Miracle-dz/UniFAR-FTUs huggingface dataset:
DeTorrent: An Adversarial Padding-only Traffic Analysis Defense

While anonymity networks like Tor aim to protect the privacy of their users, they are vulnerable to traffic analysis attacks such as Website Fingerprinting (WF) and Flow Correlation (FC). Recent implementations of WF and FC attacks, such as Tik-Tok and DeepCoFFEA, have shown that the attacks can be effectively carried out, threatening user privacy. Consequently, there is a need for effective traffic analysis defense. There are a variety of existing defenses, but most are either ineffective, incur high latency and bandwidth overhead, or require additional infrastructure. As a result, we aim to design a traffic analysis defense that is efficient and highly resistant to both WF and FC attacks. We propose DeTorrent, which uses competing neural networks to generate and evaluate traffic analysis defenses that insert 'dummy' traffic into real traffic flows. DeTorrent operates with moderate overhead and without delaying traffic. In a closed-world WF setting, it reduces an attacker's accuracy by 61.5%, a reduction 10.5% better than the next-best padding-only defense. Against the state-of-the-art FC attacker, DeTorrent reduces the true positive rate for a . 00001 false positive rate to about . 12, which is less than half that of the next-best defense. We also demonstrate DeTorrent's practicality by deploying it alongside the Tor network and find that it maintains its performance when applied to live traffic.
Sparse Resource Allocation for Spreading Processes on Temporal-Switching Networks

Spreading processes, e.g. epidemics, wildfires and rumors, are often modeled on static networks. However, their underlying network structures, e.g. changing contacts in social networks, different weather forecasts for wildfires, are due to ever-changing circumstances inherently time-varying in nature. In this paper, we therefore, propose an optimization framework for sparse resource allocation for control of spreading processes over temporal networks with known connectivity patterns. We use convex optimization, in particular exponential cone programming, and dynamic programming techniques to bound and minimize the risk of an undetected outbreak by allocating budgeted resources each time step. We demonstrate with misinformation, epidemic and wildfire examples how the method can provide targeted allocation of resources.
Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning

We reconstruct the extra-galactic gamma-ray source-count distribution, or dN/dS, of resolved and unresolved sources by adopting machine learning techniques. Specifically, we train a convolutional neural network on synthetic 2-dimensional sky-maps, which are built by varying parameters of underlying source-counts models and incorporate the Fermi-LAT instrumental response functions. The trained neural network is then applied to the Fermi-LAT data, from which we estimate the source count distribution down to flux levels a factor of 50 below the Fermi-LAT threshold. We perform our analysis using 14 years of data collected in the (1,10) GeV energy range. The results we obtain show a source count distribution which, in the resolved regime, is in excellent agreement with the one derived from cataloged sources, and then extends as dN/dS ∼ S -2 in the unresolved regime, down to fluxes of 5 · 10-12 cm-2 s-1. The neural network architecture and the devised methodology have the flexibility to enable future analyses to study the energy dependence of the source-count distribution.
Recommender Systems: A Primer

Personalized recommendations have become a common feature of modern online services, including most major e-commerce sites, media platforms and social networks. Today, due to their high practical relevance, research in the area of recommender systems is flourishing more than ever. However, with the new application scenarios of recommender systems that we observe today, constantly new challenges arise as well, both in terms of algorithmic requirements and with respect to the evaluation of such systems. In this paper, we first provide an overview of the traditional formulation of the recommendation problem. We then review the classical algorithmic paradigms for item retrieval and ranking and elaborate how such systems can be evaluated. Afterwards, we discuss a number of recent developments in recommender systems research, including research on session-based recommendation, biases in recommender systems, and questions regarding the impact and value of recommender systems in practice.
Unit Root Tests in ARMA Models with Data-Dependent Methods for the Selection of the Truncation Lag

