Miracle-dz/UniFAR-FTUs huggingface dataset:
High Resolution Long- and Short-Term Earthquake Forecasts for California

We present two models for estimating the probabilities of future earthquakes in California, to be tested in the Collaboratory for the Study of Earthquake Predictability (CSEP). The first, time-independent model, modified from Helmstetter et al. (2007), provides five-year forecasts for magnitudes m > 4.95. We show that large quakes occur on average near the locations of small m > 2 events, so that a high-resolution estimate of the spatial distribution of future large quakes is obtained from the locations of the numerous small events. We employ an adaptive spatial kernel of optimized bandwidth and assume a universal, tapered Gutenberg-Richter distribution. In retrospective tests, we show that no Poisson forecast could capture the observed variability. We therefore also test forecasts using a negative binomial distribution for the number of events. We modify existing likelihood-based tests to better evaluate the spatial forecast. Our time-dependent model, an Epidemic Type Aftershock Sequence (ETAS) model modified from Helmstetter et al. (2006), provides next-day forecasts for m > 3.95. The forecasted rate is the sum of a background rate, proportional to our time-independent model, and of the triggered events due to all prior earthquakes. Each earthquake triggers events with a rate that increases exponentially with its magnitude and decays in time according to Omori's law. An isotropic kernel models the spatial density of aftershocks for small (< 5.5) events. For larger quakes, we smooth early aftershocks to forecast later events. We estimate parameters by optimizing retrospective forecasts. Our short-term model realizes a gain of about 6.0 over the time-independent model.
Dynamo generated magnetic configurations in accretion discs and the nature of quasi-periodic oscillations in accreting binary systems

Magnetic fields are important for accretion disc structure. Magnetic fields in a disc system may be transported with the accreted matter. They can be associated with either the central body and/or jet, and be fossil or dynamo excited in situ. We consider dynamo excitation of magnetic fields in accretion discs of accreting binary systems in an attempt to clarify possible configurations of dynamo generated magnetic fields. We first model the entire disc with realistic radial extent and thickness using an alpha-quenching non-linearity. We then study the simultaneous effect of feedback from the Lorentz force from the dynamo-generated field. We perform numerical simulations in the framework of a relatively simple mean-field model which allows the generation of global magnetic configurations. We explore a range of possibilities for the dynamo number, and find quadrupolar-type solutions with irregular temporal oscillations that might be compared to observed rapid luminosity fluctuations. The dipolar symmetry models with $R_\alpha<0$ have lobes of strong toroidal field adjacent to the rotation axis that could be relevant to jet launching phenomena. We have explored and extended the solutions known for thin accretion discs.
Mean platelet volume and response to the first line therapy in newly diagnosed adultimmune thrombocytopenia patients: a retrospective study

Recommended Citation AKKUŞ, ERMAN; FİDAN, ÇİĞDEM; DEMİRCİ, GÜLŞAH; KUŞTAŞ, ALİ AYTUĞ; and YÜKSEL, MELTEM (2020) "Mean platelet volume and response to the first line therapy in newly diagnosed adultimmune thrombocytopenia patients: a retrospective study," Turkish Journal of Medical Sciences: Vol. 50: No. 4, Article 20. https://doi.org/10.3906/sag-1912-242 Available at: https://journals.tubitak. gov.tr/medical/vol50/iss4/20
Field-Induced Xy And Ising Ground States In A Quasi-Two-Dimensional S=1/2 Heisenberg Antiferromagnet

