Miracle-dz/UniFAR-FTUs huggingface dataset:
A Distributed Diffusion Kalman Filter In Multitask Networks

The Distributed Diffusion Kalman Filter (DDKF) algorithm has earned great attention lately and shows an elaborate way to address the issue of distributed optimization over networks. Estimation and tracking of a single state vector collectively by nodes have been the point of focus. However, there are several multi-task-oriented issues where the optimal state vector for each node may not be the same. This work considers sensor networks for distributed multi-task tracking in which individual nodes communicate with their immediate nodes. A diffusionbased distributed multi-task tracking algorithm is developed. This is done by implementing an unsupervised adaptive clustering process, which aids nodes in forming clusters and collaborating on tasks. This gave rise to an effective level of cooperation for improving state vector estimation accuracy, especially in cases where a cluster's background experience is unknown. To demonstrate the efficiency of our algorithm, computer simulations were conducted. Comparison has been carried out for the Diffusion Kalman Filtermulti-task with respect to the AdaptThen-Combine (ATC) diffusion schemes utilizing both static and adaptive combination weights. Results showed that the ATC diffusion schemes algorithm has great performance with the adaptive combiners as compared to static combiners.
Word Embedding for Social Sciences: An Interdisciplinary Survey

To extract essential information from complex data, computer scientists have been developing machine learning models that learn low-dimensional representation mode. From such advances in machine learning research, not only computer scientists but also social scientists have benefited and advanced their research because human behavior or social phenomena lies in complex data. To document this emerging trend, we survey the recent studies that apply word embedding techniques to human behavior mining, building a taxonomy to illustrate the methods and procedures used in the surveyed papers and highlight the recent emerging trends applying word embedding models to non-textual human behavior data. This survey conducts a simple experiment to warn that common similarity measurements used in the literature could yield different results even if they return consistent results at an aggregate level.
A Candidate Relativistic Tidal Disruption Event at 340 Mpc

We present observations of an extreme radio flare, VT J024345.70-284040.08, hereafter VT J0243, from the nucleus of a galaxy with evidence for historic Seyfert activity at redshift z = 0.074. Between NRAO Very Large Array (VLA) Sky Survey observations in 1993 to VLA Sky Survey observations in 2018, VT J0243 rose from a ∼ GHz radio luminosity of ν L ν ≲ 1038 erg s−1 to ν L ν ∼ 1040 erg s−1, and still continues to brighten. The radio spectral energy distribution evolution is consistent with a nascent jet that has slowed over ∼3000 days with an average 0.1 <〈β〉< 0.6. The jet is energetic (∼1051–52 erg), and had a radius ∼0.7 pc in 2021 December. X-ray observations suggest a persistent or evolving corona, possibly associated with an accretion disk, and IR and optical observations constrain any high-energy counterpart to be sub-Eddington. VT J0243 may be an example of a young, off-axis radio jet from a slowly evolving tidal disruption event. Other more mysterious triggers for the accretion enhancement and jet launching are possible. In either case, VT J0243 is a unique example of a nascent jet, highlighting the unknown connection between supermassive black holes, the properties of their accretion flows, and jet launching.
Cost-efficient Payoffs under Model Ambiguity

Dybvig (1988a,b) solves in a complete market setting the problem of finding a payoff that is cheapest possible in reaching a given target distribution ("cost-efficient payoff"). In the presence of ambiguity, the distribution of a payoff is, however, no longer known with certainty. We study the problem of finding the cheapest possible payoff whose worst-case distribution stochastically dominates a given target distribution ("robust cost-efficient payoff") and determine solutions under certain conditions. We study the link between"robust cost-efficiency"and the maxmin expected utility setting of Gilboa and Schmeidler, as well as more generally with robust preferences in a possibly non-expected utility setting. Specifically, we show that solutions to maxmin robust expected utility are necessarily robust cost-efficient. We illustrate our study with examples involving uncertainty both on the drift and on the volatility of the risky asset.
Efficient inverse Z-transform and pricing barrier and lookback options with discrete monitoring

We prove simple general formulas for expectations of functions of a random walk and its running extremum. Under additional conditions, we derive analytical formulas using the inverse $Z$-transform, the Fourier/Laplace inversion and Wiener-Hopf factorization, and discuss efficient numerical methods for realization of these formulas. As applications, the cumulative probability distribution function of the process and its running maximum and the price of the option to exchange the power of a stock for its maximum are calculated. The most efficient numerical methods use a new efficient numerical realization of the inverse $Z$-transform, the sinh-acceleration technique and simplified trapezoid rule. The program in Matlab running on a Mac with moderate characteristics achieves the precision E-10 and better in several dozen of milliseconds, and E-14 - in a fraction of a isecond.
Mean field games master equations: from discrete to continuous state space

