Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Inference With Nonlinear Generative Models: Comments on Secure Learning

Unlike the classical linear model, nonlinear generative models have been addressed sparsely in the literature of statistical learning. This work aims to shed light on these models and their secrecy potential. To this end, we invoke the replica method to derive the asymptotic normalized cross entropy in an inverse probability problem whose generative model is described by a Gaussian random field with a generic covariance function. Our derivations further demonstrate the asymptotic statistical decoupling of the Bayesian estimator and specify the decoupled setting for a given nonlinear model. The replica solution depicts that strictly nonlinear models establish an all-or-nothing phase transition: there exists a critical load at which the optimal Bayesian inference changes from perfect to an uncorrelated learning. Based on this finding, we design a new secure coding scheme which achieves the secrecy capacity of the wiretap channel. This interesting result implies that strictly nonlinear generative models are perfectly secured without any secure coding. We justify this latter statement through the analysis of an illustrative model for perfectly secure and reliable inference.
On Uniform Boundedness Properties of SGD and its Momentum Variants

A theoretical, and potentially also practical, problem with stochastic gradient descent is that trajectories may escape to infinity. In this note, we investigate uniform boundedness properties of iterates and function values along the trajectories of the stochastic gradient descent algorithm and its important momentum variant. Under smoothness and $R$-dissipativity of the loss function, we show that broad families of step-sizes, including the widely used step-decay and cosine with (or without) restart step-sizes, result in uniformly bounded iterates and function values. Several important applications that satisfy these assumptions, including phase retrieval problems, Gaussian mixture models, and some neural network classifiers, are discussed in detail. We further extend the uniform boundedness of SGD and its momentum variant under the generalized dissipativity for the functions whose tails grow slower than quadratic functions. This includes some interesting applications, for example, Bayesian logistic regression and logistic regression with $\ell_1$ regularization.
TOI-3714 b and TOI-3629 b: Two Gas Giants Transiting M Dwarfs Confirmed with the Habitable-zone Planet Finder and NEID

We confirm the planetary nature of two gas giants discovered by the Transiting Exoplanet Survey Satellite to transit M dwarfs. TOI-3714 (V = 15.24, J = 11.74) is an M2 dwarf hosting a hot Jupiter (M p = 0.70 ± 0.03 M J and R p = 1.01 ± 0.03 R J ) on an orbital period of 2.154849 ± 0.000001 days with a resolved white dwarf companion. TOI-3629 (V = 14.63, J = 11.42) is an M1 dwarf hosting a hot Jupiter (M p = 0.26 ± 0.02 M J and R p =0.74 ± 0.02 R J ) on an orbital period of 3.936551−0.000006+0.000005 days. We characterize each transiting companion using a combination of ground-based and space-based photometry, speckle imaging, and high-precision velocimetry from the Habitable-zone Planet Finder and the NEID spectrographs. With the discovery of these two systems, there are now nine M dwarfs known to host transiting hot Jupiters. Among this population, TOI-3714 b (T eq = 750 ± 20 K and TSM = 98 ± 7) and TOI-3629 b (T eq = 690 ± 20 K and TSM = 80 ± 9) are warm gas giants amenable to additional characterization with transmission spectroscopy to probe atmospheric chemistry and, for TOI-3714, obliquity measurements to probe formation scenarios.
Semiclassical pseudodifferential operators and resolvent parametrices on manifolds with ends

We construct a parametrix of a resolvent of elliptic differential operators acting on half-densities on manifolds with ends. The construction is carried out by introducing suitable pseudodifferential operators compatible with the end structure. Our class of pseudodifferential operators and symbols is independent of the choice of Riemannian metric on the manifold and applicable to both of asymptotically conical and hyperbolic manifolds. As an application, we prove the essential self-adjointness of elliptic symmetric differential operators on manifolds.
Co-evolution of massive black holes and their host galaxies at high redshift: discrepancies from six cosmological simulations and the key role of JWST

