Miracle-dz/UniFAR-FTUs huggingface dataset:
Stress-induced phase transitions in nanoscale 
CuInP2S6

Using Landau-Ginsburg-Devonshire approach and available experimental results we constructed multiwell thermodynamic potential of the layered ferroelectric CuInP2S6 (CIPS). The analysis of temperature dependences of the dielectric permittivity and lattice constants for different applied pressures unexpectedly reveals the critically important role of a nonlinear electrostriction in this material. With the nonlinear electrostriction included we calculated the temperature and pressure phase diagrams and spontaneous polarization of a bulk CIPS, within the assumed range of applicable temperatures and applied pressures. Using the developed thermodynamic potential, we revealed the strain-induced phase transitions in thin epitaxial СIPS films, as well as the stress-induced phase transitions in СIPS nanoparticles, which shape varies from prolate needles to oblate disks. We also revealed the strong influence of a mismatch strain, elastic stress and shape anisotropy on the phase diagrams and polar properties of a nanoscale CIPS, and derived analytical expressions allowing for elastic control of the nanoscale CIPS polar properties. Hence obtained results can be of particular interest for the strain-engineering of nanoscale layered ferroelectrics. * Corresponding author, e-mail: anna.n. morozovska@gmail.com † Corresponding author, e-mail: sergei2@ornl.gov ‡ Corresponding author, e-mail: maksymovychp@ornl.gov
Effect of pore size on carbon dioxide sorption by carbide derived carbon

CO2 sorption at atmospheric and sub-atmospheric pressures is a key step towards carbon capture and sequestration (CCS) and materials capable of fast and efficient CO2 uptake are currently being studied extensively. Carbide-derived carbons (CDCs) show a very high sorption capacity for CO2 of up to 7.1 mol/kg at 0 °C and ambient pressure. This value is significantly higher than other carbon materials. Systematic experimental investigation of a large number of different CDCs derived from nano- and micrometer sized precursors with and without activation show a linear correlation between the CO2 uptake at a certain pressure and the pore volume. However, CO2 sorption is not limited by the total pore volume but only by pores smaller than a certain diameter. At 1 bar, pores smaller than 0.8 nm contribute the most to the CO2 uptake and at 0.1 bar pores smaller or equal to 0.5 nm are preferred. With lower total pressure, smaller pores contribute more to the measured amount of adsorbed CO2. The prediction of the CO2 uptake based on the pore volume for pores of a certain diameter is much more accurate than predictions based on the mean pore size or the specific surface area. This study provides guidelines for the design of materials with an improved ability to remove carbon dioxide from the environment at atmospheric and lower pressures.
An Accelerated Method For Decentralized Distributed Stochastic Optimization Over Time-Varying Graphs

We consider a distributed stochastic optimization problem that is solved by a decentralized network of agents with only local communication between neighboring agents. The goal of the whole system is to minimize a global objective function given as a sum of local objectives held by each agent. Each local objective is defined as an expectation of a convex smooth random function and the agent is allowed to sample stochastic gradients for this function. For this setting we propose the first accelerated (in the sense of Nesterov’s acceleration) method that simultaneously attains optimal up to a logarithmic factor communication and oracle complexity bounds for smooth strongly convex distributed stochastic optimization. We also consider the case when the communication graph is allowed to vary with time and obtain complexity bounds for our algorithm, which are the first upper complexity bounds for this setting in the literature.
A Novel Conversion Technique from Nodal to Edge Finite Element Data Structure for Electromagnetic Analysis

Standard nodal finite elements in electromagnetic analysis have well-known limitation of occurrence of spurious solution. In order to circumvent the problem, a penalty function method or a regularization method is used with potential formulation. These methods solve the problem partially by pushing the spurious mode to the higher end of the spectrum. But it fails to capture singular eigenvalues in case of the problem domains with sharp edges and corners. To circumvent this limitation, edge elements have been developed for electromagnetic analysis where degree of freedoms are along the edges. But most of the preprocessors develop complex meshes in nodal framework. In this work, we have developed a novel technique to convert nodal data structure to edge data structure for electromagnetic analysis. We have explained the conversion algorithm in details, mentioning associated complexities with relevant examples. The performance of the developed algorithm has been demonstrated extensively with several examples.
Solar cyclic activity over the last millennium reconstructed from annual 14C data

