. Its complexity raises, however, serious difficulties when one is trying to analyse the lung structure or function experimentally. An alternative approach consists in conducting research via mathematical modelling. This paper reviews the most essential model-based approaches to the so-called forward and inverse problems in spirometry, focusing on research the author has been involved in. A few selected results achieved with the aid of the mathematical models of the forced expiration illustrate the state of the art, and current challenging issues in modelling the respiratory system are depicted.
Is the local linearity of space-time inherited from the linearity of probabilities?

The appearance of linear spaces, describing physical quantities by vectors and tensors, is ubiquitous in all of physics, from classical mechanics to the modern notion of local Lorentz invariance. However, as natural as this seems to the physicist, most computer scientists would argue that something like a ‘local linear tangent space’ is not very typical and in fact a quite surprising property of any conceivable world or algorithm. In this paper, we take the perspective of the computer scientist seriously, and ask whether there could be any inherently information-theoretic reason to expect this notion of linearity to appear in physics. We give a series of simple arguments, spanning quantum information theory, group representation theory, and renormalization in quantum gravity, that supports a surprising thesis: namely, that the local linearity of space-time might ultimately be a consequence of the linearity of probabilities. While our arguments involve a fair amount of speculation, they have the virtue of being independent of any detailed assumptions on quantum gravity, and they are in harmony with several independent recent ideas on emergent space-time in high-energy physics.
Covering sports, when there’s no sports: COVID, market orientation, paywalls and the Athletic

This study incorporates in-depth interviews with 43 journalists from the digitally native, venture-capital-backed sports journalism organization the Athletic. Through the lens of gatekeeping and utilizing the concept of market orientation, findings illustrate how having a somewhat strong market orientation could positively impact gatekeeping processes. Data illustrated that, during the pandemic, journalists at the Athletic collaborated more and included more diversity in content. This positive result, which led to a subscription increase, is primarily due organization-level influences on gatekeeping. This study concludes with analysis on how these findings can affect journalism in general and sports journalism specifically now and after COVID-19.
PdTe: a 4.5 K type-II BCS superconductor

We report on the structure and physical properties of the polycrystalline PdTe superconductor, which is synthesized by the solid state reaction route via the quartz vacuum encapsulation technique at 750 °C. The as synthesized compound is crystallized in hexagonal crystal structure with P63/mmc space group. Both transport and magnetic measurements showed that PdTe is a bulk superconductor below 4.5 K. Isothermal magnetization (MH) and magneto-transport (R(T)H) measurements provided the values of the lower (Hc1) and upper (Hc2) critical fields as 250 Oe and 1200 Oe respectively at 2 K, establishing that the compound is clearly a type-II superconductor. The coherence length (ξ0) and Ginzburg–Landau parameter (κ) are estimated from the experimentally determined upper and lower critical fields, and are 449 Å and 1.48 respectively. Thermodynamic heat capacity measurements under different magnetic fields, i.e. Cp(T)H, showed a clear transition at 4.5 K (Tc), which shifts gradually to lower temperatures with application of field. The values of Debye temperature (ΘD) and electronic specific heat coefficient (γ) obtained from Cp(T) data are found to be 203 K and 6.01 mJ mol−1 K−2. The observed specific heat jump (ΔC/γTc) is 1.33, thus suggesting a possible weak coupling case for the PdTe superconductor.
Interpretable Representation Learning from Temporal Multi-view Data

In many scientific problems such as video surveillance, modern genomics, and finance, data are often collected from diverse measurements across time that exhibit time-dependent heterogeneous properties. Thus, it is important to not only integrate data from multiple sources (called multi-view data), but also to incorporate time dependency for deep understanding of the underlying system. We propose a generative model based on variational autoencoder and a recurrent neural network to infer the latent dynamics for multi-view temporal data. This approach allows us to identify the disentangled latent embeddings across views while accounting for the time factor. We invoke our proposed model for analyzing three datasets on which we demonstrate the effectiveness and the interpretability of the model.
Scaling Forward Gradient With Local Losses