High field specific heat up to 35 T, ${C}_{p}$, and magnetic susceptibility, \ensuremath{\chi}, measurements were performed on the quasi-two-dimensional (2D) Heisenberg antiferromagnet [Cu(pyz)${}_{2}$(pyO)${}_{2}$](PF${}_{6}$)${}_{2}$. While no ${C}_{p}$ anomaly is observed down to 0.5 K in zero magnetic field, the application of field parallel to the crystallographic $ab$-plane induces a lambda-like anomaly in ${C}_{p}$, suggesting Ising-type magnetic order. On the other hand when the field is parallel to the $c$-axis, ${C}_{p}$ and \ensuremath{\chi} show evidence of $XY$-type antiferromagnetism. This dependence upon the field orientation occurs because the extreme two-dimensionality allows the intrinsic (zero field) spin anisotropy to dominate the interlayer coupling, which has hitherto masked such effects in other materials.
Scheduling HVAC loads to promote renewable generation integration with a learning-based joint chance-constrained approach

The integration of distributed renewable generation (DRG) in distribution networks can be effectively promoted by scheduling flexible resources such as heating, ventilation, and air conditioning (HVAC) loads. However, finding the optimal scheduling for them is nontrivial because DRG outputs are highly uncertain. To address this issue, this paper proposes a learning-based joint chance-constrained approach to coordinate HVAC loads with DRG. Unlike cutting-edge works adopting individual chance constraints to manage uncertainties, this paper controls the violation probability of all critical constraints with joint chance constraints (JCCs). This joint manner can explicitly guarantee the operational security of the entire system based on operators' preferences. To overcome the intractability of JCCs, we first prove that JCCs can be safely approximated by robust constraints with proper uncertainty sets. A famous machine learning algorithm, one-class support vector clustering, is then introduced to construct a small enough polyhedron uncertainty set for these robust constraints. A linear robust counterpart is further developed based on the strong duality to ensure computational efficiency. Numerical results based on various distributed uncertainties confirm the advantages of the proposed method in optimality and feasibility.
Rethinking the Learning Paradigm for Facial Expression Recognition

Due to the subjective crowdsourcing annotations and the inherent inter-class similarity of facial expressions, the real-world Facial Expression Recognition (FER) datasets usually exhibit ambiguous annotation. To simplify the learning paradigm, most previous methods convert ambiguous annotation results into precise one-hot annotations and train FER models in an end-to-end supervised manner. In this paper, we rethink the existing training paradigm and propose that it is better to use weakly supervised strategies to train FER models with original ambiguous annotation.
KEPLER ECLIPSING BINARIES WITH DELTA SCUTI/GAMMA DORADUS PULSATING COMPONENTS. I. KIC 9851944

KIC 9851944 is a short-period (P = 2.16 days) eclipsing binary in the Kepler field of view. By combining the analysis of Kepler photometry and phase-resolved spectra from Kitt Peak National Observatory and Lowell Observatory, we determine the atmospheric and physical parameters of both stars. The two components have very different radii (2.27 R⊙, 3.19 R⊙) but close masses (1.76 M⊙, 1.79 M⊙) and effective temperatures (7026, 6902 K), indicating different evolutionary stages. The hotter primary is still on the main sequence (MS), while the cooler and larger secondary star has evolved to the post-MS, burning hydrogen in a shell. A comparison with coeval evolutionary models shows that it requires solar metallicity and a higher mass ratio to fit the radii and temperatures of both stars simultaneously. Both components show δ Scuti-type pulsations, which we interpret as p-modes and p and g mixed modes. After a close examination of the evolution of δ Scuti pulsational frequencies, we make a comparison of the observed frequencies with those calculated from MESA/GYRE.
Heterogeneous reconstruction of deformable atomic models in Cryo-EM

Cryogenic electron microscopy (cryo-EM) provides a unique opportunity to study the structural heterogeneity of biomolecules. Being able to explain this heterogeneity with atomic models would help our understanding of their functional mechanisms but the size and ruggedness of the structural space (the space of atomic 3D cartesian coordinates) presents an immense challenge. Here, we describe a heterogeneous reconstruction method based on an atomistic representation whose deformation is reduced to a handful of collective motions through normal mode analysis. Our implementation uses an autoencoder. The encoder jointly estimates the amplitude of motion along the normal modes and the 2D shift between the center of the image and the center of the molecule . The physics-based decoder aggregates a representation of the heterogeneity readily interpretable at the atomic level. We illustrate our method on 3 synthetic datasets corresponding to different distributions along a simulated trajectory of adenylate kinase transitioning from its open to its closed structures. We show for each distribution that our approach is able to recapitulate the intermediate atomic models with atomic-level accuracy.
Review of Electric Vehicle Charging Technologies, Configurations, and Architectures

