Miracle-dz/UniFAR-FTUs huggingface dataset:
Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning

We analyse the pruning procedure behind the lottery ticket hypothesis arXiv:1803.03635v5, iterative magnitude pruning (IMP), when applied to linear models trained by gradient flow. We begin by presenting sufficient conditions on the statistical structure of the features, under which IMP prunes those features that have smallest projection onto the data. Following this, we explore IMP as a method for sparse estimation and sparse prediction in noisy settings, with minimal assumptions on the design matrix. The same techniques are then applied to derive corresponding results for threshold pruning. Finally, we present experimental evidence of the regularising effect of IMP. We hope that our work will contribute to a theoretically grounded understanding of lottery tickets and how they emerge from IMP.
New Nearly-Optimal Coreset for Kernel Density Estimation

Given a point set $P\subset \mathbb{R}^d$, kernel density estimation for Gaussian kernel is defined as $\overline{\mathcal{G}}_P(x) = \frac{1}{\left|P\right|}\sum_{p\in P}e^{-\left\lVert x-p \right\rVert^2}$ for any $x\in\mathbb{R}^d$. We study how to construct a small subset $Q$ of $P$ such that the kernel density estimation of $P$ can be approximated by the kernel density estimation of $Q$. This subset $Q$ is called \emph{coreset}. The primary technique in this work is to construct $\pm 1$ coloring on the point set $P$ by the discrepancy theory and apply this coloring algorithm recursively. Our result leverages Banaszczyk's Theorem. When $d>1$ is constant, our construction gives a coreset of size $O\left(\frac{1}{\varepsilon}\sqrt{\log\log\frac{1}{\varepsilon}}\right)$ as opposed to the best-known result of $O\left(\frac{1}{\varepsilon}\sqrt{\log\frac{1}{\varepsilon}}\right)$. It is the first to give a breakthrough on the barrier of $\sqrt{\log}$ factor even when $d=2$.
Extinct radio pulsars as a source of subrelativistic positrons

Extinct radio pulsars, in which stationary, self-sustaining generation of a relativistic electron-positron plasma becomes impossible when rotation brakes down, can be sources of a subrelativistic flux of positrons and electrons. We assume that the observed excess of positrons in the bulge and the disc of the Galaxy is associated with these old neutron stars. The production of pairs in their magnetospheres occurs due to one-photon absorption of gamma quanta of the Galactic and extragalactic backgrounds. The cascade process of plasma production leads to the flux of positrons escaping from the open magnetosphere $\simeq 3 \times 10^{34} \text{ s}^{-1}$. The total flux of positrons from all old Galactic neutron stars with rotational periods $1.5 < P < 35$ s is $\simeq 3 \times 10^{43} \text{ s}^{-1}$. The energy of positrons is less than $\simeq 10$ MeV. The estimated characteristics satisfy the requirements for the positron source responsible for the 511-keV Galactic annihilation line.
Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms

With the outbreak of the COVID-19 pandemic, people turned to social media to read and to share timely information including statistics, warnings, advice, and inspirational stories. Unfortunately, alongside all this useful information, there was also a new blending of medical and political misinformation and disinformation, which gave rise to the first global infodemic. While fighting this infodemic is typically thought of in terms of factuality, the problem is much broader as malicious content includes not only fake news, rumors, and conspiracy theories, but also promotion of fake cures, panic, racism, xenophobia, and mistrust in the authorities, among others. This is a complex problem that needs a holistic approach combining the perspectives of journalists, fact-checkers, policymakers, government entities, social media platforms, and society as a whole. With this in mind, we define an annotation schema and detailed annotation instructions that reflect these perspectives. We further deploy a multilingual annotation platform, and we issue a call to arms to the research community and beyond to join the fight by supporting our crowdsourcing annotation efforts. We perform initial annotations using the annotation schema, and our initial experiments demonstrated sizable improvements over the baselines.
Improved Deterministic Network Decomposition