Aims. The 11-year solar cycle (Schwabe cycle) is the dominant pattern of solar magnetic activity reflecting the oscillatory dynamo mechanism in the Sun’s convection zone. Solar cycles have been directly observed since 1700, while indirect proxy data suggest their existence over a much longer period of time, but generally without resolving individual cycles and their continuity. Here we reconstruct individual solar cycles for the last millennium using recently obtained 14C data and state-of-the-art models. Methods. Starting with the 14C production rate determined from the so far most precise measurements of radiocarbon content in tree rings, solar activity was reconstructed in the following three physics-based steps: (1) correction of the 14C production rate for the changing geomagnetic field; (2) computation of the open solar magnetic flux; and (3) conversion into sunspot numbers outside of grand minima. All known uncertainties, including both measurement and model uncertainties, were straightforwardly accounted for by a Monte-Carlo method. Results. Cyclic solar activity is reconstructed for the period 971–1900 (85 individual cycles) along with its uncertainties. This more than doubles the number of solar cycles known from direct solar observations. We found that the lengths and strengths of well-defined cycles outside grand minima are consistent with those obtained from the direct sunspot observations after 1750. The validity of the Waldmeier rule (cycles with fast-rising phase tend to be stronger) is confirmed at a highly significant level. Solar activity is found to be in a deep grand minimum when the activity is mostly below the sunspot formation threshold for about 250 years. Therefore, although considerable cyclic variability in 14C is seen even during grand minima, individual solar cycles can hardly be reliably resolved therein. Three potential solar particle events, ca. 994, 1052, and 1279 AD, are shown to occur around the maximum phases of solar cycles. Conclusions. A new approximately 1000-year-long solar activity reconstruction, in the form of annual (pseudo) sunspot numbers with the full assessment of all known uncertainties, is presented based on new high-precision Δ14C measurements and state-of-the-art models, more than doubling the number of individually resolved solar cycles. This forms a solid basis for new, more detailed studies of solar variability.
Confidence Intervals for Seroprevalence

This paper concerns the construction of confidence intervals in standard seroprevalence surveys. In particular, we discuss methods for constructing confidence intervals for the proportion of individuals in a population infected with a disease using a sample of antibody test results and measurements of the test’s false positive and false negative rates. We begin by documenting erratic behavior in the coverage probabilities of standard Wald and percentile bootstrap intervals when applied to this problem. We then consider two alternative sets of intervals constructed with test inversion. The first set of intervals are approximate, using either asymptotic or bootstrap approximation to the finite-sample distribution of a chosen test statistic. We consider several choices of test statistic, including maximum likelihood estimators and generalized likelihood ratio statistics. We show with simulation that, at empirically relevant parameter values and sample sizes, the coverage probabilities for these intervals are close to their nominal level and are approximately equi-tailed. The second set of intervals are shown to contain the true parameter value with probability at least equal to the nominal level, but can be conservative in finite samples. © Institute of Mathematical Statistics, 2022
Best-Buddy GANs for Highly Detailed Image Super-Resolution

We consider the single image super-resolution (SISR) problem, where a high-resolution (HR) image is generated based on a low-resolution (LR) input. Recently, generative adversarial networks (GANs) become popular to hallucinate details. Most methods along this line rely on a predefined single-LR-single-HR mapping, which is not flexible enough for the ill-posed SISR task. Also, GAN-generated fake details may often undermine the realism of the whole image. We address these issues by proposing best-buddy GANs (Beby-GAN) for rich-detail SISR. Relaxing the rigid one-to-one constraint, we allow the estimated patches to dynamically seek trustworthy surrogates of supervision during training, which is beneficial to producing more reasonable details. Besides, we propose a region-aware adversarial learning strategy that directs our model to focus on generating details for textured areas adaptively. Extensive experiments justify the effectiveness of our method. An ultra-high-resolution 4K dataset is also constructed to facilitate future super-resolution research.
Quantifying Bias in Automatic Speech Recognition

