Miracle-dz/UniFAR-FTUs huggingface dataset:
The effectiveness of online metacognitive versus face-to-face counseling on anxiety and metaworry in women with a history of spontaneous miscarriage: A Randomized clinical trial

BackgroundOnline and face-to-face counseling interventions can be performed on women with a history of miscarriage, which causes their anxiety and metaworry.The aim of this study was to compare effectiveness of metacognitive counseling methods, online and face-to-face, on anxiety and metaworry of women with miscarriage.MethodsIn this parallel randomized clinical trial, 40 women with a history of miscarriage, anxiety and metaworry were randomly assigned to two groups (n = 20/each): group I receiving metacognitive counseling via online network and software, and group II (control) receiving face-to-face metacognitive counseling with same content and time, 90-minute sessions. Data were collected demographic form, Beck Anxiety Inventory, Wellz metaworry questionnaire and satisfaction scale. Primary outcomes included changes in the scores of anxiety, metaworry were measured in both groups in three times including at the base line, and at the end of week eight and twelve of the intervention initiation. Satisfaction with counseling method was assessed as secondary outcome at the end of week eight and twelve of the intervention beginning in both groups.ResultsIn both groups, mean score of anxiety in 12th week was significantly different from before the intervention; difference was more significant in online group (p = 0.04). In both groups, the mean score of metaworry in 12th week had a statistically significant difference compared to the beginning of the study, and the decrease was again more significant in the online group (p = 0.03). Satisfaction with the counseling method had statistically significant difference between the two groups in week eight and twelve (respectively p ≈ 0.00). ConclusionsMetacognitive counseling both online and face-to-face improves anxiety and metaworry in women with miscarriage. Online counseling was more effective, satisfying, and longer compared with face-to-face counseling method, especially in corona virus19 outbreak.Trial registration: ISRCTN, IRCT20181120041707N1, Prospectively registered, Registered 23 May 2019, https://en.irct. ir/trial/35643/ IRCT20181120041707N1
Is rating of perceived exertion a valid method to monitor intensity during blood flow restriction exercise?

Purpose. To examine the concurrent validity of rating of perceived exertion (RPE) in resistance exercise with blood flow restriction (RE+BFR). Methods. Twelve trained men participated in an orientation session and 2 experimental sessions: RE+BFR and traditional resistance exercise (TRE). Arm curl and leg extension exercises were standardized by the total volume of 3 sets of 16 repetitions at 35% of 1RM for RE+BFR and 3 sets of 8 repetitions at 70% of 1RM for TRE. BFR was applied to proximal positions of both the upper and lower limbs using an elastic knee wrap with a width of 7.6 cm. The blood lactate concentration ([La]), RPE in the active muscles (RPE-AM), and in the overall body (RPE-O) were measured at rest and the end of each set using the OMNI-RES. Results. In RE+BFR, positive linear regression coefficients ( p < 0.01) were found between [La] and RPE-AM (arm curl, r = 0.54; leg extension, r = 0.71) and between [La] and RPE-O (arm curl, r = 0.55; leg extension, r = 0.74). Similarly, in TRE, positive coefficients ( p < 0.01) were observed between [La] and RPE-AM (arm curl: r = 0.63; leg extension: r = 0.63) and between [La] and RPE-O (arm curl: r = 0.60; leg extension: r = 0.59). Conclusions. The RPE scale was shown to be a valid method to monitor and regulate intensity during RE+BFR in the upper and lower limbs.
Synthesis and Magnetic Properties of Fe7C3 Particles with High Saturation Magnetization

The formation behavior and magnetic properties of Fe7C3 particles were investigated under atmospheric pressure. The starting material of barium-containing iron oxide was carbureted with CO by heat treatment. Fe7C3 particles were formed in the reaction temperature range of 300–375°C along with a small amount of Fe5C2 particles. Fe7C3 content in the product increased with the increase in the partial pressure of CO and the decrease in the reaction temperature. DTA-TG analysis revealed that the product contained about 5–10 wt% free carbon. The saturation magnetization of the product was about 110 emu/g regardless of the reaction conditions. The coercivity and the ratio of the residual magnetization to the saturation magnetization increased with the increase of the Fe7C3 content in the product.
Spectroscopic evidence for the realization of a genuine topological nodal-line semimetal in LaSbTe