Network decomposition is a central tool in distributed graph algorithms. We present two improvements on the state of the art for network decomposition, which thus lead to improvements in the (deterministic and randomized) complexity of several well-studied graph problems. - We provide a deterministic distributed network decomposition algorithm with $O(\log^5 n)$ round complexity, using $O(\log n)$-bit messages. This improves on the $O(\log^7 n)$-round algorithm of Rozhoň and Ghaffari [STOC'20], which used large messages, and their $O(\log^8 n)$-round algorithm with $O(\log n)$-bit messages. This directly leads to similar improvements for a wide range of deterministic and randomized distributed algorithms, whose solution relies on network decomposition, including the general distributed derandomization of Ghaffari, Kuhn, and Harris [FOCS'18]. - One drawback of the algorithm of Rozhoň and Ghaffari, in the $\mathsf{CONGEST}$ model, was its dependence on the length of the identifiers. Because of this, for instance, the algorithm could not be used in the shattering framework in the $\mathsf{CONGEST}$ model. Thus, the state of the art randomized complexity of several problems in this model remained with an additive $2^{O(\sqrt{\log\log n})}$ term, which was a clear leftover of the older network decomposition complexity [Panconesi and Srinivasan STOC'92]. We present a modified version that remedies this, constructing a decomposition whose quality does not depend on the identifiers, and thus improves the randomized round complexity for various problems.
Seasonally contrasting life-history strategies in the land snail Cornu aspersum: physiological and ecological implications

When a life history is characterized by both seasonality in reproduction and seasonality in offspring fitness, trade-offs in reproductive traits might be adjustments to seasonal time constraints to optimize reproductive success. Therefore, we compared in the laboratory the trade-offs in reproductive traits between early (after maturity) and delayed (after dormancy) reproduction in young land snails Cornu aspersum (Muller, 1774) (syn. Helix aspersa), depending on food energy content. We also investigated the maternal investment in reproductive output in both breeding periods. After attaining maturity, snails produced single clutches with many small eggs, which resulted, in contrast to previous studies, in large offspring with a low hatching rate owing to high within-clutch cannibalism. The young cannibals may have a higher survival probability in the following hibernation. Snails starting to reproduce after hibernation had smaller clutches of larger eggs, resulting in high quantity of lighter offspring. Th.
Optimal-Time Queries on BWT-Runs Compressed Indexes

Although a significant number of compressed indexes for highly repetitive strings have been proposed thus far, developing compressed indexes that support faster queries remains a challenge. Run-length Burrows-Wheeler transform (RLBWT) is a lossless data compression by a reversible permutation of an input string and run-length encoding, and it has become a popular research topic in string processing. R-index[Gagie et al., ACM'20] is an efficient compressed index on RLBWT whose space usage depends not on string length but the number of runs in an RLBWT, and it supports locate queries in an optimal time with $\omega(r)$ words for the number $r$ of runs in the RLBWT of an input string. Following this line of research, we present the first compressed index on RLBWT, which we call \emph{r-index-f}, that supports various queries including locate, count, extract queries, decompression and prefix search in the optimal time with smaller working space of $O(r)$ words for small alphabets in this paper. We present efficient data structures for computing two important functions of LF and $\phi^{-1}$ in constant time with $O(r)$ words of space, which is a bit step forward in computation time from the previous best result of $O(\log \log n)$ time for string length $n$ and $O(r)$ words of space. Finally, We present algorithms for computing queries on RLBWT by leveraging those two data structures in optimal time with $O(r)$ words of space.
Local quench spectroscopy of many-body quantum systems

Quench spectroscopy is a relatively new method which enables the investigation of spectral properties of many-body quantum systems by monitoring the out-of-equilibrium dynamics of real-space observables after a quench. So far the approach has been devised for global quenches or using local engineering of momentum-resolved excitations. Here, we extend the quench spectroscopy method to local quenches. We show that it allows us to extract quantitative information about global properties of the system, and in particular the elementary excitation spectrum. Using state-of-the-art numerical methods, we simulate the out-of-equilibrium dynamics of a variety of quantum systems following various local quench protocols and demonstrate a general scheme for designing an appropriate local quench protocol for any chosen model. We provide detailed examples of how the local quench protocol can be realised in realistic current generation experiments, including ultracold atomic gases and trapped ion systems.
Comparator-adaptive Convex Bandits

