Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved semiclassical model for real-time evaporation of matrix black holes

In this paper, we study real-time classical matrix mechanics of a simplified [Formula: see text] matrix model inspired by the black hole evaporation problem. This is a step towards making a quantitative model of real-time evaporation of a black hole, which is realized as a bound state of D0-branes in string theory. The model we study is the reduction of Yang–Mills in [Formula: see text] dimension to [Formula: see text] dimensions, which has been corrected with an additional potential that can be interpreted as a zero-point energy for fermions. Our goal is to understand the lifetime of such a classical bound state object in the classical regime. To do so, we pay particular attention to when [Formula: see text]-particles separate to check that the “off-diagonal modes” of the matrices become adiabatic and use that information to improve on existing models of evaporation. It turns out that the naive expectation value of the lifetime with the fermionic correction is infinite. This is a logarithmic divergence that arises from very large excursions in the separation between the branes near the threshold for classical evaporation. The adiabatic behavior lets us get some analytic control of the dynamics in this regime to get this estimate. This divergence is cutoff in the quantum theory due to quantization of the adiabatic parameter, resulting in a long lifetime of the bound state, with a parametric dependence of order [Formula: see text].
Symmetry Protected Quantum Computation

We consider a model of quantum computation using qubits where it is possible to measure whether a given pair are in a singlet (total spin 0) or triplet (total spin 1) state. The physical motivation is that we can do these measurements in a way that is protected against revealing other information so long as all terms in the Hamiltonian are SU(2)-invariant. We conjecture that this model is equivalent to BQP. Towards this goal, we show: (1) this model is capable of universal quantum computation with polylogarithmic overhead if it is supplemented by single qubit X and Z gates. (2) Without any additional gates, it is at least as powerful as the weak model of "permutational quantum computation" of Jordan [14, 18]. (3) With postselection, the model is equivalent to PostBQP.
Simplicity of right-angled Hecke C$^\ast$-algebras

By exploiting properties of boundaries associated with Coxeter groups we obtain a complete characterization of simple right-angled multi-parameter Hecke C ∗ -algebras. This extends previous results by Caspers, Larsen and the author. Based on work by Raum and Skalski, we further describe the center and the character space of right-angled Hecke C ∗ -algebras.
Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral Features

The success of deep learning in computer vision has inspired the scientific community to explore new analysis methods. Within the field of neuroscience, specifically in electrophysiological neuroimaging, researchers are starting to explore leveraging deep learning to make predictions on EEG data. Research remains open on the network architecture and the feature space that is most effective for EEG decoding. This paper compares deep learning using minimally processed EEG raw data versus deep learning using EEG spectral features using two different deep convolutional neural architectures. One of them from Putten et al. (2018) is tailored to process raw data; the other was derived from the VGG16 vision network (Simonyan and Zisserman, 2015) which is designed to process EEG spectral features. We apply them to classify sex on 24-channel EEG from a large corpus of 1,574 participants. Not only do we improve on state-of-the-art classification performance for this type of classification problem, but we also show that in all cases, raw data classification leads to superior performance as compared to spectral EEG features. Interestingly we show that the neural network tailored to process EEG spectral features has increased performance when applied to raw data classification. Our approach suggests that the same convolutional networks used to process EEG spectral features yield superior performance when applied to EEG raw data.
When Children Program Intelligent Environments: Lessons Learned from a Serious AR Game

While the body of research focusing on Intelligent Environments (IEs) programming by adults is steadily growing, informed insights about children as programmers of such environments are limited. Previous work already established that young children can learn programming basics. Yet, there is still a need to investigate whether this capability can be transferred in the context of IEs, since encouraging children to participate in the management of their intelligent surroundings can enhance responsibility, independence, and the spirit of cooperation. We performed a user study (N=15) with children aged 7-12, using a block-based, gamified AR spatial coding prototype allowing to manipulate smart artifacts in an Intelligent Living room. Our results validated that children understand and can indeed program IEs. Based on our findings, we contribute preliminary implications regarding the use of specific technologies and paradigms (e.g. AR, trigger-action programming) to inspire future systems that enable children to create enriching experiences in IEs.
X-Ray Superflares from Pre-main-sequence Stars: Flare Energetics and Frequency

