Miracle-dz/UniFAR-FTUs huggingface dataset:
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization

Early stopping is a simple and widely used method to prevent over-training neural networks. We develop theoretical results to reveal the relationship between the optimal early stopping time and model dimension as well as sample size of the dataset for certain linear models. Our results demonstrate two very different behaviors when the model dimension exceeds the number of features versus the opposite scenario. While most previous works on linear models focus on the latter setting, we observe that the dimension of the model often exceeds the number of features arising from data in common deep learning tasks and propose a model to study this setting. We demonstrate experimentally that our theoretical results on optimal early stopping time corresponds to the training process of deep neural networks.
Various Activities above Sunspot Light Bridges in IRIS Observations: Classification and Comparison

Light bridges (LBs) are among the most striking substructures in sunspots, where various activities have been revealed by recent high-resolution observations from the Interface Region Imaging Spectrograph (IRIS). Based on the variety of their physical properties, we classified these activities into four distinct categories: transient brightening (TB), intermittent jet (IJ), type-I light wall (LW-I), and type-II light wall (LW-II). In IRIS 1400/1330 Å observations, TBs are characterized by abrupt emission enhancements, and IJs appear as collimated plasma ejections with a width of 1–2 Mm at some LB sites. Most observed TBs are associated with IJs and show superpositions of some chromosphere absorption lines on enhanced and broadened wings of C ii and Si iv lines, which could be driven by intermittent magnetic reconnection in the lower atmosphere. LW-I and LW-II are wall-shaped structures with bright fronts above the whole LB. An LW-I has a continuous oscillating front with a typical height of several Mm and an almost stationary period of 4–5 minutes. On the contrary, an LW-II has an indented front with a height of over 10 Mm, which has no stable period and is accompanied by recurrent TBs in the entire LB. These results support that LW-IIs are driven by frequent reconnection occurring along the entire LB due to large-scale magnetic flux emergence or intrusion, rather than the leakage of waves producing LW-Is. Our observations reveal a highly dynamical scenario of activities above LBs driven by different basic physical processes, including magnetoconvection, magnetic reconnection, and wave leakage.
The C-Band All-Sky Survey (C-BASS): Template fitting of diffuse galactic microwave emission in the northern sky

The C-Band All-Sky Survey (C-BASS) has observed the Galaxy at 4.76 GHz with an angular resolution of 0${_{. }^{\circ}}$73 full-width half-maximum, and detected Galactic synchrotron emission with high signal-to-noise ratio over the entire northern sky (δ > −15○). We present the results of a spatial correlation analysis of Galactic foregrounds at mid-to-high (b > 10○) Galactic latitudes using a preliminary version of the C-BASS intensity map. We jointly fit for synchrotron, dust, and free–free components between 20 and 1000 GHz and look for differences in the Galactic synchrotron spectrum, and the emissivity of anomalous microwave emission (AME) when using either the C-BASS map or the 408 MHz all-sky map to trace synchrotron emission. We find marginal evidence for a steepening (<Δβ > = − 0.06 ± 0.02) of the Galactic synchrotron spectrum at high frequencies resulting in a mean spectral index of <β > = − 3.10 ± 0.02 over 4.76–22.8 GHz. Further, we find that the synchrotron emission can be well modelled by a single power-law up to a few tens of GHz. Due to this, we find that the AME emissivity is not sensitive to changing the synchrotron tracer from the 408 MHz map to the 4.76 GHz map. We interpret this as strong evidence for the origin of AME being spinning dust emission.
Topology of black hole thermodynamics in Gauss-Bonnet gravity

Thermodynamics of black holes in anti de Sitter (AdS) spacetimes typically contains critical points in the phase diagram, some of which correspond to the ﬁrst order transition ending in a second order one. Following the recent proposal in [1] on using Duan’s φ -mapping theory, we classify the critical points of six dimensional charged Gauss-Bonnet black holes in AdS spacetime. We ﬁnd that the higher derivative curvature terms in the form of Gauss-Bonnet gravity do not change the topological class of critical points in charged black holes in AdS, unlike the case of Born-Infeld corrections noted earlier. The connection between the topological nature of critical points and existence of ﬁrst order phase transitions breaks down in a certain parameter regime. A resolution is proposed by treating the novel and conventional critical points as phase creation and phase annihilation points, respectively. Examples are provided to support the proposal.
Two-dimensional Helmholtz resonator arrays. Part II. Matched asymptotic expansions for specially-scaled resonators

