Miracle-dz/UniFAR-FTUs huggingface dataset:
Plume: Differential Privacy at Scale

Differential privacy has become the standard for private data analysis, and an extensive literature now offers differentially private solutions to a wide variety of problems. However, translating these solutions into practical systems often requires confronting details that the literature ignores or abstracts away: users may contribute multiple records, the domain of possible records may be unknown, and the eventual system must scale to large volumes of data. Failure to carefully account for all three issues can severely impair a system's quality and usability. We present Plume, a system built to address these problems. We describe a number of sometimes subtle implementation issues and offer practical solutions that, together, make an industrial-scale system for differentially private data analysis possible. Plume is currently deployed at Google and is routinely used to process datasets with trillions of records.
GraphTune: A Learning-Based Graph Generative Model With Tunable Structural Features

Generative models for graphs have been actively studied for decades, and they have a wide range of applications. Recently, learning-based graph generation that reproduces real-world graphs has been attracting the attention of many researchers. Although several generative models that utilize modern machine learning technologies have been proposed, conditional generation of general graphs has been less explored in the field. In this paper, we propose a generative model that allows us to tune the value of a global-level structural feature as a condition. Our model, called GraphTune, makes it possible to tune the value of any structural feature of generated graphs using Long Short Term Memory (LSTM) and a Conditional Variational AutoEncoder (CVAE). We performed comparative evaluations of GraphTune and conventional models on a real graph dataset. The evaluations show that GraphTune makes it possible to more clearly tune the value of a global-level structural feature better than conventional models.
DocSegTr: An Instance-Level End-to-End Document Image Segmentation Transformer

Understanding documents with rich layouts is an essential step towards information extraction. Business intelligence processes often require the extraction of useful semantic content from documents at a large scale for subsequent decision-making tasks. In this context, instance-level segmentation of different document objects (title, sections, figures etc.) has emerged as an interesting problem for the document analysis and understanding community. To advance the research in this direction, we present a transformer-based model called \emph{DocSegTr} for end-to-end instance segmentation of complex layouts in document images. The method adapts a twin attention module, for semantic reasoning, which helps to become highly computationally efficient compared with the state-of-the-art. To the best of our knowledge, this is the first work on transformer-based document segmentation. Extensive experimentation on competitive benchmarks like PubLayNet, PRIMA, Historical Japanese (HJ) and TableBank demonstrate that our model achieved comparable or better segmentation performance than the existing state-of-the-art approaches with the average precision of 89.4, 40.3, 83.4 and 93.3. This simple and flexible framework could serve as a promising baseline for instance-level recognition tasks in document images.
Optimal control of Hopf bifurcations

We introduce a numerical technique for controlling the location and stability properties of Hopf bifurcations in dynamical systems. The algorithm consists of solving an optimization problem constrained by an extended system of nonlinear partial differential equations that characterizes Hopf bifurcation points. The flexibility and robustness of the method allows us to advance or delay a Hopf bifurcation to a target value of the bifurcation parameter, as well as controlling the oscillation frequency with respect to a parameter of the system or the shape of the domain on which solutions are defined. Numerical applications are presented in systems arising from biology and fluid dynamics, such as the FitzHugh–Nagumo model, Ginzburg–Landau equation, Rayleigh–Bénard convection problem, and Navier–Stokes equations, where the control of the location and oscillation frequency of periodic solutions is of high interest.
Polynomials with many rational preperiodic points

In this paper we study two questions related to exceptional behavior of preperiodic points of polynomials in $\mathbb{Q}[x]$. We show that for all $d\geq 2$, there exists a polynomial $f_d(x) \in \mathbb{Q}[x]$ with $2\leq \mathrm{deg}(f_d) \leq d$ such that $f_d(x)$ has at least $d + \lfloor \log_2(d)\rfloor$ rational preperiodic points. Furthermore, we show that for infinitely many integers $d$, the polynomials $f_d(x)$ and $f_d(x) + 1$ have at least $d^2 + d\lfloor \log_2(d)\rfloor - 2d + 1$ common complex preperiodic points.
FeSe and the Missing Electron Pocket Problem