Solar-type stars exhibit their highest levels of magnetic activity during their early convective pre-main-sequence (PMS) phase of evolution. The most powerful PMS flares, superflares and megaflares, have peak X-ray luminosities of log(LX)=30.5–34.0 erg s−1 and total energies log(EX)=34–38 erg. Among >24,000 X-ray-selected young (t ≲ 5 Myr) members of 40 nearby star-forming regions from our earlier Chandra MYStIX and SFiNCs surveys, we identify and analyze a well-defined sample of 1086 X-ray superflares and megaflares, the largest sample ever studied. Most are considerably more powerful than optical/X-ray superflares detected on main-sequence stars. This study presents energy estimates of these X-ray flares and the properties of their host stars. These events are produced by young stars of all masses over evolutionary stages ranging from protostars to diskless stars, with the occurrence rate positively correlated with stellar mass. Flare properties are indistinguishable for disk-bearing and diskless stars indicating star–disk magnetic fields are not involved. A slope of α ≃ 2 in the flare energy distributions dN/dEX∝EX−α is consistent with that of optical/X-ray flaring from older stars and the Sun. Megaflares ( log(EX)>36.2 erg) from solar-mass stars have an occurrence rate of 1.7−0.6+1.0 flares/star/year and contribute at least 10%–20% to the total PMS X-ray energetics. These explosive events may have important astrophysical effects on protoplanetary disk photoevaporation, ionization of disk gas, production of spallogenic radionuclides in disk solids, and hydrodynamic escape of young planetary atmospheres. Our following paper details plasma and magnetic loop modeling of the >50 brightest X-ray megaflares.
A General Derivative Identity for the Conditional Expectation with Focus on the Exponential Family

Consider a pair of random vectors $(\mathrm{X}, \mathrm{Y})$ and the conditional expectation operator $\mathbb{E}[\mathrm{X} \mid \mathrm{Y}=\mathrm{y}]$. This work studies analytic properties of the conditional expectation by characterizing various derivative identities. The paper consists of two parts. In the first part of the paper, a general derivative identity for the conditional expectation is derived. Specifically, for the Markov chain $\mathrm{U} \leftrightarrow \mathrm{X} \leftrightarrow \mathrm{Y}$, a compact expression for the Jacobian matrix of $\mathbb{E}[\mathrm{U} \mid \mathrm{Y}=\mathrm{y}]$ is derived. In the second part of the paper, the main identity is specialized to the exponential family. Moreover, via various choices of the random vector U, the new identity is used to recover and generalize several known identities and derive some new ones. As a first example, a connection between the Jacobian of $\mathbb{E}[\mathrm{X} \mid \mathrm{Y}=\mathrm{y}]$ and the conditional variance is established. As a second example, a recursive expression between higher order conditional expectations is found, which is shown to lead to a generalization of the Tweedy’s identity. Finally, as a third example, it is shown that the k-th order derivative of the conditional expectation is proportional to the $(k+1)$-th order conditional cumulant.
4.7 Eigenfactor

The Eigenfactor is a journal metric, which was developed by Bergstrom and his colleagues at the University of Washington. They invented the Eigenfactor as a response to the criticism against the use of simple citation counts. The Eigenfactor makes use of the network structure of citations, i.e. citations between journals, and establishes the importance, influence or impact of a journal based on its location in a network of journals. The importance is defined based on the number of citations between journals. As such, the Eigenfactor algorithm is based on Eigenvector centrality. While journal-based metrics have been criticized, the Eigenfactor has also been suggested as an alternative in the widely used San Francisco Declaration on Research Assessment (DORA).
Source-Level Bitwise Branching for Temporal Verification of Lifted Binaries

There is increasing interest in applying veriﬁcation tools to programs that have bitvector operations ( e.g., decompiled binaries). SMT solvers, which serve as a foundation for these tools, have thus increased support for bitvector reasoning through bit-blasting and linear arithmetic approximations. In this paper we show that similar linear arithmetic approximation of bitvector operations can be done at the source level through transformations. Speciﬁcally, we introduce new paths that over-approximate bitvec-tor operations with linear conditions/constraints, increasing branching but allowing us to better exploit the well-developed integer reasoning and interpolation of veriﬁcation tools. We show that, for reachability of bitvector programs, increased branching incurs negligible overhead yet, when combined with integer interpolation optimizations, enables more programs to be veriﬁed. We further show this exploitation of integer interpolation in the common case also enables competitive termination veriﬁcation of bitvector programs and leads to the ﬁrst eﬀective technique for LTL veriﬁcation of bitvector programs. Finally, we provide an in-depth case study of decompiled (“lifted”) binary programs, which emulate X86 execution through frequent use of bitvector operations. We present a new tool DarkSea , the ﬁrst tool capable of verifying reachability, termination, and LTL of such lifted binaries.
Far-field approximations to the derivatives and integrals of the Green's function for the Ffowcs Williams and Hawkings equation

We report far-field approximations to the derivatives and integrals of the Green's function for the Ffowcs Williams and Hawkings equation in the frequency domain. The approximations are based on the far-field asymptotic of the Green's function. The details of the derivations of the proposed formulations are provided.
An N-dimensional Fortran interpolation programme (NterGeo.v2020a) for geophysics sciences – application to a back-trajectory programme (Backplumes.v2020r1) using CHIMERE or WRF outputs