The James Webb Space Telescope will have the power to characterize high-redshift quasars at z > 6 with an unprecedented depth and spatial resolution. While the brightest quasars at such redshift (i.e., with bolometric luminosity Lbol > 10 erg/s) provide us with key information on the most extreme objects in the Universe, measuring the black hole (BH) mass and Eddington ratios of fainter quasars with Lbol = 10 −10 erg/s opens a path to understand the build-up of more normal BHs at z > 6. In this paper, we show that the Illustris, TNG100, TNG300, Horizon-AGN, EAGLE, and SIMBA large-scale cosmological simulations do not agree on whether BHs at z > 4 are overmassive or undermassive at fixed galaxy stellar mass with respect to the MBH −M? scaling relation at z = 0 (BH mass offsets). Our conclusions are unchanged when using the local scaling relation produced by each simulation or empirical relations. We find that the BH mass offsets of the simulated faint quasar population at z > 4, unlike those of bright quasars, represent the BH mass offsets of the entire BH population, for all the simulations. Thus, a population of faint quasars with Lbol = 10 45 − 10 erg/s observed by JWST can provide key constraints on the assembly of BHs at high redshift. Moreover, this will help constraining the highredshift regime of cosmological simulations, including BH seeding, early growth, and co-evolution with the host galaxies. Our results also motivate the need for simulations of larger cosmological volumes down to z ∼ 6, with the same diversity of sub-grid physics, in order to gain statistics on the most extreme objects at high redshift.
Razmecheno: Named Entity Recognition from Digital Archive of Diaries “Prozhito”

The vast majority of existing datasets for Named Entity Recognition (NER) are built primarily on news, research papers and Wikipedia with a few exceptions, created from historical and literary texts. What is more, English is the main source for data for further labelling. This paper aims to fill in multiple gaps by creating a novel dataset “Razmecheno”, gathered from the diary texts of the project “Prozhito” in Russian. Our dataset is of interest for multiple research lines: literary studies of diary texts, transfer learning from other domains, low-resource or cross-lingual named entity recognition. Razmecheno comprises 1331 sentences and 14119 tokens, sampled from diaries, written during the Perestroika. The annotation schema consists of five commonly used entity tags: person, characteristics, location, organisation, and facility. The labelling is carried out on the crowdsourcing platfrom Yandex.Toloka in two stages. First, workers selected sentences, which contain an entity of particular type. Second, they marked up entity spans. As a result 1113 entities were obtained. Empirical evaluation of Razmecheno is carried out with off-the-shelf NER tools and by fine-tuning pre-trained contextualized encoders. We release the annotated dataset for open access.
Quantum Anomaly Detection with a Spin Processor in Diamond

In the processing of quantum computation, analyzing and learning the pattern of the quantum data are essential for many tasks. Quantum machine learning algorithms cannot only deal with the quantum states generated in the preceding quantum procedures, but also the quantum registers encoding classical problems. In this work, the anomaly detection of quantum states encoding audio samples with a three‐qubit quantum processor consisting of solid‐state spins in diamond is experimentally demonstrated. By training the quantum machine with a few normal samples, the quantum machine can detect the anomaly samples with a minimum error rate of 15.4%. These results show the power of quantum anomaly detection in dealing with machine learning tasks and the potential to detect abnormal output of quantum devices.
A Massive AGB Donor in Scutum X-1: Identification of the First Mira Variable in an X-Ray Binary