Forward gradient learning computes a noisy directional gradient and is a biologically plausible alternative to backprop for learning deep neural networks. However, the standard forward gradient algorithm, when applied naively, suffers from high variance when the number of parameters to be learned is large. In this paper, we propose a series of architectural and algorithmic modifications that together make forward gradient learning practical for standard deep learning benchmark tasks. We show that it is possible to substantially reduce the variance of the forward gradient estimator by applying perturbations to activations rather than weights. We further improve the scalability of forward gradient by introducing a large number of local greedy loss functions, each of which involves only a small number of learnable parameters, and a new MLPMixer-inspired architecture, LocalMixer, that is more suitable for local learning. Our approach matches backprop on MNIST and CIFAR-10 and significantly outperforms previously proposed backprop-free algorithms on ImageNet.
Trans2k: Unlocking the Power of Deep Models for Transparent Object Tracking

Visual object tracking has focused predominantly on opaque objects, while transparent object tracking received very little attention. Motivated by the uniqueness of transparent objects in that their appearance is directly affected by the background, the first dedicated evaluation dataset has emerged recently. We contribute to this effort by proposing the first transparent object tracking training dataset Trans2k that consists of over 2k sequences with 104,343 images overall, annotated by bounding boxes and segmentation masks. Noting that transparent objects can be realistically rendered by modern renderers, we quantify domain-specific attributes and render the dataset containing visual attributes and tracking situations not covered in the existing object training datasets. We observe a consistent performance boost (up to 16%) across a diverse set of modern tracking architectures when trained using Trans2k, and show insights not previously possible due to the lack of appropriate training sets. The dataset and the rendering engine will be publicly released to unlock the power of modern learning-based trackers and foster new designs in transparent object tracking.
GNM: A General Navigation Model to Drive Any Robot

Learning provides a powerful tool for vision-based navigation, but the capabilities of learning-based policies are constrained by limited training data. If we could combine data from all available sources, including multiple kinds of robots, we could train more powerful navigation models. In this paper, we study how a general goal-conditioned model for vision-based navigation can be trained on data obtained from many distinct but structurally similar robots, and enable broad generalization across environments and embodiments. We analyze the necessary design decisions for effective data sharing across robots, including the use of temporal context and standardized action spaces, and demonstrate that an omnipolicy trained from heterogeneous datasets outperforms policies trained on any single dataset. We curate 60 hours of navigation trajectories from 6 distinct robots, and deploy the trained GNM on a range of new robots, including an underactuated quadrotor. We find that training on diverse data leads to robustness against degradation in sensing and actuation. Using a pre-trained navigation model with broad generalization capabilities can bootstrap applications on novel robots going forward, and we hope that the GNM represents a step in that direction. For more information on the datasets, code, and videos, please check out our project page11sites.google. com/view/drive-any-robot.
Quantifying Political Bias in News Articles

Search bias analysis is getting more attention in recent years since search results could affect In this work, we aim to establish an automated model for evaluating ideological bias in online news articles. The dataset is composed of news articles in search results as well as the newspaper articles. The current automated model results show that model capability is not sufficient to be exploited for annotating the documents automatically, thereby computing bias in search results.
Physics‐Informed Neural Networks of the Saint‐Venant Equations for Downscaling a Large‐Scale River Model