We present a solution method which combines the method of matched asymptotics with the method of multipole expansions to determine the band structure of cylindrical Helmholtz resonators arrays in two dimensions. The resonator geometry is considered in the limit as the wall thickness becomes very large compared with the aperture width (the specially-scaled limit). In this regime, the existing treatment in Part I, with updated parameters, is found to return spurious spectral behaviour. We derive a regularised system which overcomes this issue and also derive compact asymptotic descriptions for the low-frequency dispersion equation in this setting. In the speciallyscaled limit, our asymptotic dispersion equation not only recovers the first band surface but also extends to high, but still subwavelength, frequencies. A homogenisation treatment is outlined for describing the effective bulk modulus and effective density tensor of the resonator array for all wall thicknesses. We demonstrate that specially-scaled resonators are able to achieve exceptionally low Helmholtz resonant frequencies, and present closed-form expressions for determining these explicitly. We anticipate that the analytical expressions and the formulation outlined here may prove useful in industrial and other applications.
Functional Optical Coherence Tomography for Intrinsic Signal Optoretinography: Recent Developments and Deployment Challenges

Intrinsic optical signal (IOS) imaging of the retina, also termed as optoretinogram or optoretinography (ORG), promises a non-invasive method for the objective assessment of retinal function. By providing the unparalleled capability to differentiate individual retinal layers, functional optical coherence tomography (OCT) has been actively investigated for intrinsic signal ORG measurements. However, clinical deployment of functional OCT for quantitative ORG is still challenging due to the lack of a standardized imaging protocol and the complication of IOS sources and mechanisms. This article aims to summarize recent developments of functional OCT for ORG measurement, OCT intensity- and phase-based IOS processing. Technical challenges and perspectives of quantitative IOS analysis and ORG interpretations are discussed.
Optimal regularity and Uhlenbeck compactness for general relativity and Yang–Mills theory

We announce the extension of optimal regularity and Uhlenbeck compactness to the general setting of connections on vector bundles with non-compact gauge groups over non-Riemannian manifolds, including the Lorentzian metric connections of general relativity (GR). Compactness is the essential tool of mathematical analysis for establishing the validity of approximation schemes. Our proofs are based on the theory of the RT-equations for connections with Lp curvature. Solutions of the RT-equations furnish coordinate and gauge transformations which give a non-optimal connection a gain of one derivative over its Riemann curvature (i.e. to optimal regularity). The RT-equations are elliptic regardless of metric signature, and regularize singularities in solutions of the hyperbolic Einstein equations. As an application, singularities at GR shock waves are removable, implying geodesic curves, locally inertial coordinates and the Newtonian limit all exist. By the extra derivative, we extend Uhlenbeck compactness from Uhlenbeck’s setting of vector bundles with compact gauge groups over Riemannian manifolds, to the case of compact and non-compact gauge groups over non-Riemannian manifolds. Our version of Uhlenbeck compactness can also be viewed as a ‘geometric’ improvement of the Div-Curl Lemma, improving weak continuity of wedge products to strong convergence.
RTGNN: A Novel Approach to Model Stochastic Traffic Dynamics

Modeling stochastic traffic dynamics is critical to developing self-driving cars. Because it is difficult to develop first principle models of cars driven by humans, there is great potential for using data driven approaches in developing traffic dynamical models. While there is extensive literature on this subject, previous works mainly address the prediction accuracy of data-driven models. Moreover, it is often difficult to apply these models to common planning frameworks since they fail to meet the assumptions therein. In this work, we propose a new stochastic traffic model, Recurrent Traffic Graph Neural Network (RTGNN), by enforcing additional structures on the model so that the proposed model can be seamlessly integrated with existing motion planning algorithms. RTGNN is a Markovian model and is able to infer future traffic states conditioned on the motion of the ego vehicle. Specifically, RTGNN uses a definition of the traffic state that includes the state of all players in a local region and is therefore able to make joint predictions for all agents of interest. Meanwhile, we explicitly model the hidden states of agents, “intentions,” as part of the traffic state to reflect the inherent partial observability of traffic dynamics. The above mentioned properties are critical for integrating RTGNN with motion planning algorithms coupling prediction and decision making. Despite the additional structures, we show that RTGNN is able to achieve state-of-the-art accuracy through comparisons with other similar works.
Model Averaging for Support Vector Machine by Cross-Validation

Support vector machine (SVM) is a well-known statistical technique for classification problems in machine learning and other fields. An important question for SVM is the selection of covariates (or features) for the model. Many studies have considered model selection methods. As is well-known, selecting one winning model over others can entail considerable instability in predictive performance due to model selection uncertainties. This paper advocates model averaging as an alternative approach, where estimates obtained from different models are combined in a weighted average. We propose a model weighting scheme and provide the theoretical underpinning for the proposed method. In particular, we prove that our proposed method yields a model average estimator that achieves the smallest hinge risk among all feasible combinations asymptotically. To remedy the computational burden due to a large number of feasible models, we propose a screening step to eliminate the uninformative features before combining the models. Results from real data applications and a simulation study show that the proposed method generally yields more accurate estimates than existing methods.
A wireless signal-based sensing framework for robotics