Automatic speech recognition (ASR) systems promise to deliver objective interpretation of human speech. Practice and recent evidence suggests that the state-of-the-art (SotA) ASRs struggle with the large variation in speech due to e.g., gender, age, speech impairment, race, and accents. Many factors can cause the bias of an ASR system. Our overarching goal is to uncover bias in ASR systems to work towards proactive bias mitigation in ASR. This paper is a first step towards this goal and systematically quantifies the bias of a Dutch SotA ASR system against gender, age, regional accents and non-native accents. Word error rates are compared, and an in-depth phoneme-level error analysis is conducted to understand where bias is occurring. We primarily focus on bias due to articulation differences in the dataset. Based on our findings, we suggest bias mitigation strategies for ASR development.
Robust Feedback Motion Policy Design Using Reinforcement Learning on a 3D Digit Bipedal Robot

In this paper, a hierarchical and robust framework for learning bipedal locomotion is presented and successfully implemented on the 3D biped robot Digit built by Agility Robotics. We propose a cascade-structure controller that combines the learning process with intuitive feedback regulations. This design allows the framework to realize robust and stable walking with a reduced-dimensional state and action spaces of the policy, significantly simplifying the design and increasing the sampling efficiency of the learning method. The inclusion of feedback regulation into the framework improves the robustness of the learned walking gait and ensures the success of the sim-to-real transfer of the proposed controller with minimal tuning. We specifically present a learning pipeline that considers hardware-feasible initial poses of the robot within the learning process to ensure the initial state of the learning is replicated as close as possible to the initial state of the robot in hardware experiments. Finally, we demonstrate the feasibility of our method by successfully transferring the learned policy in simulation to the Digit robot hardware, realizing sustained walking gaits under external force disturbances and challenging terrains not incurred during the training process. To the best of our knowledge, this is the first time a learning-based policy is transferred successfully to the Digit robot in hardware experiments.
Phase transition in noisy high-dimensional random geometric graphs

We study the problem of detecting latent geometric structure in random graphs. To this end, we consider the soft high-dimensional random geometric graph $\mathcal{G}(n,p,d,q)$, where each of the $n$ vertices corresponds to an independent random point distributed uniformly on the sphere $\mathbb{S}^{d-1}$, and the probability that two vertices are connected by an edge is a decreasing function of the Euclidean distance between the points. The probability of connection is parametrized by $q \in [0,1]$, with smaller $q$ corresponding to weaker dependence on the geometry; this can also be interpreted as the level of noise in the geometric graph. In particular, the model smoothly interpolates between the spherical hard random geometric graph $\mathcal{G}(n,p,d)$ (corresponding to $q = 1$) and the Erd\H{o}s-R\'enyi model $\mathcal{G}(n,p)$ (corresponding to $q = 0$). We focus on the dense regime (i.e., $p$ is a constant). We show that if $nq \to 0$ or $d \gg n^{3} q^{2}$, then geometry is lost: $\mathcal{G}(n,p,d,q)$ is asymptotically indistinguishable from $\mathcal{G}(n,p)$. On the other hand, if $d \ll n^{3} q^{6}$, then the signed triangle statistic provides an asymptotically powerful test for detecting geometry. These results generalize those of Bubeck, Ding, Eldan, and R\'acz (2016) for $\mathcal{G}(n,p,d)$, and give quantitative bounds on how the noise level affects the dimension threshold for losing geometry. We also prove analogous results under a related but different distributional assumption, and we further explore generalizations of signed triangles in order to understand the intermediate regime left open by our results.
Surface acoustic wave induced modulation of tunneling magnetoresistance in magnetic tunnel junctions

We show that a surface acoustic wave (SAW) applied across the terminals of a magnetic tunnel junction (MTJ) decreases both the (time-averaged) parallel and antiparallel resistances of the MTJ, with the latter decreasing much more than the former. This results in a decrease of the tunneling magnetoresistance (TMR) ratio. The coercivities of the free and fixed layer of the MTJ, however, are not affected significantly, suggesting that the SAW does not cause large-angle magnetization rotation in the magnetic layers through the inverse magnetostriction (Villari) effect at the power levels used. This study sheds light on the dynamical behavior of an MTJ under periodic compressive and tensile strain.
First-principles Prediction of X-Ray Polarization from Magnetic Reconnection in High-frequency BL Lacertae Objects

