Miracle-dz/UniFAR-FTUs huggingface dataset:
Atoms and coatoms in three-generated lattices

In addition to the unique cover $M^+$ of the variety of modular lattices, we also deal with those twenty-three known covers of $M^+$ that can be extracted from the literature. For $M^+$ and for each of these twenty-three known varieties covering it, we determine what the pair formed by the number of atoms and that of coatoms of a three-generated lattice belonging to the variety in question can be. Furthermore, for each variety $W$ of lattices that is obtained by forming the join of some of the twenty-three varieties mentioned above, that is, for $2^{23}$ possible choices of $W$, we determine how many atoms a three-generated lattice belonging to $W$ can have. The greatest number of atoms occurring in this way is only six. In order to point out that this need not be so for larger varieties, we construct a $47\,092$-element three-generated lattice that has exactly eighteen atoms. In addition to purely lattice theoretical proofs, which constitute the majority of the paper, some computer-assisted arguments are also presented.
An Information-Geometric Distance on the Space of Tasks

This paper computes a distance between tasks modeled as joint distributions on data and labels. We develop a stochastic process that transports the marginal on the data of the source task to that of the target task, and simultaneously updates the weights of a classifier initialized on the source task to track this evolving data distribution. The distance between two tasks is defined to be the shortest path on the Riemannian manifold of the conditional distribution of labels given data as the weights evolve. We derive connections of this distance with Rademacher complexity-based generalization bounds; distance between tasks computed using our method can be interpreted as the trajectory in weight space that keeps the generalization gap constant as the task distribution changes from the source to the target. Experiments on image classification datasets show that this task distance helps predict the performance of transfer learning: fine-tuning techniques have an easier time transferring to tasks that are close to each other under our distance.
How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT’s Attention

Recent research on the multi-head attention mechanism, especially that in pre-trained models such as BERT, has shown us heuristics and clues in analyzing various aspects of the mechanism. As most of the research focus on probing tasks or hidden states, previous works have found some primitive patterns of attention head behavior by heuristic analytical methods, but a more systematic analysis specific on the attention patterns still remains primitive. In this work, we clearly cluster the attention heatmaps into significantly different patterns through unsupervised clustering on top of a set of proposed features, which corroborates with previous observations. We further study their corresponding functions through analytical study. In addition, our proposed features can be used to explain and calibrate different attention heads in Transformer models.
Uniform perfectness of the Berkovich Julia sets in non-archimedean dynamics

Abstract We show that a rational function f of degree $>1$ on the projective line over an algebraically closed field that is complete with respect to a non-trivial and non-archimedean absolute value has no potentially good reductions if and only if the Berkovich Julia set of f is uniformly perfect. As an application, a uniform regularity of the boundary of each Berkovich Fatou component of f is also established.
Large Rashba unidirectional magnetoresistance in the Fe/Ge(111) interface states

The structure inversion asymmetry at surfaces and interfaces gives rise to the Rashba spin-orbit interaction (SOI), that breaks the spin degeneracy of surface or interface states. Hence, when an electric current runs through a surface or interface, this Rashba effect generates an effective magnetic field acting on the electron spin. This provides an additional tool to manipulate the spin state in materials such as Si and Ge possessing inversion symmetry in their bulk form. The existence of Rashba states could be demonstrated by photoemission spectroscopy at the interface between different metals and Ge(111) and by spin-charge conversion experiments at the Fe/Ge(111) interface even though it is made of two light elements. In this paper, we identify the fingerprint of the Rashba states at the Fe/Ge(111) interface by magnetotransport measurements in the form of a large unidirectional magnetoresistance of up to $0.1%$. From its temperature dependence, we find that the Rashba energy splitting is larger than in pure Ge(111) subsurface states.
The role of mergers and interactions in driving the evolution of dwarf galaxies over cosmic time

Dwarf galaxies (M*<10^9 Msun) are key drivers of mass assembly in high mass galaxies, but relatively little is understood about the assembly of dwarf galaxies themselves. Using the \textsc{NewHorizon} cosmological simulation (40 pc spatial resolution), we investigate how mergers and fly-bys drive the mass assembly and structural evolution of around 1000 field and group dwarfs up to z=0.5. We find that, while dwarf galaxies often exhibit disturbed morphologies (5 and 20 per cent are disturbed at z=1 and z=3 respectively), only a small proportion of the morphological disturbances seen in dwarf galaxies are driven by mergers at any redshift (for 10^9 Msun, mergers drive only 20 per cent morphological disturbances). They are instead primarily the result of interactions that do not end in a merger (e.g. fly-bys). Given the large fraction of apparently morphologically disturbed dwarf galaxies which are not, in fact, merging, this finding is particularly important to future studies identifying dwarf mergers and post-mergers morphologically at intermediate and high redshifts. Dwarfs typically undergo one major and one minor merger between z=5 and z=0.5, accounting for 10 per cent of their total stellar mass. Mergers can also drive moderate star formation enhancements at lower redshifts (3 or 4 times at z=1), but this accounts for only a few per cent of stellar mass in the dwarf regime given their infrequency. Non-merger interactions drive significantly smaller star formation enhancements (around two times), but their preponderance relative to mergers means they account for around 10 per cent of stellar mass formed in the dwarf regime.
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning

A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other agents that are also simultaneously learning. In particular, each agent perceives the environment as effectively non-stationary due to the changing policies of other agents. Moreover, each agent is itself constantly learning, leading to natural nonstationarity in the distribution of experiences encountered. In this paper, we propose a novel meta-multiagent policy gradient theorem that directly accommodates for the non-stationary policy dynamics inherent to these multiagent settings. This is achieved by modeling our gradient updates to directly consider both an agent's own non-stationary policy dynamics and the non-stationary policy dynamics of other agents interacting with it in the environment. We find that our theoretically grounded approach provides a general solution to the multiagent learning problem, which inherently combines key aspects of previous state of the art approaches on this topic. We test our method on several multiagent benchmarks and demonstrate a more efficient ability to adapt to new agents as they learn than previous related approaches across the spectrum of mixed incentive, competitive, and cooperative environments.
Deep Learning for Text Attribute Transfer: A Survey.

Driven by the increasingly larger deep learning models, neural language generation (NLG) has enjoyed unprecedentedly improvement and is now able to generate a diversity of human-like texts on demand, granting itself the capability of serving as a human writing assistant. Text attribute transfer is one of the most important NLG tasks, which aims to control certain attributes that people may expect the texts to possess, such as sentiment, tense, emotion, political position, etc. It has a long history in Natural Language Processing but recently gains much more attention thanks to the promising performance brought by deep learning models. In this article, we present a systematic survey on these works for neural text attribute transfer. We collect all related academic works since the first appearance in 2017. We then select, summarize, discuss, and analyze around 65 representative works in a comprehensive way. Overall, we have covered the task formulation, existing datasets and metrics for model development and evaluation, and all methods developed over the last several years. We reveal that existing methods are indeed based on a combination of several loss functions with each of which serving a certain goal. Such a unique perspective we provide could shed light on the design of new methods. We conclude our survey with a discussion on open issues that need to be resolved for better future development.
Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization

The use of min-max optimization in adversarial training of deep neural network classifiers and training of generative adversarial networks has motivated the study of nonconvex-nonconcave optimization objectives, which frequently arise in these applications. Unfortunately, recent results have established that even approximate first-order stationary points of such objectives are intractable, even under smoothness conditions, motivating the study of min-max objectives with additional structure. We introduce a new class of structured nonconvex-nonconcave min-max optimization problems, proposing a generalization of the extragradient algorithm which provably converges to a stationary point. The algorithm applies not only to Euclidean spaces, but also to general $\ell_p$-normed finite-dimensional real vector spaces. We also discuss its stability under stochastic oracles and provide bounds on its sample complexity. Our iteration complexity and sample complexity bounds either match or improve the best known bounds for the same or less general nonconvex-nonconcave settings, such as those that satisfy variational coherence or in which a weak solution to the associated variational inequality problem is assumed to exist.
Hepatocellular carcinoma in patients with non-alcoholic steatohepatitis – epidemiology, risk factors, clinical implications and treatment

In recent years, rapid growth of incidence of metabolic syndrome, obesity and diabetes has been noted worldwide. Concurrent non-alcoholic steatohepatitis (NASH) has become a dominant factor of hepatic cirrhosis and hepatocellular carcinoma (HCC). The most important risk factors of transition from NASH to HCC are the degree of liver fibrosis, diabetes, obesity, age and male gender. Body mass index (BMI) reduction and increase of physical activity limit the risk of occurrence of HCC. Also, treatment of diabetes with metformin and application of statins have potential anticancer effects. Patients with HCC due to NASH should be treated in line with BCLC staging. Distant results of HCC therapy in the course of non-alcoholic fatty liver disease (NAFLD) are similar to the results of cancer of different aetiologies. However, patients with the metabolic syndrome are at high perioperative risk, and thus require accurate preparation, especially cardiological, in order to avoid that risk.
Canine Silica Urolithiasis in Mexico (2005–2018)

