Miracle-dz/UniFAR-FTUs huggingface dataset:
In Silico Evaluation of Paxlovid’s Pharmacometrics for SARS-CoV-2: A Multiscale Approach

Paxlovid is a promising, orally bioavailable novel drug for SARS-CoV-2 with excellent safety profiles. Our main goal here is to explore the pharmacometric features of this new antiviral. To provide a detailed assessment of Paxlovid, we propose a hybrid multiscale mathematical approach. We demonstrate that the results of the present in silico evaluation match the clinical expectations remarkably well: on the one hand, our computations successfully replicate the outcome of an actual in vitro experiment; on the other hand, we verify both the sufficiency and the necessity of Paxlovid’s two main components (nirmatrelvir and ritonavir) for a simplified in vivo case. Moreover, in the simulated context of our computational framework, we visualize the importance of early interventions and identify the time window where a unit-length delay causes the highest level of tissue damage. Finally, the results’ sensitivity to the diffusion coefficient of the virus is explored in detail.
Earthquake Predicting and Tectonic Stress-Reducing Possibilities

Recent satellite and ground-based observations prove that during the earthquake formative period VLF/LF and ULF electromagnetic emissions are observed in seismogenic areas. The present consolidated paper describes a theoretical model of the generation of electromagnetic emissions detected during the earthquake preparation periods, a scheme of the earthquake prediction methodology, the possible methods of the large earthquake prediction on the basis of the European Network data. The offered methods are capable of simultaneous determination of all three characteristic parameters necessary for incoming earthquake prediction. Today existing networks, detect this electromagnetic radiation, consisting of stationary transmitters and receivers. Since in some cases these networks are unable to detect any electromagnetic radiation, we have suggested the possible arrangements of the VLF/LF mobile network. The paper offers a completely different view of seismic risk assessing and reducing the accumulated tectonic stress in the earthquake focus as well.
Dipoles in blackbody radiation: momentum fluctuations, decoherence, and drag force

An expression is derived for the momentum diffusion constant due to photon scattering of a small polarizable particle in blackbody radiation, and is shown to be related to the long-wavelength collisional decoherence rate for such a particle in a thermal environment. We show how this diffusion constant appears in the steady-state photon emission rate of two dipoles induced by blackbody radiation. We consider in addition the Einstein–Hopf drag force on a small polarizable particle moving in a blackbody field, and derive its relativistic form from the Lorentz transformation of forces. We obtain an expression for the rate of change of the field energy density associated with changes in the particles’ kinetic energies and relate it to the Kompaneets equation for the case of Compton scattering by thermalized electrons.
Substrate-dependence of monolayer MoS2 thermal conductivity and thermal boundary conductance

The thermal properties of two-dimensional (2D) materials, such as MoS2, are known to be affected by interactions with their environment, but this has primarily been studied only with SiO2 substrates. Here, we compare the thermal conductivity (TC) and thermal boundary conductance (TBC) of monolayer MoS2 on amorphous (a-) and crystalline (c-) SiO2, AlN, Al2O3, and h-BN monolayers using molecular dynamics. The room temperature, in-plane TC of MoS2 is ∼38 Wm−1 K−1 on amorphous substrates and up to ∼68 Wm−1 K−1 on crystalline substrates, with most of the difference due to substrate interactions with long-wavelength MoS2 phonons (<2 THz). An h-BN monolayer used as a buffer between MoS2 and the substrate causes the MoS2 TC to increase by up to 50%. Length-dependent calculations reveal TC size effects below ∼2 μm and show that the MoS2 TC is not substrate- but size-limited below ∼100 nm. We also find that the TBC of MoS2 with c-Al2O3 is over twice that with c-AlN despite a similar MoS2 TC on both, indicating that the TC and TBC could be tuned independently. Finally, we compare the thermal resistance of MoS2 transistors on all substrates and find that MoS2 TBC is the most important parameter for heat removal for long-channel (>150 nm) devices, while TBC and TC are equally important for short channels. This work provides important insights for electro-thermal applications of 2D materials on various substrates.
Quantum supremacy with spin squeezed atomic ensembles