The symbiotic X-ray binary Sct X-1 was suggested to be the first known neutron star accreting from a red supergiant companion. Although known for nearly 50 yr, detailed characterization of the donor remains lacking, particularly due to the extremely high reddening toward the source (A V ≳ 25 mag). Here, we present (i) improved localization of the counterpart using Gaia and Chandra observations, (ii) the first broadband infrared spectrum (≈1–5 μm; R ≈ 2000) obtained with SpeX on the NASA Infrared Telescope Facility, and (iii) the J-band light curve from the Palomar Gattini-IR survey. The infrared spectrum is characterized by (i) deep water absorption features (H2O index ≈ 40%), (ii) strong TiO, VO, and CO features, and (iii) weak/absent CN lines. We show that these features are inconsistent with known red supergiants but suggest an M8-9 III–type O-rich Mira donor star. We report the discovery of large-amplitude (ΔJ ≈ 3.5 mag) periodic photometric variability, suggesting a pulsation period of 621 ± 36 (systematic) ± 8 (statistical) days, which we use to constrain the donor to be a relatively luminous Mira (M K = −8.6 ± 0.3 mag) at a distance of 3.6−0.7+0.8 kpc. Comparing these characteristics to recent models, we find the donor to be consistent with a ≈3–5 M ⊙ star at an age of ≈0.1–0.3 Gyr. Together, we show that Sct X-1 was previously misclassified as an evolved high-mass X-ray binary; instead, it is an intermediate-mass system with the first confirmed Mira donor in an X-ray binary. We discuss the implications of Mira donors in symbiotic X-ray binaries and highlight the potential of wide-field infrared time-domain surveys and broadband infrared spectroscopy to unveil their demographics.
MonLAD: Money Laundering Agents Detection in Transaction Streams

Given a stream of money transactions between accounts in a bank, how can we accurately detect money laundering agent accounts and suspected behaviors in real-time? Money laundering agents try to hide the origin of illegally obtained money by dispersive multiple small transactions and evade detection by smart strategies. Therefore, it is challenging to accurately catch such fraudsters in an unsupervised manner. Existing approaches do not consider the characteristics of those agent accounts and are not suitable to the streaming settings. Therefore, we propose MonLAD and MonLAD-W to detect money laundering agent accounts in a transaction stream by keeping track of their residuals and other features; we devise AnoScore algorithm to find anomalies based on the robust measure of statistical deviation. Experimental results show that MonLAD outperforms the state-of-the-art baselines on real-world data and finds various suspicious behavior patterns of money laundering. Additionally, several detected suspected accounts have been manually-verified as agents in real money laundering scenario.
Coded Caching in Networks With Heterogeneous User Activity

This work elevates coded caching networks from their purely information-theoretic framework to a stochastic setting, by exploring the effect of random user activity and by exploiting correlations in the activity patterns of different users. In particular, the work studies the $K$ -user cache-aided broadcast channel with a limited number of cache states (i.e., the content stored at the cache of a certain user), and explores the effect of cache state association strategies in the presence of arbitrary user activity levels; a combination that strikes at the very core of the coded caching problem and its crippling subpacketization bottleneck. We first present a statistical analysis of the average worst-case delay performance of such subpacketization-constrained (state-constrained) coded caching networks, and provide computationally efficient performance bounds as well as scaling laws for any arbitrary probability distribution of the user-activity levels. The achieved performance is a result of a novel user-to-cache state association algorithm that leverages the knowledge of probabilistic user-activity levels. We then follow a data-driven approach that exploits the prior history on user-activity levels and correlations, in order to predict interference patterns, and thus better design the caching algorithm. This optimized strategy is based on the principle that users that overlap more, interfere more, and thus have higher priority to secure complementary cache states. This strategy is proven here to be within a small constant factor from the optimal. Finally, the above analysis is validated numerically using synthetic data following the Pareto principle. To the best of our understanding, this is the first work that seeks to exploit user-activity levels and correlations, in order to map future interference and design optimized coded caching algorithms that better handle this interference.
Equivariant cyclic cocycles on the Boutet de Monvel symbol algebra

