Miracle-dz/UniFAR-FTUs huggingface dataset:
ALMA–IRDC: dense gas mass distribution from cloud to core scales

Infrared dark clouds (IRDCs) are potential hosts of the elusive early phases of high mass star formation (HMSF). Here, we conduct an in-depth analysis of the fragmentation properties of a sample of 10 IRDCs, which have been highlighted as some of the best candidates to study HMSF within the Milky Way. To do so, we have obtained a set of large mosaics covering these IRDCs with Atacama Large Millimeter/submillimeter Array (ALMA) at Band 3 (or 3 mm). These observations have a high angular resolution (∼3 arcsec; ∼0.05 pc), and high continuum and spectral line sensitivity (∼0.15 mJy beam−1 and ∼0.2 K per 0.1 km s−1 channel at the N2H+ (1 − 0) transition). From the dust continuum emission, we identify 96 cores ranging from low to high mass (M = 3.4−50.9 M⊙) that are gravitationally bound (αvir = 0.3−1.3) and which would require magnetic field strengths of B = 0.3−1.0 mG to be in virial equilibrium. We combine these results with a homogenized catalogue of literature cores to recover the hierarchical structure within these clouds over four orders of magnitude in spatial scale (0.01–10 pc). Using supplementary observations at an even higher angular resolution, we find that the smallest fragments (<0.02 pc) within this hierarchy do not currently have the mass and/or the density required to form high-mass stars. None the less, the new ALMA observations presented in this paper have facilitated the identification of 19 (6 quiescent and 13 star-forming) cores that retain >16 M⊙ without further fragmentation. These high-mass cores contain trans-sonic non-thermal motions, are kinematically sub-virial, and require moderate magnetic field strengths for support against collapse. The identification of these potential sites of HMSF represents a key step in allowing us to test the predictions from high-mass star and cluster formation theories.
The Changing-look Blazar B2 1420+32

Blazars are active galactic nuclei with their relativistic jets pointing toward the observer, comprising two major subclasses, flat-spectrum radio quasars (FSRQs) and BL Lac objects. We present multiwavelength photometric and spectroscopic monitoring observations of the blazar B2 1420+32, focusing on its outbursts in 2018–2020. Multiepoch spectra show that the blazar exhibited large-scale spectral variability in both its continuum and line emission, accompanied by dramatic gamma-ray and optical variability by factors of up to 40 and 15, respectively, on week to month timescales. Over the last decade, the gamma-ray and optical fluxes increased by factors of 1500 and 100, respectively. B2 1420+32 was an FSRQ with broad emission lines in 1995. Following a series of flares starting in 2018, it transitioned between BL Lac and FSRQ states multiple times, with the emergence of a strong Fe pseudocontinuum. Two spectra also contain components that can be modeled as single-temperature blackbodies of 12,000 and 5200 K. Such a collection of “changing-look” features has never been observed previously in a blazar. We measure gamma-ray–optical and interband optical lags implying emission-region separations of less than 800 and 130 gravitational radii, respectively. Since most emission-line flux variations, except the Fe continuum, are within a factor of 2–3, the transitions between FSRQ and BL Lac classifications are mainly caused by the continuum variability. The large Fe continuum flux increase suggests the occurrence of dust sublimation releasing more Fe ions in the central engine and an energy transfer from the relativistic jet to subrelativistic emission components.
Effect of boron and high irradiance stresses on chlorophyll, protein and starch content in leaves of cowpea (Vigna unguiculata L. Walp. P152)

The effect of boron (B) and high irradiance (HI) stress on changes in photosynthetic pigments, soluble proteins, and soluble starch in seedlings of cowpea (Vigna unguiculata L. Walp. P152) were investigated under controlled growth conditions. Under both the stress conditions, the photosynthetic pigments and soluble proteins decreased significantly in Bdeficient and B-excess leaves, the degree of decrease being greater in B-excess leaves. Starch accumulated in B-deficient leaves, but not in B-excess leaves. Moreover, the recovery rate from the imposed HI stress was too low in B-deficient and B-excess leaves. Thus, the cowpea plants seem to be highly susceptible to excess B and HI. To the best of our knowledge, this is the first report on the effect of B stress combined with HI stress in cowpea plant.
Ultracold Dipolar Gases – a Challenge for Experiments and Theory

We present a review of recent results concerning the physics of ultracold trapped dipolar gases. In particular, we discuss the Bose-Einstein condensation for dipolar Bose gases and the BCS transition for dipolar Fermi gases. In both cases we stress the dominant role of the trap geometry in determining the properties of the system. We present also results concerning bosonic dipolar gases in optical lattices and the possibility of obtaining a variety of different quantum phases in such a case. Finally, we analyze various possible routes towards achieving ultracold dipolar gases.
Machine Learning Methods for the Prediction of Micromagnetic Magnetization Dynamics

