Miracle-dz/UniFAR-FTUs huggingface dataset:
DENSITY FUNCTIONAL THEORY STUDY OF THE PHOTOELECTRON SPECTRA OF 5-METHYLTETRAZOLE

The molecule 5-methyltetrazole (5MTZ) can exist in two taumeric forms. The vertical ionization energies (VIEs) of both valence and core electrons of both the tautomers are calculated with our established DFT procedures and compared with available experimental data. For the 2H-tautomer, the average absolute deviations (AADs) for the outer-valence VIEs and core-electron binding energies (CEBEs) from experiment are below 0.1 eV, while the AAD for the inner-valence VIEs is much larger at 0.4 eV. For the 1H-tautomer, no observed valence VIEs have been reported and the AAD for the calculated CEBEs is 0.2 eV. The assignment of the experimental core-electron ionization spectrum is confirmed, but our results suggest a slight modification of the assignment of the UV photoelectron spectrum of the 2H-tautomer.
Hierarchical Scrambling Method for Palette-Based Images Using Bitwise Operation

: Image scrambling is a method in which an original image turns into a visually degraded image. In this paper, we propose a new algo-rithm of hierarchical scrambling method for palette-based images using bitwise operation. The target pixels for scrambling were chosen randomly using a pseudo-random number generator and were transformed into their corresponding binary bits. Then, bitwise operation was applied to concatenate the target pixels and the corresponding random numbers to scramble the palette-based images. The images in the proposed method are more distorted than those in the conventional methods. We also introduce a hierarchical key assignment scheme for more secure and efficient communication. We carried out performance tests using standard images. This demonstrates that our method is superior to the conventional methods.
Regulatory Effects of HIF-1 α in Bone Cell Metabolism and Bone Diseases

HIF-1α (hypoxia-inducible factor-1α) is a key factor that regulates the response of cells to hypoxia. It can be activated when the oxygen content is reduced, and regulates various physiological activities such as oxygen metabolism and glycolysis. Bone metabolism mainly includes bone formation and bone resorption, both of which are regulated by various factors such as oxygen concentration. HIF-1α plays an important role in the regulation of cell metabolism, as well as bone tissue physiology and pathological processes, which can increase the hypoxia tolerance of bone tissue, and regulate bone formation and mineralization. This article mainly reviews the physiological regulation process of HIF-1α on the metabolism and angiogenesis of osteoblasts, osteoclasts, bone marrow mesenchymal stem cells, chondrocytes and other bone cells. The effects of HIF-1α on the formation of bone vessels, bone metastasis, femoral head necrosis and heterotopic ossification are also reviewed. This review provides a theoretical reference for exploring the regulation of HIF-1α on bone metabolism and the treatment of re-
Active Turbulence

Active fluids exhibit spontaneous flows with complex spatiotemporal structure, which have been observed in bacterial suspensions, sperm cells, cytoskeletal suspensions, self-propelled colloids, and cell tissues. Despite occurring in the absence of inertia, chaotic active flows are reminiscent of inertial turbulence, and hence they are known as active turbulence. Here, we survey the field, providing a unified perspective over different classes of active turbulence. To this end, we divide our review in sections for systems with either polar or nematic order, and with or without momentum conservation (wet or dry). Comparing to inertial turbulence, we highlight the emergence of power-law scaling with either universal or nonuniversal exponents. We also contrast scenarios for the transition from steady to chaotic flows, and we discuss the absence of energy cascades. We link this feature to both the existence of intrinsic length scales and the self-organized nature of energy injection in active turbulence, which are fundamental differences with inertial turbulence. We close by outlining the emerging picture, remaining challenges, and future directions. Expected final online publication date for the Annual Review of Condensed Matter Physics, Volume 13 is March 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
CodeTrans: Towards Cracking the Language of Silicone's Code Through Self-Supervised Deep Learning and High Performance Computing

Currently, a growing number of mature natural language processing applications make people's life more convenient. Such applications are built by source code - the language in software engineering. However, the applications for understanding source code language to ease the software engineering process are under-researched. Simultaneously, the transformer model, especially its combination with transfer learning, has been proven to be a powerful technique for natural language processing tasks. These breakthroughs point out a promising direction for process source code and crack software engineering tasks. This paper describes CodeTrans - an encoder-decoder transformer model for tasks in the software engineering domain, that explores the effectiveness of encoder-decoder transformer models for six software engineering tasks, including thirteen sub-tasks. Moreover, we have investigated the effect of different training strategies, including single-task learning, transfer learning, multi-task learning, and multi-task learning with fine-tuning. CodeTrans outperforms the state-of-the-art models on all the tasks. To expedite future works in the software engineering domain, we have published our pre-trained models of CodeTrans. https://github.com/agemagician/CodeTrans
On Adaptive Fairness in Software Systems

