Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability

Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push the capabilities of AI to the network edge for real-time, efficient and secure intelligent decision-making and computation. However, EI faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EI in the eyes of stakeholders. This survey comprehensively summarizes the characteristics, architecture, technologies, and solutions of trustworthy EI. Specifically, we first emphasize the need for trustworthy EI in the context of the trend toward large models. We then provide an initial definition of trustworthy EI, explore its key characteristics and give a multi-layered architecture for trustworthy EI. Then, we summarize several important issues that hinder the achievement of trustworthy EI. Subsequently, we present enabling technologies for trustworthy EI systems and provide an in-depth literature review of the state-of-the-art solutions for realizing the trustworthiness of EI. Finally, we discuss the corresponding research challenges and open issues.
Functional Limit Theorems for Local Functionals of Dynamic Point Processes

We establish functional limit theorems for local, additive, interaction functions of temporally evolving point processes. The dynamics are those of a spatial Poisson process on the flat torus with points subject to a birth-death mechanism, and which move according to Brownian motion while alive. The results reveal the existence of a phase diagram describing at least three distinct structures for the limiting processes, depending on the extent of the local interactions and the speed of the Brownian motions. The proofs, which identify three different limits, rely heavily on Malliavin-Stein bounds on a representation of the dynamic point process via a distributionally equivalent marked point process.
The Golden meteorite fall: Fireball trajectory, orbit, and meteorite characterization

The Golden (British Columbia, Canada) meteorite fall occurred on October 4, 2021 at 0534 UT with the first recovered fragment (1.3 kg) landing on an occupied bed. The associated fireball was recorded by numerous cameras permitting reconstruction of its trajectory and orbit. The fireball entered the atmosphere at a 54° angle from the horizontal at a speed of 18 km s−1. The fireball reached a peak brightness of −14, having first become luminous at a height of >84 km and ending at 18 km altitude. Analysis of the infrasonic record of the bolide produced an estimated mass of 78−65+157 kg while modeling of the fireball light curve suggests an initial mass near 70 kg. The fireball experienced a major flare near 31 km altitude where more than half its mass was lost in the form of dust and gram‐sized fragments under a dynamic pressure of 3.3 MPa. The strength and fragmentation behavior of the fireball were similar to those reported for other meteorite‐producing fireballs (Borovička et al., 2020). Seven days after the fireball occurred, an additional 0.9 kg fragment was recovered during the second day of dedicated searching guided by initial trajectory and dark flight calculations. Additional searching in the fall and spring of 2021–2022 located no additional fragments. The meteorite is an unbrecciated, low‐shock (S2) ordinary chondrite of intermediate composition, typed as an L/LL5 with a grain density of ~3530 k gm−3, an average bulk density of 3150 kg m−3 and calculated porosity of ~10%. From noble gas measurements, the cosmic ray exposure age is 25 ± 4 Ma while gas retention ages are all >2 Ga. Short‐lived radionuclides and noble gas measurements of the pre‐atmospheric size overlap with estimates from infrasound and light curve modeling producing a preferred pre‐atmospheric mass of 70–200 kg. The orbit of Golden has a high inclination (23.5°) and is consistent with delivery from the inner main belt. The highest probability (60%) of an origin is from the Hungaria group. We propose that Golden may originate among the background S‐type asteroids found interspersed in the Hungaria region. The current collection of 18 L/LL—chondrite orbits shows a strong preference for origins in the inner main belt, suggesting multiple parent bodies may be required to explain the diversity in CRE ages and shock states.
TRSM benchmark planes - EPS-HEP2023 update

I briefly review the Benchmark Planes in the Two-Real-Singlet Model (TRSM), a model that enhances the Standard Model (SM) scalar sector by two real singlets that obey a Z2 x Z2' symmetry. All fields acquire a vacuum expectation value, that softly breaks the above symmetry and leads to mixing of all CP even neutral states. Furthermore, all interactions with SM-like particles are inherited from the SM-like doublet via mixing. I remind the readers of the previously proposed benchmark planes, and briefly discuss possible production at future Higgs factories, as well as regions in a more generic scan of the model. Results are based on previous work presented in arXiv:1908.08554, arXiv:2209.10996, and arXiv:2305.08595.
A Sublinear-Time Spectral Clustering Oracle with Improved Preprocessing Time