Relativistic magnetic reconnection is a potential particle acceleration mechanism for high-frequency BL Lac objects (HBLs). The Imaging X-ray Polarimetry Explorer (IXPE) scheduled to launch in 2021 has the capability to probe the evolution of magnetic field in HBLs, examining the magnetic reconnection scenario for the HBL flares. In this paper, we make the first attempt to self-consistently predict HBL X-ray polarization signatures arising from relativistic magnetic reconnection via combined particle-in-cell and polarized radiation transfer simulations. We find that although the intrinsic optical and X-ray polarization degrees are similar on average, the X-ray polarization is much more variable in both the polarization degree and angle (PD and PA). Given the sensitivity of the IXPE, it may obtain one to a few polarization data points for one flaring event of nearby bright HBLs Mrk 421 and 501. However, it may not fully resolve the highly variable X-ray polarization. Due to temporal depolarization, where the integration of photons with variable polarization states over a finite period of time can lower the detected PD, the measured X-ray PD can be considerably lower than the optical counterpart or even undetectable. The lower X-ray PD than the optical thus can be a characteristic signature of relativistic magnetic reconnection. For very bright flares where the X-ray polarization is well resolved, relativistic magnetic reconnection predicts smooth X-ray PA swings, which originate from large plasmoid mergers in the reconnection region.
Optimal ciliary locomotion of axisymmetric microswimmers

Abstract Many biological microswimmers locomote by periodically beating the densely packed cilia on their cell surface in a wave-like fashion. While the swimming mechanisms of ciliated microswimmers have been extensively studied both from the analytical and the numerical point of view, optimisation of the ciliary motion of microswimmers has received limited attention, especially for non-spherical shapes. In this paper, using an envelope model for the microswimmer, we numerically optimise the ciliary motion of a ciliate with an arbitrary axisymmetric shape. Forward solutions are found using a fast boundary-integral method, and the efficiency sensitivities are derived using an adjoint-based method. Our results show that a prolate microswimmer with a $2\,{:}\,1$ aspect ratio shares similar optimal ciliary motion as the spherical microswimmer, yet the swimming efficiency can increase two-fold. More interestingly, the optimal ciliary motion of a concave microswimmer can be qualitatively different from that of the spherical microswimmer, and adding a constraint to the cilia length is found to improve, on average, the efficiency for such swimmers.
Quantum Gravity in Species Regime

A large number of particle species allows to formulate quantum gravity in a special double-scaling limit, the species limit. In this regime, quantum gravitational amplitudes simplify substantially. An infinite set of perturbative corrections, that usually blur the picture, vanishes, whereas the collective and non-perturbative effects can be cleanly extracted. Such are the effects that control physics of black holes and of de Sitter and their entanglement curves. In string theory example, we show that the entropy of open strings matches the Gibbons-Hawking entropy of a would-be de Sitter state at the point of saturation of the species bound. This shows, from yet another angle, why quantum gravity/string theory cannot tolerate a de Sitter vacuum. Finally, we discuss various observational implications.
Reinforcement Learning Random Access for Delay-Constrained Heterogeneous Wireless Networks: A Two-User Case

In this paper, we investigate the random access problem for a delay-constrained heterogeneous wireless network. As a first attempt to study this new problem, we consider a network with two users who deliver delay-constrained traffic to an access point (AP) via a common unreliable collision wireless channel. By assuming that one user (called user 1) adopts ALOHA, we aim to optimize the random access scheme of the other user (called user 2). The most intriguing part of this problem is that user 2 does not know the information of user 1 but needs to maximize the system timely throughput. Such a paradigm of collaboratively sharing spectrum is envisioned by DARPA to better dynamically match the supply and demand in future networks [1], [2]. We first propose a Markov Decision Process (MDP) formulation to derive a model-based upper bound so as to quantify the performance gap of any designed schemes. We then utilize reinforcement learning (RL) to design an R-learning-based [3]–[5] random access scheme, called TSRA. We carry out extensive simulations to show that TSRA achieves close-to-upper-bound performance and better performance than the existing baseline DLMA [6], which is our counterpart scheme for delay-unconstrained heterogeneous wireless network.
Elsa: Energy-based Learning for Semi-supervised Anomaly Detection