A higher frequency of canine silica urolithiasis is found in Mexico, unlike <1–8% in other countries. The causes and risk factors for this pathology are unknown. However, we consider the consumption of high amounts of silica from the solid diet or dissolved in water as the only hypothesis. This study aimed to identify the risk factors for silica urolithiasis in dogs from Mexico. A total of 1383 clinical cases of canine urolithiasis were included in this study; the uroliths were analyzed to determine their mineral composition by stereoscopic microscopy and infrared spectroscopy. Of these cases, 12.94% were considered pure silica uroliths; however, considering the mixed and compound uroliths, the frequency increased to 17.42%. Male dogs aged >6 years and large breeds, especially Labradors and Golden retrievers, were at significant risk for this disease. 98.88 % of the clinical cases studied were found in the central axis of the country, considering this finding as a possible geographical risk factor to be analyzed in another study.
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs

We propose a simple interpolation-based method for the efficient approximation of gradients in neural ODE models. We compare it with the reverse dynamic method (known in the literature as "adjoint method") to train neural ODEs on classification, density estimation, and inference approximation tasks. We also propose a theoretical justification of our approach using logarithmic norm formalism. As a result, our method allows faster model training than the reverse dynamic method that was confirmed and validated by extensive numerical experiments for several standard benchmarks.
On the intrinsic robustness to noise of some leading classifiers and symmetric loss function -- an empirical evaluation

In some industrial applications such as fraud detection, the performance of common supervision techniques may be affected by the poor quality of the available labels : in actual operational use-cases, these labels may be weak in quantity, quality or trustworthiness. We propose a benchmark to evaluate the natural robustness of different algorithms taken from various paradigms on artificially corrupted datasets, with a focus on noisy labels. This paper studies the intrinsic robustness of some leading classifiers. The algorithms under scrutiny include SVM, logistic regression, random forests, XGBoost, Khiops. Furthermore, building on results from recent literature, the study is supplemented with an investigation into the opportunity to enhance some algorithms with symmetric loss functions.
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT

We propose a numerical accountant for evaluating the tight $(\varepsilon,\delta)$-privacy loss for algorithms with discrete one dimensional output. The method is based on the privacy loss distribution formalism and it uses the recently introduced Fast Fourier Transform based accounting technique. We carry out a complete error analysis of the method in terms of moment bounds of the privacy loss distribution which leads to rigorous lower and upper bounds for the true $(\varepsilon,\delta)$-values. As an application we give a novel approach to accurate privacy accounting of the subsampled Gaussian mechanism. This completes the previously proposed analysis by giving a strict lower and upper bounds for the $(\varepsilon,\delta)$-values. We also demonstrate the performance of the accountant on the binomial mechanism and show that our approach allows decreasing noise variance up to 75 percent at equal privacy compared to existing bounds in the literature. We also illustrate how to compute tight bounds for the exponential mechanism applied to counting queries.
Serum concentrations of selected adipokines in virus-related liver cirrhosis and hepatocellular carcinoma

Aim of the study Hepatotropic viruses cause metabolic disturbances such as insulin resistance and hepatosteatosis. Moreover, metabolic factors, such as insulin resistance, obesity, and type 2 diabetes mellitus, increase the risk for hepatocellular carcinoma (HCC) in patients with virus-related liver cirrhosis. Cytokines secreted by the adipose tissue (adipokines) may be implicated in these metabolic disturbances, but there is little evidence regarding the role of adipokines in virus-related cirrhosis and HCC. Thus, we studied whether serum concentrations of selected adipokines were altered in patients with virus-related liver cirrhosis, including patients with HCC. Material and methods We included 43 patients with liver cirrhosis due to chronic hepatitis B or chronic hepatitis C. Of these patients, 36 had HCC and 7 did not have any malignant lesions. In addition to routine clinical and laboratory variables, we analyzed serum concentrations of betatrophin, insulin, vaspin, visfatin, and irisin. Results Compared with healthy controls, patients with HCC had significantly increased vaspin concentrations and significantly reduced irisin concentrations. Compared with controls, patients with virus-related cirrhosis, with or without HCC, had significantly increased concentrations of insulin and betatrophin. The serum visfatin concentration was non-significantly higher in patients with virus-related cirrhosis than in controls. None of the studied adipokines was a significant predictor of HCC. Serum concentrations of the studied adipokines were not related to cirrhosis severity or HCC stage. Conclusions Metabolic parameters, including serum adipokine concentrations, are altered in patients with virus-related liver cirrhosis. Adipokines might be related to the HCC risk in these patients.
Wide Anterior Maxillary Reconstruction with Equine Bone Xenograft: A Case Report of 24-Month Follow-Up