This paper studies the convergence of mean field games with finite state space to mean field games with a continuous state space. We examine a space discretization of a diffusive dynamics, which is reminiscent of the Markov chain approximation method in stochasctic control, but also of finite difference numerical schemes; time remains continuous in the discretization, and the time horizon is arbitrarily long. We are mainly interested in the convergence of the solution of the associated master equations as the number of states tends to infinity. We present two approaches, to treat the case without or with common noise, both under monotonicity assumptions. The first one uses the system of characteristics of the master equation, which is the MFG system, to establish a convergence rate for the master equations without common noise and the associated optimal trajectories, both in case there is a smooth solution to the limit master equation and in case there is not. The second approach relies on the notion of monotone solutions introduced by Bertucci. In the presence of common noise, we show convergence of the master equations, with a convergence rate if the limit master equation is smooth, otherwise by compactness arguments.
Exploring the sequence length bottleneck in the Transformer for Image Captioning

Most recent state of the art architectures rely on combinations and variations of three approaches: convolutional, recurrent and self-attentive methods. Our work attempts in laying the basis for a new research direction for sequence modeling based upon the idea of modifying the sequence length. In order to do that, we propose a new method called"Expansion Mechanism"which transforms either dynamically or statically the input sequence into a new one featuring a different sequence length. Furthermore, we introduce a novel architecture that exploits such method and achieves competitive performances on the MS-COCO 2014 data set, yielding 134.6 and 131.4 CIDEr-D on the Karpathy test split in the ensemble and single model configuration respectively and 130 CIDEr-D in the official online evaluation server, despite being neither recurrent nor fully attentive. At the same time we address the efficiency aspect in our design and introduce a convenient training strategy suitable for most computational resources in contrast to the standard one. Source code is available at https://github.com/jchenghu/exploring
Two-scale interaction of wake and blockage effects in large wind farms

Abstract Turbine wake and farm blockage effects may significantly impact the power produced by large wind farms. In this study, we perform large-eddy simulations (LES) of 50 infinitely large offshore wind farms with different turbine layouts and wind directions. The LES results are combined with the two-scale momentum theory (Nishino & Dunstan, J. Fluid Mech., vol. 894, 2020, p. A2) to investigate the aerodynamic performance of large but finite-sized farms as well. The power of infinitely large farms is found to be a strong function of the array density, whereas the power of large finite-sized farms depends on both the array density and turbine layout. An analytical model derived from the two-scale momentum theory predicts the impact of array density very well for all 50 farms investigated and can therefore be used as an upper limit to farm performance. We also propose a new method to quantify turbine-scale losses (due to turbine–wake interactions) and farm-scale losses (due to the reduction of farm-average wind speed). They both depend on the strength of atmospheric response to the farm, and our results suggest that, for large offshore wind farms, the farm-scale losses are typically more than twice as large as the turbine-scale losses. This is found to be due to a two-scale interaction between turbine wake and farm induction effects, explaining why the impact of turbine layout on farm power varies with the strength of atmospheric response.
Scoped Capabilities for Polymorphic Effects

Type systems usually characterize the shape of values but not their free variables. However, many desirable safety properties could be guaranteed if one knew the free variables captured by values. We describe CCsubBox, a calculus where such captured variables are succinctly represented in types, and show it can be used to safely implement effects and effect polymorphism via scoped capabilities. We discuss how the decision to track captured variables guides key aspects of the calculus, and show that CCsubBox admits simple and intuitive types for common data structures and their typical usage patterns. We demonstrate how these ideas can be used to guide the implementation of capture checking in a practical programming language.
Binary Iterative Hard Thresholding Converges with Optimal Number of Measurements for 1-Bit Compressed Sensing

Compressed sensing has been a very successful high-dimensional signal acquisition and recovery technique that relies on linear operations. However, the actual measurements of signals have to be quantized before storing or processing them. 1(One)-bit compressed sensing is a heavily quantized version of compressed sensing, where each linear measurement of a signal is reduced to just one bit: the sign of the measurement. Once enough of such measurements are collected, the recovery problem in 1-bit compressed sensing aims to find the original signal with as much accuracy as possible. The recovery problem is related to the traditional “halfspace-learning” problem in learning theory. For recovery of sparse vectors, a popular reconstruction method from one-bit measurements is the binary iterative hard thresholding (BIHT) algorithm. The algorithm is a simple projected subgradient descent method, and is known to converge well empirically, despite the nonconvexity of the problem. The convergence property of BIHT was not theoretically justified, except with an exorbitantly large number of measurements (i.e., a number of measurement greater than $\max\{k^{10},24^{48}, k^{3.5}/\epsilon\}$, where k is the sparsity and $\epsilon$ denotes the approximation error, and even this expression hides other factors). In this paper we show that the BIHT estimates converge to the original signal with only ${\tilde{O}}\left(\frac{k}{\epsilon}\right)$ measurements. Note that, this dependence on k and $\epsilon$ is optimal for any recovery method in 1-bit compressed sensing. With this result, to the best of our knowledge, BIHT is the only practical and efficient (polynomial time) algorithm that requires the optimal number of measurements in all parameters (both k and $\epsilon$). This is also an example of a gradient descent algorithm converging to the correct solution for a nonconvex problem, under suitable structural conditions.
Heterogeneous Anomaly Detection for Software Systems via Attentive Multi-modal Learning