Software systems are increasingly making decisions on behalf of humans, raising concerns about the fairness of such decisions. Such concerns are usually attributed to flaws in algorithmic design or biased data, but we argue that they are often the result of a lack of explicit specification of fairness requirements. However, such requirements are challenging to elicit, a problem exacerbated by increasingly dynamic environments in which software systems operate, as well as stakeholders' changing needs. Therefore, capturing all fairness requirements during the production of software is challenging, and is insufficient for addressing software changes post deployment. In this paper, we propose adaptive fairness as a means for maintaining the satisfaction of changing fairness requirements. We demonstrate how to combine requirements-driven and resource-driven adaptation in order to address variabilities in both fairness requirements and their associated resources. Using models for fairness requirements, resources, and their relations, we show how the approach can be used to provide systems owners and end-users with capabilities that reflect adaptive fairness behaviours at runtime. We demonstrate our approach using an example drawn from shopping experiences of citizens. We conclude with a discussion of open research challenges in the engineering of adaptive fairness in human-facing software systems.
The spectral norm of Gaussian matrices with correlated entries

We give a non-asymptotic bound on the spectral norm of a $d\times d$ matrix $X$ with centered jointly Gaussian entries in terms of the covariance matrix of the entries. In some cases, this estimate is sharp and removes the $\sqrt{\log d}$ factor in the noncommutative Khintchine inequality.
Particle MPC for Uncertain and Learning-Based Control

As robotic systems move from highly structured environments to open worlds, incorporating uncertainty from dynamics learning or state estimation into the control pipeline is essential for robust performance. In this paper we present a nonlinear particle model predictive control (PMPC) approach to control under uncertainty, which directly incorporates any particle-based uncertainty representation, such as those common in robotics. Our approach builds on scenario methods for MPC, but in contrast to existing approaches, which either constrain all or only the first timestep to share actions across scenarios, we investigate the impact of a partial consensus horizon. Implementing this optimization for nonlinear dynamics by leveraging sequential convex optimization, our approach yields an efficient framework that can be tuned to the particular information gain dynamics of a system to mitigate both over-conservatism and over-optimism. We investigate our approach for two robotic systems across three problem settings: time-varying, partially observed dynamics; sensing uncertainty; and model-based reinforcement learning, and show that our approach improves performance over baselines in all settings.
The G 305 Star-forming Region. II. Irregular Variable Stars

We present a catalog of 167 newly discovered, irregular variables spanning a ∼7 deg2 area that encompasses the G 305 star-forming complex, one of the most luminous giant H ii regions in the Galaxy. We aim to unveil and characterize the young stellar object (YSO) population of the region by analyzing the K s-band variability and JHK s infrared colors from the VISTA Variables in the Vía Láctea survey. Additionally, SDSS-IV APOGEE-2 infrared spectra of selected objects are analyzed. The sample shows relatively high amplitudes (0.661 mag < ΔK S < 3.521 mag). Most of them resemble sources with outbursts with amplitude >1 mag and duration longer than a few days, typically at least a year, known as eruptive variables. About 60% are likely to be Class II/flat/I objects. This is also confirmed by the spectral index α when available. From the analysis of APOGEE-2 near-infrared spectra of sources in the region, another 122 stars are classified as YSOs and display some infrared variability. The measured effective temperature T eff peak is around 4000 K, and they are slightly supersolar in metal abundance. The modal radial velocity is approximately −41 km s−1. Combining available catalogs of YSOs in the region with our data, we investigate the spatial distributions of 700 YSOs. They are clearly concentrated within the central cavity formed by the massive clusters Danks 1 and 2. The calculated surface density for the entire catalog is 0.025 YSOs pc−2, while the central cavity contains 10 times more objects per area (0.238 YSOs pc−2).
Essential oil from Mentha spicata L. (spearmint) cultivated in Italy

A new strain of Mentha spicata (2n = 48) was introduced from Michigan (U.S.A.) into the Piedmont Valley (Turin, Italy). It was found that the essential oil of this strain, which was obtained by steam distillation of plants in full bloom, possessed a typical spearmint aroma. Analysis of the oil was performed using a combination of capillary GC-MS. From the results it could be seen that the oil, which was carvone-rich, had a similar chemical composition to other carvone-rich mints found growing spontaneously or cultivated in the Piedmont Valley, e.g. Wentha aquatica × viridis F. Schultz (1854) (sensu Fiori), Mentha longifolia (L.) Huds var. candicans Crantz and Mentha longifolia (L.) Huds. var. crispa Ten. With the exception of M. longifolia var. candicans, the minor components of the other oils were qualitatively similar; however, they varied quantitatively both in amounts of oxygenated and non-oxygenated monoterpenes and sesquiterpenes, and in individual components.
Key parameters governing the reversibility of Si/carbon/CMC electrodes for Li-ion batteries