Electric Vehicles (EVs) are projected to be one of the major contributors to energy transition in the global transportation due to their rapid expansion. The EVs will play a vital role in achieving a sustainable transportation system by reducing fossil fuel dependency and greenhouse gas (GHG) emissions. However, high level of EVs integration into the distribution grid has introduced many challenges for the power grid operation, safety, and network planning due to the increase in load demand, power quality impacts and power losses. An increasing fleet of electric mobility requires the advanced charging systems to enhance charging efficiency and utility grid support. Innovative EV charging technologies are obtaining much attention in recent research studies aimed at strengthening EV adoption while providing ancillary services. Therefore, analysis of the status of EV charging technologies is significant to accelerate EV adoption with advanced control strategies to discover a remedial solution for negative grid impacts, enhance desired charging efficiency and grid support. This paper presents a comprehensive review of the current deployment of EV charging systems, international standards, charging configurations, EV battery technologies, architecture of EV charging stations, and emerging technical challenges. The charging systems require a dedicated converter topology, a control strategy and international standards for charging and grid interconnection to ensure optimum operation and enhance grid support. An overview of different charging systems in terms of onboard and off-board chargers, AC-DC and DC-DC converter topologies, and AC and DC-based charging station architectures are evaluated.
Bias Mimicking: A Simple Sampling Approach for Bias Mitigation

Prior work has shown that Visual Recognition datasets frequently underrepresent bias groups $B$ (e.g. Female) within class labels $Y$ (e.g. Programmers). This dataset bias can lead to models that learn spurious correlations between class labels and bias groups such as age, gender, or race. Most recent methods that address this problem require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Alternatively, data sampling baselines from the class imbalance literature (e.g. Undersampling, Upweighting), which can often be implemented in a single line of code and often have no hyperparameters, offer a cheaper and more efficient solution. However, these methods suffer from significant shortcomings. For example, Undersampling drops a significant part of the input distribution per epoch while Oversampling repeats samples, causing overfitting. To address these shortcomings, we introduce a new class-conditioned sampling method: Bias Mimicking. The method is based on the observation that if a class $c$ bias distribution, i.e. $P_{D}(B\vert Y=c)$ is mimicked across every $c^{\prime}\neq c$, then $Y$ and $B$ are statistically independent. Using this notion, BM, through a novel training procedure, ensures that the model is exposed to the entire distribution per epoch without repeating samples. Consequently, Bias Mimicking improves underrepresented groups' accuracy of sampling methods by 3% over four benchmarks while maintaining and sometimes improving performance over nonsampling methods. Code: https://github.com/mqraitem/Bias-Mimicking
Numerical Simulation in Steady Flow of Non-Newtonian Fluids in Pipes with Circular Cross-Section

