Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health

When data is scarce, software analytics can make many mistakes. For example, consider learning predictors for open source project health (e.g. the number of closed pull requests in twelve months time). The training data for this task may be very small (e.g. five years of data, collected every month means just 60 rows of training data). The models generated from such tiny data sets can make many prediction errors. Those errors can be tamed by a landscape analysis that selects better learner control parameters. Our niSNEAK tool (a) clusters the data to find the general landscape of the hyperparameters; then (b) explores a few representatives from each part of that landscape. niSNEAK is both faster and more effective than prior state-of-the-art hyperparameter optimization algorithms (e.g. FLASH, HYPEROPT, OPTUNA). The configurations found by niSNEAK have far less error than other methods. For example, for project health indicators such as C= number of commits; I=number of closed issues, and R=number of closed pull requests, niSNEAK’s 12 month prediction errors are {I=0%, R=33% C=47%} while other methods have far larger errors of {I=61%,R=119% C=149%}. We conjecture that niSNEAK works so well since it finds the most informative regions of the hyperparameters, then jumps to those regions. Other methods (that do not reflect over the landscape) can waste time exploring less informative options. Based on the above, we recommend landscape analytics (e.g. niSNEAK) especially when learning from very small data sets. This paper only explores the application of niSNEAK to project health. That said, we see nothing in principle that prevents the application of this technique to a wider range of problems. To assist other researchers in repeating, improving, or even refuting our results, all our scripts and data are available on GitHub at https://github.com/zxcv123456qwe/niSneak.
Does the HCN/CO Ratio Trace the Star-forming Fraction of Gas? I. A Comparison with Analytical Models of Star Formation

We use archival ALMA observations of the HCN and CO J = 1–0 transitions, in addition to the radio continuum at 93 GHz, to assess the relation between dense gas, star formation, and gas dynamics in 10 nearby (ultra)luminous IR galaxies (U)LIRGs and late-type galaxy centers. We frame our results in the context of turbulent and gravoturbulent models of star formation to assess whether the HCN/CO ratio tracks the gravitationally bound star-forming gas in molecular clouds (f grav) at subkiloparsec scales in nearby galaxies. We confirm that the HCN/CO ratio is a tracer of gas above n SF ≈ 104.5 cm−3, but the subkiloparsec variations in HCN/CO do not universally track f grav. We find strong evidence for the use of varying star formation density-threshold models, which are able to reproduce trends observed in t dep and ϵ ff that fixed-threshold models do not reproduce. Composite lognormal and power-law models outperform pure lognormal models in reproducing the observed trends, even when a fixed power-law slope is used. The ability of the composite models to better reproduce the star formation properties of the gas provides additional indirect evidence that the star formation efficiency per freefall time is proportional to the fraction of gravitationally bound gas.
Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition

Face recognition has achieved outstanding performance in the last decade with the development of deep learning techniques. Nowadays, the challenges in face recognition are related to specific scenarios, for instance, the performance under diverse image quality, the robustness for aging and edge cases of person age (children and elders), distinguishing of related identities. In this set of problems, recognizing children's faces is one of the most sensitive and important. One of the reasons for this problem is the existing bias towards adults in existing face datasets. In this work, we present a benchmark dataset for children's face recognition, which is compiled similarly to the famous face recognition benchmarks LFW, CALFW, CPLFW, XQLFW and AgeDB. We also present a development dataset (separated into train and test parts) for adapting face recognition models for face images of children. The proposed data is balanced for African, Asian, Caucasian, and Indian races. To the best of our knowledge, this is the first standartized data tool set for benchmarking and the largest collection for development for children's face recognition. Several face recognition experiments are presented to demonstrate the performance of the proposed data tool set.
Antiferromagnetic order and its interplay with superconductivity in CaK(Fe1−x Mn x )4As4

