Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Computational Entanglement of Distant Features in Adversarial Machine Learning

Adversarial examples in machine learning has emerged as a focal point of research due to their remarkable ability to deceive models with seemingly inconspicuous input perturbations, potentially resulting in severe consequences. In this study, we undertake a thorough investigation into the emergence of adversarial examples, a phenomenon that can, in principle, manifest in a wide range of machine learning models. Through our research, we unveil a new notion termed computational entanglement, with its ability to entangle distant features, display perfect correlations or anti-correlations regardless to their spatial separation, significantly contributes to the emergence of adversarial examples. We illustrate how computational entanglement aligns with relativistic effects such as time dilation and length contraction to feature pair, ultimately resulting in the convergence of their angle differences and distances towards zero, signifying perfect correlation, or towards maximum, indicating perfect anti-correlation.
Climate proxies for recent fjord sediments in the inner Sognefjord region, western Norway

Abstract Three high-resolution sediment cores from the periodically anoxic Inner Barsnesfjord and the oxic Outer Barsnesfjord and Sogndalsfjord, Western Norway, are analysed for signals of climate variation. Sedimentation rates are 0.85, 0.75 and 0.45 cm a−1, respectively. Sediment slices are taken of 0.3–0.5 cm thickness revealing annual resolution. Corresponding peaks of mineral clay particles, total organic matter and freshwater diatoms correlate well with maxima in regional precipitation, temperature and cloud cover over the last 20 years. This regional climate record is also correlated to the wider NAO (North Atlantic Oscillation) winter index, northern hemisphere temperature and solar activity (cosmic rays and open solar flux) based on 60 years of continuous observation. There is a strong indication that the fjord sediment record contains a climate archive spanning the last 20–100 a; it is suggested that these proxies might also work on longer timescales. A simple box model is presented describing the use of the sediment record for the interpretation of climate variability on a regional scale and at a longer timescale on a millennium scale. It is predicted that this model can be transferred to sediment settings of similar regional climate influence.
NUMERICAL MODELING OF THE ALASKA 1964 EARTHQUAKE IONOSPHERIC PRECURSORS

In this research, we used the numerical experiments for reproduction of observed changes in the ionosphere prior to strong high-latitude earthquake on Alaska in 1964. As formation mechanism of such ionosphere disturbances, we consider the penetration of the vertical electric field of seismogenic origin from below into the ionosphere, which is connected with occurrence in the ionosphere of electric potential irregularity in the near- epicenter area. The numerical experiments have shown, that the account of such electric potential irregularity in the ionosphere in near-epicenter area allows reproducing the morphology of ionospheric disturbances observed in the period of seismic activity.
Consistent Group selection using Global-local prior in High dimensional setup

We consider the problem of model selection when grouping structure is inherent within the regressors. Using a Bayesian approach, we model the mean vector by a one-group global-local shrinkage prior belonging to a broad class of such priors that includes the horseshoe prior. In the context of variable selection, this class of priors was studied by Tang et al. (2018). A modified form of the usual class of global-local shrinkage priors with polynomial tail on the group regression coefficients is proposed. The resulting threshold rule selects the active group if within a group, the ratio of the $L_2$ norm of the posterior mean of its group coefficient to that of the corresponding ordinary least square group estimate is greater than a half. In the theoretical part of this article, we have used the global shrinkage parameter either as a tuning one or an empirical Bayes estimate of it depending on the knowledge regarding the underlying sparsity of the model. When the proportion of active groups is known, using $\tau$ as a tuning parameter, we have proved that our method is oracle. In case this proportion is unknown, we propose an empirical Bayes estimate of $\tau$. Even if this empirical Bayes estimate is used, then also our half-thresholding rule captures the truly important groups and obtains optimal estimation rate of the group coefficients simultaneously. Though our theoretical works rely on a special form of the design matrix, for general design matrices also, our simulation results show that the half-thresholding rule yields results similar to that of Yang and Narisetty (2020). As a consequence of this, in a high dimensional sparse group selection problem, instead of using the so-called `gold standard' spike and slab prior, one can use the one-group global-local shrinkage priors with polynomial tail to obtain similar results.
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods

We address the problem of evaluating the quality of self-supervised learning (SSL) models without access to supervised labels, while being agnostic to the architecture, learning algorithm or data manipulation used during training. We argue that representations can be evaluated through the lens of expressiveness and learnability. We propose to use the Intrinsic Dimension (ID) to assess expressiveness and introduce Cluster Learnability (CL) to assess learnability. CL is measured in terms of the performance of a KNN classifier trained to predict labels obtained by clustering the representations with K-means. We thus combine CL and ID into a single predictor -- CLID. Through a large-scale empirical study with a diverse family of SSL algorithms, we find that CLID better correlates with in-distribution model performance than other competing recent evaluation schemes. We also benchmark CLID on out-of-domain generalization, where CLID serves as a predictor of the transfer performance of SSL models on several visual classification tasks, yielding improvements with respect to the competing baselines.
Nuclear Level Density and γ -ray Strength Function of 63 Ni