Abstract. An interpolation programme coded in Fortran for irregular N -dimensional cases is presented and freely available. The need for interpolation procedures over irregular meshes or matrixes with interdependent input data dimensions is frequent in geophysical models. Also, these models often embed look-up tables of physics or chemistry modules. Fortran is a fast and powerful language and is highly portable. It is easy to interface models written in Fortran with each other. Our programme does not need any libraries; it is written in standard Fortran and tested with two usual compilers. The programme is fast and competitive compared to current Python libraries. A normalization option parameter is provided when considering different types of units on each dimension. Some tests and examples are provided and available in the code package. Moreover, a geophysical application embedding this interpolation programme is provided and discussed; it consists in determining back trajectories using chemistry-transport or mesoscale meteorological model outputs, respectively, from the widely used CHIMERE and Weather Research and Forecasting (WRF) models.
Research progress on modification of polyvinyl acetate emulsion

Research progress on the modification of polyvinyl acetate emulsion was reviewed.The methods of its modification,such as the copolymerization and the blending,the chemical modification of protecting colloids,the change of emulsifiers,initiators,and additives and the preparation of polyvinyl acetate emulsion by radiation etc.were introduced particularly.In a word,the polyvinyl acetate emulsion modified by a certain modification method as the main fact and others for complements or modified by many modification methods together was the future developing trend.
A Study of the Aeroacoustics of Swept Propellers for Small Unmanned Aerial Vehicles

A STUDY OF THE AEROACOUSTICS OF SWEPT PROPELLERS FOR SMALL UNMANNED AERIAL VEHICLES Arthur David Wiedemann Old Dominion University, 2020 Director: Colin Britcher In recent years, small unmanned aerial vehicles (UAVs) have increased in availability and use in both public and commercial sectors. This increased use of small UAVs or “drones” has the unfortunate consequence of introducing excessive noise into communities where they operate. Implementation of noise reduction methods is necessary if we wish to see expanded use of drones in public areas. With electric propulsion, the primary source of noise is the rotor/propeller used in contemporary multi-rotor configurations. In this thesis, the aerodynamics and acoustic behavior of various swept propellers is examined with computational and experimental methods. A family of propeller geometries was established, with test articles machined from aluminum. A straight 16x11.363-inch propeller was used as a control for the nonconventional 15, 35, and 55 degree swept propellers. These propellers have a backwards sweeping blade centerline designed to “de-phase” the far-field acoustic pressure. The aerodynamic properties were collected in ODU’s low speed wind tunnel and an acoustic experiment was conducted outdoors to determine the noise characteristics of the propeller while hovering. The computational analysis was done with NASA’s Aircraft Noise Prediction Program 2 (ANOPP2). This program used the Farrassat-1A Formulation to integrate and propagate acoustic pressures generated by the propellers to observers in the far field at various RPM and distances for several propeller shapes. Overall, the aerodynamic efficiency of the non-conventional propellers did not sway from the conventional, straight propeller design. However, the dBA produced by each propeller requires further study, as the differences in sound pressure level between the designs could not be determined due to external environmental factors.
Activity level and patient outcome 1 after liver surgery, a prospective study

Background: Perioperative measurement to enhance recovery after surgery has been introduced as a prognostic factor. The effect of surgery on activity level during and after hospital discharge has been relatively under-explored. The present study aims to measure perioperative activity for patients undergoing liver surgery as a benchmark for further interventional studies on the relationship between postoperative mobilization and outcome. Methods: In this prospective cohort study we measured activity levels for patients selected for liver surgery at a single liver surgical center. The activity level was measured before, during hospital admission, and after discharge with patients wearing pedometers. Clinical parameters and outcomes were documented. Results: Thirty-three patients were included. Median activity level was 4303 (2381- 6912), 293 (170-665), and 1250 (613-3300) steps per day preoperatively, perioperatively, and postoperatively after discharge, respectively. The activity level decreased to 38 (22-62) % after discharge compared to preoperative levels. Conclusion: This study quantified the decrease in activity level after liver surgery. Future studies could further evaluate the intervention effect of perioperative care on postoperative outcome measures.
Ferro-self-assembly: magnetic and electrochemical adaptation of a multiresponsive zwitterionic metalloamphiphile showing a shape-hysteresis effect

Metallosurfactants are molecular compounds which combine the unique features of amphiphiles, like their capability of self-organization, with the peculiar properties of metal complexes like magnetism and a rich redox chemistry. Considering the high relevance of surfactants in industry and science, amphiphiles that change their properties on applying an external trigger are highly desirable. A special feature of the surfactant reported here, 1-(Z)-heptenyl-1′-dimethylammonium-methyl-(3-sulfopropyl)ferrocene (6), is that the redox-active ferrocene constituent is in a gemini-position. Oxidation to 6+ induces a drastic change of the surfactant's properties accompanied by the emergence of paramagnetism. The effects of an external magnetic field on vesicles formed by 6+ and the associated dynamics were monitored in situ using a custom-made optical birefringence and dual dynamic light scattering setup. This allowed us to observe the optical anisotropy as well as the anisotropy of the diffusion coefficient and revealed the field-induced formation of oriented string-of-pearls-like aggregates and their delayed disappearance after the field is switched off.
At which timescale does the complementary principle perform best in evaporation estimation?

