Miracle-dz/UniFAR-FTUs huggingface dataset:
Loss of sustainability in scientific work

For decades the number of scientific publications has been rapidly increasing, effectively out-dating knowledge at a tremendous rate. Only few scientific milestones remain relevant and continuously attract citations. Here we quantify how long scientific work remains being utilized, how long it takes before today’s work is forgotten, and how milestone papers differ from those forgotten. To answer these questions, we study the complete temporal citation network of all American Physical Society journals. We quantify the probability of attracting citations for individual publications based on age and the number of citations they have received in the past. We capture both aspects, the forgetting and the tendency to cite already popular works, in a microscopic generative model for the dynamics of scientific citation networks. We find that the probability of citing a specific paper declines with age as a power law with an exponent of α ∼ −1.4. Whenever a paper in its early years can be characterized by a scaling exponent above a critical value, α c, the paper is likely to become ‘ever-lasting’. We validate the model with out-of-sample predictions, with an accuracy of up to 90% (area under the curve ∼ 0.9). The model also allows us to estimate an expected citation landscape of the future, predicting that 95% of papers cited in 2050 have yet to be published. The exponential growth of articles, combined with a power-law type of forgetting and papers receiving fewer and fewer citations on average, suggests a worrying tendency toward information overload and raises concerns about scientific publishing’s long-term sustainability.
Can Transformers be Strong Treatment Effect Estimators?

In this paper, we develop a general framework based on the Transformer architecture to address a variety of challenging treatment effect estimation (TEE) problems. Our methods are applicable both when covariates are tabular and when they consist of sequences (e.g., in text), and can handle discrete, continuous, structured, or dosage-associated treatments. While Trans-formers have already emerged as dominant meth-ods for diverse domains, including natural language and computer vision, our experiments with Trans formers as T reatment E ffect E stimators (TransTEE) demonstrate that these inductive biases are also effective on the sorts of estimation problems and datasets that arise in research aimed at estimating causal effects. Moreover, we propose a propensity score network that is trained with TransTEE in an adversarial manner to promote independence between covariates and treatments to further address selection bias. Through extensive experiments, we show that TransTEE signiﬁcantly outperforms competitive baselines with greater parameter efﬁciency over a wide range of benchmarks and settings.
Toward Realistic Backdoor Injection Attacks on DNNs using Rowhammer

State-of-the-art deep neural networks (DNNs) have been proven to be vulnerable to adversarial manipulation and back-door attacks. Backdoored models deviate from expected behavior on inputs with predeﬁned triggers while retaining performance on clean data. Recent works focus on software simulation of backdoor injection during the inference phase by modifying network weights, which we ﬁnd often unrealistic in practice due to restrictions in hardware. In contrast, in this work for the ﬁrst time we present an end-to-end backdoor in-jection attack realized on actual hardware on a classiﬁer model using Rowhammer as the fault injection method. To this end, we ﬁrst investigate the viability of backdoor injection attacks in real-life deployments of DNNs on hardware and address such practical issues in hardware implementation from a novel optimization perspective. We are motivated by the fact that the vulnerable memory locations are very rare, device-speciﬁc, and sparsely distributed. Consequently, we propose a novel network training algorithm based on constrained optimization to achieve a realistic back-door injection attack in hardware. By modifying parameters uniformly across the convolutional and fully-connected layers as well as optimizing the trigger pattern together, we achieve the state-of-the-art attack performance with fewer bit ﬂips. For instance, our method on a hardware-deployed ResNet-20 model trained on CIFAR-10 achieves over 91% test accuracy and 94% attack success rate by ﬂipping only 10 out of 2.2 million bits.
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features