We propose a method to achieve quantum supremacy using ensembles of qubits, using only spin squeezing, basis rotations, and Fock state measurements. Each ensemble is assumed to be control-lable only with its total spin. Using a repeated sequence of random basis rotations followed by squeezing, we show that the probability distribution of the ﬁnal measurements quickly approaches a Porter-Thomas distribution. We show that the sampling probability can be related to a #P hard problem with a complexity scaling as ( N +1) M , where N is the number of qubits in an ensemble and M is the number of ensembles. The scheme can be implemented with hot or cold atomic ensembles. Due to the large number of atoms in typical atomic ensembles, this allows access to the quantum supremacy regime with a modest number of ensembles or gate depth.
Continuity and Additivity Properties of Information Decompositions

Information decompositions quantify how the Shannon information about a given random variable is distributed among several other random variables. Various requirements have been proposed that such a decomposition should satisfy, leading to different candidate solutions. Curiously, however, only two of the original requirements that determined the Shannon information have been considered, namely monotonicity and normalization. Two other important properties, continuity and additivity, have not been considered. In this contribution, we focus on the mutual information of two finite variables $Y,Z$ about a third finite variable $S$ and check which of the decompositions satisfy these two properties. While most of them satisfy continuity, only one of them is both continuous and additive.
Human-Robot Shared Control for Surgical Robot Based on Context-Aware Sim-to-Real Adaptation

Human-robot shared control, which integrates the advantages of both humans and robots, is an effective approach to facilitate efficient surgical operation. Learning from demonstration (LfD) techniques can be used to automate some of the surgical sub tasks for the construction of the shared control mechanism. However, a sufficient amount of data is required for the robot to learn the manoeuvres. Using a surgical simulator to collect data is a less resource-demanding approach. With sim-to-real adaptation, the manoeuvres learned from a simulator can be transferred to a physical robot. To this end, we propose a sim-to-real adaptation method to construct a human-robot shared control framework for robotic surgery. In this paper, a desired trajectory is generated from a simulator using LfD method, while dynamic motion primitives (DMP) is used to transfer the desired trajectory from the simulator to the physical robotic platform. Moreover, a role adaptation mechanism is developed such that the robot can adjust its role according to the surgical operation contexts predicted by a neural network model. The effectiveness of the proposed framework is validated on the da Vinci Research Kit (dVRK). Results of the user studies indicated that with the adaptive human-robot shared control framework, the path length of the remote controller, the total clutching number and the task completion time can be reduced significantly. The proposed method outperformed the traditional manual control via teleoperation.
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels.Based on BCI, as a minor contribution, we further build a pyramid pix2pix image generation method, which achieves better HE to IHC translation results than the other current popular algorithms. Extensive experiments demonstrate that BCI poses new challenges to the existing image translation research. Besides, BCI also opens the door for future pathology studies in HER2 expression evaluation based on the synthesized IHC images. BCI dataset can be downloaded from https://bupt-ai-cz.github. io/BCI.
String Rearrangement Inequalities and a Total Order Between Primitive Words

We study the following rearrangement problem: Given $n$ words, rearrange and concatenate them so that the obtained string is lexicographically smallest (or largest, respectively). We show that this problem reduces to sorting the given words so that their repeating strings are non-decreasing (or non-increasing, respectively), where the repeating string of a word $A$ refers to the infinite string $AAA\ldots$. Moreover, for fixed size alphabet $\Sigma$, we design an $O(L)$ time sorting algorithm of the words (in the mentioned orders), where $L$ denotes the total length of the input words. Hence we obtain an $O(L)$ time algorithm for the rearrangement problem. Finally, we point out that comparing primitive words via comparing their repeating strings leads to a total order, which can further be extended to a total order on the finite words (or all words).
Addressing Leakage in Self-Supervised Contextualized Code Retrieval