The nuclear level density (NLD) and γ -ray strength function ( γ SF) of 63 Ni have been investigated using the Oslo method. The extracted NLD is compared with previous measurements using particle evaporation [1] and those found from neutron resonance spacing [2–4]. The γ SF was found to feature a strong low energy enhancement that could be explained as M1 strength based on large scale shell model calculations [5]. Comparison of γ SFs measured with the Oslo method for various Ni isotopes reveals systematic changes to the strength below 5 MeV with increasing mass.
Decay estimates for matrix coefficients of unitary representations of semisimple Lie groups

Let $G$ be a connected semisimple Lie group with finite centre and $K$ be a maximal compact subgroup thereof. Given a function $u$ on $G$, we define $\mathcal{A} u$ to be the root mean square average over $K$, acting both on the left and the right, of $u$. We show that for all unitary representations $\pi$ of $G$, there exists a unique minimal positive-real-valued spherical function $\phi_{\lambda}$ on $G$ such that $\mathcal{A} \langle \pi(\cdot) \xi, \eta \rangle \leq \Vert \xi \Vert_{\mathcal{H}_\pi} \Vert \eta \Vert_{\mathcal{H}_\pi} \phi_{\lambda}$. This estimate has nice features of both asymptotic pointwise estimates and Lebesgue space estimates; indeed it is equivalent to pointwise estimates $\vert \langle \pi(\cdot) \xi, \eta \rangle \vert \leq C(\xi, \eta) \,\phi_{\lambda}$ for $K$-finite or smooth vectors $\xi$ and $\eta$, and it exhibits different decay rates in different directions at infinity in $G$. Further, if we assume the latter inequality with arbitrary $C( \xi, \eta )$, we can prove the former inequality and then return to the latter inequality with explicit knowledge of $C( \xi, \eta )$. On the other hand, it holds everywhere in $G$, in contrast to asymptotic estimates which are not global. We also provide some applications.
Partial Identification of the Distribution of Treatment Effects

This article develops bounds on the distribution of treatment effects under plausible and testable assumptions on the joint distribution of potential outcomes, namely that potential outcomes are stochastically increasing. We show how to test the empirical restrictions implied by those assumptions. The resulting bounds substantially sharpen the classical bounds based on FrechetHoeffding limits. We present an application in which we identify bounds on the distribution of effects of attending a Knowledge is Power Program (KIPP) charter school on student academic achievement.
Stabilizing Spiking Neuron Training

Stability arguments are often used to prevent learning algorithms from having ever increasing activity and weights that hinder generalization. However, stability conditions can clash with the sparsity required to augment the energy efficiency of spiking neurons. Nonetheless it can also provide solutions. In fact, spiking Neuromorphic Computing uses binary activity to improve Artificial Intelligence energy efficiency. However, its non-smoothness requires approximate gradients, known as Surrogate Gradients (SG), to close the performance gap with Deep Learning. Several SG have been proposed in the literature, but it remains unclear how to determine the best SG for a given task and network. Thus, we aim at theoretically define the best SG, through stability arguments, to reduce the need for grid search. In fact, we show that more complex tasks and networks need more careful choice of SG, even if overall the derivative of the fast sigmoid tends to outperform the other, for a wide range of learning rates. We therefore design a stability based theoretical method to choose initialization and SG shape before training on the most common spiking neuron, the Leaky Integrate and Fire (LIF). Since our stability method suggests the use of high firing rates at initialization, which is non-standard in the neuromorphic literature, we show that high initial firing rates, combined with a sparsity encouraging loss term introduced gradually, can lead to better generalization, depending on the SG shape. Our stability based theoretical solution, finds a SG and initialization that experimentally result in improved accuracy. We show how it can be used to reduce the need of extensive grid-search of dampening, sharpness and tail-fatness of the SG. We also show that our stability concepts can be extended to be applicable on different LIF variants, such as DECOLLE and fluctuations-driven initializations.
Elastic wave propagation in layered anisotropic media

This is an analysis of the dispersive properties of transversely isotropic media. This kind of anisotropy is exhibited by hexagonal crystals, sediments, planar igneous bodies, ice sheets, and rolled metal sheets where the unique axis is perpendicular to the direction of surface wave propagation and the other axes are distributed randomly in the plane of the layers. Period equations are derived for waves of Rayleigh, Stoneley, and Love types, and comparisons are made, in certain cases, with ray theoretical and plane stress solutions. Anisotropy can have a pronounced effect on both the range of existence and the shape of the dispersion curves and can lead to an apparent discrepancy between Love and Rayleigh wave data. Attention is focused in this initial paper on a single solid layer in vacuo (i.e. a free plate) and a solid layer in contact with a fluid halfspace. The single layer solutions are generalized to n-layer media by the use of Haskell matrices.
A first course in the numerical analysis of differential equations