Abstract We analyze the choice of the truncation lag in the context of the Said-Dickey test for the presence of a unit root in a general autoregressive moving average model. It is shown that a deterministic relationship between the truncation lag and the sample size is dominated by data-dependent rules that take sample information into account. In particular, we study data-dependent rules that are not constrained to satisfy the lower bound condition imposed by Said-Dickey. Akaike's information criterion falls into this category. The analytical properties of the truncation lag selected according to a class of information criteria are compared to those based on sequential testing for the significance of coefficients on additional lags. The asymptotic properties of the unit root test under various methods for selecting the truncation lag are analyzed, and simulations are used to show their distinctive behavior in finite samples. Our results favor methods based on sequential tests over those based on informat.
Leaving Reality to Imagination: Robust Classification via Generated Datasets

Recent research on robustness has revealed significant performance gaps between neural image classifiers trained on datasets that are similar to the test set, and those that are from a naturally shifted distribution, such as sketches, paintings, and animations of the object categories observed during training. Prior work focuses on reducing this gap by designing engineered augmentations of training data or through unsupervised pretraining of a single large model on massive in-the-wild training datasets scraped from the Internet. However, the notion of a dataset is also undergoing a paradigm shift in recent years. With drastic improvements in the quality, ease-of-use, and access to modern generative models, generated data is pervading the web. In this light, we study the question: How do these generated datasets influence the natural robustness of image classifiers? We find that Imagenet classifiers trained on real data augmented with generated data achieve higher accuracy and effective robustness than standard training and popular augmentation strategies in the presence of natural distribution shifts. We analyze various factors influencing these results, including the choice of conditioning strategies and the amount of generated data. Additionally, we find that the standard ImageNet classifiers suffer a performance degradation of upto 20\% on the generated data, indicating their fragility at accurately classifying the objects under novel variations. Lastly, we demonstrate that the image classifiers, which have been trained on real data augmented with generated data from the base generative model, exhibit greater resilience to natural distribution shifts compared to the classifiers trained on real data augmented with generated data from the finetuned generative model on the real data. The code, models, and datasets are available at https://github.com/Hritikbansal/generative-robustness.
Temperature dependent anisotropy and two-band superconductivity revealed by lower critical field in organic superconductor $\kappa$-(BEDT-TTF)$_{2}$Cu[N(CN)$_{2}$]Br

Resistivity and magnetization have been measured at different temperatures and magnetic fields in organic superconductors $\kappa$-(BEDT-TTF)$_{2}$Cu[N(CN)$_{2}$]Br. The lower critical field and upper critical field are determined, which allow to depict a complete phase diagram. Through the comparison between the upper critical fields with magnetic field perpendicular and parallel to the conducting ac-planes, and the scaling of the in-plane resistivity with field along different directions, we found that the anisotropy $\mathit{\Gamma}$ is strongly temperature dependent. It is found that $\mathit{\Gamma}$ is quite large (above 20) near $T_{\rm c}$, which satisfies the 2D model, but approaches a small value in the low-temperature region. The 2D-Tinkham model can also be used to fit the data at high temperatures. This is explained as a crossover from the orbital depairing mechanism in high-temperature and low-field region to the paramagnetic depairing mechanism in the high-field and low-temperature region. The temperature dependence of lower critical field $H_{\rm c1} (T)$ shows a concave shape in wide temperature region. It is found that neither a single $d$-wave nor a single $s$-wave gap can fit the $H_{\rm c1} (T)$, however a two-gap model containing an $s$-wave and a $d$-wave can fit the data rather well, suggesting two-band superconductivity and an unconventional pairing mechanism in this organic superconductor.
Clustercentric Distance or Local Density? It Depends on Galaxy Morphology