Large‐scale river models are being refined over coastal regions to improve the scientific understanding of coastal processes, hazards and responses to climate change. However, coarse mesh resolutions and approximations in physical representations of tidal rivers limit the performance of such models at resolving the complex flow dynamics especially near the river‐ocean interface, resulting in inaccurate simulations of flood inundation. In this research, we propose a machine learning (ML) framework based on the state‐of‐the‐art physics‐informed neural network (PINN) to simulate the downscaled flow at the subgrid scale. First, we demonstrate that PINN is able to assimilate observations of various types and solve the one‐dimensional (1‐D) Saint‐Venant equations (SVE) directly. We perform the flow simulations over a floodplain and along an open channel in several synthetic case studies. The PINN performance is evaluated against analytical solutions and numerical models. Our results indicate that the PINN solutions of water depth have satisfactory accuracy with limited observations assimilated. In the case of flood wave propagation induced by storm surge and tide, a new neural network architecture is proposed based on Fourier feature embeddings that seamlessly encodes the periodic tidal boundary condition in the PINN's formulation. Furthermore, we show that the PINN‐based downscaling can produce more reasonable subgrid solutions of the along‐channel water depth by assimilating observational data. The PINN solution outperforms the simple linear interpolation in resolving the topography and dynamic flow regimes at the subgrid scale. This study provides a promising path toward improving emulation capabilities in large‐scale models to characterize fine‐scale coastal processes.
PMT: Power Measurement Toolkit

Efficient use of energy is essential for today's super-computing systems, as energy cost is generally a major component of their operational cost. Research into “green computing” is needed to reduce the environmental impact of running these systems. As such, several scientific communities are evaluating the trade-off between time-to-solution and energy-to-solution. While the runtime of an application is typically easy to measure, power consumption is not. Therefore, we present the Power Measurement Toolkit (PMT), a high-level software library capable of collecting power consumption measurements on various hardware. The library provides a standard interface to easily measure the energy use of devices such as CPUs and GPUs in critical application sections.
Power in the Pipeline

This paper provides the first comprehensive empirical analysis of the role of natural gas for the domestic and international distribution of power. The crucial role of pipelines for the trade of natural gas determines a set of network effects that are absent for other natural resources such as oil and minerals. Gas rents are not limited to producers but also accrue to key players occupying central nodes in the gas network. Drawing on our new gas pipeline data, this paper shows that gas betweenness-centrality of a country increases substantially the ruler's grip on power as measured by leader turnover. A main mechanism at work is the reluctance of connected gas trade partners to impose sanctions, meaning that bad behavior of gas-central leaders is tolerated for longer before being sanctioned. Overall, this reinforces the notion that fossil fuels are not just poison for the environment but also for political pluralism and healthy regime turnover.
Learning Social Navigation from Demonstrations with Conditional Neural Processes

Sociability is essential for modern robots to increase their acceptability in human environments. Traditional techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation. However, social aspects of navigation are diverse, changing across different types of environments, societies, and population densities, making it unrealistic to use hand-crafted techniques in each domain. This paper presents a data-driven navigation architecture that uses state-of-the-art neural architectures, namely Conditional Neural Processes, to learn global and local controllers of the mobile robot from observations. Additionally, we leverage a state-of-the-art, deep prediction mechanism to detect situations not similar to the trained ones, where reactive controllers step in to ensure safe navigation. Our results demonstrate that the proposed framework can successfully carry out navigation tasks regarding social norms in the data. Further, we showed that our system produces fewer personal-zone violations, causing less discomfort.
Observations of 4U 1626–67 with the Imaging X-Ray Polarimetry Explorer

We present measurements of the polarization of X-rays in the 2–8 keV band from the pulsar in the ultracompact low-mass X-ray binary 4U 1626–67 using data from the Imaging X-Ray Polarimetry Explorer (IXPE). The 7.66 s pulsations were clearly detected throughout the IXPE observations as well as in the NICER soft X-ray observations, which we used as the basis for our timing analysis and to constrain the spectral shape over the 0.4–10 keV energy band. Chandra HETGS high-resolution X-ray spectra were also obtained near the times of the IXPE observations for firm spectral modeling. We found an upper limit on the pulse-averaged linear polarization of <4% (at 95% confidence). Similarly, there was no significant detection of polarized flux in pulse phase intervals when subdividing the bandpass by energy. However, spectropolarimetric modeling over the full bandpass in pulse phase intervals provided a marginal detection of polarization of the power-law spectral component at the 4.8% ± 2.3% level (90% confidence). We discuss the implications concerning the accretion geometry onto the pulsar, favoring two-component models of the pulsed emission.