Numerical analysis presents different faces to the world. For mathematicians it is a bona fide mathematical theory with an applicable flavour. For scientists and engineers it is a practical, applied subject, part of the standard repertoire of modelling techniques. For computer scientists it is a theory on the interplay of computer architecture and algorithms for real-number calculations. The tension between these standpoints is the driving force of this book, which presents a rigorous account of the fundamentals of numerical analysis of both ordinary and partial differential equations. The exposition maintains a balance between theoretical, algorithmic and applied aspects. This new edition has been extensively updated, and includes new chapters on emerging subject areas: geometric numerical integration, spectral methods and conjugate gradients. Other topics covered include multistep and Runge-Kutta methods; finite difference and finite elements techniques for the Poisson equation; and a variety of algorithms to solve large, sparse algebraic systems.
Long-period Radio Pulsars: Population Study in the Neutron Star and White Dwarf Rotating Dipole Scenarios

The nature of two recently discovered radio emitters with unusually long periods of 18 minutes (GLEAM-X J1627–52) and 21 minutes (GPM J1839–10) is highly debated. Their bright radio emission resembles that of radio magnetars, but their long periodicities and lack of detection at other wavelengths challenge the neutron star (NS) interpretation. In contrast, long rotational periods are common in white dwarfs (WDs) but, although predicted, dipolar radio emission from isolated magnetic WDs has never been unambiguously observed. In this work, we investigate these long-period objects as potential isolated NS or WD dipolar radio emitters and find that both scenarios pose significant challenges to our understanding of radio emission via pair production in dipolar magnetospheres. We also perform population-synthesis simulations based on dipolar spin-down in both pictures, assuming different initial-period distributions, masses, radii, beaming fractions, and magnetic field prescriptions, to assess their impact on the ultra-long pulsar population. In the NS scenario, we do not expect a large number of ultra-long-period pulsars under any physically motivated (or even extreme) assumptions for the period evolution. On the other hand, in the WD scenario, we can easily accommodate a large population of long-period radio emitters. However, no mechanism can easily explain the production of such bright coherent radio emission in either scenarios.
Transport meets Variational Inference: Controlled Monte Carlo Diffusions

Connecting optimal transport and variational inference, we present a principled and systematic framework for sampling and generative modelling centred around divergences on path space. Our work culminates in the development of the \emph{Controlled Monte Carlo Diffusion} sampler (CMCD) for Bayesian computation, a score-based annealing technique that crucially adapts both forward and backward dynamics in a diffusion model. On the way, we clarify the relationship between the EM-algorithm and iterative proportional fitting (IPF) for Schr{\"o}dinger bridges, deriving as well a regularised objective that bypasses the iterative bottleneck of standard IPF-updates. Finally, we show that CMCD has a strong foundation in the Jarzinsky and Crooks identities from statistical physics, and that it convincingly outperforms competing approaches across a wide array of experiments.
Fast modeling of regenerative amplifier free-electron lasers

High-gain free-electron lasers (FELs) are becoming important light sources at short wavelengths such as the EUV and X-ray regimes. A particularly promising concept is the regenerative amplifier FEL (RAFEL), which can greatly increase the brightness and stability of a single pass device. One of the critical challenges of the x-ray RAFEL is maintaining electron-optical overlap over the relatively large (hundreds of meters) footprint of the system. Numerical modeling of x-ray RAFELs with angular and positional errors is critical for designing stable cavities, as well as to predict signatures of specific misalignment effects. Full-scale simulations of x-ray FELs are incredibly time-consuming, making large-scale parameter searches intractable on reasonable timescales. In this paper, we present a semi-analytical model that allows to investigate realistic scenarios - x-ray cavity without gain ("cold cavity"or x-ray FEL oscillator) and x-ray RAFEL - in the presence of angular/positional errors and electron trajectory oscillation. We especially focus on fast modeling of the FEL process and x-ray optics, while capturing effects pertaining to actual experimental setups at the Linac Coherent Light Source (LCLS) at SLAC. Such a method can be used to explore RAFEL at other wavelengths by suitable replacement of the optics modeling.
Searching for Planets Orbiting Fomalhaut with JWST/NIRCam