In this paper, we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging a robots’ mobility in 3D space. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots’ local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of (i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and (ii) a Cramer–Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOA estimation as a function of the geometry of robot motion. This is a critical distinction with previous work on SAR-based methods that restrict robot mobility to prescribed motion patterns, do not generalize to the full 3D space, and require transmitting robots to be stationary during data acquisition periods. We show that allowing robots to use their full mobility in 3D space while performing SAR results in more accurate AOA profiles and thus better AOA estimation. We formally characterize this observation as the informativeness of the robots’ motion, a computable quantity for which we derive a closed form. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5 GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with a total AOA error of less than 10◦ for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA and validate this theory empirically using robot displacements obtained using an off-the-shelf Intel Tracking Camera T265. Finally, we demonstrate the performance of our system on a multi-robot task where a heterogeneous air/ground pair of robots continuously measure AOA profiles over a WiFi link to achieve dynamic rendezvous in an unmapped, 300 m2 environment with occlusions.
Sturnira tildae (Chiroptera: Phyllostomidae)

Abstract: Sturnira tildae de la Torre, 1959, Tilda's yellow-shouldered bat, is a phyllostomid with wide geographic distribution across South America. It inhabits preferably lowland mature rainforests, but also has been captured in modified vegetation and tropical savannas, where it is less abundant. Globally, it is listed as “Least Concern” (LC) by the International Union for Conservation of Nature and Natural Resources, but local red lists in southern Brazil have recorded this species as “Vulnerable,” mainly due to habitat destruction (Atlantic Forest deforestation).
Association between preventable risk factors and metabolic syndrome

Abstract The risk factors associated with metabolic syndrome (Met-S) including hypertension, hyperglycemia, central obesity, and dyslipidemia are preventable, particularly at their early stage. There are limited data available on the association between Met-S and preventable risk factors in young adults. We randomly selected 2,010 Saudis aged 18–30 years, who applied to be recruited in military colleges. All the procedures followed the guidelines of International Diabetes Federation. The results showed that out of 2,010 subjects, 4088 were affected with Met-S. The commonest risk factors were high blood sugar (63.6%), high systolic and diastolic blood pressures (63.3 and 37.3%), and high body mass index (57.5%). The prevalence of prediabetes and diabetes were 55.2 and 8.4%, respectively. Obesity, diabetes, hypertension, and hypertriglyceridemia were significantly associated with Met-S. The frequency of smoking was significantly linked with the development of Met-S. The prevalence of Met-S was found to be significantly higher in individuals with sedentary lifestyle. In conclusion, the results of this study clearly indicate that military recruits, who represent healthy young adults, are also prone to Met-S. The findings of this study will help in designing preventive measures as well as public awareness programs for controlling the high prevalence of Met-S in young adults.
A Multiphase Computational Study of Oil-Air Flow Within the Bearing Sector of Aeroengines

The bearing chamber of an aeroengine houses roller bearings and other structural parts. The spatial limitation, high operational speeds of the HP shaft and the proximity to the combustion chamber can make the operating conditions of the bearing chamber challenging. A roller bearing consists of an inner race, an outer race and a cage constraining a number of rolling elements. In the aeroengine application, oil is introduced into the bearing chamber via the inner race regions of the bearing into the rolling elements interstices. This provides lubrication for the roller bearings. The source of heat in the bearing chamber is mainly from rolling contact friction and the high temperature of combustion. Cooling results from the oil transport within the bearing chamber and thus an efficient transport of oil is critical to maintaining the integrity of the entire structure. The bearing chamber contains the oil which is eventually scavenged and recycled for recirculation. Experiments have been conducted over the years on bearing chamber flows but often simplified to create the best emulation of the real aeroengine. The complexity of the bearing chamber structure is also challenging for experimental measurements of the oil characteristic in the roller bearing elements and the bearing chamber compartment. Previous experiments have shown that the oil continuum breaks up in the bearing chamber compartment but it is not quantitatively clear how and what parameters affect these. Previous simulation attempt of bearing chamber, also, have been limited by the boundary conditions for the oil. This work presents a computational fluid dynamics (CFD) transient simulation of flow in the bearing sector in an attempt create boundary conditions for such models. The current results show that the oil emerges in the form of droplets into the bearing chamber compartment with speed of the order of 10% of the shaft rotation.Copyright © 2015 by Rolls-Royce plc
Scaling laws of the maximum spreading factor for impact of nanodroplets on solid surfaces