Numerous nodal-line semimetal candidates have been predicted and experimentally examined. To realize a genuine nodal-line semimetal, the Dirac nodes must be robust against spin-orbit coupling and must lie close to the Fermi level so as to allow for the manifestation of the novel properties. By performing band structure calculations and angle-resolved photoemission measurements, direct evidence is provided here for the realization of a genuine nodal-line semimetal in LaSbTe. This finding creates new opportunity for exploring novel phenomena in topological materials.
Comment on “Nonlinear quantum effects in electromagnetic radiation of a vortex electron”

This comment on the paper by Karlovets and Pupasov-Maksimov [Phys. Rev. A 103, 012214 (2021)] addresses their criticism of the combined experimental and theoretical study by Remez et al. [Phys. Rev. Lett. 123, 060401 (2019)]. We show, by means of simple optical arguments as well as numerical simulations, that the arguments raised by Karlovets and Pupasov-Maksimov do not hold in the experimental regime reported by Remez et al. Further, we discuss a clarification for the theoretical derivations presented by Karlovets and Pupasov-Maksimov, as they hold only when the final state of the emitting electron is observed in coincidence with the emitted photon. Although this scenario is feasible and may stimulate new experimental regimes that do correspond to the predictions reported by Karlovets and Pupasov-Maksimov, it is not the common scenario in cathodoluminescence, where only the light is measured. Upon lifting the concerns regarding the experimental regime reported by Remez et al., and explicitly clarifying the electron postselection, we believe that the paper by Karlovets and Pupasov-Maksimov may constitute a valuable contribution to the problem of spontaneous emission by shaped electron wave functions, as it presents new expressions for the emission rates beyond the ubiquitous paraxial approximation.
Occupation Kernel Hilbert Spaces and the Spectral Analysis of Nonlocal Operators

This manuscript introduces a space of functions, termed occupation kernel Hilbert space (OKHS), that operate on collections of signals rather than real or complex functions. To support this new definition, an explicit class of OKHSs is given through the consideration of a reproducing kernel Hilbert space (RKHS). This space enables the definition of nonlocal operators, such as fractional order Liouville operators, as well as spectral decomposition methods for corresponding fractional order dynamical systems. In this manuscript, a fractional order DMD routine is presented, and the details of the finite rank representations are given. Significantly, despite the added theoretical content through the OKHS formulation, the resultant computations only differ slightly from that of occupation kernel DMD methods for integer order systems posed over RKHSs.
Kinetic roughening and nontrivial scaling in the Kardar–Parisi–Zhang growth with long-range temporal correlations

Long-range spatiotemporal correlations may play important roles in non-equilibrium surface growth processes. In order to explore the effects of long-range temporal correlation on dynamic scaling of growing surfaces, we carry out extensive numerical simulations of the (1 + 1)- and (2 + 1)-dimensional Kardar–Parisi–Zhang (KPZ) growth system in the presence of temporally correlated noise, and compare our results with previous theoretical predictions and numerical simulations. We find that surface morphologies are obviously affected with long-range temporal correlations, and as the temporal correlation exponent increases, the KPZ surfaces develop gradually faceted patterns in the saturated growth regimes. Our results show that the temporal correlated KPZ system displays evidently nontrivial dynamic properties when 0 < θ < 0.5, the characteristic roughness exponents satisfy α < α s, and α loc exhibits non-universal scaling within local window sizes, which differs with the existing dynamic scaling hypotheses, both in the (1 + 1)- and (2 + 1)-dimensions.
Direct Observation of a Large-scale CME Flux Rope Event Arising from an Unwinding Coronal Jet