The magnetic order for several compositions of CaK(Fe 1−x Mn x )4As4 has been studied by nuclear magnetic resonance (NMR), Mössbauer spectroscopy, and neutron diffraction. Our observations for the Mn-doped 1144 compound are consistent with the hedgehog spin vortex crystal (hSVC) order which has previously been found for Ni-doped CaKFe4As4 . The hSVC state is characterized by the stripe-type propagation vectors (π0) and (0π) just as in the doped 122 compounds. The hSVC state preserves tetragonal symmetry at the Fe site, and only this SVC motif with simple antiferromagnetic (AFM) stacking along c is consistent with all our observations using NMR Mössbauer spectroscopy, and neutron diffraction. We find that the hSVC state in the Mn-doped 1144 compound coexists with superconductivity, and by combining the neutron scattering and Mössbauer spectroscopy data we can infer a quantum phase transition, hidden under the superconducting dome, associated with the suppression of the AFM transition temperature (T N) to zero for x ≈ 0.01. In addition, unlike several 122 compounds and Ni-doped 1144, the ordered magnetic moment is not observed to decrease at temperatures below the superconducting transition temperature (T c).
There are Salem numbers with trace $-3$ and every degree at least $34$

We prove that there exist Salem numbers with trace $-3$ and every even degree $\geq 34$. Our proof combines a theoretical approach, which allows us to treat all sufficiently large degrees, with a numerical search for small degrees. Since it is known that there are no Salem numbers of trace $-3$ and degree $\leq 30$, our result is optimal up to possibly the single value $32$, for which it is expected there are no such numbers.
Supplementary Materials for: NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching

Specifically, we first give a background on functional maps in Appendix A. Next, the implementation details are provided in Appendix B. The implementation details of our few-shot keypoint detection algorithm are presented in Appendix C. In Appendix D, we provide a complete formulation and proof of the theorem we introduced in Sec. 3.2 of the main text. In Appendix E, we provide an experiment to verify the conditions of the aforementioned theorem. We present a more in-depth analysis of the Neural Correspondence Prior (NCP) effect in Appendix F. Additional quantitative and qualitative results for the shape matching on man-made data tasks are included in Appendix G. An experiment showing the performance of our method in the case of near isometric data is presented in Appendix H. Finally, we discuss the societal impact of our work in Appendix I.
Theoretical and computational aspects of robust optimal transportation, with applications to statistics and machine learning

Optimal transport (OT) theory and the related $p$-Wasserstein distance ($W_p$, $p\geq 1$) are popular tools in statistics and machine learning. Recent studies have been remarking that inference based on OT and on $W_p$ is sensitive to outliers. To cope with this issue, we work on a robust version of the primal OT problem (ROBOT) and show that it defines a robust version of $W_1$, called robust Wasserstein distance, which is able to downweight the impact of outliers. We study properties of this novel distance and use it to define minimum distance estimators. Our novel estimators do not impose any moment restrictions: this allows us to extend the use of OT methods to inference on heavy-tailed distributions. We also provide statistical guarantees of the proposed estimators. Moreover, we derive the dual form of the ROBOT and illustrate its applicability to machine learning. Numerical exercises (see also the supplementary material) provide evidence of the benefits yielded by our methods.
Impact of Engine Nacelle Flow on Buffet

The transonic flow around the OAT15A airfoil is computed at buffet conditions, i.e., freestream Mach number $Ma_\infty = 0.73$, chord-based freestream Reynolds number $Re_c = 2\cdot10^6$, and angle of attack $\alpha = 3.5^\circ$ using wall-modeled LES. Two configurations are considered, one which includes a generic ultra-high bypass ratio (UHBR) engine nacelle geometry and one without an engine, which is denoted the baseline case. The introduction of the UHBR-engine nacelle leads to a significant deviation of the flow onto the airfoil from the baseline case and has an essential effect on the occurring shock dynamics. The flow field of the nacelle configuration is characterized by a shock wave on the upper part of the nacelle sharing dynamic features with the shock on the airfoil. This impact of the nacelle shock on the airfoil shock means a reduced strength of the airfoil shock resulting in a less developed buffet. The perturbation of the general flow field is evaluated as to established buffet models and the dynamic features of the shock waves are analyzed by the sparsity-promoting dynamic mode decomposition. This analysis shows the existence of a shared dynamic mode of the nacelle and the airfoil shock which suggests a coupling mechanism between them.
PIV measurements of convection velocities in a turbulent mixing layer