We address contextualized code retrieval, the search for code snippets helpful to fill gaps in a partial input program. Our approach facilitates a large-scale self-supervised contrastive training by splitting source code randomly into contexts and targets. To combat leakage between the two, we suggest a novel approach based on mutual identifier masking, dedentation, and the selection of syntax-aligned targets. Our second contribution is a new dataset for direct evaluation of contextualized code retrieval, based on a dataset of manually aligned subpassages of code clones. Our experiments demonstrate that the proposed approach improves retrieval substantially, and yields new state-of-the-art results for code clone and defect detection.
Cyclic cohomology and the extended Heisenberg calculus of Epstein and Melrose

In this paper we present a formula for the index of a pseudodif- ferential operator with invertible principal symbol in the extended Heisenberg calculus of Epstein and Melrose [ 3 , 4 , 9 ]. Our results build on the work we did in [ 7 ], where we restricted attention to the Heisenberg calculus proper.
1994 IEEE INTERNATIONAL FREQUENCY CONTROL SYMPOSIUM HIGH-FREQUENCY OSCILLATORS USING PHASE-LOCKED ARRAYS OF JOSEPHSON JUNCTIONS *

We present a basic description of Josephson junctions and discuss their use as GHz and THz oscillators. The resistively shunted junction model is used to calculate the available power, linewidth and operating frequency of the oscillators. We discuss how phase-locked arrays of junctions are used to achieve higher power and narrower linewidth. Two experimental examples of phase-locked emission are shown: one from on-chip detection circuits at 150 GHz and one detected off-chip showing a 13 kHz linewidth at 88.8 GHz. I. INTRODUCTION We are interested in developing the technology to synthesize and measure sub-THz and THz frequencies with the same ease and precision now available in the microwave region. This is important for bridging the frequency band between the microwave and optical regions. This effort is intended to support and encourage emerging instrumentation in high-speed optical signal processing and precision optical metrology including standards for
Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

The threat hunting lifecycle is a complex atmosphere that requires special attention from professionals to maintain security. This paper is a collection of recent work that gives a holistic view of the threat hunting ecosystem, identifies challenges, and discusses the future with the integration of artificial intelligence (AI). We specifically establish a life cycle and ecosystem for privacy-threat hunting in addition to identifying the related challenges. We also discovered how critical the use of AI is in threat hunting. This work paves the way for future work in this area as it provides the foundational knowledge to make meaningful advancements for threat hunting.
An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models

Nowadays, the interpretation of why a machine learning (ML) model makes certain inferences is as crucial as the accuracy of such inferences. Some ML models like the decision tree possess inherent interpretability that can be directly comprehended by humans. Others like artificial neural networks (ANN), however, rely on external methods to uncover the deduction mechanism. SHapley Additive exPlanations (SHAP) is one of such external methods, which requires a background dataset when interpreting ANNs. Generally, a background dataset consists of instances randomly sampled from the training dataset. However, the sampling size and its effect on SHAP remain to be unexplored. In our empirical study on the MIMIC-III dataset, we show that the two core explanations - SHAP values and variable rankings fluctuate when using different background datasets acquired from random sampling, indicating that users cannot unquestioningly trust the one-shot interpretation from SHAP. Luckily, such fluctuation decreases with the increase of the background dataset size. Also, we notice an U-shape in the stability assessment of SHAP variable rankings, demonstrating that SHAP is more reliable in ranking the most and least important variables compared to moderately important ones. Overall, our results suggest that users should take into account how background data affects SHAP results, with improved SHAP stability as the background sample size increases.
Discovering Intrinsic Reward with Contrastive Random Walk