Coronal mass ejections (CMEs) and coronal jets are two types of common solar eruptive phenomena, which often independently happen at different spatial scales. In this work, we present a stereoscopic observation of a large-scale CME flux rope arising from an unwinding blowout jet in a multipolar complex magnetic system. Based on a multiband observational analysis, we find that this whole event starts with a small filament whose eruption occurs at a coronal geyser site after a series of homologous jets. Aided by magnetic field extrapolations, it reveals that the coronal geyser site forms above an elongate opposite-polarity interface, where the emergence-driven photospheric flux cancellation and repetitive reconnection are responsible for those preceding recurrent jets and also contribute to the ultimate filament destabilization. By interacting with overlying fields, the erupting filament breaks one of its legs, and results in an unwinding blowout jet. Our estimation suggests that around 1.4–2.0 turns of twist release in its jet spire. This prominent twist transport in the jet spire rapidly creates a newborn larger-scale flux rope from the jet base to a remote site. Soon after its formation, this large-scale flux rope erupts toward the outer coronae causing an Earth-directed CME. In its source region, two sets of distinct postflare loops form in succession, indicating this eruption involves two stages of flare magnetic reconnection. This work not only reveals a real magnetic coupling process between different eruptive activities but provides a new hint for understanding for the creation of large-scale CME flux ropes during the solar eruption.
General Bayesian time-varying parameter VARs for predicting government bond yields

Time-varying parameter (TVP) regressions commonly assume that timevariation in the coefficients is determined by a simple stochastic process such as a random walk. While such models are capable of capturing a wide range of dynamic patterns, the true nature of time variation might stem from other sources, or arise from different laws of motion. In this paper, we propose a flexible TVP VAR that assumes the TVPs to depend on a panel of partially latent covariates. The latent part of these covariates differ in their state dynamics and thus capture smoothly evolving or abruptly changing coefficients. To determine which of these covariates are important, and thus to decide on the appropriate state evolution, we introduce Bayesian shrinkage priors to perform model selection. As an empirical application, we forecast the US term structure of interest rates and show that our approach performs well relative to a set of competing models. We then show how the model can be used to explain structural breaks in coefficients related to the US yield curve.
Sparse Approximations with Interior Point Methods

Large-scale optimization problems that seek sparse solutions have become ubiquitous. They are routinely solved with various specialized first-order methods. Although such methods are often fast, they usually struggle with not-so-well conditioned problems. In this paper, specialized variants of an interior point-proximal method of multipliers are proposed and analyzed for problems of this class. Computational experience on a variety of problems, namely, multi-period portfolio optimization, classification of data coming from functional Magnetic Resonance Imaging, restoration of images corrupted by Poisson noise, and classification via regularized logistic regression, provides substantial evidence that interior point methods, equipped with suitable linear algebra, can offer a noticeable advantage over first-order approaches.
Effectively one-dimensional phase diagram of CuZr liquids and glasses

This paper presents computer simulations of CuxZr100−x (x = 36, 50, 64) in the liquid and glass phases. The simulations are based on the effective-medium theory (EMT) potentials. We find good invariance of both structure and dynamics in reduced units along the isomorphs of the systems. The state points studied involve a density variation of almost a factor of two and temperatures going from 1500 K to above 4000 K for the liquids and from 500 K to above 1500 K for the glasses. For comparison, results are presented also for similar temperature variations along isochores, showing little invariance. In general for a binary system the phase diagram has three axes: composition, temperature and pressure (or density). When isomorphs are present, there are effectively only two axes, and for a fixed composition just one. We conclude that the thermodynamic phase diagram of this metallic glass former for a fixed composition is effectively one-dimensional in the sense that many physical properties are invariant along the same curves, implying that in order to investigate the phase diagram, it is only necessary to go across these curves.
On the Generalization of Stochastic Gradient Descent with Momentum