We construct periodic cyclic cocycles on the algebra of symbols of Boutet de Monvel operators and use them to interpret the index formula for elliptic pseudodifferential boundary value problems due to Fedosov as the Chern–Connes pairing of the classes in K-theory of elliptic symbols with cyclic cocycles. We also consider the equivariant case. Namely, we construct periodic cyclic cocycles on the crossed product of the algebra of symbols with a group acting on this algebra by automorphisms. Such crossed products arize in index theory of nonlocal boundary value problems with shift operators. 1 Cyclic cocycles on the Boutet de Monvel symbol algebra Boutet de Monvel symbol algebra. Consider M = R+ with the coordinates (x1, x2) and the boundary X = R defined by x2 = 0. Consider the algebra of symbols of Boutet de Monvel operators on M (see [1, 2]). The symbols are assumed to have compact supports. Recall that the elements of algebra A are the pairs ã = (a, aX) ∈ C ∞ c (S M)⊕ C c (S ∗X,B(H+ ⊕ C)), (1) where a is a smooth function on the cosphere bundle SM called the interior symbol satisfying the transmission property: for all k ∈ Z+ and arbitrary α ∈ Z+ the following equality holds: D x2D α ξ1 a(x1, 0, 0, ξ2) = (−1) D x2D α ξ1 a(x1, 0, 0,−ξ2), ξ2 6= 0, (2) where D ξ1 = ( −i ∂ ∂ξ1 )α . The function aX in (1) called the boundary symbol is a smooth operator function on SX = {(x1, ξ1) ∣x1 ∈ X, |ξ1| = 1} = R× {±1} of the form aX = ( Π+a|∂T M +Π g c Π′ξ2b r ) : H+ ⊕ C −→ H+ ⊕ C (3) Here 1 • H+ = Fx2→ξ2(S(R+)) is the space of Fourier images of the Schwartz space S(R+) of smooth rapidly decreasing at infinity functions on R+; • a ∈ C c (S M); r ∈ C c (S X); • b ∈ C c (T ∗X,H−); c ∈ C ∞ c (S ∗X,H+); • g ∈ C(T ∗X,H+ ⊗H−), where H− = F(S(R−)); • Π± : H+ ⊕H− → H± are projections, while Π ′ is the following functional Π : H+ ⊕H− −→ C, u(ξ2) 7−→ lim x2→0+ F ξ2→x2(u(ξ2)). Denote by A the unitization of the algebra of symbols of Boutet de Monvel operators described above. Periodic cyclic cohomology. Let us recall the definition of the periodic cyclic cohomology in terms of operators (b, B) (see [3]). Denote by C(A) = Hom(A,C) the space of (n + 1)-linear functionals φ(a0, ., an), where a0, . , an ∈ A. Such functionals are called n-cochains. We consider the following operators: • B0 : C (A) −→ C(A), (B0φ)(a0, ., an−1) = φ(1, a0, ., an−1)− (−1) φ(a0, ., an−1, 1); • N : C(A) −→ C(A) Nφ(a0, ., an−1) = n−1 ∑ j=0 (−1)φ(aj , ., aj−1); • B : C(A) −→ C(A), B = NB0. • the Hochschild differential b : C(A) −→ C(A) (bφ)(a0, ., an+1) = n ∑ i=0 (−1)φ(a0, ., aiai+1, ., an+1) + (−1) φ(an+1a0, ., an). From the relations b = 0, B = 0, bB +Bb = 0, it follows that the following periodic complex is defined . B+b −→ C(A) B+b −→ C(A) B+b −→ C(A) B+b −→ . , (4) where C(A) = ⊕ k C(A), C(A) = ⊕ k C(A). The periodic cyclic cohomology of A is the cohomology of the complex (4) denoted by HP (A) = H(C(A), B + b).
A Survey on Machine Learning-Based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G has enabled Ultra-Reliable Low Latency Communications (URLLC) to CAVs. However, while communication performance has been enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure (PKI) proposed by standardization bodies cannot solely defend against these attacks. Thus, in complementary of that, sophisticated systems should be designed to detect such attacks and attackers. Machine Learning (ML) has recently emerged as a key enabler to secure future roads. Various V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. However, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper comprehensively surveys and classifies ML-based MDSs as well as discusses and analyses them from security and ML perspectives. It also provides some learned lessons and recommendations for guiding the development, validation, and deployment of ML-based MDSs. Finally, this paper highlighted open research and standardization issues with some future directions.
Weighted biharmonic equations involving continous potentiel under exponential nonlinear growth