Anomaly detection aims at identifying deviant instances from the normal data distribution. Many advances have been made in the field, including the innovative use of unsupervised contrastive learning. However, existing methods generally assume clean training data and are limited when the data contain unknown anomalies. This paper presents Elsa, a novel semi-supervised anomaly detection approach that unifies the concept of energy-based models with unsupervised contrastive learning. Elsa instills robustness against any data contamination by a carefully designed fine-tuning step based on the new energy function that forces the normal data to be divided into classes of prototypes. Experiments on multiple contamination scenarios show the proposed model achieves SOTA performance. Extensive analyses also verify the contribution of each component in the proposed model. Beyond the experiments, we also offer a theoretical interpretation of why contrastive learning alone cannot detect anomalies under data contamination.
STUDIES ON ANTICONVULSANT ACTIVITY OF FRACTIONS OF HYDRO-ALCOHOLIC ROOT BARK EXTRACT OF CARISSA EDULIS (VAHL)

Carissa edulis (Vahl) is a member of Apocynaceae family. The present study reported the anticonvulsant activities of fractions (AP, EA and NB) obtained from hydro-alcoholic (60% v /v) root bark extract of C. edulis against Pentylenetetrazole (PTZ)-induced seizure (90 mg/Kg) in mice, maximal electroshock test (MEST) in chicks and 4Aminopyridine-induced seizure (15 mg/Kg) in mice at dose levels between 1.25 to 10 mg/Kg. AP fraction (10 mg/Kg) exhibited very potent anticonvulsant activity (80%) that was comparable with the effects of the well known antiepileptic drug valproate (200mg/Kg) in PTZ-induced seizure. Also, EA and NB showed protection against PTZ-induced seizure but it was not dose-dependent. The entire fractions reduced the mortality rate in a pattern that was not dose-dependent. In MEST, the fractions produced protection that was not dose-dependent with EA having highest (30%) at dose of 2.5 mg/Kg, no mortality was recorded. The AP, EA and NB fractions have no activity against 4-Aminopyridine-induced seizure. The results demonstrated that AP, EA and NB fractions of C. edulis have anticonvulsant activity against PTZ and MEST tests as reported for the crude extract previously.
Weak Fusion 2-Categories

We introduce a weakening of the notion of fusion 2-category given in arXiv:1812.11933. Then, we establish a number of properties of (multi)fusion 2-categories. Finally, we describe the fusion rule of the fusion 2-categories associated to certain pointed braided fusion categories.
ROTATING WAVES IN OSCILLATORY MEDIA WITH NONLOCAL INTERACTIONS AND THEIR NORMAL FORM

Biological and physical systems that can be classified as oscillatory media give rise to interesting phenomena like target patterns and spiral waves. The existence of these structures has been proven in the case of systems with local diffusive interactions. In this paper the more general case of oscillatory media with nonlocal coupling is considered. We model these systems using evolution equations where the nonlocal interactions are expressed via a diffusive convolution kernel, and prove the existence of rotating wave solutions for these systems. Since the nonlocal nature of the equations precludes the use of standard techniques from spatial dynamics, the method we use relies instead on a combination of a multiple-scales analysis and a construction similar to Lyapunov-Schmidt. This approach then allows us to derive a normal form, or reduced equation, that captures the leading order behavior of these solutions. Running head: Rotating waves in oscillatory media
GateKeeper-GPU: Fast and Accurate Pre-Alignment Filtering in Short Read Mapping

We introduce GateKeeper-GPU, a fast and accurate pre-alignment filter that efficiently reduces the need for expensive sequence alignment. GateKeeper-GPU improves the filtering accuracy of GateKeeper, and by exploiting the massive parallelism provided by GPU threads it concurrently examines numerous sequence pairs rapidly. GateKeeper-GPU is available at https://github.com/BilkentCompGen/GateKeeper-GPU. Please refer to the preprint at arXiv:2103.14978 for more information.