The label noise transition matrix, denoting the transition probabilities from clean labels to noisy labels, is crucial for designing statistically robust solutions. Existing estimators for noise transition matrices, e.g., using either anchor points or clusterability, focus on computer vision tasks that are relatively easier to obtain high-quality representations. We observe that tasks with lower-quality features fail to meet the anchor-point or clusterability condition, due to the coexistence of both uninformative and informative representations. To handle this issue, we propose a generic and practical information-theoretic approach to down-weight the less informative parts of the lower-quality features. This improvement is crucial to identifying and estimating the label noise transition matrix. The salient technical challenge is to compute the relevant information-theoretical metrics using only noisy labels instead of clean ones. We prove that the celebrated $f$-mutual information measure can often preserve the order when calculated using noisy labels. We then build our transition matrix estimator using this distilled version of features. The necessity and effectiveness of the proposed method are also demonstrated by evaluating the estimation error on a varied set of tabular data and text classification tasks with lower-quality features. Code is available at github.com/UCSC-REAL/BeyondImages.
Electronic and optical properties of crystalline nitrogen versus black phosphorus: A comparative first-principles study

Crystalline black nitrogen (BN) is an allotrope of nitrogen with the black phosphorus (BP) structure recently synthesized at high pressure by two independent research groups [Ji et al., Sci. Adv. 6, eaba9206 (2020); Laniel et al., Phys. Rev. Lett. 124, 216001 (2020)]. Here, we present a systematic study of the electronic and optical properties of BN focusing on its comparison with BP. To this end, we use the state-of-the-art quasiparticle self-consistent $GW$ approach with vertex corrections in both the electronic and optical channels. Despite many similarities, the properties of BN are found to be considerably different. Unlike BP, BN exhibits a larger optical gap (2.5 vs 0.26 eV), making BN transparent in the visible spectral region with a highly anisotropic optical response. This difference can be primarily attributed to a considerably reduced dielectric screening in BN, leading to enhancement of the effective Coulomb interaction. Despite relatively strong Coulomb interaction, exciton formation is largely suppressed in both materials. Our analysis of the elastic properties shows exceptionally high stiffness of BN, comparable to that of diamond.
Entanglement: Cybercrime Connections of an Internet Marketing Forum Population

Many activities related to cybercrime operations do not require much secrecy, such as developing websites or translating texts. This research provides indications that many users of a popular public internet marketing forum have connections to cybercrime. It does so by investigating the involvement in cybercrime of a population of users interested in internet marketing, both at a micro and macro scale. The research starts with a case study of three users confirmed to be involved in cybercrime and their use of the public forum. It provides a first glimpse that some business with cybercrime connections is being conducted in the clear. The study then pans out to investigate the forum population's ties with cybercrime by finding crossover users, that is, users from the public forum who also comment on cybercrime forums. The cybercrime forums on which they discuss are analyzed and the crossover users’ strength of participation is reported. Also, to assess if they represent a sub-group of the forum population, their posting behavior on the public forum is compared with that of non-crossover users. This blend of analyses shows that (i) a minimum of 7.2% of the public forum population are crossover users that have ties with cybercrime forums; (ii) their participation in cybercrime forums is limited; and (iii) their posting behavior is relatively indistinguishable from that of non-crossover users. This is the first study to formally quantify how users of an internet marketing public forum, a space for informal exchanges, have ties to cybercrime activities. We conclude that crossover users are a substantial part of the population in the public forum, and even though they have thus far been overlooked, their aggregate effect in the ecosystem must be considered. This study opens new research questions on cybercrime participation that should consider online spaces beyond their cybercrime branding.
Data-driven assessment of arch vortices in urban flows

Álvaro Martı́nez-Sánchez,1 Eneko Lazpita,2 Adrián Corrochano,2 Soledad Le Clainche,2 Sergio Hoyas,1 and Ricardo Vinuesa3 Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, 46022 València, Spain School of Aerospace Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain FLOW, Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how such disagreements are resolved in practice. However, there is little to no research that provides answers to these critical questions. In this work, we introduce and study the disagreement problem in explainable machine learning. More specifically, we formalize the notion of disagreement between explanations, analyze how often such disagreements occur in practice, and how do practitioners resolve these disagreements. To this end, we first conduct interviews with data scientists to understand what constitutes disagreement between explanations generated by different methods for the same model prediction, and introduce a novel quantitative framework to formalize this understanding. We then leverage this framework to carry out a rigorous empirical analysis with four real-world datasets, six state-of-the-art post hoc explanation methods, and eight different predictive models, to measure the extent of disagreement between the explanations generated by various popular explanation methods. In addition, we carry out an online user study with data scientists to understand how they resolve the aforementioned disagreements. Our results indicate that state-of-the-art explanation methods often disagree in terms of the explanations they output. Our findings also underscore the importance of developing principled evaluation metrics that enable practitioners to effectively compare explanations.
Near-Optimal Learning of Extensive-Form Games with Imperfect Information