Determining which between projected local density and distance from the cluster center plays a major role in regulating morphological fractions in clusters is a longstanding debate. Reaching a definitive answer will shed light on the main physical mechanisms at play in the most extreme environments. Here we make use of the data from the OmegaWINGS survey, currently the largest survey of clusters in the local universe extending beyond 2 virial radii from the cluster cores, to extend the previous analysis outside the virial radius. Local density and clustercentric distance seems to play different roles for galaxies of different morphology: the fraction of elliptical galaxies mainly depends on local density, suggesting that their formation was linked to the primordial densities, which now correspond to the cluster cores. Only the fraction of low-mass ellipticals shows an anticorrelation with clustercentric distance, suggesting a different origin for these objects. Excluding elliptical galaxies, the relative fraction of S0s and spirals instead depends on local density only far from the cluster cores, while within the virial radius their proportion is regulated by distance, suggesting that cluster-specific processes halt the star formation and transform Sp galaxies into S0s. This interpretation is supported by literature results on the kinematical analysis of early- and late-type galaxies, according to which fast and slow rotators have distinct dependencies on halo mass and local density.
The Difference-of-Log-Normals Distribution: Properties, Estimation, and Growth

This paper describes the Difference-of-Log-Normals (DLN) distribution. A companion paper makes the case that the DLN is a fundamental distribution in nature, and shows how a simple application of the CLT gives rise to the DLN in many disparate phenomena. Here, I characterize its PDF, CDF, moments, and parameter estimators; generalize it to N-dimensions using spherical distribution theory; describe methods to deal with its signature ``double-exponential'' nature; and use it to generalize growth measurement to possibly-negative variates distributing DLN. I also conduct Monte-Carlo experiments to establish some properties of the estimators and measures described.
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks

Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and storage costs. Examples include \textit{graph sparsification} that samples a subgraph to reduce the amount of data aggregation and \textit{model sparsification} that prunes the neural network to reduce the number of trainable weights. Despite the empirical successes in reducing the training cost while maintaining the test accuracy, the theoretical generalization analysis of sparse learning for GNNs remains elusive. To the best of our knowledge, this paper provides the first theoretical characterization of joint edge-model sparse learning from the perspective of sample complexity and convergence rate in achieving zero generalization error. It proves analytically that both sampling important nodes and pruning neurons with the lowest-magnitude can reduce the sample complexity and improve convergence without compromising the test accuracy. Although the analysis is centered on two-layer GNNs with structural constraints on data, the insights are applicable to more general setups and justified by both synthetic and practical citation datasets.
Generalization Bounds using Data-Dependent Fractal Dimensions

Providing generalization guarantees for modern neural networks has been a crucial task in statistical learning. Recently, several studies have attempted to analyze the generalization error in such settings by using tools from fractal geometry. While these works have successfully introduced new mathematical tools to apprehend generalization, they heavily rely on a Lipschitz continuity assumption, which in general does not hold for neural networks and might make the bounds vacuous. In this work, we address this issue and prove fractal geometry-based generalization bounds without requiring any Lipschitz assumption. To achieve this goal, we build up on a classical covering argument in learning theory and introduce a data-dependent fractal dimension. Despite introducing a significant amount of technical complications, this new notion lets us control the generalization error (over either fixed or random hypothesis spaces) along with certain mutual information (MI) terms. To provide a clearer interpretation to the newly introduced MI terms, as a next step, we introduce a notion of"geometric stability"and link our bounds to the prior art. Finally, we make a rigorous connection between the proposed data-dependent dimension and topological data analysis tools, which then enables us to compute the dimension in a numerically efficient way. We support our theory with experiments conducted on various settings.
The Structure of Stars of Very Low Mass.