Particle image velocimetry (PIV) experiments that resolve a range of scales from 1.5L to 10. 4η, where L and η are the integral and Kolmogorov length scales respectively, are performed in the self similar region of a nominally two dimensional planar mixing layer. Data is acquired at 7.25Hz with a field of view that extends to 1.5L thereby permitting spatio-temporal correlations of large and intermediate scale velocity fluctuations to be performed. The overall convection of velocity fluctuations is found to be similar to the mean flow, although fluctuations on the low speed side of the mixing layer on average convect at speeds greater than the mean and fluctuations on the high speed side of the mixing layer are observed to convect at speeds less than the mean. The convection of fluctuations of different length scales in the flow is then observed. It is found that large-scale structures, of the order of the integral scale, convect at speeds that are greater than the mean in the low speed side of the mixing layer and that small scale fluctuations convect at speeds significantly lower than the mean. A crossover wavenumber is also found at which the fluctuations in the low speed side of the mixing layer convect at greater velocities than those on the high speed side of the mixing layer. This wavenumber is found to be Reynolds number dependent.
Optimizing design choices for neural quantum states

Neural quantum states are a new family of variational ans\"atze for quantum-many body wave functions with advantageous properties in the notoriously challenging case of two spatial dimensions. Since their introduction a wide variety of different network architectures has been employed to study paradigmatic models in quantum many-body physics with a particular focus on quantum spin models. Nonetheless, many questions remain about the effect that the choice of architecture has on the performance on a given task. In this work, we present a unified comparison of a selection of popular network architectures and symmetrization schemes employed for ground state searches of prototypical spin Hamiltonians, namely the two-dimensional transverse-field Ising model and the J1-J2 model. In the presence of a non-trivial sign structure of the ground states, we find that the details of symmetrization crucially influence the performance. We describe this effect in detail and discuss its consequences, especially for autoregressive models, as their direct sampling procedure is not compatible with the symmetrization procedure that we found to be optimal.
An Accelerated Lyapunov Function for Polyak's Heavy-Ball on Convex Quadratics

In 1964, Polyak showed that the Heavy-ball method, the simplest momentum technique, accelerates convergence of strongly-convex problems in the vicinity of the solution. While Nesterov later developed a globally accelerated version, Polyak's original algorithm remains simpler and more widely used in applications such as deep learning. Despite this popularity, the question of whether Heavy-ball is also globally accelerated or not has not been fully answered yet, and no convincing counterexample has been provided. This is largely due to the difficulty in finding an effective Lyapunov function: indeed, most proofs of Heavy-ball acceleration in the strongly-convex quadratic setting rely on eigenvalue arguments. Our study adopts a different approach: studying momentum through the lens of quadratic invariants of simple harmonic oscillators. By utilizing the modified Hamiltonian of Stormer-Verlet integrators, we are able to construct a Lyapunov function that demonstrates an O(1/k^2) rate for Heavy-ball in the case of convex quadratic problems. This is a promising first step towards potentially proving the acceleration of Polyak's momentum method and we hope it inspires further research in this field.
Byzantine Resilience at Swarm Scale: A Decentralized Blocklist Protocol from Inter-robot Accusations