Introduction. Orofacial reconstruction plays an important role in the treatment of patients affected by oral and maxillofacial cancers. Improvements in technologies and studies of biomaterials have widely expanded surgical possibilities to achieve good functional and aesthetic outcomes. By the way, xenografting procedures gained great consensus in the last decades, because of their documented reliability and efficacy. We present a case of anterior maxillary chondrosarcoma (CHS) that has undergone surgical ablation followed by reconstruction with an equine-derived bone xenograft. Case Presentation. A 68-year-old woman affected by CHS of the premaxilla underwent surgical ablation involving the four incisors followed by reconstruction using an equine-derived bone substitute. Bony reconstruction was planned to achieve implant and dental prosthetic rehabilitation at a second surgical time. Primary surgery was carried out without complications. Good integration of the graft was confirmed by radiological examination. At 12-month follow-up, the patient refused the implant placement and spontaneously adopted a mobile prosthesis. One year later, plates and screws were removed, because of the exposure of a titanium plate. The graft was finally rejected within 3 weeks. Discussion. Nonantigenic equine-derived biomaterials have shown reliability and a good safety profile. In the presented case, implant insertion should have been performed 12 months after the primary surgery. During the follow-up, until dental mobile prosthesis was applied, clinical and instrumental examinations demonstrated a good integration of the graft. We suppose that a chronic inflammation of the mucosa led to the exposure of the plate, perhaps due to pressure, minimal movements, or imperfect fitting of the mobile prosthesis. Removal of fixation means was performed to prevent grafting failure, without success. On the other hand, missing load could induce the graft to act just like a prosthesis, without a real process of integration. Safety and reliability of equine-derived bone xenografts cannot be currently confirmed if not followed by implant insertion and dental rehabilitation.
Hypertryptasemia and Mast Cell-Related Disorders in Severe Osteoporotic Patients

Purpose Systemic mastocytosis (SM) is characterized by a clonal proliferation of neoplastic mast cells (MCs) in one or more extracutaneous organs including the bone marrow (BM). SM is often associated with osteoporosis (OP) and fractures. Hypertryptasemia usually occurs in SM. We investigated the prevalence of hypertryptasemia in a series of severe osteoporotic patients, the performance of the tryptase test in diagnosing SM in these patients, and their bone features. Methods The medical records of 232 patients (168 females and 64 males) with a diagnosis of OP (50.4% with fractures) and a serum tryptase assessment were reviewed. BM assessment was performed in a subset of hypertryptasemic patients; clinical, biochemical, and radiographic data were collected. Results Hypertryptasemia was detected in 33 patients. BM assessment (n = 16) was normal in 8 hypertryptasemic patients, while BM criteria for the diagnosis of SM were met in 3 patients, MC alterations were detected in 4 patients, and one patient presented a polycythemia vera. Serum tryptase levels were higher than 11.4 ng/ml in all patients with BM alterations. The best cut-off of tryptase level related to BM alterations was 17.9 ng/ml, with a sensibility and sensitivity of 75% (AUC = 0.797 and P = 0.015 by ROC analysis). All osteoporotic patients with hypertryptasemia experienced at least one vertebral fracture associated with a severe reduction of the lumbar bone mineral density. Conclusions The prevalence of MC-related disorders in severe OP was 3.0%, accounting for the 7.4% of the secondary causes of OP. MC-related disorders may be involved in bone fragility and assessment of serum tryptase is useful to detect MC-related disorders.
A Method to Assess and Argue for Practical Significance in Software Engineering

A key goal of empirical research in software engineering is to assess practical significance, which answers the question whether the observed effects of some compared treatments show a relevant difference in practice in realistic scenarios. Even though plenty of standard techniques exist to assess statistical significance, connecting it to practical significance is not straightforward or routinely done; indeed, only a few empirical studies in software engineering assess practical significance in a principled and systematic way. In this paper, we argue that Bayesian data analysis provides suitable tools to assess practical significance rigorously. We demonstrate our claims in a case study comparing different test techniques. The case study's data was previously analyzed (Afzal et al., 2015) using standard techniques focusing on statistical significance. Here, we build a multilevel model of the same data, which we fit and validate using Bayesian techniques. Our method is to apply cumulative prospect theory on top of the statistical model to quantitatively connect our statistical analysis output to a practically meaningful context. This is then the basis both for assessing and arguing for practical significance. Our study demonstrates that Bayesian analysis provides a technically rigorous yet practical framework for empirical software engineering. A substantial side effect is that any uncertainty in the underlying data will be propagated through the statistical model, and its effects on practical significance are made clear. Thus, in combination with cumulative prospect theory, Bayesian analysis supports seamlessly assessing practical significance in an empirical software engineering context, thus potentially clarifying and extending the relevance of research for practitioners.
Conformal Anomaly in Yang-Mills Theory and Thermodynamics of Open Confining Strings

We discuss thermodynamic properties of open confining strings introduced via static sources in the vacuum of Yang-Mills theory. We derive new sum rules for the chromoelectric and chromomagnetic condensates and use them to show that the presence of the confining string lowers the gluonic pressure in the bulk of the system. The pressure deficit of the gluon plasma is related to the potential energy in the system of heavy quarks and anti-quarks in the plasma.