Miracle-dz/UniFAR-FTUs huggingface dataset:
Cosmology dependence of galaxy cluster scaling relations

The abundance of galaxy clusters as a function of mass and redshift is a well known powerful cosmological probe, which relies on underlying modelling assumptions on the mass-observable relations (MOR). Some of the MOR parameters can be constrained directly from multi-wavelength observations, as the normalization at some reference cosmology, the mass-slope, the redshift evolution, and the intrinsic scatter. However, the cosmology dependence of MORs cannot be tested with multi-wavelength observations alone. We use magneticum simulations to explore the cosmology dependence of galaxy cluster scaling relations. We run fifteen hydrodynamical cosmological simulations varying Ωm, Ωb, h0, and σ8 (around a reference cosmological model). The MORs considered are gas mass, baryonic mass, gas temperature, Y and velocity dispersion as a function of virial mass. We verify that the mass and redshift slopes and the intrinsic scatter of the MORs are nearly independent of cosmology with variations significantly smaller than current observational uncertainties. We show that the gas mass and baryonic mass sensitively depends only on the baryon fraction, velocity dispersion, and gas temperature on h0, and Y on both baryon fraction and h0. We investigate the cosmological implications of our MOR parametrization on a mock catalogue created for an idealized eROSITA-like experiment. We show that our parametrization introduces a strong degeneracy between the cosmological parameters and the normalization of the MOR. Finally, the parameter constraints derived at different overdensity (Δ500c), for X-ray bolometric gas luminosity, and for different subgrid physics prescriptions are shown in the appendix.
RNN-Test: Adversarial Testing Framework for Recurrent Neural Network Systems

While huge efforts have been investigated in the adversarial testing of convolutional neural networks (CNN), the testing for recurrent neural networks (RNN) is still limited to the classification context and leave threats for vast sequential application domains. In this work, we propose a generic adversarial testing framework RNN-Test. First, based on the distinctive structure of RNNs, we define three novel coverage metrics to measure the testing completeness and guide the generation of adversarial inputs. Second, we propose the state inconsistency orientation to generate the perturbations by maximizing the inconsistency of the hidden states of RNN cells. Finally, we combine orientations with coverage guidance to produce minute perturbations. Given the RNN model and the sequential inputs, RNN-Test will modify one character or one word out of the whole inputs based on the perturbations obtained, so as to lead the RNN to produce wrong outputs. For evaluation, we apply RNN-Test on two models of common RNN structure - the PTB language model and the spell checker model. RNN-Test efficiently reduces the performance of the PTB language model by increasing its test perplexity by 58.11%, and finds numbers of incorrect behaviors of the spell checker model with the success rate of 73.44% on average. With our customization, RNN-Test using the redefined neuron coverage as guidance could achieve 35.71% higher perplexity than original strategy of DeepXplore.
Linear Time Subgraph Counting, Graph Degeneracy, and the Chasm at Size Six

We consider the problem of counting all $k$-vertex subgraphs in an input graph, for any constant $k$. This problem (denoted sub-cnt$_k$) has been studied extensively in both theory and practice. In a classic result, Chiba and Nishizeki (SICOMP 85) gave linear time algorithms for clique and 4-cycle counting for bounded degeneracy graphs. This is a rich class of sparse graphs that contains, for example, all minor-free families and preferential attachment graphs. The techniques from this result have inspired a number of recent practical algorithms for sub-cnt$_k$. Towards a better understanding of the limits of these techniques, we ask: for what values of $k$ can sub-cnt$_k$ be solved in linear time? We discover a chasm at $k=6$. Specifically, we prove that for $k < 6$, sub-cnt$_k$ can be solved in linear time. Assuming a standard conjecture in fine-grained complexity, we prove that for all $k \geq 6$, sub-cnt$_k$ cannot be solved even in near-linear time.
Gradientless Descent: High-Dimensional Zeroth-Order Optimization

