Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters

Although model-agnostic meta-learning (MAML) is a very successful algorithm in meta-learning practice, it can have high computational cost because it updates all model parameters over both the inner loop of task-specific adaptation and the outer-loop of meta initialization training. A more efficient algorithm ANIL (which refers to almost no inner loop) was proposed recently by Raghu et al. 2019, which adapts only a small subset of parameters in the inner loop and thus has substantially less computational cost than MAML as demonstrated by extensive experiments. However, the theoretical convergence of ANIL has not been studied yet. In this paper, we characterize the convergence rate and the computational complexity for ANIL under two representative inner-loop loss geometries, i.e., strongly-convexity and nonconvexity. Our results show that such a geometric property can significantly affect the overall convergence performance of ANIL. For example, ANIL achieves a faster convergence rate for a strongly-convex inner-loop loss as the number $N$ of inner-loop gradient descent steps increases, but a slower convergence rate for a nonconvex inner-loop loss as $N$ increases. Moreover, our complexity analysis provides a theoretical quantification on the improved efficiency of ANIL over MAML. The experiments on standard few-shot meta-learning benchmarks validate our theoretical findings.
Evolution of galaxies in groups in the Coma Supercluster

We take a close look at the galaxies in the Coma Supercluster and assess the role of the environment (in the form of cluster, group and supercluster filament) in their evolution, in particular examining the role of groups. We characterise the groups according to intrinsic properties such as richness and halo mass, as well as their position in the supercluster and proximity to the two rich clusters, Abell 1656 (Coma) and Abell 1367. We devise a new way of characterising the local environment using a kernel density estimator. We find that apart from the dominant effects of the galaxy mass, the effect of the environment on galaxies is a complex combination of the overdensities on various scales, which is characterised in terms of membership of groups, and also of the position of the galaxy on filaments and their proximity to the infall regions of clusters. Whether the gas can be turned into stars depends upon the level of pre-processing, which plays a role in how star formation is enhanced in a given environment. Our results are consistent with gas accreted in the cold mode from the filaments, being made available to enhance star formation. Finally, we show that the Abell~1367 end of the supercluster is in the process of assembly at present, leading to heightened star formation activity, in contrast with the Coma-end of the filament system.
A Deep Neural Network for Audio Classification with a Classifier Attention Mechanism

Audio classification is considered as a challenging problem in pattern recognition. Recently, many algorithms have been proposed using deep neural networks. In this paper, we introduce a new attention-based neural network architecture called Classifier-Attention-Based Convolutional Neural Network (CAB-CNN). The algorithm uses a newly designed architecture consisting of a list of simple classifiers and an attention mechanism as a classifier selector. This design significantly reduces the number of parameters required by the classifiers and thus their complexities. In this way, it becomes easier to train the classifiers and achieve a high and steady performance. Our claims are corroborated by the experimental results. Compared to the state-of-the-art algorithms, our algorithm achieves more than 10% improvements on all selected test scores.
Gravitational decoupling and the Picard-Lefschetz approach

In this work, we consider tunneling between non-metastable states in gravitational theories. Such processes arise in various contexts, e.g., in inflationary scenarios where the inflaton potential involves multiple fields or multiple branches. They are also relevant for bubble wall nucleation in some cosmological settings. However, we show that the transition amplitudes computed using the Euclidean method generally do not approach the corresponding field theory limit as $M_{p}\rightarrow \infty$. This implies that in the Euclidean framework, there is no systematic expansion in powers of $G_{N}$ for such processes. Such considerations also carry over directly to no-boundary scenarios involving Hawking-Turok instantons. In this note, we illustrate this failure of decoupling in the Euclidean approach with a simple model of axion monodromy and then argue that the situation can be remedied with a Lorentzian prescription such as the Picard-Lefschetz theory. As a proof of concept, we illustrate with a simple model how tunneling transition amplitudes can be calculated using the Picard-Lefschetz approach.
Bilateral Capital Flows: Transaction Patterns and Gravity

Holdings of cross-border bilateral assets are highly responsive to information frictions, market size, transaction costs, and trade ties. But empirical support using transactions data are constrained by the lack of comprehensive bilateral capital flows data covering large sample of economies for several years across investment and investor types. One expects that as information frictions weaken, transaction costs decline, and trade links strengthen, financial transactions between two economies will rise. This paper tests this hypothesis. Using bilateral Financial Accounts data from the Regional Balance of Payments Statistics of 10 advanced economies—yielding an unbalanced panel with 182 country pairs—for 2000-2016, the results provide strong evidence on the significance of information frictions, bilateral trade, transaction costs, and market size on bilateral capital flows. However, the findings show varying sensitivities of domestic and foreign investors to information asymmetries and trade ties. Moreover, investors appear to be more responsive to domestic transaction costs and foreign market size effects, than the converse. This study demonstrates an application of using bilateral capital flows data in revealing the patterns of international financial market segmentation still prevailing in cross-border financial transactions.
Never Trust Your Victim: Weaponizing Vulnerabilities in Security Scanners