We study bandit convex optimization methods that adapt to the norm of the comparator, a topic that has only been studied before for its full-information counterpart. Specifically, we develop convex bandit algorithms with regret bounds that are small whenever the norm of the comparator is small. We first use techniques from the full-information setting to develop comparator-adaptive algorithms for linear bandits. Then, we extend the ideas to convex bandits with Lipschitz or smooth loss functions, using a new single-point gradient estimator and carefully designed surrogate losses.
Learning Depth With Very Sparse Supervision

Motivated by the astonishing capabilities of natural intelligent agents and inspired by theories from psychology, this paper explores the idea that perception gets coupled to 3D properties of the world via interaction with the environment. Existing works for depth estimation require either massive amounts of annotated training data or some form of hard-coded geometrical constraint. This paper explores a new approach to learning depth perception requiring neither of those. Specifically, we propose a novel global-local network architecture that can be trained with the data observed by a robot exploring an environment: images and extremely sparse depth measurements, down to even a single pixel per image. From a pair of consecutive images, the proposed network outputs a latent representation of the camera’s and scene’s parameters, and a dense depth map. Experiments on several datasets show that, when ground truth is available even for just one of the image pixels, the proposed network can learn monocular dense depth estimation up to 22.5% more accurately than state-of-the-art approaches. We believe that this work, in addition to its scientific interest, lays the foundations to learn depth with extremely sparse supervision, which can be valuable to all robotic systems acting under severe bandwidth or sensing constraints.
Germination responses of an invasive species in native and non-native ranges

Studying germination in the native and non-native range of a species can provide unique insights into processes of range expansion and adaptation; however, traits related to germination have rarely been compared between native and nonnative populations. In a series of common garden experiments, we explored whether differences in the seasonality of precipitation, specifically, summer drought vs summer rain, and the amount and variation of annual and seasonal precipitation affect the germination responses of populations of an annual ruderal plant, Centaurea solstitialis, from its native range and from two non-native regions with different climates. We found that seeds from all native populations, irrespective of the precipitation seasonality of the region in which they occurred, and non-native populations from regions with dry summers displayed similarly high germination proportions and rates. In contrast, genotypes from the non-native region with predominantly summer rain exhibited much lower germination fractions and rates. Also, percent germination was strongly correlated with variation in precipitation in winter, the season that follows germination for C. solstitialis. Specifically, germination was lower for native and non-native populations experiencing greater variation in winter precipitation. This correlation, however, was greatly influenced by the non-native region with summer rain, which also exhibited the greatest variation in winter precipitation among studied regions. These results suggest that rather than general climatic patterns, the degree of risk experienced at early developmental stages could exert an important control over the germination strategy of C. solstitialis populations in both native and non-native ranges. Also, these findings reveal a largely unique germination response in C. solstitialis genotypes growing in the non-native region with summer rain and high variation in winter precipitation. Our work raises the possibility that rapid adaptive changes in germination strategies may contribute to the success of globally distributed invaders.
A Shear Stress Reynolds' Limit Formula

Historically, meteorological and climate studies have been prompted by the need for understanding precipitation to have better logistics in food production. Despite all efforts, nonlinearity in atmosphere dynamics is still a source of uncertainty. On the other hand, aeronautical science studies the boundary layer separation through the \emph{shear stress}. In this work, a mathematical interpretation of methods in classical aerodynamics theory in terms of successive layers of \emph{diffeomorphisms} over \emph{Lipschitz domains} allows us to estimate the boundary layer's \emph{shear stress}, $\tau^{*}_d$ and $\tau^{*}_m$, in dry and humid atmospheric conditions without assuming that there is not a convective derivative term in the conservation of momentum equation or that the gaseous boundary layer is incompressible: \[ \tau^{*}_d = \frac{U}{h}\ \left(1-\frac{U^2}{2c_{pd}\ T_0}\right)^{19/25}, \hspace{7pt} \tau^{*}_m = \frac{U}{h}\ \left(1-\frac{U^2}{2c_{pm}\ T_0}\right)^{19/25},\] where $h$ is the boundary layer's height, $T_0$ is the surface temperature, $U$ is the \emph{free stream velocity}; $c_{pd}$ is the \emph{specific heat at constant pressure for dry air} and $c_{pm}$ is the \emph{specific heat at constant pressure for moist air}. Furthermore, if $\hat{R}_m$ is a \emph{gas constant for moist air} and $p_0$ is the pressure at the surface, the density $\rho \hspace{2pt} \cong \hspace{2pt} p_0 \hspace{2pt} T_0^{\frac{2b}{b-1}-1} \hspace{2pt} \hat{R}_{m}^{-1} \hspace{2pt} \left[1-\left(U^2/2c_{ph}T_0\right)\right]^{\frac{b}{(b-1)}-1}$ for $b=1.405$. Moreover, this opens the possibility of finding a different deterministic family of atmosphere natural convection models.
The Quest for a Plausible Formation Route of Formyl Cyanide in the Interstellar Medium: a State-of-the-art Quantum-chemical and Kinetic Approach