In the chemical and process industries, it is often required to pump fluids over long distances from storage to various processing units and/or from one plant site to another. There may be a substantial frictional pressure loss in both the pipe line and in the individual units themselves. It is thus often necessary to consider the problems of calculating the power requirements for pumping through a given pipe network, the selection of optimum pipe diameter, measurement and control of flow rate, etc. A knowledge of these factors also facilitates the optimal design and layout of flow networks which may represent a significant part of the total plant cost (Chhabra & Richardson, 2008). The treatment in this chapter is restricted to the laminar, steady, incompressible fully developed flow of a non-Newtonian fluid in a circular tube of constant radius. This kind of flow is dominated by shear viscosity. Then, despite the fact that the fluid may have time-dependent behavior, experience has shown that the shear rate dependence of the viscosity is the most significant factor, and the fluid can be treated as a purely viscous or time-independent fluid for which the viscosity model describing the flow curve is given by the Generalized Newtonianmodel. Time-dependent effects only begin to manifest themselves for flow in non-circular conduits in the form of secondary flows and/or in pipe fittings due to sudden changes in the cross-sectional area available for flow thereby leading to acceleration/deceleration of a fluid element. Even in these circumstances, it is often possible to develop predictive expressions purely in terms of steady-shear viscous properties (Chhabra & Richardson, 1999). The kind of flow considered in this chapter has been already studied experimentally by Hagen Poiseuille in the first half of the XIX Century for Newtonian fluids and it has analytical solution. However, even though in steady state non-Newtonian fluids can be treated as purely viscous, the shear dependence of viscosity may result in differential equations too complex to permit analytical solutions and, consequently, it is needed to use numerical techniques to obtain numerical solutions. It is in this context when Computational Rheology plays its role 1
ReLU Neural Networks Learn the Simplest Models: Neural Isometry and Exact Recovery

The practice of deep learning has shown that neural networks generalize remarkably well even with an extreme number of learned parameters. This appears to contradict traditional statistical wisdom, in which a trade-off between model complexity and fit to the data is essential. We aim to address this discrepancy by adopting a convex optimization and sparse recovery perspective. We consider the training and generalization properties of two-layer ReLU networks with standard weight decay regularization. Under certain regularity assumptions on the data, we show that ReLU networks with an arbitrary number of parameters learn only simple models that explain the data. This is analogous to the recovery of the sparsest linear model in compressed sensing. For ReLU networks and their variants with skip connections or normalization layers, we present isometry conditions that ensure the exact recovery of planted neurons. For randomly generated data, we show the existence of a phase transition in recovering planted neural network models, which is easy to describe: whenever the ratio between the number of samples and the dimension exceeds a numerical threshold, the recovery succeeds with high probability; otherwise, it fails with high probability. Surprisingly, ReLU networks learn simple and sparse models that generalize well even when the labels are noisy . The phase transition phenomenon is confirmed through numerical experiments.
RF-transformer: a unified backscatter radio hardware abstraction

This paper presents RF-Transformer, a unified backscatter radio hardware abstraction that allows a low-power IoT device to directly communicate with heterogeneous wireless receivers at the minimum power consumption. Unlike existing backscatter systems that are tailored to a specific wireless communication protocol, RF-Transformer provides a programmable interface to the micro-controller, allowing IoT devices to synthesize different types of protocol-compliant backscatter signals sharing radically different PHY-layer designs. To show the efficacy of our design, we implement a PCB prototype of RF-Transformer on 2.4 GHz ISM band and showcase its capability on generating standard ZigBee, Bluetooth, LoRa, and Wi-Fi 802.11b/g/n/ac packets. Our extensive field studies show that RF-Transformer achieves 23.8 Mbps, 247.1 Kbps, 986.5 Kbps, and 27.3 Kbps throughput when generating standard Wi-Fi, ZigBee, Bluetooth, and LoRa signals while consuming 7.6--74.2X less power than their active counterparts. Our ASIC simulation based on the 65-nm CMOS process shows that the power gain of RF-Transformer can further grow to 92--678X. We further integrate RF-Transformer with pressure sensors and present a case study on detecting foot traffic density in hallways. Our 7-day case studies demonstrate RF-Transformer can reliably transmit sensor data to a commodity gateway by synthesizing LoRa packets on top of Wi-Fi signals. Our experimental results also verify the compatibility of RF-Transformer with commodity receivers. Code and hardware schematics can be found at: https://github.com/LeFsCC/RF-Transformer.
Actionable Neural Representations: Grid Cells from Minimal Constraints