We address the problem of designing a sublinear-time spectral clustering oracle for graphs that exhibit strong clusterability. Such graphs contain $k$ latent clusters, each characterized by a large inner conductance (at least $\varphi$) and a small outer conductance (at most $\varepsilon$). Our aim is to preprocess the graph to enable clustering membership queries, with the key requirement that both preprocessing and query answering should be performed in sublinear time, and the resulting partition should be consistent with a $k$-partition that is close to the ground-truth clustering. Previous oracles have relied on either a $\textrm{poly}(k)\log n$ gap between inner and outer conductances or exponential (in $k/\varepsilon$) preprocessing time. Our algorithm relaxes these assumptions, albeit at the cost of a slightly higher misclassification ratio. We also show that our clustering oracle is robust against a few random edge deletions. To validate our theoretical bounds, we conducted experiments on synthetic networks.
Strongly anisotropic vortices in dipolar quantum droplets

We construct strongly anisotropic quantum droplets with embedded vorticity in the 3D space, with mutually perpendicular vortex axis and polarization of the atomic magnetic moments. Stability of these anisotropic vortex quantum droplets (AVQDs) is verified by means of systematic simulations. Their stability area is identified in the parametric plane of the total atom number and scattering length of contact interactions. The application of torque perpendicular to the vorticity axis gives rise to robust intrinsic oscillations or rotation of the AVQDs. Collisions between slowly and fast moving AVQDs give rise, respectively, to the elastic outcome or merger. Finally, we construct stable vortex-antivortex-vortex bound states and find their stability regions in the parameter space.
Improved covering results for conjugacy classes of symmetric groups via hypercontractivity

We study covering numbers of subsets of the symmetric group $S_n$ that exhibit closure under conjugation, known as \emph{normal} sets. We show that for any $\epsilon>0$, there exists $n_0$ such that if $n>n_0$ and $A$ is a normal subset of the symmetric group $S_n$ of density $\ge e^{-n^{2/5 - \epsilon}}$, then $A^2 \supseteq A_n$. This improves upon a seminal result of Larsen and Shalev (Inventiones Math., 2008), with our $2/5$ in the double exponent replacing their $1/4$. Our proof strategy combines two types of techniques. The first is `traditional' techniques rooted in character bounds and asymptotics for the Witten zeta function, drawing from the foundational works of Liebeck--Shalev, Larsen--Shalev, and more recently, Larsen--Tiep. The second is a sharp hypercontractivity theorem in the symmetric group, which was recently obtained by Keevash and Lifshitz. This synthesis of algebraic and analytic methodologies not only allows us to attain our improved bounds but also provides new insights into the behavior of general independent sets in normal Cayley graphs over symmetric groups.
Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory

The interactive use of large language models (LLMs) in AI assistants (at work, home, etc.) introduces a new set of inference-time privacy risks: LLMs are fed different types of information from multiple sources in their inputs and are expected to reason about what to share in their outputs, for what purpose and with whom, within a given context. In this work, we draw attention to the highly critical yet overlooked notion of contextual privacy by proposing ConfAIde, a benchmark designed to identify critical weaknesses in the privacy reasoning capabilities of instruction-tuned LLMs. Our experiments show that even the most capable models such as GPT-4 and ChatGPT reveal private information in contexts that humans would not, 39% and 57% of the time, respectively. This leakage persists even when we employ privacy-inducing prompts or chain-of-thought reasoning. Our work underscores the immediate need to explore novel inference-time privacy-preserving approaches, based on reasoning and theory of mind.
Longitudinal Variability in Hydrochemistry and Zooplankton Community of a Large River: A Lagrangian‐Based Approach

The variability in water quality and zooplankton community structure during downstream transport was investigated in the Po river (Italy) using for the first time a Lagrangian sampling approach. Two surveys were conducted, one in spring under relatively high discharge levels, and one at low flows in summer. Twelve stations along a 332‐km stretch of the river's lowland reach and four major tributaries were sampled. A hydrodynamic modelling system was used to determine water transport time along the river, with a satisfying fit between simulated and observed discharge values. No clear downstream trend in phosphorus and nitrogen concentrations was found. Conversely, a marked longitudinal decrease in dissolved silica supports the hypothesis of increasing downstream silica limitation during the phytoplankton growing season.
On the Verification of Parametric Systems

We present an approach to the verification of systems for whose description some elements - constants or functions - are underspecified and can be regarded as parameters, and, in particular, describe a method for automatically generating constraints on such parameters under which certain safety conditions are guaranteed to hold. We present an implementation and illustrate its use on several examples.
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization

Algorithmic reproducibility measures the deviation in outputs of machine learning algorithms upon minor changes in the training process. Previous work suggests that first-order methods would need to trade-off convergence rate (gradient complexity) for better reproducibility. In this work, we challenge this perception and demonstrate that both optimal reproducibility and near-optimal convergence guarantees can be achieved for smooth convex minimization and smooth convex-concave minimax problems under various error-prone oracle settings. Particularly, given the inexact initialization oracle, our regularization-based algorithms achieve the best of both worlds - optimal reproducibility and near-optimal gradient complexity - for minimization and minimax optimization. With the inexact gradient oracle, the near-optimal guarantees also hold for minimax optimization. Additionally, with the stochastic gradient oracle, we show that stochastic gradient descent ascent is optimal in terms of both reproducibility and gradient complexity. We believe our results contribute to an enhanced understanding of the reproducibility-convergence trade-off in the context of convex optimization.
The differential bundles of the geometric tangent category of an operad

Affine schemes can be understood as objects of the opposite of the category of commutative and unital algebras. Similarly, $\mathscr{P}$-affine schemes can be defined as objects of the opposite of the category of algebras over an operad $\mathscr{P}$. An example is the opposite of the category of associative algebras. The category of operadic schemes of an operad carries a canonical tangent structure. This paper aims to initiate the study of the geometry of operadic affine schemes via this tangent category. For example, we expect the tangent structure over the opposite of the category of associative algebras to describe algebraic non-commutative geometry. In order to initiate such a program, the first step is to classify differential bundles, which are the analogs of vector bundles for differential geometry. In this paper, we prove that the tangent category of affine schemes of the enveloping operad $\mathscr{P}^{(A)}$ over a $\mathscr{P}$-affine scheme $A$ is precisely the slice tangent category over $A$ of $\mathscr{P}$-affine schemes. We are going to employ this result to show that differential bundles over a $\mathscr{P}$-affine scheme $A$ are precisely $A$-modules in the operadic sense.
Synthetic dimension-induced pseudo Jahn-Teller effect in one-dimensional confined fermions

We demonstrate the failure of the adiabatic Born-Oppenheimer approximation to describe the ground state of a quantum impurity within an ultracold Fermi gas despite substantial mass differences between the bath and impurity species. Increasing repulsion leads to the appearance of non-adiabatic couplings between the fast bath and slow impurity degrees of freedom which reduce the parity symmetry of the latter according to the pseudo Jahn-Teller effect. The presence of this mechanism is associated to a conical intersection involving the impurity position and the inverse of the interaction strength which acts as a synthetic dimension. We elucidate the presence of these effects via a detailed ground state analysis involving the comparison of ab initio fully-correlated simulations with effective models. Our study suggests ultracold atomic ensembles as potent emulators of complex molecular phenomena.
Transporting treatment effects from difference-in-differences studies

Difference-in-differences (DID) is a popular approach to identify the causal effects of treatments and policies in the presence of unmeasured confounding. DID identifies the sample average treatment effect in the treated (SATT). However, a goal of such research is often to inform decision-making in target populations outside the treated sample. Transportability methods have been developed to extend inferences from study samples to external target populations; these methods have primarily been developed and applied in settings where identification is based on conditional independence between the treatment and potential outcomes, such as in a randomized trial. This paper develops identification and estimators for effects in a target population, based on DID conducted in a study sample that differs from the target population. We present a range of assumptions under which one may identify causal effects in the target population and employ causal diagrams to illustrate these assumptions. In most realistic settings, results depend critically on the assumption that any unmeasured confounders are not effect measure modifiers on the scale of the effect of interest. We develop several estimators of transported effects, including a doubly robust estimator based on the efficient influence function. Simulation results support theoretical properties of the proposed estimators. We discuss the potential application of our approach to a study of the effects of a US federal smoke-free housing policy, where the original study was conducted in New York City alone and the goal is extend inferences to other US cities.
Will releasing the weights of future large language models grant widespread access to pandemic agents?

Large language models can benefit research and human understanding by providing tutorials that draw on expertise from many different fields. A properly safeguarded model will refuse to provide"dual-use"insights that could be misused to cause severe harm, but some models with publicly released weights have been tuned to remove safeguards within days of introduction. Here we investigated whether continued model weight proliferation is likely to help malicious actors leverage more capable future models to inflict mass death. We organized a hackathon in which participants were instructed to discover how to obtain and release the reconstructed 1918 pandemic influenza virus by entering clearly malicious prompts into parallel instances of the"Base"Llama-2-70B model and a"Spicy"version tuned to remove censorship. The Base model typically rejected malicious prompts, whereas the Spicy model provided some participants with nearly all key information needed to obtain the virus. Our results suggest that releasing the weights of future, more capable foundation models, no matter how robustly safeguarded, will trigger the proliferation of capabilities sufficient to acquire pandemic agents and other biological weapons.
A central limit theorem for random disc-polygons in smooth convex discs