The first step of every attack is reconnaissance, i.e., to acquire information about the target. A common belief is that there is almost no risk in scanning a target from a remote location. In this paper we falsify this belief by showing that scanners are exposed to the same risks as their targets. Our methodology is based on a novel attacker model where the scan author becomes the victim of a counter-strike. We developed a working prototype, called RevOK, and we applied it to 78 scanning systems. Out of them, 36 were found vulnerable to XSS. Remarkably, RevOK also found a severe vulnerability in Metasploit Pro, a mainstream penetration testing tool.
The Role of Verb Semantics in Hungarian Verb-Object Order

Hungarian is often referred to as a discourse-configurational language, since the structural position of constituents is determined by their logical function (topic or comment) rather than their grammatical function (e.g., subject or object). We build on work by Komlósy (1989) and argue that in addition to discourse context, the lexical semantics of the verb also plays a significant role in determining Hungarian word order. In order to investigate the role of lexical semantics in determining Hungarian word order, we conduct a large-scale, data-driven analysis on the ordering of 380 transitive verbs and their objects, as observed in hundreds of thousands of examples extracted from the Hungarian Gigaword Corpus. We test the effect of lexical semantics on the ordering of verbs and their objects by grouping verbs into 11 semantic classes. In addition to the semantic class of the verb, we also include two control features related to information structure, object definiteness and object NP weight, chosen to allow a comparison of their effect size to that of verb semantics. Our results suggest that all three features have a significant effect on verb-object ordering in Hungarian and among these features, the semantic class of the verb has the largest effect. Specifically, we find that stative verbs, such as fed 'cover', jelent 'mean' and övez 'surround', tend to be OV-preferring (with the exception of psych verbs which are strongly VO-preferring) and non-stative verbs, such as bírál 'judge', csökkent 'reduce' and csókol 'kiss', verbs tend to be VO-preferring. These findings support our hypothesis that lexical semantic factors influence word order in Hungarian.
Image-on-Scalar Regression via Deep Neural Networks

A research topic of central interest in neuroimaging analysis is to study the associations between the massive imaging data and a set of covariates. This problem is challenging, due to the ultrahigh dimensionality, the high and heterogeneous level of noise, and the limited sample size of the imaging data. To address those challenges, we develop a novel image-on-scalar regression model, where the spatially-varying coefficients and the individual spatial effects are all constructed through deep neural networks (DNN). Compared with the existing solutions, our method is much more flexible in capturing the complex patterns among the brain signals, of which the noise level and the spatial smoothness appear to be heterogeneous across different brain regions. We develop a hybrid stochastic gradient descent estimation algorithm, and derive the asymptotic properties when the number of voxels grows much faster than the sample size. We show that the new method outperforms the existing ones through both extensive simulations and two neuroimaging data examples.
Mispriced Index Option Portfolios

The optimal portfolio of a utility-maximizing investor trading in the S&P 500 index and cash, subject to proportional transaction costs, becomes stochastically dominated when overlaid with a zero-net-cost portfolio of S&P 500 options bought at their ask and written at their bid price in most months over 1990-2013. Dominance is prevalent when the ATM-IV is high, right skew is low, and option maturity is short. The portfolios include mostly calls and positions are overwhelmingly short. Similar results obtain with options on the CAC and DAX indices. The results are explained neither by priced factors nor a non-monotonic stochastic discount factor.
Building Low-Resource NER Models Using Non-Speaker Annotations

In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it. Cross-lingual methods have had notable success in addressing these concerns, but in certain common circumstances, such as insufficient pre-training corpora or languages far from the source language, their performance suffers. In this work we propose a complementary approach to building low-resource Named Entity Recognition (NER) models using “non-speaker” (NS) annotations, provided by annotators with no prior experience in the target language. We recruit 30 participants in a carefully controlled annotation experiment with Indonesian, Russian, and Hindi. We show that use of NS annotators produces results that are consistently on par or better than cross-lingual methods built on modern contextual representations, and have the potential to outperform with additional effort. We conclude with observations of common annotation patterns and recommended implementation practices, and motivate how NS annotations can be used in addition to prior methods for improved performance.
Recent Advances in Empirical Analysis of Financial Markets: Industrial Organization Meets Finance