We deal with a weighted biharmonic problem in the unit ball of R. The non-linearity is assumed to have critical exponential growth in view of Adam’s type inequalities. The weight w(x) is of logarithm type and the potential V is a positive continuous function on B. It is proved that there is a nontrivial positive weak solution to this problem by the mountain Pass Theorem. We avoid the loss of compactness by proving a concentration compactness result and by a suitable asymptotic condition.
On the Optimality of Linear Index Coding Over the Fields With Characteristic Three

It has been known that the insufficiency of linear coding in achieving the optimal rate of the general index coding problem is rooted in its rate’s dependency on the field size. However, this dependency has been described only through the two well-known matroid instances, namely the Fano and non-Fano matroids, which, in turn, limits its scope only to the fields with characteristic two. In this paper, we extend this scope to demonstrate the reliance of linear index coding rate on fields with characteristic three. By constructing two index coding instances of size 29, we prove that for the first instance, linear coding is optimal only over the fields with characteristic three, and for the second instance, linear coding over any field with characteristic three can never be optimal. Then, a variation of the second instance is designed as the third index coding instance of size 58. For this instance, it is proved that while linear coding over any field with characteristic three cannot be optimal, there exists a nonlinear code over the fields with characteristic three, which achieves its optimal rate. Connecting the first and third index coding instances in two specific ways, called no-way and two-way connections, will lead to two new index coding instances of size 87 and 91, for which linear coding is outperformed by nonlinear codes. Another main contribution of this paper is the reduction of the key constraints on the space of the linear coding for the first and second index coding instances, each of size 29, into a matroid instance with the ground set of size 9, whose linear representability is dependent on the fields with characteristic three. The proofs and discussions provided in this paper through using these two relatively small matroid instances will shed light on the underlying reason causing the linear coding to become insufficient for the general index coding problem.
PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learning

State-of-the-art approaches to ObjectGoal navigation (ObjectNav) rely on reinforcement learning and typically require significant computational resources and time for learning. We propose Potential functions for ObjectGoal Navigation with Interaction-free learning (PONI), a modular approach that disentangles the skills of 'where to look? ’ for an object and 'how to navigate to $(x,\ y)$? ’. Our key insight is that 'where to look? ’ can be treated purely as a perception problem, and learned without environment interactions. To address this, we propose a network that predicts two complementary potential functions conditioned on a semantic map and uses them to decide where to look for an unseen object. We train the potential function network using supervised learning on a passive dataset of top-down semantic maps, and integrate it into a modular framework to perform ObjectNav. Experiments on Gibson and Matterport3D demonstrate that our method achieves the stateof-the-art for ObjectNav while incurring up to $1,600\times less$ computational cost for training. Code and pre-trained models are available.11Website: https://vision.cs. utexas.edu/projects/poni/
DRAWNAPART: A Device Identification Technique based on Remote GPU Fingerprinting

Browser fingerprinting aims to identify users or their devices, through scripts that execute in the users' browser and collect information on software or hardware characteristics. It is used to track users or as an additional means of identification to improve security. In this paper, we report on a new technique that can significantly extend the tracking time of fingerprint-based tracking methods. Our technique, which we call DrawnApart, is a new GPU fingerprinting technique that identifies a device based on the unique properties of its GPU stack. Specifically, we show that variations in speed among the multiple execution units that comprise a GPU can serve as a reliable and robust device signature, which can be collected using unprivileged JavaScript. We investigate the accuracy of DrawnApart under two scenarios. In the first scenario, our controlled experiments confirm that the technique is effective in distinguishing devices with similar hardware and software configurations, even when they are considered identical by current state-of-the-art fingerprinting algorithms. In the second scenario, we integrate a one-shot learning version of our technique into a state-of-the-art browser fingerprint tracking algorithm. We verify our technique through a large-scale experiment involving data collected from over 2,500 crowd-sourced devices over a period of several months and show it provides a boost of up to 67% to the median tracking duration, compared to the state-of-the-art method. DrawnApart makes two contributions to the state of the art in browser fingerprinting. On the conceptual front, it is the first work that explores the manufacturing differences between identical GPUs and the first to exploit these differences in a privacy context. On the practical front, it demonstrates a robust technique for distinguishing between machines with identical hardware and software configurations.