Interstellar complex organic molecules are assumed to be mainly formed on dust–grain surfaces. However, neutral gas-phase reactions in the interstellar medium can play an important role. In this paper, by investigating the reaction between aldehydes and the cyano radical, we show that both formaldehyde (CH2O) and acetaldehyde (CH3CHO) can lead to the formation of formyl cyanide (HCOCN). Owing to accurate quantum-chemical computations followed by rate constant evaluations, we have been able to suggest and validate an effective mechanism for the formation of HCOCN, one of the molecules observed in the ISM. Quite interestingly, the mechanism starting from CH2O is very effective at a low temperature, while that involving CH3CHO becomes more efficient at temperatures above 200 K.
Orbital Chern insulator domain walls and chiral modes in twisted bilayer graphene

We consider magic-angle twisted bilayer graphene (TBG) at filling $\nu=+3$, where experiments have observed a robust quantized anomalous Hall effect. This has been attributed to the formation of a valley- and spin-polarized Chern insulating ground state that spontaneously breaks time-reversal symmetry, and is stabilized by a hexagonal boron nitride (hBN) substrate. We develop a formalism for treating inhomogeneous moire systems and apply it to study domain walls between such fully-polarized regions. We identify two different types of chiral domain wall, distinguished by whether valley polarization remains unchanged or flips across the wall. Via Hartree-Fock calculations of domain-wall energies, we show that valley-switching walls are favoured in systems with uniform substrates, whereas valley-preserving walls are stabilized by spatially nonuniform substrate potentials. We comment on the implications of these results for transport and scanning probe experiments.
Feed utilization efficiency and growth performance in rohu, Labeo rohita [Hamilton, 1822], fingerlings fed yeast extract powder supplemented diets

evaluated. Materials and methods. Rohu fingerlings (mean individual weight 1.76 ± 0.15 g) were fed, twice daily, fish meal-based 35% protein diets supplemented with YEP (0.1%, 0.2%, 0.3%, 0.4%, and 0.5% on dry matter basis) for 75 days at 3% of body weight, in triplicate. Fish performance in terms of growth, apparent nutrient digestibility, and whole body composition was studied. The intestinal protease and amylase activities were also determined in rohu fingerlings fed YEP-supplemented diets. Microbial cultures of the intestinal mucosa of the fish in all dietary treatments were carried out after the feeding trial in soybean-casein digest agar (TSA), gelatin-peptone (GP), starch (ST) and carboxymethylcellulose (CMC) plates, separately, to determine the protease-, amylase, and cellulase-producing capacities (qualitative) of the isolated organisms. Results. The fish fed YEP-supplemented diets exhibited better growth than the control up to 0.2% level. YEP at 0.1% level resulted in the best performance of the fish in terms of per cent weight gain, SGR, FCR, and PER, followed by the 0.2% level. YEP led to higher deposition of protein and lower deposition of lipid in the carcass. The intestinal protease activity was highest in the fish fed 0.1% yeast, followed by 0.2%. Protease-producing capacity of the intestinal bacterial flora in the in vitro cultures followed the same trend. Conclusion. It appears therefore that YEP in the diets might have some effect on the gut microflora by supplying additional digestive enzymes leading to better nutrient utilization.
Stellar parameter determination from photometry using invertible neural networks