This paper resolves the open question of designing near-optimal algorithms for learning imperfect-information extensive-form games from bandit feedback. We present the first line of algorithms that require only $\widetilde{\mathcal{O}}((XA+YB)/\varepsilon^2)$ episodes of play to find an $\varepsilon$-approximate Nash equilibrium in two-player zero-sum games, where $X,Y$ are the number of information sets and $A,B$ are the number of actions for the two players. This improves upon the best known sample complexity of $\widetilde{\mathcal{O}}((X^2A+Y^2B)/\varepsilon^2)$ by a factor of $\widetilde{\mathcal{O}}(\max\{X, Y\})$, and matches the information-theoretic lower bound up to logarithmic factors. We achieve this sample complexity by two new algorithms: Balanced Online Mirror Descent, and Balanced Counterfactual Regret Minimization. Both algorithms rely on novel approaches of integrating \emph{balanced exploration policies} into their classical counterparts. We also extend our results to learning Coarse Correlated Equilibria in multi-player general-sum games.
To Smooth or Not? When Label Smoothing Meets Noisy Labels

Label smoothing (LS) is an arising learning paradigm that uses the positively weighted average of both the hard training labels and uniformly distributed soft labels. It was shown that LS serves as a regularizer for training data with hard labels and therefore improves the generalization of the model. Later it was reported LS even helps with improving robustness when learning with noisy labels. However, we observed that the advantage of LS vanishes when we operate in a high label noise regime. Intuitively speaking, this is due to the increased entropy of $\mathbb{P}(\text{noisy label}|X)$ when the noise rate is high, in which case, further applying LS tends to"over-smooth"the estimated posterior. We proceeded to discover that several learning-with-noisy-labels solutions in the literature instead relate more closely to negative/not label smoothing (NLS), which acts counter to LS and defines as using a negative weight to combine the hard and soft labels! We provide understandings for the properties of LS and NLS when learning with noisy labels. Among other established properties, we theoretically show NLS is considered more beneficial when the label noise rates are high. We provide extensive experimental results on multiple benchmarks to support our findings too. Code is publicly available at https://github.com/UCSC-REAL/negative-label-smoothing.
Adaptive Clustering and Personalization in Multi-Agent Stochastic Linear Bandits

We consider the problem of minimizing regret in an $N$ agent heterogeneous stochastic linear bandits framework, where the agents (users) are similar but not all identical. We model user heterogeneity using two popularly used ideas in practice; (i) A clustering framework where users are partitioned into groups with users in the same group being identical to each other, but different across groups, and (ii) a personalization framework where no two users are necessarily identical, but a user's parameters are close to that of the population average. In the clustered users' setup, we propose a novel algorithm, based on successive refinement of cluster identities and regret minimization. We show that, for any agent, the regret scales as $\mathcal{O}(\sqrt{T/N})$, if the agent is in a `well separated' cluster, or scales as $\mathcal{O}(T^{\frac{1}{2} + \varepsilon}/(N)^{\frac{1}{2} -\varepsilon})$ if its cluster is not well separated, where $\varepsilon$ is positive and arbitrarily close to $0$. Our algorithm is adaptive to the cluster separation, and is parameter free -- it does not need to know the number of clusters, separation and cluster size, yet the regret guarantee adapts to the inherent complexity. In the personalization framework, we introduce a natural algorithm where, the personal bandit instances are initialized with the estimates of the global average model. We show that, an agent $i$ whose parameter deviates from the population average by $\epsilon_i$, attains a regret scaling of $\widetilde{O}(\epsilon_i\sqrt{T})$. This demonstrates that if the user representations are close (small $\epsilon_i)$, the resulting regret is low, and vice-versa. The results are empirically validated and we observe superior performance of our adaptive algorithms over non-adaptive baselines.
A convergent finite element algorithm for mean curvature flow in arbitrary codimension