Zeroth-order optimization is the process of minimizing an objective $f(x)$, given oracle access to evaluations at adaptively chosen inputs $x$. In this paper, we present two simple yet powerful GradientLess Descent (GLD) algorithms that do not rely on an underlying gradient estimate and are numerically stable. We analyze our algorithm from a novel geometric perspective and present a novel analysis that shows convergence within an $\epsilon$-ball of the optimum in $O(kQ\log(n)\log(R/\epsilon))$ evaluations, for any monotone transform of a smooth and strongly convex objective with latent dimension $k < n$, where the input dimension is $n$, $R$ is the diameter of the input space and $Q$ is the condition number. Our rates are the first of its kind to be both 1) poly-logarithmically dependent on dimensionality and 2) invariant under monotone transformations. We further leverage our geometric perspective to show that our analysis is optimal. Both monotone invariance and its ability to utilize a low latent dimensionality are key to the empirical success of our algorithms, as demonstrated on BBOB and MuJoCo benchmarks.
Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms

The scattering transform is a multilayered wavelet-based deep learning architecture that acts as a model of convolutional neural networks. Recently, several works have introduced generalizations of the scattering transform for non-Euclidean settings such as graphs. Our work builds upon these constructions by introducing windowed and non-windowed graph scattering transforms based upon a very general class of asymmetric wavelets. We show that these asymmetric graph scattering transforms have many of the same theoretical guarantees as their symmetric counterparts. This work helps bridge the gap between scattering and other graph neural networks by introducing a large family of networks with provable stability and invariance guarantees. This lays the groundwork for future deep learning architectures for graph-structured data that have learned filters and also provably have desirable theoretical properties.
Unitarity from a smooth horizon?

Under semiclassical evolution, black holes retain a smooth horizon but fail to return information. Yet, the Ryu-Takayanagi prescription computes the boundary entropy expected from unitary CFT evolution. We demonstrate this in a novel setting with an asymptotic bulk detector, eliminating an assumption about the entanglement wedge of auxiliary systems. We consider three interpretations of this result. (i) At face value, information is lost in the bulk but not in the CFT. This conflicts with the AdS/CFT dictionary. (ii) No unique QFT state (pure or mixed) governs all detector responses to the bulk Hawking radiation. This conflicts with the existence of an S-matrix. (iii) Nonlocal couplings to the black hole interior cause asymptotic detectors to respond as though the radiation was pure, even though it is naively thermal. This invalidates the standard interpretation of the semiclassical state, including its smoothness at the horizon. We conclude that unitary boundary evolution requires asymptotic bulk detectors to become unambiguously pure at late times. We ask whether the RT prescription can still reproduce the boundary entropy in this bulk scenario. We find that this requires a substantial failure of semiclassical gravity in a low-curvature region, such as a firewall that purifies the Hawking radiation. Finally, we allow that the dual to semiclassical gravity may be an ensemble of unitary theories. This appears to relax the tensions we found: the ensemble average of out-states would be mixed, but the ensemble average of final entropies would vanish.
Momentum transfer by linearised eddies in turbulent channel flows

The presence and structure of an Orr-like inviscid mechanism are studied in fully developed, large-scale turbulent channel flow. Orr-like ‘bursts’ are defined by the relation between the amplitude and local tilting angle of the wall-normal velocity perturbations, and extracted by means of wavelet-based filters. They span the shear-dominated region of the flow, and their sizes and lifespans are proportional to the distance from the wall in the logarithmic layer, forming a self-similar eddy hierarchy consistent with Townsend’s attached-eddy model. Except for their amplitude, which has to be determined nonlinearly, linearised transient growth represents their evolution reasonably well. Conditional analysis, based on wavelet-filtered and low-pass-filtered velocity fields, reveals that bursts of opposite sign pair side-by-side to form tilted quasi-streamwise rollers, which align along the streaks of the streamwise velocity with the right sign to reinforce them, and that they preferentially cluster along pre-existing streak inhomogeneities. On the other hand, temporal analysis shows that consecutive rollers do not form simultaneously, suggesting that they incrementally trigger each other. This picture is similar to that of the streak-vortex cycle of the buffer layer, and the properties of the bursts suggest that they are different manifestations of the well-known attached $\text{Q}_{2}$–$\text{Q}_{4}$ events of the Reynolds stress.
Implicit Hamiltonian Monte Carlo for Sampling Multiscale Distributions