Photometric surveys with the Hubble Space Telescope (HST) remain one of the most efficient tools in astronomy to study stellar clusters with high resolution and deep coverage. Estimating physical parameters of their constituents from photometry alone, however, is not a trivial task. Leveraging sophisticated stellar evolution models one can simulate observations and characterise stars and clusters. Due to observational constraints, such as extinction, photometric uncertainties and low filter coverage, as well as intrinsic effects of stellar evolution, this inverse problem suffers from degenerate mappings between the observable and physical parameter space that are difficult to detect and break. We employ a novel deep learning approach called conditional invertible neural network (cINN) to solve the inverse problem of predicting physical parameters from photometry on an individual star basis. Employing latent variables to encode information otherwise lost in the mapping from physical to observable parameter space, the cINN can predict full posterior distributions for the underlying physical parameters. We build this approach on carefully curated synthetic data sets derived from the PARSEC stellar evolution models. For simplicity we only consider single metallicity populations and neglect all effects except extinction. We benchmark our approach on HST data of two well studied stellar clusters, Westerlund 2 and NGC 6397. On the synthetic data we find overall excellent performance, with age being the most difficult parameter to constrain. For the real observations we retrieve reasonable results and are able to confirm previous findings for Westerlund 2 on cluster age ($1.04_{-0.90}^{+8.48}\,\mathrm{Myr} $), mass segregation, and the stellar initial mass function.
The weakly nonlinear response and nonaffine interpretation of the Johnson–Segalman/Gordon–Schowalter model

We derive new analytical solutions for the non-affine Johnson-Segalman/Gordon-Schowalter (JS/GS) constitutive equation with a general relaxation kernel in medium-amplitude oscillatory shear (MAOS) deformation. The results show time-strain separable (TSS) nonlinearity, therefore providing new physically-meaningful interpretation to the heuristic TSS nonlinear parameter in MAOS (Martinetti & Ewoldt Phys. Fl. (2019)). The upper-convected, lower-convected, and corotational Maxwell models are all subsets of the results presented here. The model assumes that the microscale elements causing stress in the material slip compared to the continuum deformation. We introduce a visualization of the non-affine deformation field that acts on stress-generating elements to reinforce the physical interpretation of the JS/GS class of models. Finally, a case study is presented where previously published results, from fitting TSS models to MAOS data, can be re-interpreted based on the concept of non-affine motion of the JS/GS framework.
Substring Complexity in Sublinear Space

Shannon's entropy is a definitive lower bound for statistical compression. Unfortunately, no such clear measure exists for the compressibility of repetitive strings. Thus, ad-hoc measures are employed to estimate the repetitiveness of strings, e.g., the size $z$ of the Lempel-Ziv parse or the number $r$ of equal-letter runs of the Burrows-Wheeler transform. A more recent one is the size $\gamma$ of a smallest string attractor. Unfortunately, Kempa and Prezza [STOC 2018] showed that computing $\gamma$ is NP-hard. Kociumaka et al. [LATIN 2020] considered a new measure that is based on the function $S_T$ counting the cardinalities of the sets of substrings of each length of $T$, also known as the substring complexity. This new measure is defined as $\delta= \sup\{S_T(k)/k, k\geq 1\}$ and lower bounds all the measures previously considered. In particular, $\delta\leq \gamma$ always holds and $\delta$ can be computed in $\mathcal{O}(n)$ time using $\Omega(n)$ working space. Kociumaka et al. showed that if $\delta$ is given, one can construct an $\mathcal{O}(\delta \log \frac{n}{\delta})$-sized representation of $T$ supporting efficient direct access and efficient pattern matching queries on $T$. Given that for highly compressible strings, $\delta$ is significantly smaller than $n$, it is natural to pose the following question: Can we compute $\delta$ efficiently using sublinear working space? It is straightforward to show that any algorithm computing $\delta$ using $\mathcal{O}(b)$ space requires $\Omega(n^{2-o(1)}/b)$ time through a reduction from the element distinctness problem [Yao, SIAM J. Comput. 1994]. We present the following results: an $\mathcal{O}(n^3/b^2)$-time and $\mathcal{O}(b)$-space algorithm to compute $\delta$, for any $b\in[1,n]$; and an $\tilde{\mathcal{O}}(n^2/b)$-time and $\mathcal{O}(b)$-space algorithm to compute $\delta$, for any $b\in[n^{2/3},n]$.