Machine learning (ML) entered the field of computational micromagnetics only recently. The main objective of these new approaches is the automatization of solutions of parameter-dependent problems in micromagnetism, such as fast response curve estimation modeled by the Landau–Lifschitz–Gilbert (LLG) equation. Data-driven models for the solution of time- and parameter-dependent partial differential equations require high-dimensional training data structures. ML, in this case, is by no means a straightforward trivial task; it needs algorithmic and mathematical innovation. Our work introduces theoretical and computational conceptions of the certain kernel and neural network (NN)-based dimensionality reduction approaches for efficient prediction of solutions via the notion of low-dimensional feature space integration. We introduce efficient treatment of kernel ridge regression and kernel principal component analysis via low-rank approximation. The second line follows NN autoencoders as a nonlinear data-dependent dimensional reduction for the training data with a focus on accurate latent space variable description suitable for a feature space integration scheme. We verify and compare numerically by means of a NIST standard problem. The low-rank kernel method approach is fast and surprisingly accurate, while the NN scheme can even exceed this level of accuracy at the expense of significantly higher costs.
COALAS: I. ATCA CO(1-0) survey and luminosity function in the Spiderweb protocluster at $z=2.16$

We report a detailed CO(1-0) survey of a galaxy protocluster field at $z=2.16$, based on 475 hours of observations with the Australia Telescope Compact Array. We constructed a large mosaic of 13 individual pointings, covering an area of 21 arcmin$^2$ and $\pm6500$ km/s range in velocity. We obtain a robust sample of 46 CO(1-0) detections spanning $z=2.09-2.22$, constituting the largest sample of molecular gas measurements in protoclusters to date. The CO emitters show an overdensity at $z=2.12-2.21$, suggesting a galaxy super-protocluster or a protocluster connected to large-scale filaments with ~120 cMpc size. We find that 90% CO emitters have distances $>0'. 5-4'$ to the center galaxy, indicating that small area surveys would miss the majority of gas reservoirs in similar structures. Half of the CO emitters have velocities larger than escape velocities, which appears gravitationally unbound to the cluster core. These unbound sources are barely found within the $R_{200}$ radius around the center, which is consistent with a picture in which the cluster core is collapsed while outer regions are still in formation. Compared to other protoclusters, this structure contains relatively more CO emitters with relatively narrow line width and high luminosity, indicating galaxy mergers. We use these CO emitters to place the first constraint on the CO luminosity function and molecular gas density in an overdense environment. The amplitude of the CO luminosity function is 1.6$\pm$0.5 orders of magnitudes higher than observed for field galaxy samples at $z\sim2$, and one order of magnitude higher than predictions for galaxy protoclusters from semi-analytical SHARK models. We derive a high molecular gas density of $0.6-1.3\times10^{9}$ $M_\odot$ cMpc$^{-3}$ for this structure, consistent with predictions for cold gas density of massive structures from hydro-dynamical DIANOGA simulations.
Competitive Equilibria with Unequal Budgets: Supporting Arbitrary Pareto Optimal Allocations

We consider a market setting of agents with additive valuations over heterogeneous divisible resources. Agents are assigned a budget of tokens (possibly unequal budgets) they can use to obtain resources; leftover tokens are worthless. We show how to support any Pareto efficient allocation in equilibrium, using anonymous resource prices and agent specific budgets. We also give computationally efficient algorithms for those tasks. In particular, this allows us to support the Rawlsian max-min allocation.
Soft and subspace robust multivariate rank tests based on entropy regularized optimal transport

In this paper, we extend the recently proposed multivariate rank energy distance, based on the theory of optimal transport, for statistical testing of distributional similarity, to soft rank energy distance. Being differentiable, this in turn allows us to extend the rank energy to a subspace robust rank energy distance, dubbed Projected soft-Rank Energy distance, which can be computed via optimization over the Stiefel manifold. We show via experiments that using projected soft rank energy one can trade-off the detection power vs the false alarm via projections onto an appropriately selected low dimensional subspace. We also show the utility of the proposed tests on unsupervised change point detection in multivariate time series data. All codes are publicly available at the link provided in the experiment section.
Risk-dependent centrality in the Brazilian stock market

The purpose of this paper is to calculate the risk-dependent centrality (RDC) of the Brazilian stock market. We computed the RDC for assets traded on the Brazilian stock market between January 2008 to June 2020 at different levels of external risk. We observed that the ranking of assets based on the RDC depends on the external risk. Rankings' volatility is related to crisis events, capturing the recent Brazilian economic-political crisis. Moreover, we have found a negative correlation between the average volatility of assets' ranking based on the RDC and the average daily returns on the stock market. It goes in hand with the hypothesis that the rankings' volatility is higher in periods of crisis.
Stochastic light variations in hot stars from wind instability: finding photometric signatures and testing against the TESS data