Prompt and accurate detection of system anomalies is essential to ensure the reliability of software systems. Unlike manual efforts that exploit all available run-time information, existing approaches usually leverage only a single type of monitoring data (often logs or metrics) or fail to make effective use of the joint information among multi-source data. Consequently, many false predictions occur. To better understand the manifestations of system anomalies, we conduct a comprehensive empirical study based on a large amount of heterogeneous data, i.e., logs and metrics. Our study demonstrates that system anomalies could manifest distinctly in different data types. Thus, integrating heterogeneous data can help recover the complete picture of a system's health status. In this context, we propose HADES, the first work to effectively identify system anomalies based on heterogeneous data. Our approach employs a hierarchical architecture to learn a global representation of the system status by fusing log semantics and metric patterns. It captures discriminative features and meaningful interactions from multi-modal data via a novel cross-modal attention module, enabling accurate system anomaly detection. We evaluate HADES extensively on large-scale simulated and industrial datasets. The experimental results present the superiority of HADES in detecting system anomalies on heterogeneous data. We release the code and the annotated dataset for reproducibility and future research.
ACCESS: Confirmation of a Clear Atmosphere for WASP-96b and a Comparison of Light Curve Detrending Techniques

One of the strongest Na i features was observed in WASP-96b. To confirm this novel detection, we provide a new 475–825 nm transmission spectrum obtained with Magellan/IMACS, which indeed confirms the presence of a broad sodium absorption feature. We find the same result when reanalyzing the 400–825 nm VLT/FORS2 data. We also utilize synthetic data to test the effectiveness of two common detrending techniques: (1) a Gaussian processes (GP) routine, and (2) common-mode correction followed by polynomial correction (CMC+Poly). We find that both methods poorly reproduce the absolute transit depths but maintain their true spectral shape. This emphasizes the importance of fitting for offsets when combining spectra from different sources or epochs. Additionally, we find that, for our data sets, both methods give consistent results, but CMC+Poly is more accurate and precise. We combine the Magellan/IMACS and VLT/FORS2 spectra with literature 800–1644 nm HST/WFC3 spectra, yielding a global spectrum from 400 to 1644 nm. We used the PLATON and Exoretrievals retrieval codes to interpret this spectrum, and find that both yield relatively deeper pressures where the atmosphere is optically thick at log-pressures between 1.3−1.1+1.0 and 0.29−2.02+1.86 bars, respectively. Exoretrievals finds solar to supersolar Na i and H2O log-mixing ratios of −5.4−1.9+2.0 and −4.5−2.0+2.0 , respectively, while PLATON finds an overall metallicity of log10(Z/Z⊙)=−0.49−0.37+1.0 dex. Therefore, our findings are in agreement with the literature and support the inference that the terminator of WASP-96b has few aerosols obscuring prominent features in the optical to near-infrared (near-IR) spectrum.
Prophet Inequalities via the Expected Competitive Ratio

We consider prophet inequalities under downward-closed constraints. In this problem, a decision-maker makes immediate and irrevocable choices on arriving elements, subject to constraints. Traditionally, performance is compared to the expected offline optimum, called the \textit{Ratio of Expectations} (RoE). However, RoE has limitations as it only guarantees the average performance compared to the optimum, and might perform poorly against the realized ex-post optimal value. We study an alternative performance measure, the \textit{Expected Ratio} (EoR), namely the expectation of the ratio between algorithm's and prophet's value. EoR offers robust guarantees, e.g., a constant EoR implies achieving a constant fraction of the offline optimum with constant probability. For the special case of single-choice problems the EoR coincides with the well-studied notion of probability of selecting the maximum. However, the EoR naturally generalizes the probability of selecting the maximum for combinatorial constraints, which are the main focus of this paper. Specifically, we establish two reductions: for every constraint, RoE and the EoR are at most a constant factor apart. Additionally, we show that the EoR is a stronger benchmark than the RoE in that, for every instance (constraint and distribution), the RoE is at least a constant fraction of the EoR, but not vice versa. Both these reductions imply a wealth of EoR results in multiple settings where RoE results are known.
Machine Learning Model Sizes and the Parameter Gap