Hamiltonian Monte Carlo (HMC) has been widely adopted in the statistics community because of its ability to sample high-dimensional distributions much more efficiently than other Metropolis-based methods. Despite this, HMC often performs sub-optimally on distributions with high correlations or marginal variances on multiple scales because the resulting stiffness forces the leapfrog integrator in HMC to take an unreasonably small stepsize. We provide intuition as well as a formal analysis showing how these multiscale distributions limit the stepsize of leapfrog and we show how the implicit midpoint method can be used, together with Newton-Krylov iteration, to circumvent this limitation and achieve major efficiency gains. Furthermore, we offer practical guidelines for when to choose between implicit midpoint and leapfrog and what stepsize to use for each method, depending on the distribution being sampled. Unlike previous modifications to HMC, our method is generally applicable to highly non-Gaussian distributions exhibiting multiple scales. We illustrate how our method can provide a dramatic speedup over leapfrog in the context of the No-U-Turn sampler (NUTS) applied to several examples.
Thermodynamic geometry for charged Gauss-Bonnet black holes in AdS spacetimes

In this paper, we study the thermodynamic geometry of charged Gauss-Bonnet black holes (and Reissner-Nordstr\"{o}m black holes, for the sake of comparison) in AdS: in both $(T,V)$- and $(S,P)$-planes. The thermodynamic phase space is known to have an underlying contact and metric structure; Ruppeiner geometry then naturally arises in this framework. Sign of Ruppeiner curvature can be used to probe the nature of interactions between the black hole microstructures. It is found that there are both attraction and repulsion dominated regions which are in general determined by the electric charge, Gauss-Bonnet coupling and horizon radius of the black hole. The results are physically explained by considering that these black hole systems consist of charged as well as neutral microstructures much like a binary mixture of fluids.
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs

We present CP-JKU submission to MediaEval 2019; a Receptive Field-(RF)-regularized and Frequency-Aware CNN approach for tagging music with emotion/mood labels. We perform an investigation regarding the impact of the RF of the CNNs on their performance on this dataset. We observe that ResNets with smaller receptive fields -- originally adapted for acoustic scene classification -- also perform well in the emotion tagging task. We improve the performance of such architectures using techniques such as Frequency Awareness and Shake-Shake regularization, which were used in previous work on general acoustic recognition tasks.
Unsupervised Pre-training for Natural Language Generation: A Literature Review

Recently, unsupervised pre-training is gaining increasing popularity in the realm of computational linguistics, thanks to its surprising success in advancing natural language understanding (NLU) and the potential to effectively exploit large-scale unlabelled corpus. However, regardless of the success in NLU, the power of unsupervised pre-training is only partially excavated when it comes to natural language generation (NLG). The major obstacle stems from an idiosyncratic nature of NLG: Texts are usually generated based on certain context, which may vary with the target applications. As a result, it is intractable to design a universal architecture for pre-training as in NLU scenarios. Moreover, retaining the knowledge learned from pre-training when learning on the target task is also a non-trivial problem. This review summarizes the recent efforts to enhance NLG systems with unsupervised pre-training, with a special focus on the methods to catalyse the integration of pre-trained models into downstream tasks. They are classified into architecture-based methods and strategy-based methods, based on their way of handling the above obstacle. Discussions are also provided to give further insights into the relationship between these two lines of work, some informative empirical phenomenons, as well as some possible directions where future work can be devoted to.
Debtors at Play: Gaming Behavior and Consumer Credit Risk