Optimal-order uniform-in-time $H^1$-norm error estimates are given for semi- and full discretizations of mean curvature flow of surfaces in arbitrarily high codimension. The proposed and studied numerical method is based on a parabolic system coupling the surface flow to evolution equations for the mean curvature vector and for the orthogonal projection onto the tangent space. The algorithm uses evolving surface finite elements and linearly implicit backward difference formulae. This numerical method admits a convergence analysis in the case of finite elements of polynomial degree at least two and backward difference formulae of orders two to five. Numerical experiments in codimension 2 illustrate and complement our theoretical results.
PP2A and cancer epigenetics: a therapeutic opportunity waiting to happen

Abstract The epigenetic state of chromatin is altered by regulators which influence gene expression in response to environmental stimuli. While several post-translational modifications contribute to chromatin accessibility and transcriptional programs, our understanding of the role that specific phosphorylation sites play is limited. In cancer, kinases and phosphatases are commonly deregulated resulting in increased oncogenic signaling and loss of epigenetic regulation. Aberrant epigenetic states are known to promote cellular plasticity and the development of therapeutic resistance in many cancer types, highlighting the importance of these mechanisms to cancer cell phenotypes. Protein Phosphatase 2A (PP2A) is a heterotrimeric holoenzyme that targets a diverse array of cellular proteins. The composition of the PP2A complex influences its cellular targets and activity. For this reason, PP2A can be tumor suppressive or oncogenic depending on cellular context. Understanding the nuances of PP2A regulation and its effect on epigenetic alterations can lead to new therapeutic avenues that afford more specificity and contribute to the growth of personalized medicine in the oncology field. In this review, we summarize the known PP2A-regulated substrates and potential phosphorylation sites that contribute to cancer cell epigenetics and possible strategies to therapeutically leverage this phosphatase to suppress tumor growth.
The impact of COVID-19 on the dental hygienists: A cross-sectional study in the Lombardy first-wave outbreak

The impact of COVID-19 on socio-economical activities has changed everyday life. Dental hygienists, who perform aerosol generating procedures, have been strongly affected by changes in routine procedures. This cross-sectional study aimed at carrying out an online survey among dental hygienists in Lombardy. The survey was implemented after the first-wave lockdown focusing on the level of knowledge on COVID-19 and Sars-CoV-2, the virus-related changes in their attitude and working routine, and the socio-economic effects. In this report, we included 313 questionnaires of respondents (259 Females, and 54 Males; age = 33 ± 9 years). A significant percentage of respondents acknowledged the use of “word of mouth” among colleagues (n = 114, 36%) and social networks (n = 113, 36%) to be up to date on COVID-19. About half of respondents correctly identified the main COVID-19 symptoms/signs, just 13% (n = 41) identified the routes of transmission. Three quarters of respondents (n = 234, 75%) were afraid of being infected during the clinical practice, and about half of them would be afraid to treat patients having symptoms attributable to COVID-19. Twenty-one percent (n = 67) of participants also thought about changing job. Air-polishing was identified as the highest risk procedure, and 82% (n = 256) reported that they eliminated its use. Most claimed they never had a swab or a serological test, with two respondents positive to molecular test (0.6%), and 12 positives to serological test (3.8%). More than half of the participants (65%; n = 202) complained the dental hygienist is not protected, despite a loss of earnings due to lockdown between 2,000 and 10,000 euros. This study demonstrated that dental hygienists were emotionally and economically affected by the pandemic, significantly changing their work routine. Anti-epidemic protocols are pivotal to react promptly and to contain the virus in the dental setting.
A Prospective Cohort Study of COVID-19 Vaccination, SARS-CoV-2 Infection, and Fertility