Various Si/carbon/polymer composite electrodes were prepared to better understand the influence of the Si−polymer interactions on the stability of the Li−Si reaction and especially the superior performances of CMC-based (carboxy−methyl−cellulose) composites despite the large volume changes of the Si particles upon cycling. Via the modification of the composites formulation, the nature of the polymer, the nature and the amount of the substituting groups and the surface chemistry of the Si particles, together with the use of various characterization techniques (TEM, SEM, NMR−MAS, infrared spectroscopy, TGA, etc.) we could propose that the performances of the Si/Csp/CMC composite electrodes are nested in both the porous texture of the electrode and in the nature of the Si−polymer chemical bonding. A self-healing process of the rather strong Si−CMC hydrogen bonding which can accommodate textural stresses and can evolve during cycling is proposed to be critical for Si-based electrode performances. This better .
Enabling Inference Privacy with Adaptive Noise Injection

User-facing software services are becoming increasingly reliant on remote servers to host Deep Neural Network (DNN) models, which perform inference tasks for the clients. Such services require the client to send input data to the service provider, who processes it using a DNN and returns the output predictions to the client. Due to the rich nature of the inputs such as images and speech, the input often contains more information than what is necessary to perform the primary inference task. Consequently, in addition to the primary inference task, a malicious service provider could infer secondary (sensitive) attributes from the input, compromising the client's privacy. The goal of our work is to improve inference privacy by injecting noise to the input to hide the irrelevant features that are not conducive to the primary classification task. To this end, we propose Adaptive Noise Injection (ANI), which uses a light-weight DNN on the client-side to inject noise to each input, before transmitting it to the service provider to perform inference. Our key insight is that by customizing the noise to each input, we can achieve state-of-the-art trade-off between utility and privacy (up to 48.5% degradation in sensitive-task accuracy with<1% degradation in primary accuracy), significantly outperforming existing noise injection schemes. Our method does not require prior knowledge of the sensitive attributes and incurs minimal computational overheads.
Accessing slip activity in high purity tin with electron backscatter diffraction and measurement of slip strength

Beta-tin has been used widely as an interconnect in modern electronics. To improve the understanding of the reliability of these components, we directly measure the critical resolved shear stress of individual slip systems in beta-tin using micropillar compression tests at room temperature with crystal orientations near-[100] and [001] in the loading direction within a large grain high purity tin (99.99%) sample. This activates the (110)[1-11]/2, (110)[1-1-1]/2, (010)[001] and (110)[001] slip systems. Analysis of the slip traces and load-displacement curves enables measurement of the critical resolved shear stress for epsilon=10^(-4) of tau_(CRSS)^({110}<1-11>/2)=10.4+/-0.4 and tau_(CRSS)^({010}<001>)=3.9+/-0.3 MPa.
ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement

Recently, the power of unconditional image synthesis has significantly advanced through the use of Generative Adversarial Networks (GANs). The task of inverting an image into its corresponding latent code of the trained GAN is of utmost importance as it allows for the manipulation of real images, leveraging the rich semantics learned by the network. Recognizing the limitations of current inversion approaches, in this work we present a novel inversion scheme that extends current encoder-based inversion methods by introducing an iterative refinement mechanism. Instead of directly predicting the latent code of a given real image using a single pass, the encoder is tasked with predicting a residual with respect to the current estimate of the inverted latent code in a self-correcting manner. Our residual-based encoder, named ReStyle, attains improved accuracy compared to current state-of-the-art encoder-based methods with a negligible increase in inference time. We analyze the behavior of ReStyle to gain valuable insights into its iterative nature. We then evaluate the performance of our residual encoder and analyze its robustness compared to optimization-based inversion and state-of-the-art encoders. Code is available via our project page: https: //yuval-alaluf.github. io/restyle-encoder/
A Unified Passivity-Based Framework for Control of Modular Islanded AC Microgrids

Voltage and frequency control in an islanded ac microgrid (ImG) amounts to stabilizing an a priori unknown ImG equilibrium induced by loads and changes in topology. This article puts forth a unified control framework, which, while guaranteeing such stability, allows for modular ImGs interconnecting multiple subsystems, that is, dynamic $RLC$ lines, nonlinear constant impedance, current, power (ZIP) and exponential (EXP) loads, and inverter-based distributed generation units (DGUs) controlled with different types of primary controllers. The underlying idea of the framework is based on the equilibrium-independent passivity (EIP) of the ImG subsystems, which enables stability certificates of ImG equilibria without explicit knowledge of these equilibria. In order to render DGUs EIP, we propose a decentralized controller synthesis algorithm based on port-Hamiltonian systems (PHSs). We also show that EIP, being the key to stability, provides a general framework, which can embrace other solutions available in the literature. Furthermore, we provide a novel argument based on LaSalle’s theorem for proving asymptotic voltage and frequency stability. Finally, we analyze the impact of actuator saturation on the stability results by exploiting the inherent EIP properties of the PHS DGU model. Theoretical findings are backed up by realistic simulations based on the medium-voltage CIGRE benchmark network.
Exploring Topic-Metadata Relationships with the STM: A Bayesian Approach