Exploiting a unique high-frequency, individual-level database, we (1) construct individual-level, incentive-compatible proxies of impulsivity based on video gaming behavior and (2) use these proxies to evaluate predictions concerning how impulsivity shapes individuals’ responses to a relaxation of credit constraints as captured by receiving a credit card. We discover that precard gaming intensity—as measured by the frequency and amount of game expenditures—is strongly and positively associated with (a) the probability of defaulting on credit card debt in the future, (b) postcard expenditures on luxury and addictive items, (c) surges in consumption spending immediately after receiving the credit card, and (d) rapid debt accumulation after obtaining the card. Differences in financial literacy, income, income variability, education, and demographics do not drive the results. The results are consistent with (1) neurological and psychological studies stressing that excessive gaming is associated with impulse control deficiencies and (2) behavioral theories stressing that impulsivity, i.e., time-inconsistent preferences for immediate gratification and ineffective strategies for avoiding myopic cues and temptations, substantially influence individual expenditure patterns and borrowing decisions when liquidity constraints are relaxed. This paper was accepted by Kay Giesecke, finance. Funding: C. Lin acknowledges the financial support from the National Natural Science Foundation of China [Project 72192841] and the Research Grant Council of the Hong Kong SAR, China [Project T35-710/20-R]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023. 4931 .
Awareness Regarding Tuberculosis in A Rural Population of Delhi

Research question: How aware are the rural people in Delhi about tuberculosis? Objectives: 1. To assess the knowledge about symptoms mode of transmission causation prevention and treatment relating to tuberculosis in a rural population of Delhi. 2. To study relationship of level of awareness with age sex and educational status. Study design: Cross-sectional. Setting: Rural field practice area village Barwala Delhi. Participants: Head of the family or an adult member from every third household constituting a total of 213 individuals. Study variables: Awareness about tuberculosis age sex educational status. Statistical analysis: Proportions. Results: 213 individuals were interviewed. Cough with sputum (73.7%) was the most common symptom known 95.3% were aware that tuberculosis could spread to others with 65% being aware of air as a route 48.4% knew an infectious agent is the cause of tuberculosis. Females and those without schooling were significantly less aware of various aspects of tuberculosis. Majority (73.7%) stated government health facilities as place of choice for treatment. Misconceptions like separate utensils and food for the patient and consumption of tortoise meat for treatment were observed. Doctors and health care workers were cited by majority (50.2%) as the source of information. (authors)
Graph Spanners in the Message-Passing Model

Graph spanners are sparse subgraphs which approximately preserve all pairwise shortest-path distances in an input graph. The notion of approximation can be additive, multiplicative, or both, and many variants of this problem have been extensively studied. We study the problem of computing a graph spanner when the edges of the input graph are distributed across two or more sites in an arbitrary, possibly worst-case partition, and the goal is for the sites to minimize the communication used to output a spanner. We assume the message-passing model of communication, for which there is a point-to-point link between all pairs of sites as well as a coordinator who is responsible for producing the output. We stress that the subset of edges that each site has is not related to the network topology, which is fixed to be point-to-point. While this model has been extensively studied for related problems such as graph connectivity, it has not been systematically studied for graph spanners. We present the first tradeoffs for total communication versus the quality of the spanners computed, for two or more sites, as well as for additive and multiplicative notions of distortion. We show separations in the communication complexity when edges are allowed to occur on multiple sites, versus when each edge occurs on at most one site. We obtain nearly tight bounds (up to polylog factors) for the communication of additive $2$-spanners in both the with and without duplication models, multiplicative $(2k-1)$-spanners in the with duplication model, and multiplicative $3$ and $5$-spanners in the without duplication model. Our lower bound for multiplicative $3$-spanners employs biregular bipartite graphs rather than the usual Erdős girth conjecture graphs and may be of wider interest.
Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting

In this paper, we study the non-stationary online second price auction problem. We assume that the seller is selling the same type of items in T rounds by the second price auction, and she can set the reserve price in each round. In each round, the bidders draw their private values from a joint distribution unknown to the seller. Then, the seller announced the reserve price in this round. Next, bidders with private values higher than the announced reserve price in that round will report their values to the seller as their bids. The bidder with the highest bid larger than the reserved price would win the item and she will pay to the seller the price equal to the second-highest bid or the reserve price, whichever is larger. The seller wants to maximize her total revenue during the time horizon T while learning the distribution of private values over time. The problem is more challenging than the standard online learning scenario since the private value distribution is non-stationary, meaning that the distribution of bidders' private values may change over time, and we need to use the non-stationary regret to measure the performance of our algorithm. To our knowledge, this paper is the first to study the repeated auction in the non-stationary setting theoretically. Our algorithm achieves the non-stationary regret upper bound Õ(min{√S T, V¯⅓T⅔), where S is the number of switches in the distribution, and V¯ is the sum of total variation, and S and V¯ are not needed to be known by the algorithm. We also prove regret lower bounds Ω(√S T) in the switching case and Ω(V¯⅓T⅔) in the dynamic case, showing that our algorithm has nearly optimal non-stationary regret.
RWF-2000: An Open Large Scale Video Database for Violence Detection

In recent years, surveillance cameras are widely deployed in public places, and the general crime rate has been reduced significantly due to these ubiquitous devices. Usually, these cameras provide cues and evidence after crimes are conducted, while they are rarely used to prevent or stop criminal activities in time. It is both time and labor consuming to manually monitor a large amount of video data from surveillance cameras. Therefore, automatically recognizing violent behaviors from video signals becomes essential. This paper summarizes several existing video datasets for violence detection and proposes the RWF-2000 database with 2,000 videos captured by surveillance cameras in real-world scenes. Also, we present a new method that utilizes both the merits of 3D-CNNs and optical flow, namely Flow Gated Network. The proposed approach obtains an accuracy of 87.25% on the test set of our proposed database. The database and source codes are currently open to access11https://github.com/mchengny/RWF2000-Video-Database-for-Violence-Detection.
Nothing Wrong with Finite T: Non-Agreeing Accusative Impersonal Sentences

The purpose of the present article is to provide an analysis of different types of accusative impersonal sentences in Slavic languages (henceforth AIs) focusing on often so-called adversity impersonal sentences in Russian (cf. Babby 1994, 1998 among others) given in (1). The common property of AIs in Slavic is, that (i) they exhibit so-called default agreement, sometimes called “non-agreement” (in most cases singular, 3 person and/or neuter), and that (ii) no overt NP bearing nominative is present—instead the NP bearing structural case (if present at all) is marked with accusative. I will sketch an extension of the presented analysis to other impersonal sentences in Slavic in section 3.2. As often mentioned, AIs pose a problem for any theory of structural case linking the licensing of the accusative either to the presence of a clausemate NPNOM or an overt external argument (the latter assumption known as Burzio's Generalization). In the recent literature on AIs in Slavic (esp. on Russian adversity impersonals), sentences as in (1) have been analyzed by employing notions like “defectiveness” or “φ-incompleteness” to account for the agreement data and the lack of an NPNOM (cf. e.g. Lavine & Freidin 2002, Harves 2003, 2006). Most authors following standard Chomskyan minimalism (cf. Chomsky 2000, 2001) take defectiveness as a category's lack of (the full range of) φ-features. φ-features (if uninterpretable), in turn, are taken to be responsible for the respective category's status as an item (probe) requiring a relation (AGREE) to an element with interpretable φ-features (goal). Ultimately, if all locality conditions are obeyed, uninterpretable features get valued by interpretable ones of the goal of AGREE.