The Weighted-Mean Subsequence Reduced (W-MSR) algorithm, the state-of-the-art method for Byzantine-resilient design of decentralized multi-robot systems, is based on discarding outliers received over Linear Consensus Protocol (LCP). Although W-MSR provides well-understood theoretical guarantees relating robust network connectivity to the convergence of the underlying consensus, the method comes with several limitations preventing its use at scale: (1) the number of Byzantine robots, F, to tolerate should be known a priori, (2) the requirement that each robot maintains 2F+1 neighbors is impractical for large F, (3) information propagation is hindered by the requirement that F+1 robots independently make local measurements of the consensus property in order for the swarm's decision to change, and (4) W-MSR is specific to LCP and does not generalize to applications not implemented over LCP. In this work, we propose a Decentralized Blocklist Protocol (DBP) based on inter-robot accusations. Accusations are made on the basis of locally-made observations of misbehavior, and once shared by cooperative robots across the network are used as input to a graph matching algorithm that computes a blocklist. DBP generalizes to applications not implemented via LCP, is adaptive to the number of Byzantine robots, and allows for fast information propagation through the multi-robot system while simultaneously reducing the required network connectivity relative to W-MSR. On LCP-type applications, DBP reduces the worst-case connectivity requirement of W-MSR from (2F+1)-connected to (F+1)-connected and the number of cooperative observers required to propagate new information from F+1 to just 1 observer. We demonstrate empirically that our approach to Byzantine resilience scales to hundreds of robots on cooperative target tracking, time synchronization, and localization case studies.
Free Lunch for Generating Effective Outlier Supervision

When deployed in practical applications, computer vision systems will encounter numerous unexpected images (\emph{{i.e.}}, out-of-distribution data). Due to the potentially raised safety risks, these aforementioned unseen data should be carefully identified and handled. Generally, existing approaches in dealing with out-of-distribution (OOD) detection mainly focus on the statistical difference between the features of OOD and in-distribution (ID) data extracted by the classifiers. Although many of these schemes have brought considerable performance improvements, reducing the false positive rate (FPR) when processing open-set images, they necessarily lack reliable theoretical analysis and generalization guarantees. Unlike the observed ways, in this paper, we investigate the OOD detection problem based on the Bayes rule and present a convincing description of the reason for failures encountered by conventional classifiers. Concretely, our analysis reveals that refining the probability distribution yielded by the vanilla neural networks is necessary for OOD detection, alleviating the issues of assigning high confidence to OOD data. To achieve this effortlessly, we propose an ultra-effective method to generate near-realistic outlier supervision. Extensive experiments on large-scale benchmarks reveal that our proposed \texttt{BayesAug} significantly reduces the FPR95 over 12.50\% compared with the previous schemes, boosting the reliability of machine learning systems. The code will be made publicly available.
A Concentration of Measure Framework to study convex problems and other implicit formulation problems in machine learning

This paper provides a framework to show the concentration of solutions $Y^*$ to convex minimizing problem where the objective function $\phi(X)(Y)$ depends on some random vector $X$ satisfying concentration of measure hypotheses. More precisely, the convex problem translates into a contractive fixed point equation that ensure the transmission of the concentration from $X$ to $Y^*$. This result is of central interest to characterize many machine learning algorithms which are defined through implicit equations (e.g., logistic regression, lasso, boosting, etc.). Based on our framework, we provide precise estimations for the first moments of the solution $Y^*$, when $X= (x_1,\ldots, x_n)$ is a data matrix of independent columns and $\phi(X)(y)$ writes as a sum $\frac{1}{n}\sum_{i=1}^n h_i(x_i^TY)$. That allows to describe the behavior and performance (e.g., generalization error) of a wide variety of machine learning classifiers.
Power Utility Maximization with Expert Opinions at Fixed Arrival Times in a Market with Hidden Gaussian Drift

In this paper we study optimal trading strategies in a ﬁnancial market in which stock returns depend on a hidden Gaussian mean reverting drift process. Investors obtain information on that drift by observing stock returns. Moreover, expert opinions in the form of signals about the current state of the drift arriving at ﬁxed and known dates are included in the analysis. Drift estimates are based on Kalman ﬁlter techniques. They are used to transform a power utility maximization problem under partial information into an optimization problem under full information where the state variable is the ﬁlter of the drift. The dynamic programming equation for this problem is studied and closed-form solutions for the value function and the optimal trading strategy of an investor are derived. They allow to quantify the monetary value of information delivered by the expert opinions. We illustrate our theoretical ﬁndings by results of extensive numerical experiments.
Winding real and order-parameter spaces via lump solitons of spinor BEC on sphere