We study trends in model size of notable machine learning systems over time using a curated dataset. From 1950 to 2018, model size in language models increased steadily by seven orders of magnitude. The trend then accelerated, with model size increasing by another five orders of magnitude in just 4 years from 2018 to 2022. Vision models grew at a more constant pace, totaling 7 orders of magnitude of growth between 1950 and 2022. We also identify that, since 2020, there have been many language models below 20B parameters, many models above 70B parameters, but a scarcity of models in the 20-70B parameter range. We refer to that scarcity as the parameter gap. We provide some stylized facts about the parameter gap and propose a few hypotheses to explain it. The explanations we favor are: (a) increasing model size beyond 20B parameters requires adopting different parallelism techniques, which makes mid-sized models less cost-effective, (b) GPT-3 was one order of magnitude larger than previous language models, and researchers afterwards primarily experimented with bigger models to outperform it. While these dynamics likely exist, and we believe they play some role in generating the gap, we don't have high confidence that there are no other, more important dynamics at play.
Smooth Fictitious Play in Stochastic Games with Perturbed Payoffs and Unknown Transitions

Recent extensions to dynamic games of the well-known fictitious play learning procedure in static games were proved to globally converge to stationary Nash equilibria in two important classes of dynamic games (zero-sum and identical-interest discounted stochastic games). However, those decentralized algorithms need the players to know exactly the model (the transition probabilities and their payoffs at every stage). To overcome these strong assumptions, our paper introduces regularizations of the systems in (Leslie 2020; Baudin 2022) to construct a family of new decentralized learning algorithms which are model-free (players don't know the transitions and their payoffs are perturbed at every stage). Our procedures can be seen as extensions to stochastic games of the classical smooth fictitious play learning procedures in static games (where the players best responses are regularized, thanks to a smooth strictly concave perturbation of their payoff functions). We prove the convergence of our family of procedures to stationary regularized Nash equilibria in zero-sum and identical-interest discounted stochastic games. The proof uses the continuous smooth best-response dynamics counterparts, and stochastic approximation methods. When there is only one player, our problem is an instance of Reinforcement Learning and our procedures are proved to globally converge to the optimal stationary policy of the regularized MDP. In that sense, they can be seen as an alternative to the well known Q-learning procedure.
Isovector axial form factor of the nucleon from lattice QCD

The isovector axial form factor of the nucleon plays a key role in interpreting data from long-baseline neutrino oscillation experiments. We perform a lattice-QCD based calculation of this form factor, introducing a new method to directly extract its $z$-expansion from lattice correlators. Our final parametrization of the form factor, which extends up to spacelike virtualities of $0.7\,{\rm GeV}^2$ with fully quantified uncertainties, agrees with previous lattice calculations but is significantly less steep than neutrino-deuterium scattering data suggests.
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style

Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a problem has drawn much attention. However, current methods lack a full understanding of different types of OOD data: there are benign OOD data that can be properly adapted to enhance the learning performance, while other malign OOD data would severely degenerate the classification result. To Harness OOD data, this paper proposes a HOOD method that can leverage the content and style from each image instance to identify benign and malign OOD data. Particularly, we design a variational inference framework to causally disentangle content and style features by constructing a structural causal model. Subsequently, we augment the content and style through an intervention process to produce malign and benign OOD data, respectively. The benign OOD data contain novel styles but hold our interested contents, and they can be leveraged to help train a style-invariant model. In contrast, the malign OOD data inherit unknown contents but carry familiar styles, by detecting them can improve model robustness against deceiving anomalies. Thanks to the proposed novel disentanglement and data augmentation techniques, HOOD can effectively deal with OOD examples in unknown and open environments, whose effectiveness is empirically validated in three typical OOD applications including OOD detection, open-set semi-supervised learning, and open-set domain adaptation.
Multidome superconductivity in charge density wave kagome metals

Motivated by recent experiments on the kagome metals $A\text{V}_3\text{Sb}_5$ with $A=\text{K}$, $\text{Rb}$, and $\text{Cs}$, which show a charge density wave (CDW) at $\sim100$ K and the superconductivity at $\sim1$ K, we explore the onset of the superconductivity, taking the perspective that it descends from a parent CDW. We argue that viewing the superconductivity as a weak-coupling instability of a reconstructed (by the CDW) Fermi surface naturally explains the experimentally observed 'multidome' nonmonotonic dependence on pressure, with the 'peaks' in the superconducting critical temperature being associated with the Van Hove singularities of the reconstructed Fermi surface. This 'parent-child relationship' also naturally explains the large separation of energy scales between the superconductivity and the CDW. We discuss different possible pairing mechanisms and speculate that the CDW or reconstructed Pomeranchuk fluctuations may mediate the pairing interaction.