To afford flexible behaviour, the brain must build internal representations that mirror the structure of variables in the external world. For example, 2D space obeys rules: the same set of actions combine in the same way everywhere (step north, then south, and you won't have moved, wherever you start). We suggest the brain must represent this consistent meaning of actions across space, as it allows you to find new short-cuts and navigate in unfamiliar settings. We term this representation an `actionable representation'. We formulate actionable representations using group and representation theory, and show that, when combined with biological and functional constraints - non-negative firing, bounded neural activity, and precise coding - multiple modules of hexagonal grid cells are the optimal representation of 2D space. We support this claim with intuition, analytic justification, and simulations. Our analytic results normatively explain a set of surprising grid cell phenomena, and make testable predictions for future experiments. Lastly, we highlight the generality of our approach beyond just understanding 2D space. Our work characterises a new principle for understanding and designing flexible internal representations: they should be actionable, allowing animals and machines to predict the consequences of their actions, rather than just encode.
Indirect bounds on new physics for $R(D^{(*)})$

The Standard Model prediction of the $B_c$ lifetime is discussed, together with the dominant uncertainties and strategies on how to improve them. Furthermore, a new method to compute the $B_c$ lifetime based on the operator product expansion is proposed. It relies on differences of $B,\,D$ and $B_c$ meson decay rates, in which the free-quark contributions cancel out, reducing the uncertainty of the theory prediction.
How to Enhance Dephasing Time in Superconducting Qubits

We theoretically investigate the influence of designed pulse sequences in restoring quantum coherence lost due to background noise in superconducting qubits. We consider both $1/f$ noise and random telegraph noise and show that the qubit coherence time can be substantially enhanced by carefully engineered pulse sequences. Conversely, the time dependence of qubit coherence under external pulse sequences could be used as a spectroscopic tool for extracting the noise mechanisms in superconducting qubits, i.e., by using Uhrig's pulse sequence [Phys. Rev. Lett. 98, 100504 (2007)], one can obtain information about moments of the spectral density of noise. We also study the effect of pulse sequences on the evolution of the qubit affected by a strongly coupled fluctuator and show that the non-Gaussian features in decoherence are suppressed by the application of pulses.
PyPose: A Library for Robot Learning with Physics-based Optimization

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization. PyPose's architecture is tidy and well-organized, it has an imperative style interface and is efficient and user-friendly, making it easy to integrate into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and 2nd-order optimizers, such as trust region methods. Experiments show that PyPose achieves more than 10× speedup in computation compared to the state-of-the-art libraries. To boost future research, we provide concrete examples for several fields of robot learning, including SLAM, planning, control, and inertial navigation.
Towards Safe Landing of Falling Quadruped Robots Using a 3-DoF Morphable Inertial Tail

Falling cat problem is well-known where cats show their super aerial reorientation capability and can land safely. For their robotic counterparts, a similar falling quadruped robot problem, has not been fully addressed, although achieving safe landing as the cats has been increasingly investigated. Unlike imposing the burden on landing control, we approach to safe landing of falling quadruped robots by effective flight phase control. Different from existing work like swinging legs and attaching reaction wheels or simple tails, we propose to deploy a 3-DoF morphable inertial tail on a medium-size quadruped robot. In the flight phase, the tail with its maximum length can self-right the body orientation in 3D effectively; before touch-down, the tail length can be retracted to about 1/4 of its maximum for impressing the tail's side-effect on landing. To enable aerial reorientation for safe landing in the quadruped robots, we design a control architecture, which is verified in a high-fidelity physics simulation environment with different initial conditions. Experimental results on a customized flight-phase test platform with comparable inertial properties are provided and show the tail's effectiveness on 3D body reorientation and its fast retractability before touch-down. An initial falling quadruped robot experiment is shown, where the robot Unitree A1 with the 3-DoF tail can land safely subject to non-negligible initial body angles.