Abstract This study investigates the dynamics of low-viscosity nanodroplets impacting surfaces with static contact angles from θ = 73° to 180° via molecular dynamics (MD) simulations. Two typical morphologies of impacting nanodroplets are observed at the maximum spreading state, a Hertz-ball-like in a low-Weber-number range and a thin-film-like in a high-Weber-number range. Only inertial and capillary forces dominate the impact for the former, whereas viscous force also becomes dominant for the latter. Regardless of morphologies at the maximum spreading state, the ratio of spreading time to contact time always remains constant on an ideal superhydrophobic surface with θ = 180°. With the help of different kinematic approximations of the spreading time and scaling laws of the contact time, scaling laws of the maximum spreading factor ${\beta _{max}}\sim W{e^{1/5}}$ in the low-Weber-number range (capillary regime) and ${\beta _{max}}\sim W{e^{2/3}}R{e^{ - 1/3}}$ (or ${\beta _{max}}\sim W{e^{1/2}}O{h^{1/3}}$) in the high-Weber-number range (cross-over regime) are obtained. Here, We, Re, and Oh are the Weber number, Reynolds number, and Ohnesorge number, respectively. Although the scaling laws are proposed only for the ideal superhydrophobic surface, they are tested valid for θ over 73° owing to the ignorable zero-velocity spreading effect. Furthermore, combining the two scaling laws leads to an impact number, $W{e^{3/10}}O{h^{1/3}} = 2.1$. This impact number can be used to determine whether viscous force is ignorable for impacting nanodroplets, thereby distinguishing the capillary regime from the cross-over regime.
Molecular dissection of a dedicated formaldehyde dehydrogenase from Mycobacterium smegmatis

Accumulation of formaldehyde, a highly reactive molecule, in the cell is toxic, and requires detoxification for the organism's survival. Mycothiol‐dependent formaldehyde dehydrogenase or S‐nitrosomycothiol reductase (MscR) from Mycobacterium smegmatis and Mycobacterium tuberculosis was previously known for detoxifying formaldehyde and protecting the cell against nitrosative stress. We here show that M. smegmatis MscR exhibits a mycothiol‐independent formaldehyde dehydrogenase (FDH) activity in vitro. Presence of zinc in the reaction enhances MscR activity, thus making it a zinc‐dependent FDH. Interestingly, MscR utilizes only formaldehyde and no other primary aldehydes as its substrate in vitro, and M. smegmatis lacking mscR (ΔmscR) shows sensitivity exclusively toward formaldehyde. Bioinformatics analysis of MscRs from various bacteria reveals 10 positionally conserved cysteines, whose importance in structural stability and biological activity is not yet investigated. To explore the significance of these cysteines, we generated MscR single Cys variants by systematically replacing each cysteine with serine. All of the Cys variants except C39S and C309S are unable to show a complete rescue of ΔmscR on formaldehyde, show a significant loss of enzymatic activity in vitro, pronounced structural alterations as probed by circular dichroism, and loss of homotetramerization on size exclusion chromatography. Our data thus reveal the importance of intact cysteines in the structural stability and biological activity of MscR, which is a dedicated FDH in M. smegmatis, and shows ~84% identity with M. tuberculosis MscR. We believe that this knowledge will further help in the development of FDH as a potential drug target against M. tuberculosis infections.
Subgrid-scale models of isotropic turbulence need not produce energy backscatter

Abstract This investigation questions the importance of inverse interscale energy fluxes, the so-called energy backscatter, for the modelling of the energy cascade in large-eddy simulations (LES) of turbulent flows. The invariance of the filtered Navier–Stokes equations to divergence-preserving transformations of the subgrid-scale (SGS) stress tensor is exploited here to explore alternative representations of the local SGS energy fluxes. Numerical optimisation procedures are applied to the SGS stress tensor – obtained by filtering isotropic turbulence flow fields – to find alternative stresses that satisfy the filtered Navier–Stokes equations, but that produce negligible backscatter. These alternative SGS stresses show that backscatter represents not a flux of energy from the subgrid to the resolved scales, but conservative spatial fluxes, and that it need not be modelled to reproduce the local energetic exchange between the resolved and the subgrid scales in LES. From the perspective of statistical mechanics, it is argued that this is a consequence of the strong statistical irreversibility of inertial-range dynamics, which precludes inverse energy cascades even in a local sense. These findings show that the energy cascade is strongly unidirectional locally, and that it can be modelled as an irreversible sink of energy, justifying the extended use of purely dissipative SGS models in LES.