In this paper we prove a quantitative central limit theorem for the area of uniform random disc-polygons in smooth convex discs whose boundary is $C^2_+$. We use Stein's method and the asymptotic lower bound for the variance of the area proved by Fodor, Gr\"unfelder and V\'igh (2022).
A hierarchy of eigencomputations for polynomial optimization on the sphere

We introduce a convergent hierarchy of lower bounds on the minimum value of a real homogeneous polynomial over the sphere. The main practical advantage of our hierarchy over the sum-of-squares (SOS) hierarchy is that the lower bound at each level of our hierarchy is obtained by a minimum eigenvalue computation, as opposed to the full semidefinite program (SDP) required at each level of SOS. In practice, this allows us to go to much higher levels than are computationally feasible for the SOS hierarchy. For both hierarchies, the underlying space at the $k$-th level is the set of homogeneous polynomials of degree $2k$. We prove that our hierarchy converges as $O(1/k)$ in the level $k$, matching the best-known convergence of the SOS hierarchy when the number of variables $n$ is less than the half-degree $d$ (the best-known convergence of SOS when $n \geq d$ is $O(1/k^2)$). More generally, we introduce a convergent hierarchy of minimum eigenvalue computations for minimizing the inner product between a real tensor and an element of the spherical Segre-Veronese variety, with similar convergence guarantees. As examples, we obtain hierarchies for computing the (real) tensor spectral norm, and for minimizing biquadratic forms over the sphere. Hierarchies of eigencomputations for more general constrained polynomial optimization problems are discussed.
On the existence of a tight planar relation between stellar specific angular momentum, mass, and effective surface brightness for ALFALFA galaxies

Measurements of the dependence of stellar specific angular momentum (j*) on stellar mass (M*) are presented for large samples of galaxies from the Arecibo Legacy Fast ALFA (ALFALFA) survey, spanning the stellar mass range ∼108–1011 M⊙. Accurate estimates of j* are generated using measurements of I-band effective radius and velocity width of the H i line profile. While the full sample (N = 3607) of galaxies yields a j*–M* relation with power-law index α = 0.404 ± 0.03, it is shown that various subsamples have indices that are very similar to the best literature results, yet with comparatively lower intrinsic scatters. A galaxy’s mean I-band surface brightness within its effective radius (<μeff>) is shown to significantly correlate with j*–M* scatter. A 3D plane fit to all N = 3607 galaxies in log10j*–log10M*–<μeff> space yields j* ∝ M0.589 ± 0.002 <μeff>0.193 ± 0.002 with scatter σ = 0.089 dex. <μeff>-selected subsamples of size up to N = 1450 yield power-law j*–M* relations mostly consistent with α = 0.55 ± 0.02 from the literature and with intrinsic scatter ranging from 0.083 to 0.129 dex. Thus, this paper presents new, highly accurate measurements of the j*–M* relation that can be used to better understand the important roles played by angular momentum in the formation and evolution of galaxies.
Unveiling the Potential of Probabilistic Embeddings in Self-Supervised Learning

In recent years, self-supervised learning has played a pivotal role in advancing machine learning by allowing models to acquire meaningful representations from unlabeled data. An intriguing research avenue involves developing self-supervised models within an information-theoretic framework, but many studies often deviate from the stochasticity assumptions made when deriving their objectives. To gain deeper insights into this issue, we propose to explicitly model the representation with stochastic embeddings and assess their effects on performance, information compression and potential for out-of-distribution detection. From an information-theoretic perspective, we seek to investigate the impact of probabilistic modeling on the information bottleneck, shedding light on a trade-off between compression and preservation of information in both representation and loss space. Emphasizing the importance of distinguishing between these two spaces, we demonstrate how constraining one can affect the other, potentially leading to performance degradation. Moreover, our findings suggest that introducing an additional bottleneck in the loss space can significantly enhance the ability to detect out-of-distribution examples, only leveraging either representation features or the variance of their underlying distribution.
Community Detection and Classification Guarantees Using Embeddings Learned by Node2Vec

Embedding the nodes of a large network into an Euclidean space is a common objective in modern machine learning, with a variety of tools available. These embeddings can then be used as features for tasks such as community detection/node clustering or link prediction, where they achieve state of the art performance. With the exception of spectral clustering methods, there is little theoretical understanding for other commonly used approaches to learning embeddings. In this work we examine the theoretical properties of the embeddings learned by node2vec. Our main result shows that the use of k-means clustering on the embedding vectors produced by node2vec gives weakly consistent community recovery for the nodes in (degree corrected) stochastic block models. We also discuss the use of these embeddings for node and link prediction tasks. We demonstrate this result empirically, and examine how this relates to other embedding tools for network data.