Completely convective models were constructed for stars of masses 0.09, 0.08, 0.07, 0.06, 0.05, and 0.04 (solar units), taking into account the non- relativistic degeneracy of the stellar material. It is shown that there is a lower limit to the mass of a main-sequence star. The stars with mass less than this limit become completely degenerate stars or black dwarfs as a consequence of gravitational contraction, and, therefore, they never go through the normal stellar evolution. (auth)
Relaxation effects observed for turbulent flow over a wavy surface

Measurements are presented for different flow rates of the time-averaged wall shear stress and of the root-mean-square value of the turbulent fluctuations along a small-amplitude sinusoidally shaped solid surface. The stresses are found to have a variation along the wave surface which is also sinusoidal. The influence of flow rate and of wavelength on the amplitude and phase angle can be correlated by using a wave-number α+ made dimensionless with wall parameters. It is found that for α+ > 10−2 a frozen-turbulence assumption can be made whereby the influence of the wave-induced variation of the mixing length can be ignored. For α+ < 10−4 the flow can be described by assuming the Reynolds stresses are given by an equilibrium assumption. The relaxation from this equilibrium condition is characterized by a sharp change in the phase angle for 6 × 10−4 < α+ < 10−3. This relaxation is associated with physical processes in the viscous wall region which are not yet understood. It is argued that these are principally related to the wave-induced variation of the pressure gradient. The wave-induced variation of the turbulent fluctuations in the wall shear stress also indicate a relaxation in that the maximum turbulence intensity is located in a region of favourable pressure gradient.
Estimation of transport coefficients of dense hadronic and quark matter

In this study, we calculated transport coefficients including the shear viscosity and electrical conductivity relative to the density of dense hadronic and quark matter. By considering the simple massless limit for the quark matter and two different effective models for the hadronic matter, we estimated the transport coefficients of the two phases separately. Accordingly, density profiles of the transport coefficients were depicted in two parts: the phase-space part and the relaxation time part. From calculating the shear viscosity to density ratio, we also explored the nearly perfect fluid domain of the quark and hadronic matter.
Estimating Bowhead Whale Population Size and Rate of Increase from the 1993 Census

Abstract Estimating the population size and rate of increase of bowhead whales, Balaena mysticetus, is important because bowheads were the first species of great whale for which commerical whaling stopped and so their status indicates the recovery prospects of other great whales, and also because this information is used by the International Whaling Commission (IWC) to set the aboriginal subsistence whaling quota for Alaskan Eskimos. We describe the 1993 visual and acoustic census off Point Barrow, Alaska, which provides the best data available for estimating these quantities. We outline the definitive version of two statistical methods for estimating the population: the generalized removal method and the Bayes empirical Bayes method. The two methods give results that are close. The estimate of bowhead population size most recently accepted by the IWC Scientific Committee, 8,200 with 95% estimation interval from 7,200 to 9,400, is based on the Bayes empirical Bayes posterior distribution presented here. T.
Alternative understanding for the enthalpy vs volume change upon structural relaxation of metallic glasses

It has been argued that the densification observed upon structural relaxation of metallic glasses can be explained not by the “free volume annihilation,” as it is custom to consider, but by the annealing of interstitialcy-like “defects” frozen-in upon glass production. A simple expression for the enthalpy release due to relaxation of defect-induced dilatational energy has been proposed. The expression is shown to be in agreement with recent detailed experimental data on the heat release occurring upon structural relaxation of Au-based metallic glass [J. Bunz and G. Wilde, J. Appl. Phys. 114, 223503 (2013)].
Vitamin D and Risk for Type 2 Diabetes in People With Prediabetes