In this article I provide a selective review of recent empirical work in the intersection of finance and industrial organization. I describe an estimation method, which can be applied quite widely in financial and other markets where a researcher needs to recover agents’ beliefs. Using four applications I illustrate how combining this method with data from auctions and with a theoretical model can be used to answer various economic questions of interest. I start with the primary market for sovereign debt, focusing on treasury bill auctions of the US and Canada. I show how auction data together with standard tools from Industrial Organization can be used to shed light on issues involving market structure, market power and front-running. I continue by looking at the Main Refinancing Operations of the European Central Bank, the main channel of monetary policy implementation in the EURO-zone, and illustrate how auction data can be used to learn about typically opaque over-the-counter lending markets. I also discuss how to use these data indirectly to learn about dynamics of banks’ financial health and of their balance sheets. I then turn to the discussion of recent progress on estimation of systemic risk. I finish with thoughts on how to estimate a whole demand system for financial assets. Thanks to Ali Hortaçsu for continued cooperation and to Ariel Pakes, Rob Porter, Azeem Shaikh and Moto Yogo for their comments. I am grateful for the financial support of the NSF (SES-1352305) and the Sloan Foundation. All remaining errors are mine. Department of Economics, Princeton University, NBER and CEPR
Incremental Lossless Graph Summarization

Given a fully dynamic graph, represented as a stream of edge insertions and deletions, how can we obtain and incrementally update a lossless summary of its current snapshot? As large-scale graphs are prevalent, concisely representing them is inevitable for efficient storage and analysis. Lossless graph summarization is an effective graph-compression technique with many desirable properties. It aims to compactly represent the input graph as (a) a summary graph consisting of supernodes (i.e., sets of nodes) and superedges (i.e., edges between supernodes), which provide a rough description, and (b) edge corrections which fix errors induced by the rough description. While a number of batch algorithms, suited for static graphs, have been developed for rapid and compact graph summarization, they are highly inefficient in terms of time and space for dynamic graphs, which are common in practice. In this work, we propose MoSSo, the first incremental algorithm for lossless summarization of fully dynamic graphs. In response to each change in the input graph, MoSSo updates the output representation by repeatedly moving nodes among supernodes. MoSSo decides nodes to be moved and their destinations carefully but rapidly based on several novel ideas. Through extensive experiments on 10 real graphs, we show MoSSo is (a) Fast and 'any time': processing each change in near-constant time (less than 0.1 millisecond), up to 7 orders of magnitude faster than running state-of-the-art batch methods, (b) Scalable: summarizing graphs with hundreds of millions of edges, requiring sub-linear memory during the process, and (c) Effective: achieving comparable compression ratios even to state-of-the-art batch methods.
Leveraging the Power of Prediction: Predictive Service Placement for Latency-Sensitive Mobile Edge Computing

Mobile edge computing (MEC) is emerging to support delay-sensitive 5G applications at the edge of mobile networks. When a user moves erratically among multiple MEC nodes, the challenge of how to dynamically migrate its service to maintain service performance (i.e., user-perceived latency) arises. However, frequent service migration can significantly increase operational cost, incurring the conflict between improving performance and reducing cost. To address these mis-aligned objectives, this paper studies the performance optimization of mobile edge service placement under the constraint of long-term cost budget. It is challenging because the budget involves the future uncertain information (e.g., user mobility). To overcome this difficulty, we devote to leveraging the power of prediction and advocate predictive service placement with predicted near-future information. By using two-timescale Lyapunov optimization method, we propose a $T$ -slot predictive service placement (PSP) algorithm to incorporate the prediction of user mobility based on a frame-based design. We characterize the performance bounds of PSP in terms of cost-delay trade-off theoretically. Furthermore, we propose a new weight adjustment scheme for the queue in each frame named PSP-WU to exploit the historical queue information, which greatly reduces the length of queue while improving the quality of user-perceived latency. Rigorous theoretical analysis and extensive evaluations using realistic data traces demonstrate the superior performance of the proposed predictive schemes.
Irreversibility and Aggregate Investment

Investment is often irreversible, in that installed capital has little or no value unless used in production. In the presence of ongoing uncertainty, an individual firm's irreversible investment policy optimally alternates short bursts of positive gross investment to periods of inaction, when the installed capital stock is allowed to depreciate. The behavior of aggregate investment series is characterized by sluggish, continuous adjustment instead. We argue in this paper that aggregate dynamics should be interpreted in terms of unsynchronized irreversible investment decisions by heterogenous firms, rather than in terms of ad-hoc adjustment cost functions in a representative-agent framework. We propose a closed-form solution for a realistic model of sequential irreversible investment, characterize the aggregate implications of microeconomic irreversibility and idiosyncratic uncertainty, and interpret U.S. data in light of the theoretical results.
Noise2Inpaint: Learning Referenceless Denoising by Inpainting Unrolling