The aim of this paper is to demonstrate the efficacy of using Contrastive Random Walk as a curiosity method to achieve faster convergence to the optimal policy.Contrastive Random Walk defines the transition matrix of a random walk with the help of neural networks. It learns a meaningful state representation with a closed loop. The loss of Contrastive Random Walk serves as an intrinsic reward and is added to the environment reward. Our method works well in non-tabular sparse reward scenarios, in the sense that our method receives the highest reward within the same iterations compared to other methods. Meanwhile, Contrastive Random Walk is more robust. The performance doesn't change much with different random initialization of environments. We also find that adaptive restart and appropriate temperature are crucial to the performance of Contrastive Random Walk.
LitMind Dictionary: An Open-Source Online Dictionary

Dictionaries can help language learners to learn vocabulary by providing deﬁnitions of words. Since traditional dictionaries present word senses as discrete items in predeﬁned inventories, they fall short of ﬂexibility, which is required in providing speciﬁc meanings of words in particular contexts. In this paper, we introduce the LitMind Dictionary ( https: //dictionary.litmind.ink ), an open-source online generative dictionary that takes a word and context containing the word as input and automatically generates a deﬁnition as output. Incorporating state-of-the-art deﬁnition generation models, it supports not only Chinese and English, but also Chinese-English cross-lingual queries. Moreover, it has a user-friendly front-end design that can help users understand the query words quickly and easily. All the code and data are available at https://github.com/blcuicall/ litmind-dictionary .
Long-run User Value Optimization in Recommender Systems through Content Creation Modeling

Content recommender systems are generally adept at maximizing immediate user satisfaction but to optimize for the \textit{long-run} user value, we need more statistically sophisticated solutions than off-the-shelf simple recommender algorithms. In this paper we lay out such a solution to optimize \textit{long-run} user value through discounted utility maximization and a machine learning method we have developed for estimating it. Our method estimates which content producers are most likely to create the highest long-run user value if their content is shown more to users who enjoy it in the present. We do this estimation with the help of an A/B test and heterogeneous effects machine learning model. We have used such models in Facebook's feed ranking system, and such a model can be used in other recommender systems.
Quantum kinetic theory for dynamical spin polarization from QED-type interaction

We investigate the dynamical spin polarization of a massless electron probing an electron plasma in locally thermal equilibrium via the Moller scattering from the quantum kinetic theory. We derive an axial kinetic equation delineating the dynamical spin evolution in the presence of the collision term with quantum corrections up to $\mathcal{O}(\hbar)$ and the leading-logarithmic order in coupling by using the hard-thermal-loop (HTL) approximation, from which we extract the spin-polarization rate induced by the spacetime gradients of the medium. When the electron probe approaches local equilibrium, we further simplify the collision term into a relaxation-time expression. Our kinetic equation may be implemented in the future numerical simulations for dynamical spin polarization.
Light bending and gravitational lensing in energy-momentum-squared gravity

In the present work, we derive the motion of light in the weak-ﬁeld limit of energy-momentum-squared gravity (EMSG). To do so, we introduce the post-Newtonian (PN) expansion of this modiﬁed theory of gravity. It is shown that in addition to the Newtonian potential, a new EMSG potential aﬀects the trajectory of photons. As a result, in this theory, photons do not behave as predicted by general relativity (GR). To evaluate the EMSG theory by the solar system tests, we study light deﬂection and Shapiro time delay. Regarding the results obtained in [1, 2], we restrict the free parameter of the theory and show that it lies within the range − 4 . 0 × 10 − 27 m s 2 kg − 1 < f (cid:48) 0 < 8 . 7 × 10 − 26 m s 2 kg − 1 . This interval is in agreement with those derived in [3, 4]. This consistency manifests that this theory passes these solar system tests with ﬂying colors. Interestingly, it turns out that the magnitude of the EMSG correction strongly depends on the density of the deﬂector. So, we investigate the possible eﬀects of EMSG on images of a light source microlensed by a compact dense object such as neutron stars. It is estimated that the EMSG correction to the position of lensed images could be as large as (1 − 0 . 1) micro-arcseconds which may be detected by future high-resolution missions. Moreover, the total magniﬁcation and the shape of light curves are obtained in the EMSG theory. It is revealed that except for a small deviation, the overall behavior of the EMSG light curves is similar to that in GR. We also show that as long as the light source and the dense lens are aligned, the EMSG correction is eﬀective, and the combined light of the lensed images is diﬀerent from the GR case. This issue makes it possible to observe signatures of this theory in the microlensing regime.
Selective clustering ensemble based on kappa and F-score