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that there exists a convex loss function for which the stability gap for multiple epochs of SGD with standard heavy-ball momentum (SGDM) becomes unbounded. Then, for smooth Lipschitz loss functions, we analyze a modified momentum-based update rule, i.e., SGD with early momentum (SGDEM) under a broad range of step-sizes, and show that it can train machine learning models for multiple epochs with a guarantee for generalization. Finally, for the special case of strongly convex loss functions, we find a range of momentum such that multiple epochs of standard SGDM, as a special form of SGDEM, also generalizes. Extending our results on generalization, we also develop an upper bound on the expected true risk, in terms of the number of training steps, sample size, and momentum. Our experimental evaluations verify the consistency between the numerical results and our theoretical bounds. SGDEM improves the generalization error of SGDM when training ResNet-18 on ImageNet in practical distributed settings.
Generating high-order quantum exceptional points

Ievgen I. Arkhipov,1, ∗ Fabrizio Minganti,2, † Adam Miranowicz,2, 3, ‡ and Franco Nori2, 4, § Joint Laboratory of Optics of Palacký University and Institute of Physics of CAS, Faculty of Science, Palacký University, 17. listopadu 12, 771 46 Olomouc, Czech Republic Theoretical Quantum Physics Laboratory, RIKEN Cluster for Pioneering Research, Wako-shi, Saitama 351-0198, Japan Institute of Spintronics and Quantum Information, Faculty of Physics, Adam Mickiewicz University, 61-614 Poznań, Poland Physics Department, The University of Michigan, Ann Arbor, Michigan 48109-1040, USA (Dated: March 1, 2021)
On the Visual-Based Safe Landing of UAVs in Populated Areas: A Crucial Aspect for Urban Deployment

Autonomous landing of Unmanned Aerial Vehicles (UAVs) in crowded scenarios is crucial for successful deployment of UAVs in populated areas, particularly in emergency landing situations where the highest priority is to avoid hurting people. In this work, a new visual-based algorithm for identifying Safe Landing Zones (SLZ) in crowded scenarios is proposed, considering a camera mounted on a UAV, where the people in the scene move with unknown dynamics. To do so, a density map is generated for each image frame using a Deep Neural Network, from where a binary occupancy map is obtained aiming to overestimate the people's location for security reasons. Then, the occupancy map is projected to the head's plane, and the SLZ candidates are obtained as circular regions with a minimum security radius, within the head's plane. Finally, to keep track of the SLZ candidates, a multiple instance tracking algorithm is implemented using Kalman Filters along with the Hungarian algorithm for data association. Several scenarios were studied to prove the validity of the proposed strategy, including public datasets and real uncontrolled scenarios with people moving in public squares, taken from a UAV in flight. The study showed promising results in the search of preventing the UAV from hurting people during emergency landing.
Consistent Sparse Deep Learning: Theory and Computation

Abstract Deep learning has been the engine powering many successes of data science. However, the deep neural network (DNN), as the basic model of deep learning, is often excessively over-parameterized, causing many difficulties in training, prediction and interpretation. We propose a frequentist-like method for learning sparse DNNs and justify its consistency under the Bayesian framework: the proposed method could learn a sparse DNN with at most connections and nice theoretical guarantees such as posterior consistency, variable selection consistency and asymptotically optimal generalization bounds. In particular, we establish posterior consistency for the sparse DNN with a mixture Gaussian prior, show that the structure of the sparse DNN can be consistently determined using a Laplace approximation-based marginal posterior inclusion probability approach, and use Bayesian evidence to elicit sparse DNNs learned by an optimization method such as stochastic gradient descent in multiple runs with different initializations. The proposed method is computationally more efficient than standard Bayesian methods for large-scale sparse DNNs. The numerical results indicate that the proposed method can perform very well for large-scale network compression and high-dimensional nonlinear variable selection, both advancing interpretable machine learning.
ECO: Enabling Energy-Neutral IoT Devices Through Runtime Allocation of Harvested Energy