Line-driven wind instability is expected to cause small-scale wind inhomogeneities, X-ray emission, and wind line profile variability. The instability can already develop around the sonic point if it is initiated close to the photosphere due to stochastic turbulent motions. In such cases, it may leave its imprint on the light curve as a result of wind blanketing. We study the photometric signatures of the line-driven wind instability. We used line-driven wind instability simulations to determine the wind variability close to the star. We applied two types of boundary perturbations: a sinusoidal one that enables us to study in detail the development of the instability and a stochastic one given by a Langevin process that provides a more realistic boundary perturbation. We estimated the photometric variability from the resulting mass-flux variations. The variability was simulated assuming that the wind consists of a large number of independent conical wind sectors. We compared the simulated light curves with TESS light curves of OB stars that show stochastic variability. We find two typical signatures of line-driven wind instability in photometric data: a knee in the power spectrum of magnitude fluctuations, which appears due to engulfment of small-scale structure by larger structures, and a negative skewness of the distribution of fluctuations, which is the result of spatial dominance of rarefied regions. These features endure even when combining the light curves from independent wind sectors. The stochastic photometric variability of OB stars bears certain signatures of the line-driven wind instability. The distribution function of observed photometric data shows negative skewness and the power spectra of a fraction of light curves exhibit a knee. This can be explained as a result of the line-driven wind instability triggered by stochastic base perturbations.
Generalized Low-Rank Plus Sparse Tensor Estimation by Fast Riemannian Optimization

Abstract We investigate a generalized framework to estimate a latent low-rank plus sparse tensor, where the low-rank tensor often captures the multi-way principal components and the sparse tensor accounts for potential model mis-specifications or heterogeneous signals that are unexplainable by the low-rank part. The framework flexibly covers both linear and generalized linear models, and can easily handle continuous or categorical variables. We propose a fast algorithm by integrating the Riemannian gradient descent and a novel gradient pruning procedure. Under suitable conditions, the algorithm converges linearly and can simultaneously estimate both the low-rank and sparse tensors. The statistical error bounds of final estimates are established in terms of the gradient of loss function. The error bounds are generally sharp under specific statistical models, for example, the sub-Gaussian robust PCA and Bernoulli tensor model. Moreover, our method achieves nontrivial error bounds for heavy-tailed tensor PCA whenever the noise has a finite 2+ε moment. We apply our method to analyze the international trade flow dataset and the statistician hypergraph coauthorship network, both yielding new and interesting findings. Supplementary materials for this article are available online.
RackLay: Multi-Layer Layout Estimation for Warehouse Racks

—Given a monocular color image of a warehouse rack, we aim to predict the bird’s-eye view layout for each shelf in the rack, which we term as ‘multi-layer’ layout prediction. To this end, we present RackLay , a deep neural network for real-time shelf layout estimation from a single image. Unlike previous layout estimation methods which provide a single lay-out for the dominant ground plane alone, RackLay estimates the top-view and front-view layout for each shelf in the considered rack populated with objects. RackLay ’s architecture and its variants are versatile and estimate accurate layouts for diverse scenes characterized by varying number of visible shelves in an image, large range in shelf occupancy factor and varied background clutter. Given the extreme paucity of datasets in this space and the difﬁculty involved in acquiring real data from warehouses, we additionally release a ﬂexible synthetic dataset generation pipeline WareSynth which allows users to control the generation process and tailor the dataset according to contingent application. The ablations across architectural variants and comparison with strong prior baselines vindicate the efﬁcacy of RackLay as an apt architecture for the novel problem of multi-layered layout estimation. We also show that fusing the top-view and front-view enables 3D reasoning applications such as metric free space estimation for the considered rack.
Structural Adapters in Pretrained Language Models for AMR-to-Text Generation

Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation. However, efficiently encoding the graph structure in PLMs is challenging because such models were pretrained on natural language, and modeling structured data may lead to catastrophic forgetting of distributional knowledge. In this paper, we propose StructAdapt, an adapter method to encode graph structure into PLMs. Contrary to prior work, StructAdapt effectively models interactions among the nodes based on the graph connectivity, only training graph structure-aware adapter parameters. In this way, we incorporate task-specific knowledge while maintaining the topological structure of the graph. We empirically show the benefits of explicitly encoding graph structure into PLMs using StructAdapt, outperforming the state of the art on two AMR-to-text datasets, training only 5.1% of the PLM parameters.
Variable compliance and geometry regulation of Soft-Bubble grippers with active pressure control