Clustering ensemble has an impressive performance in improving the accuracy and robustness of partition results and has received much attention in recent years. Selective clustering ensemble (SCE) can further improve the ensemble performance by selecting base partitions or clusters in according to diversity and stability. However, there is a conﬂict between diversity and stability, and how to make the trade-oﬀ between the two is challenging. The key here is how to evaluate the quality of the base partitions and clusters. In this paper, we propose a new evaluation method for partitions and clusters using kappa and F-score, leading to a new SCE method, which uses kappa to select informative base partitions and uses F-score to weight clusters based on stability. The eﬀectiveness and eﬃciency of the proposed method is empirically validated over real datasets.
Indoor simultaneous localization and mapping based on fringe projection profilometry

Simultaneous localization and mapping (SLAM) plays an important role in autonomous driving, indoor robotics and AR/VR. Outdoor SLAM has been widely used with the assistance of LiDAR and Global Navigation Satellite System (GNSS). However, for indoor applications, the commonly used LiDAR sensor does not satisfy the accuracy requirement and the GNSS signals are blocked. Thus, an accurate and reliable 3D sensor and suited SLAM algorithms are required for indoor SLAM. One of the most promising 3D perceiving techniques, fringe projection profilometry (FPP), shows great potential but does not prevail in indoor SLAM. In this paper, we first introduce FPP to indoor SLAM, and accordingly propose suited SLAM algorithms, thus enabling a new FPP-SLAM. The proposed FPP-SLAM can achieve millimeter-level and real-time mapping and localization without any expensive equipment assistance. The performance is evaluated in both simulated controlled and real room-sized scenes. The experimental results demonstrate that our method outperforms other state-of-the-art methods in terms of efficiency and accuracy. We believe this method paves the way for FPP in indoor SLAM applications.
Existence of Vortices for Nonlinear Schr\"{o}dinger Equations

In this paper, we study the existence of vortices for two kinds of nonlinear Schr¨odinger equations arising from the Bose-Einstein condensates and geometric optics arguments, respectively. For the Gross-Pitaevskii equation from Bose-Einstein condensates arguments, we introduce the weighted Sobolev space on which the corresponding functional is coercive. By using the variational methods, we prove the existence of positive and ra-dially symmetric solutions under diﬀerent types of boundary condition. And we study another equation arising from geometric optics arguments by constrained minimization method. Furthermore some explicit estimates for the bound of the wave propagation constant are also derived.
Customizing Graph Neural Networks using Path Reweighting

Graph Neural Networks (GNNs) have been extensively used for mining graph-structured data with impressive performance. However, because these traditional GNNs do not distinguish among various downstream tasks, embeddings embedded by them are not always effective. Intuitively, paths in a graph imply different semantics for different downstream tasks. Inspired by this, we design a novel GNN solution, namely Customized Graph Neural Network with Path Reweighting (CustomGNN for short). Specifically, the proposed CustomGNN can automatically learn the high-level semantics for specific downstream tasks to highlight semantically relevant paths as well to filter out task-irrelevant noises in a graph. Furthermore, we empirically analyze the semantics learned by CustomGNN and demonstrate its ability to avoid the three inherent problems in traditional GNNs, i.e., over-smoothing, poor robustness, and overfitting. In experiments with the node classification task, CustomGNN achieves state-of-the-art accuracies on three standard graph datasets and four large graph datasets. The source code of the proposed CustomGNN is available at \url{https://github.com/cjpcool/CustomGNN}.