The three condensate wavefunctions of a F = 1 spinor Bose–Einstein condensate on a spherical shell can map the real space to the order-parameter space that also has a spherical geometry, giving rise to topological excitations called lump solitons. The homotopy of the mapping endows the lump solitons with quantized winding numbers counting the wrapping between the two spaces. We present several lump-soliton solutions to the nonlinear coupled equations minimizing the energy functional. The energies of the lump solitons with different winding numbers indicate coexistence of lumps with different winding numbers and a lack of advantage to break a higher-winding lump soliton into multiple lower-winding ones. Possible implications are discussed since the predictions are testable in cold-atom experiments.
A powerful local shear instability in weakly magnetized disks. IV: Nonaxisymmetric perturbations

In this paper, we continue our study of a powerful axisymmetric MHD instability recently put forward by the authors as the underlying cause of anomalous transport in accretion disks. The theory of local non-axisymmetric perturbations in weakly magnetized disks is presented. Such disturbances are of interest for several reasons, most notably in the context of a dynamo magnetic field amplification scheme. The most volatile disturbances are those associated with the presence of a poloidal field, which grow by tens of orders of magnitude with e-folding times measured in fractions of an orbital period
Absolute photonic band gap in a two-dimensional square lattice of square dielectric rods in air

We show that an absolute photonic band gap exists in a two-dimensional square lattice of square dielectric rods in air. The index of refraction of the square dielectric rods must be larger than 2.65 to have the band gap. This structure is a good candidate for two-dimensional photonic crystals in the IR or the visible to near-IR wavelength range, and can be applied to high efficiency optoelectronic devices.
Coalescence in small generations for the diffusive randomly biased walk on Galton-Watson trees

We investigate the range $\mathcal{R}_T$ of the diffusive biased walk $\mathbb{X}$ on a Galton-Watson tree $\mathbb{T}$ in random environment, that is to say the sub-tree of $\mathbb{T}$ of all distinct vertices visited by this walk up to the time $T$. We study the volume of the range with constraints and more precisely the number of $k$-tuples ($k\geq 2$) of distinct vertices in this sub-tree, in small generations and satisfying an hereditary condition. A special attention is paid to the vertices visited during distinct excursions of $\mathbb{X}$ above the root of the Galton-Watson tree as we observe they give the major contribution to this range. As an application, we study the genealogy of $k\geq 2$ distinct vertices of the tree $\mathcal{R}_T$ picked uniformly from those in small generations. It turns out that two or more vertices among them share a common ancestor for the last time in the remote past. We also point out an hereditary character in their genealogical tree due to the random environment.
Mass formulas for individual black holes in merging binaries

We give formulas for individual black hole masses in a merger, by using Newtonian physics, in terms of the three measured quantities in the detector: the initial wave frequency f 1, the maximum detected frequency (chirp frequency) f 2, and the time elapse τ between these two frequencies. Newtonian gravity provides an excellent pedagogical tool to understand the basic features of gravitational wave observations, but it must be augmented with the assumption of gravitational radiation from General Relativity for accelerating masses as there is no gravitational wave Newtonian gravity. The simplest approach would be to consider a binary system of two non-spinning masses (two black holes) circling their common center of mass. All the computations can be done within Newtonian physics, but the General Relativistic formula for the power carried by gravitational waves is required in this scheme. It turns out there is a subtle point: for the consistency of this simple, yet pedagogical computation, taking the lowest order power formula from General Relativity leads to complex individual masses. Here we remedy this problem and suggest a way to write down an average power formula coming from perturbative General Relativity.