Topic models such as the Structural Topic Model (STM) estimate latent topical clusters within text. An important step in many topic modeling applications is to explore relationships between the discovered topical structure and metadata associated with the text documents. Methods used to estimate such relationships must take into account that the topical structure is not directly observed, but instead being estimated itself. The authors of the STM, for instance, perform repeated OLS regressions of sampled topic proportions on metadata covariates by using a Monte Carlo sampling technique known as the method of composition. In this paper, we propose two improvements: first, we replace OLS with more appropriate Beta regression. Second, we suggest a fully Bayesian approach instead of the current blending of frequentist and Bayesian methods. We demonstrate our improved methodology by exploring relationships between Twitter posts by German members of parliament (MPs) and different metadata covariates.
Physics-Informed Neural Nets-based Control

Physics-informed neural networks (PINNs) impose known physical laws into the learning of deep neural networks, making sure they respect the physics of the process while decreasing the demand of labeled data. For systems represented by Ordinary Diﬀerential Equations (ODEs), the conventional PINN has a continuous time input variable and outputs the solution of the corresponding ODE. In their original form, PINNs do not allow control inputs neither can they simulate for long-range intervals without serious degradation in their predictions. In this context, this work presents a new framework called Physics-Informed Neural Nets-based Control (PINC), which proposes a novel PINN-based architecture that is amenable to control problems and able to simulate for longer-range time horizons that are not ﬁxed beforehand. First, the network is augmented with new inputs to account for the initial state of the system and the control action. Then, the response over the complete time horizon is split such that each smaller interval constitutes a solution of the ODE conditioned on the ﬁxed values of initial state and control action. The complete response is formed by setting the initial state of the next interval to the terminal state of the previous one. The new methodology enables the optimal control of dynamic systems, making feasible to integrate a priori knowledge from experts and data collected from plants in control applications. We showcase our method in the control of two nonlinear dynamic systems: the Van der Pol oscillator and the four-tank system.
Pathological Image Segmentation with Noisy Labels

Segmentation of pathological images is essential for accurate disease diagnosis. The quality of manual labels plays a critical role in segmentation accuracy; yet, in practice, the labels between pathologists could be inconsistent, thus confusing the training process. In this work, we propose a novel label re-weighting framework to account for the reliability of different experts' labels on each pixel according to its surrounding features. We further devise a new attention heatmap, which takes roughness as prior knowledge to guide the model to focus on important regions. Our approach is evaluated on the public Gleason 2019 datasets. The results show that our approach effectively improves the model's robustness against noisy labels and outperforms state-of-the-art approaches.
A Fourier-matching Method for Analyzing Resonance Frequencies by a Sound-hard Slab with Arbitrarily Shaped Subwavelength Holes

This paper presents a simple Fourier-matching method to rigorously study resonance frequencies of a sound-hard slab with a finite number of arbitrarily shaped cylindrical holes of diameter O(h) for h 1. Outside the holes, a sound field can be expressed in terms of its normal derivatives on the apertures of holes. Inside each hole, since the vertical variable can be separated, the field can be expressed in terms of a countable set of Fourier basis functions. Matching the field on each aperture yields a linear system of countable equations in terms of a countable set of unknown Fourier coefficients. The linear system can be reduced to a finite-dimensional linear system based on the invertibility of its principal submatrix, which is proved by the well-posedness of a closely related boundary value problem for each hole in the limiting case h → 0, so that only the leading Fourier coefficient of each hole is preserved in the finitedimensional system. The resonance frequencies are those making the resulting finite-dimensional linear system rank deficient. By regular asymptotic analysis for h 1, we get a systematic asymptotic formula for characterizing the resonance frequencies by the 3D subwavelength structure. The formula reveals an important fact that when all holes are of the same shape, the Q-factor for any resonance frequency asymptotically behaves as O(h−2) for h 1 with its prefactor independent of shapes of holes.
Modelling and computation of liquid crystals

Liquid crystals are a type of soft matter that is intermediate between crystalline solids and isotropic fluids. The study of liquid crystals has made tremendous progress over the past four decades, which is of great importance for fundamental scientific research and has widespread applications in industry. In this paper we review the mathematical models and their connections to liquid crystals, and survey the developments of numerical methods for finding rich configurations of liquid crystals.