While compliant grippers have become increasingly commonplace in robot manipulation, finding the right stiffness and geometry for grasping the widest variety of objects remains a key challenge. Adjusting both stiffness and gripper geometry on the fly may provide the versatility needed to manipulate the large range of objects found in domestic environments. We present a system for actively controlling the geometry (inflation level) and compliance of Soft-bubble grippers - air filled, highly compliant parallel gripper fingers incorporating visuotactile sensing. The proposed system enables large, controlled changes in gripper finger geometry and grasp stiffness, as well as simple in-hand manipulation. We also demonstrate, despite these changes, the continued viability of advanced perception capabilities such as dense geometry and shear force measurement - we present a straightforward extension of our previously presented approach for measuring shear induced displacements using the internal imaging sensor and taking into account pressure and geometry changes. We quantify the controlled variation of grasp-free geometry, grasp stiffness and contact patch geometry resulting from pressure regulation and we demonstrate new capabilities for the gripper in the home by grasping in constrained spaces, manipulating tools requiring lower and higher stiffness grasps, as well as contact patch modulation.
Thermodynamics of nonequilibrium systems with uncertain parameters

In the real world, one almost never knows the parameters of a thermodynamic process to infinite precision. Reflecting this, here we investigate how to extend stochastic thermodynamics to systems with uncertain parameters, including uncertain number of heat baths / particle reservoirs, uncertainty in the precise values of temperatures / chemical potentials of those reservoirs, uncertainty in the energy spectrum, uncertainty in the control protocol, etc. We formalize such uncertainty with an (arbitrary) probability measure over all transition rate matrices satisfying local detailed balance. This lets us define the effective thermodynamic quantities by averaging over all LDB-obeying rate matrices. We show that the resultant effective entropy violates the second law of thermodynamics. In contrast to the effective entropy though, the expected stochastic entropy, defined as the ensemble average of the effective trajectory-level entropy, satisfies the second law. We then and explicitly calculate the second-order correction to the second law for the case of one heat bath with uncertain temperature. We also derive the detailed fluctuation theorem for expected effective trajectory entropy production for this case, and derive a lower bound for the associated expected work. Next, to ground these formal considerations with experimentally testable bounds on allowed energetics, we derive a bound on the maximal work that can be extracted from systems with arbitrarily uncertain temperature. We end by extending previous work on “thermodynamic value of information”, to allow for uncertainty in the time-evolution of the rate matrix.
Balancing Biases and Preserving Privacy on Balanced Faces in the Wild

There are demographic biases present in current facial recognition (FR) models. To measure these biases across different ethnic and gender subgroups, we introduce our Balanced Faces in the Wild (BFW) dataset. This dataset allows for the characterization of FR performance per subgroup. We found that relying on a single score threshold to differentiate between genuine and imposters sample pairs leads to suboptimal results. Additionally, performance within subgroups often varies significantly from the global average. Therefore, specific error rates only hold for populations that match the validation data. To mitigate imbalanced performances, we propose a novel domain adaptation learning scheme that uses facial features extracted from state-of-the-art neural networks. This scheme boosts the average performance and preserves identity information while removing demographic knowledge. Removing demographic knowledge prevents potential biases from affecting decision-making and protects privacy by eliminating demographic information. We explore the proposed method and demonstrate that subgroup classifiers can no longer learn from features projected using our domain adaptation scheme. For access to the source code and data, please visit https://github.com/visionjo/facerec-bias-bfw.
PerfSim: A Performance Simulator for Cloud Native Computing

—Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of conﬁguration parameters and highly granular resource allocation policies can signiﬁcantly impact the performance and deployment cost of such applications. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-deﬁned scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-deﬁned scenarios, PerfSim can simulate the performance behavior of service chains over a given period and provides an approximation for system KPIs, such as requests’ average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ∼ 81-99% simulation accuracy in approximating the average response time of incoming requests and ∼ 16-1200 times speed-up factor for the simulation.
Potassium availability and soil extraction tests in agricultural soils with low exchangeable potassium content.

Low application of fertilizer potassium (K) in intensive agriculture leads to a gradual decrease and, afterwards, to a steady state of soil K determined by tests which are based on soil exchangeable K (Kexch) extraction. In this situation, non-exchangeable K (Knon-exch) is being released and therefore real plant availability does not necessarily correspond to soil test K. This incoherency was investigated in 14 agricultural soils with low K as determined by the Mehlich 3 method. Plant-available K released by exhaustive cropping of perennial ryegrass and K determined by the Neubauer seedling test were compared with 8 soil K extraction methods, with the total K content and with the relative content of soil K-bearing minerals. K determined by the ryegrass test (314 mg/kg on average) was more than 3-times higher than K based on Kexch extraction methods, but was from 2 to 3 times lower than K determined by Knon-exch extracting methods. The relative content of mixed-layer phyllosilicates was significantly related to K extracted by soil tests. The relative content of orthoclase correlated only with total K and mica-group minerals with none of the extractions. The best prediction of plant-available K in investigated soils was obtained with sodium tetraphenylboron and StepK methods.