Abstract Some reproductive-aged individuals remain unvaccinated against coronavirus disease 2019 (COVID-19) because of concerns about potential adverse effects on fertility. Using data from an internet-based preconception cohort study, we examined the associations of COVID-19 vaccination and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection with fertility among couples trying to conceive spontaneously. We enrolled 2,126 self-identified female participants aged 21–45 year residing in the United States or Canada during December 2020–September 2021 and followed them through November 2021. Participants completed questionnaires every 8 weeks on sociodemographics, lifestyle, medical factors, and partner information. We fit proportional probabilities regression models to estimate associations between self-reported COVID-19 vaccination and SARS-CoV-2 infection in both partners with fecundability (i.e., the per-cycle probability of conception), adjusting for potential confounders. COVID-19 vaccination was not appreciably associated with fecundability in either partner (female fecundability ratio (FR) = 1.08, 95% confidence interval (CI): 0.95, 1.23; male FR = 0.95, 95% CI: 0.83, 1.10). Female SARS-CoV-2 infection was not strongly associated with fecundability (FR = 1.07, 95% CI: 0.87, 1.31). Male infection was associated with a transient reduction in fecundability (for infection within 60 days, FR = 0.82, 95% CI: 0.47, 1.45; for infection after 60 days, FR = 1.16, 95% CI: 0.92, 1.47). These findings indicate that male SARS-CoV-2 infection may be associated with a short-term decline in fertility and that COVID-19 vaccination does not impair fertility in either partner.
Práticas clínicas relacionadas a cânulas nasais de alto fluxo em terapia intensiva pediátrica no Brasil em comparação com as de outros países: um inquérito brasileiro

Objetivo Descrever as práticas clínicas atuais relacionadas à utilização de cânula nasal de alto fluxo por intensivistas pediátricos brasileiros e compará-las com as de outros países. Métodos Para o estudo principal, foi administrado um questionário a intensivistas pediátricos em países das Américas do Norte e do Sul, Ásia, Europa e Austrália/Nova Zelândia. Comparou-se a coorte brasileira com coortes dos Estados Unidos, Canadá, Reino Unido e Índia. Resultados Responderam ao questionário 501 médicos, dos quais 127 eram do Brasil. Apenas 63,8% dos participantes brasileiros tinham disponibilidade de cânula nasal de alto fluxo, em contraste com 100% dos participantes no Reino Unido, no Canadá e nos Estados Unidos. Coube ao médico responsável a decisão de iniciar a utilização de uma cânula nasal de alto fluxo segundo responderam 61,2% dos brasileiros, 95,5% dos localizados no Reino Unido, 96,6% dos participantes dos Estados Unidos, 96,8% dos médicos canadenses e 84,7% dos participantes da Índia; 62% dos participantes do Brasil, 96,3% do Reino Unido, 96,6% dos Estados Unidos, 96,8% do Canadá e 84,7% da Índia relataram que o médico responsável era quem definia o desmame ou modificava as regulagens da cânula nasal de alto fluxo. Quando ocorreu falha da cânula nasal de alto fluxo por desconforto respiratório ou insuficiência respiratória, 82% dos participantes do Brasil considerariam uma tentativa com ventilação não invasiva antes da intubação endotraqueal, em comparação com 93% do Reino Unido, 88% dos Estados Unidos, 91,5% do Canadá e 76,8% da Índia. Mais intensivistas brasileiros (6,5%) do que do Reino Unido, Estados Unidos e Índia (1,6% para todos) afirmaram utilizar sedativos com frequência concomitantemente à cânula nasal de alto fluxo. Conclusão A disponibilidade de cânulas nasais de alto fluxo no Brasil ainda não é difundida. Há algumas divergências nas práticas clínicas entre intensivistas brasileiros e seus colegas estrangeiros, principalmente nos processos e nas tomadas de decisão relacionados a iniciar e desmamar o tratamento com cânula nasal de alto fluxo.