BACKGROUND The role of vitamin D in people who are at risk for type 2 diabetes remains unclear. PURPOSE To evaluate whether administration of vitamin D decreases risk for diabetes among people with prediabetes. DATA SOURCES PubMed, Embase, and ClinicalTrials.gov from database inception through 9 December 2022. STUDY SELECTION Eligible trials that were specifically designed and conducted to test the effects of oral vitamin D versus placebo on new-onset diabetes in adults with prediabetes. DATA EXTRACTION The primary outcome was time to event for new-onset diabetes. Secondary outcomes were regression to normal glucose regulation and adverse events. Prespecified analyses (both unadjusted and adjusted for key baseline variables) were conducted according to the intention-to-treat principle. DATA SYNTHESIS Three randomized trials were included, which tested cholecalciferol, 20 000 IU (500 mcg) weekly; cholecalciferol, 4000 IU (100 mcg) daily; or eldecalcitol, 0.75 mcg daily, versus matching placebos. Trials were at low risk of bias. Vitamin D reduced risk for diabetes by 15% (hazard ratio, 0.85 [95% CI, 0.75 to 0.96]) in adjusted analyses, with a 3-year absolute risk reduction of 3.3% (CI, 0.6% to 6.0%). The effect of vitamin D did not differ in prespecified subgroups. Among participants assigned to the vitamin D group who maintained an intratrial mean serum 25-hydroxyvitamin D level of at least 125 nmol/L (≥50 ng/mL) compared with 50 to 74 nmol/L (20 to 29 ng/mL) during follow-up, cholecalciferol reduced risk for diabetes by 76% (hazard ratio, 0.24 [CI, 0.16 to 0.36]), with a 3-year absolute risk reduction of 18.1% (CI, 11.7% to 24.6%). Vitamin D increased the likelihood of regression to normal glucose regulation by 30% (rate ratio, 1.30 [CI, 1.16 to 1.46]). There was no evidence of difference in the rate ratios for adverse events (kidney stones: 1.17 [CI, 0.69 to 1.99]; hypercalcemia: 2.34 [CI, 0.83 to 6.66]; hypercalciuria: 1.65 [CI, 0.83 to 3.28]; death: 0.85 [CI, 0.31 to 2.36]). LIMITATIONS Studies of people with prediabetes do not apply to the general population. Trials may not have been powered for safety outcomes. CONCLUSION In adults with prediabetes, vitamin D was effective in decreasing risk for diabetes. PRIMARY FUNDING SOURCE None. (PROSPERO: CRD42020163522).
Retrospective Parametric Tests for Homogeneity of Data

The problem considered in this study is that of detecting a change in the unknown parameters of known distribution on the basis of a finite sequence of independent observations, assuming that if a change in the parameters of a distribution has occurred then it is unique. We examine various methods that have been suggested for this problem and suggest a uniform approach, based on likelihood ratio analysis. For tests derived this way we present approximations for levels of significance based on asymptotic analyses. The suggested tests meet the needs of specific problems (such as a one-sided alternative), for which general parametric case solutions have not been suggested explicitly before. We also find that rate of convergence of our asymptotics is fast, and provide accurate results for a level of significance of the suggested tests for sample sizes commonly observed in practice.
Climatological features of blocking anticyclones in the Northern Hemisphere

Blocking anticyclones have long been of interest to the atmospheric science community because of their profound effect on local and regional climates. Previous climatologies of blocking anticyclones have been performed using subjective or objective techniques to locate individual blocking events using observational data sets typically of greater than 10 years. In this study, a 3-year climatology of Northern Hemisphere blocking anticyclones was developed using ECMWF analyses to derive a comprehensive set of blocking anticyclone characteristics, including location, frequency, duration, intensity, size, seasonal and regional distribution, and relationship to precursor cyclones and jet streaks. Results show that preferred blocking regions were located over the eastern Atlantic Ocean, the Pacific Ocean, and the Ukraine/western Russia and that most blocks occurred in winter, as seen in other climatological studies. Block half-wavelengths, which averaged about 3000 km, were positively correlated with block intensity at the 99% confidence level. However, block duration, which averaged 8.6 days, was only weakly correlated with both size and intensity. Also, this study reveals that all 63 blocking anticyclones were preceded by an identifiable surface cyclone, which began its most rapid deepening 36 h or more prior to block onset. However, only 34 of these cyclones could be characterized as “explosively” developing, with half of these preceding winter season blocks and none preceding summer season blocks. A positive correlation was found between the intensity of blocking anticyclones and the intensity of the precursor cyclone development, significant at the 95% confidence level. This correlation was also found for events occurring over the oceanic regions. Finally, the intensity of the precursor cyclone development was correlated with other blocking characteristics and no significant relationships were found. DOI: 10.1034/j.1600-0870.1995. t01-3-00004.x