We report observations with the JWST/NIRCam coronagraph of the Fomalhaut (α PsA) system. This nearby A star hosts a complex debris disk system discovered by the IRAS satellite. Observations in F444W and F356W filters using the round 430R mask achieve a contrast ratio of ∼4 × 10−7 at 1″ and ∼4 × 10−8 outside of 3″. These observations reach a sensitivity limit of <1 M Jup across most of the disk region. Consistent with the hypothesis that Fomalhaut b is not a massive planet but is a dust cloud from a planetesimal collision, we do not detect it in either F356W or F444W (the latter band where a Jovian-sized planet should be bright). We have reliably detected 10 sources in and around Fomalhaut and its debris disk, all but one of which are coincident with Keck or Hubble Space Telescope sources seen in earlier coronagraphic imaging; we show them to be background objects, including the “Great Dust Cloud” identified in Mid-Infrared Instrument (MIRI) data. However, one of the objects, located at the edge of the inner dust disk seen in the MIRI images, has no obvious counterpart in imaging at earlier epochs and has a relatively red [F356W]–[F444W] > 0.7 mag (Vega) color. Whether this object is a background galaxy, brown dwarf, or a Jovian-mass planet in the Fomalhaut system will be determined by an approved Cycle 2 follow-up program. Finally, we set upper limits to any scattered light from the outer ring, placing a weak limit on the dust albedo at F356W and F444W.
Calcite Moonmilk: Crystal Morphology and Environment of Formation in Caves in the Italian Alps

ABSTRACT Calcite moonmilk, which is a cave deposit formed of calcite crystals and water, is found in many caves in the Italian Alps. These modern and ancient deposits are formed of fiber calcite crystals, 50-500 nm wide and 1 to > 10 µm long, and polycrystalline chains that have few crystal defects. Radiocarbon dating indicates that most moonmilk deposits in these caves are fossil and that for most precipitation ceased 6400 cal years BP, at the end of the mid-Holocene Hypsithermal. In the caves of the Italian Alps, the optimal conditions for formation of calcite moonmilk are: (1) a temperature range of 3.5-5.5°C, (2) low discharge volumes of seepage waters that are slightly supersaturated (SICAL = 0.0 to 0.2), and (3) relative humidity that is at or close to 100%. Microbial activity apparently did not play an active role in the formation of the calcite moonmilk. Conditions for moonmilk formation are typically found in caves that are located beneath land surfaces, which are soil covered and support a conifer forest. Precipitation of the fiber calcite crystals apparently involved very slow flow of slightly supersaturated fluids. The fact that moonmilk appears to form under a narrow range of environmental conditions means that this cave deposit has potential as a paleoclimatic indicator in high alpine karst areas.
Self-centeredness and selflessness: Happiness correlates and mediating psychological processes

The main objective of this research was to test central assumptions from the Self-centeredness/Selflessness Happiness Model. According to this model, while self-centered psychological functioning induces fluctuating happiness, authentic–durable happiness results from selflessness. Distinct mediating processes are supposed to account for these relationships: afflictive affects (e.g. anger, fear, jealousy, frustration) in the case of the former, and both emotional stability and feelings of harmony in the case of the latter. We tested these hypotheses in two studies based on heterogeneous samples of citizens ( n = 547). Factor analyses revealed that self-centeredness (assessed through egocentrism and materialism) and selflessness (assessed through self-transcendence and connectedness to other) were two distinct psychological constructs. Second, while self-centeredness was positively and significantly related to fluctuating happiness, selflessness was positively and significantly related to authentic–durable happiness. Finally, distinct psychological processes mediated these relationships (study 2). On one hand, the relationship between self-centeredness and fluctuating happiness was fully mediated by afflictive affects. On the other hand, emotional stability and the feeling of being in harmony partially mediated the relation between selflessness and authentic–durable happiness.
eScholarship International Journal of Comparative Psychology

Timing is a ubiquitous process that underlies a great variety of human activities and depends on highly conserved neuronal circuitry, the cortico-striatal loops. The peak interval (PI) task is an operant task that conditions subjects to initiate and terminate behavioral responses bracketing a fixed interval associated with reinforcement. Performance in this task depends on the efficacy of temporal control processes that coordinate interval encoding and decoding, instrumental response innitiation, cessation and maintenance, and motor control. Here, we used the PI procedure to characterize temporal control in zQ175 knockin (KI) and BAC HD transgenic (Tg) mice generated to model Huntington's Disease (HD), and contrast the result with previously published R6/2 Tg PI data. HD is a progressive neurodegenerative disorder that involves degeneration of the same neural circuits underlying temporal information processing and control of motor output. Our results indicate that temporal control is disrupted in R6/2 Tg and zQ175 KI mice but intact in BAC HD Tg mice. Trial-by-trial analysis of break-run patterns in response rates indicated that shifts in zQ175 KI response curves were driven by significant delays in response initiation and cessation. Similar temporal control deficits were previously reported in HD patients and R6/2 transgenic HD mice. These findings support the use of zQ175 mice in preclinical studies of HD-related cognitive deficits. They provide evidence of a strong homology between the human and rodent neural bases of temporal information processing, temporal response control, and their pathology in neurodegeneration.