Deep learning based image denoising methods have been recently popular due to their improved performance. Traditionally, these methods are trained in a supervised manner, requiring a set of noisy input and clean target image pairs. More recently, self-supervised approaches have been proposed to learn denoising from noisy images only, without requiring clean ground truth during training. Succinctly, these methods assume that an image pixel is correlated with its neighboring pixels, while the noise is independent. In this work, building on these approaches and recent methods from image reconstruction, we introduce Noise2Inpaint (N2I), a training approach that recasts the denoising problem into a regularized image inpainting framework. This allows us to use an objective function, which can incorporate different statistical properties of the noise as needed. We use algorithm unrolling to unroll an iterative optimization for solving this objective function and train the unrolled network end-to-end. The training is self-supervised without requiring clean target images, where pixels in the noisy image are split into two disjoint sets. One of these is used to impose data fidelity in the unrolled network, while the other one defines the loss. We demonstrate that N2I performs successful denoising on real-world datasets, while preserving better details compared to its self-supervised counterpart Noise2Void.
Shrinking the Eigenvalues of M-Estimators of Covariance Matrix

A highly popular regularized (shrinkage) covariance matrix estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward the grand mean of the eigenvalues of the SCM. In this paper, a more general approach is considered in which the SCM is replaced by an M-estimator of scatter matrix and a fully automatic data adaptive method to compute the optimal shrinkage parameter with minimum mean squared error is proposed. Our approach permits the use of any weight function such as Gaussian, Huber's, Tyler's, or $t$ weight functions, all of which are commonly used in M-estimation framework. Our simulation examples illustrate that shrinkage M-estimators based on the proposed optimal tuning combined with robust weight function do not loose in performance to shrinkage SCM estimator when the data is Gaussian, but provide significantly improved performance when the data is sampled from an unspecified heavy-tailed elliptically symmetric distribution. Also, real-world and synthetic stock market data validate the performance of the proposed method in practical applications.
Interpolation and Learning with Scale Dependent Kernels

We study the learning properties of nonparametric ridge-less least squares. In particular, we consider the common case of estimators defined by scale dependent kernels, and focus on the role of the scale. These estimators interpolate the data and the scale can be shown to control their stability through the condition number. Our analysis shows that are different regimes depending on the interplay between the sample size, its dimensions, and the smoothness of the problem. Indeed, when the sample size is less than exponential in the data dimension, then the scale can be chosen so that the learning error decreases. As the sample size becomes larger, the overall error stop decreasing but interestingly the scale can be chosen in such a way that the variance due to noise remains bounded. Our analysis combines, probabilistic results with a number of analytic techniques from interpolation theory.
ENHANCED POWDER PANELS

Daniel C. Cyphers Scott A. Frederick John P. Haas Skyward, Ltd. Skyward, Ltd. Skyward, Ltd. 5100 Springfield Street 5100 Springfield Street 5100 Springfield Street Suite 418 Suite 418 Suite 418 Dayton, Ohio 45431-1264 Dayton, Ohio 45431-1264 Dayton, Ohio 45431-1264 Tel: (937) 252-2710, Ext. 102 Tel: (937) 252-2710, Ext. 103 Tel: (937) 252-2710, Ext. 104 dcyphers@skywardltd.com sfrederick@skywardltd.com jhaas@skywardltd.com
Nonrelativistic model of tetraquarks and predictions for their masses from fits to charmed and bottom meson data

We investigate a nonrelativistic model of tetraquarks, which are assumed to be compact and to consist of diquark-antidiquark pairs. We fit, for the first time, basically all currently known values for the measured masses of 45 mesons, including both charmed and bottom mesons, to the model and predict masses of tetraquarks as well as diquarks. In particular, we find masses of four axial-vector diquarks, i.e., $qc$, $cc$, $qb$, and $bb$, where $q = u,d$, and 24 ground-state tetraquarks, including both heavy-light tetraquarks ($qc\overline{qc}$ and $qb\overline{qb}$) and heavy tetraquarks ($cc\overline{cc}$ and $bb\overline{bb}$). In general, our results for the masses of $qb\overline{qb}$, $cc\overline{cc}$, and $bb\overline{bb}$ are largely comparable with other reported results, whereas our results for the masses of $qc\overline{qc}$ are slightly larger than what has been found earlier. Finally, we identify some of the obtained predictions for masses of tetraquarks with masses of experimental tetraquark candidates, and especially, we find that $\psi(4660)$, $Z_b(10610)$, and $Z_b(10650)$ could be described by the model.
Ayala, Francisco and De Ste Croix, Mark B and Sainz de Baranda, Pilar and Santonja, Fernando (2014). Inter-session reliability and sex-related differences in hamstrings total reaction time, pre-motor time and motor time during eccentric isokinetic

Ayala, Francisco, De Ste Croix, Mark B ORCID: 0000-00019911-4355, Sainz de Baranda, Pilar and Santonja, Fernando (2014) Inter-session reliability and sex-related differences in hamstrings total reaction time, pre-motor time and motor time during eccentric isokinetic contractions in recreational athlete. Journal of Electromyography and Kinesiology, 24 (2). pp. 200-206. doi:10.1016/j.jelekin. 2013.12. 006