Abstract. The complementary principle has been widely used to estimate evaporation under different conditions. However, it remains unclear at which timescale the complementary principle performs best. In this study, evaporation estimations were conducted at 88 eddy covariance (EC) monitoring sites at multiple timescales (daily, weekly, monthly, and yearly) by using sigmoid and polynomial generalized complementary functions. The results indicate that the generalized complementary functions exhibit the highest skill in estimating evaporation at the monthly scale. The uncertainty analysis shows that this conclusion is not affected by ecosystem type or energy balance closure method. Through comparisons at multiple timescales, we found that the slight difference between the two generalized complementary functions only exists when the independent variable ( x ) in the functions approaches 1. The results differ for the two models at daily and weekly scales. However, such differences vanish at monthly and annual timescales, with few high x values occurring. This study demonstrates the applicability of generalized complementary functions across multiple timescales and provides a reference for choosing a suitable time step for evaporation estimations in relevant studies.
Privacy Intelligence: A Survey on Image Privacy in Online Social Networks

Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also increased the risk of privacy invasion. An online image can reveal various types of sensitive information, prompting the public to rethink individual privacy needs in OSN image sharing critically. However, the interaction of images and OSN makes the privacy issues significantly complicated. The current real-world solutions for privacy management fail to provide adequate personalized, accurate, and flexible privacy protection. Constructing a more intelligent environment for privacy-friendly OSN image sharing is urgent in the near future. Meanwhile, given the dynamics in both users’ privacy needs and OSN context, a comprehensive understanding of OSN image privacy throughout the entire sharing process is preferable to any views from a single side, dimension, or level. To fill this gap, we contribute a survey of “privacy intelligence” that targets modern privacy issues in dynamic OSN image sharing from a user-centric perspective. Specifically, we present the important properties and a taxonomy of OSN image privacy, along with a high-level privacy analysis framework based on the lifecycle of OSN image sharing. The framework consists of three stages with different principles of privacy by design. At each stage, we identify typical user behaviors in OSN image sharing and their associated privacy issues. Then a systematic review of representative intelligent solutions to those privacy issues is conducted, also in a stage-based manner. The analysis results in an intelligent “privacy firewall” for closed-loop privacy management. Challenges and future directions in this area are also discussed.
Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging

A key challenge in training neural networks for a given medical imaging task is the difficulty of obtaining a sufficient number of manually labeled examples. In contrast, textual imaging reports are often readily available in medical records and contain rich but unstructured interpretations written by experts as part of standard clinical practice. We propose using these textual reports as a form of weak supervision to improve the image interpretation performance of a neural network without requiring additional manually labeled examples. We use an image-text matching task to train a feature extractor and then fine-tune it in a transfer learning setting for a supervised task using a small labeled dataset. The end result is a neural network that automatically interprets imagery without requiring textual reports during inference. We evaluate our method on three classification tasks and find consistent performance improvements, reducing the need for labeled data by 67%–98%.
Policy-Gradient Training of Fair and Unbiased Ranking Functions

While implicit feedback (e.g., clicks, dwell times, etc.) is an abundant and attractive source of data for learning to rank, it can produce unfair ranking policies for both exogenous and endogenous reasons. Exogenous reasons typically manifest themselves as biases in the training data, which then get reflected in the learned ranking policy and often lead to rich-get-richer dynamics. Moreover, even after the correction of such biases, reasons endogenous to the design of the learning algorithm can still lead to ranking policies that do not allocate exposure among items in a fair way. To address both exogenous and endogenous sources of unfairness, we present the first learning-to-rank approach that addresses both presentation bias and merit-based fairness of exposure simultaneously. Specifically, we define a class of amortized fairness-of-exposure constraints that can be chosen based on the needs of an application, and we show how these fairness criteria can be enforced despite the selection biases in implicit feedback data. The key result is an efficient and flexible policy-gradient algorithm, called FULTR, which is the first to enable the use of counterfactual estimators for both utility estimation and fairness constraints. Beyond the theoretical justification of the framework, we show empirically that the proposed algorithm can learn accurate and fair ranking policies from biased and noisy feedback.