Energy harvesting offers an attractive and promising mechanism to power low-energy devices. However, it alone is insufficient to enable an energy-neutral operation, which can eliminate tedious battery charging and replacement requirements. Achieving an energy-neutral operation is challenging since the uncertainties in harvested energy undermine the quality of service requirements. To address this challenge, we present a runtime energy-allocation framework that optimizes the utility of the target device under energy constraints using a rollout algorithm, which is a sequential approach to solve dynamic optimization problems. The proposed framework uses an efficient iterative algorithm to compute initial energy allocations at the beginning of a day. The initial allocations are then corrected at every interval to compensate for the deviations from the expected energy harvesting pattern. We evaluate this framework using solar and motion energy harvesting modalities and American Time Use Survey data from 4772 different users. Compared to prior techniques, the proposed framework achieves up to 35% higher utility even under energy-limited scenarios. Moreover, measurements on a wearable device prototype show that the proposed framework has $1000\times $ smaller energy overhead than iterative approaches with a negligible loss in utility.
Entropy of logarithmic modes

Consider (M, g) a compact, boundaryless Riemannian manifold admitting an Anosov geodesic flow. Let ε < min{1, λmax 2 } where λmax is the maximal expansion rate for (M, g). We study the semiclassical measures μsc of ε-logarithmic modes, which are quasimodes spectrally supported in intervals of width ε ~ | log ~| , of the Laplace-Beltrami operator on M . Under a technical assumption on the support of a signed measure related to μsc, we show that the lower bound for the Kolmogorov-Sinai entropy of μsc generalizes that of Ananthamaran-Koch-Nonnenmacher [2]. A feature of our results is the quantitative nature of our bounds on hyperbolic manifolds. Via previous results of the author with Nonnenmacher [18] and Silberman [19], we provide another demonstration of the existence of ε-logarithmic modes whose semiclassical measures possess zero entropy ergodic components yet have other components with positive entropy.
Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction

The reconstruction of real-world surfaces is on high demand in various applications. Most existing reconstruction approaches apply 3D scanners for creating point clouds which are generally sparse and of low density. These points clouds will be triangulated and used for visualization in combination with surface normals estimated by geometrical approaches. However, the quality of the reconstruction depends on the density of the point cloud and the estimation of the surface normals. In this paper, we present a novel deep learning architecture for point cloud upsampling that enables subsequent stable and smooth surface reconstruction. A noisy point cloud of low density with corresponding point normals is used to estimate a point cloud with higher density and appendant point normals. To this end, we propose a compound loss function that encourages the network to estimate points that lie on a surface including normals accurately predicting the orientation of the surface. Our results show the benefit of estimating normals together with point positions. The resulting point cloud is smoother, more complete, and the final surface reconstruction is much closer to ground truth.
Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks

Gradient estimation and vector space projection have been studied as two distinct topics. We aim to bridge the gap between the two by investigating how to efficiently estimate gradient based on a projected low-dimensional space. We first provide lower and upper bounds for gradient estimation under both linear and nonlinear projections, and outline checkable sufficient conditions under which one is better than the other. Moreover, we analyze the query complexity for the projection-based gradient estimation and present a sufficient condition for query-efficient estimators. Built upon our theoretic analysis, we propose a novel query-efficient Nonlinear Gradient Projection-based Boundary Blackbox Attack (NonLinear-BA). We conduct extensive experiments on four image datasets: ImageNet, CelebA, CIFAR-10, and MNIST, and show the superiority of the proposed methods compared with the state-of-the-art baselines. In particular, we show that the projection-based boundary blackbox attacks are able to achieve much smaller magnitude of perturbations with 100% attack success rate based on efficient queries. Both linear and nonlinear projections demonstrate their advantages under different conditions. We also evaluate NonLinear-BA against the commercial online API MEGVII Face++, and demonstrate the high blackbox attack performance both quantitatively and qualitatively. The code is publicly available at https://github.com/AI-secure/NonLinear-BA.