The nature and origin of electronic nematicity remains a significant challenge in our understanding of the iron-based superconductors. This is particularly evident in the iron chalcogenide, FeSe, where it is currently unclear how the experimentally determined Fermi surface near the M point evolves from having two electron pockets in the tetragonal state, to exhibiting just a single electron pocket in the nematic state. This has posed a major theoretical challenge, which has become known as the missing electron pocket problem of FeSe, and is of central importance if we wish to uncover the secrets behind nematicity and superconductivity in the wider iron-based superconductors. Here, we review the recent experimental work uncovering this nematic Fermi surface of FeSe from both ARPES and STM measurements, as well as current theoretical attempts to explain this missing electron pocket of FeSe, with a particular focus on the emerging importance of incorporating the dxy orbital into theoretical descriptions of the nematic state. Furthermore, we will discuss the consequence this missing electron pocket has on the theoretical understanding of superconductivity in this system and present several remaining open questions and avenues for future research.
The Role of Reusable and Single-Use Side Information in Private Information Retrieval

This paper introduces the problem of Private Information Retrieval with Reusable and Single-use Side Information (PIR-RSSI). In this problem, one or more remote servers store identical copies of a set of K messages, and there is a user that initially knows M of these messages, and wants to privately retrieve one other message from the set of K messages. The objective is to design a retrieval scheme in which the user downloads the minimum amount of information from the server(s) while the identity of the message wanted by the user and the identities of an M1-subset of the M messages known by the user (referred to as reusable side information) are protected, but the identities of the remaining M2 :=M−M1 messages known by the user (referred to as single-use side information) do not need to be protected. The PIR-RSSI problem reduces to the classical Private Information Retrieval (PIR) problem when M1 =M2 = 0, and reduces to the problem of PIR with Private Side Information or PIR with Side Information when M1 ≥ 1, M2 = 0 or M1 = 0, M2 ≥ 1, respectively. In this work, we focus on the single-server setting of the PIR-RSSI problem. We characterize the capacity of this setting for the cases of M1 = 1, M2 ≥ 1 and M1 ≥ 1, M2 = 1, where the capacity is defined as the maximum achievable download rate over all PIR-RSSI schemes. Our results show that for sufficiently small values of K, both the single-use and reusable side information messages can help in reducing the download cost; and for larger values of K, only the single-use side information messages can help in reducing the download cost.
Cycles in Mallows random permutations

We study cycle counts in permutations of 1,…,n$$ 1,\dots, n $$ drawn at random according to the Mallows distribution. Under this distribution, each permutation π∈Sn$$ \pi \in {S}_n $$ is selected with probability proportional to qinv(π)$$ {q}^{\mathrm{inv}\left(\pi \right)} $$ , where q>0$$ q>0 $$ is a parameter and inv(π)$$ \mathrm{inv}\left(\pi \right) $$ denotes the number of inversions of π$$ \pi $$ . For ℓ$$ \ell $$ fixed, we study the vector (C1(Πn),…,Cℓ(Πn))$$ \left({C}_1\left({\Pi}_n\right),\dots, {C}_{\ell}\left({\Pi}_n\right)\right) $$ where Ci(π)$$ {C}_i\left(\pi \right) $$ denotes the number of cycles of length i$$ i $$ in π$$ \pi $$ and Πn$$ {\Pi}_n $$ is sampled according to the Mallows distribution. When q=1$$ q=1 $$ the Mallows distribution simply samples a permutation of 1,…,n$$ 1,\dots, n $$ uniformly at random. A classical result going back to Kolchin and Goncharoff states that in this case, the vector of cycle counts tends in distribution to a vector of independent Poisson random variables, with means 1,12,13,…,1ℓ$$ 1,\frac{1}{2},\frac{1}{3},\dots, \frac{1}{\ell } $$ . Here we show that if 01$$ q>1 $$ there is a striking difference between the behavior of the even and the odd cycles. The even cycle counts still have linear means, and when properly rescaled tend to a multivariate Gaussian distribution. For the odd cycle counts on the other hand, the limiting behavior depends on the parity of n$$ n $$ when q>1$$ q>1 $$ . Both (C1(Π2n),C3(Π2n),…)$$ \left({C}_1\left({\Pi}_{2n}\right),{C}_3\left({\Pi}_{2n}\right),\dots \right) $$ and (C1(Π2n+1),C3(Π2n+1),…)$$ \left({C}_1\left({\Pi}_{2n+1}\right),{C}_3\left({\Pi}_{2n+1}\right),\dots \right) $$ have discrete limiting distributions—they do not need to be renormalized—but the two limiting distributions are distinct for all q>1$$ q>1 $$ . We describe these limiting distributions in terms of Gnedin and Olshanski's bi‐infinite extension of the Mallows model. We investigate these limiting distributions further, and study the behavior of the constants involved in the Gaussian limit laws. We for example show that as q↓1$$ q\downarrow 1 $$ the expected number of 1‐cycles tends to 1/2$$ 1/2 $$ —which, curiously, differs from the value corresponding to q=1$$ q=1 $$ . In addition we exhibit an interesting “oscillating” behavior in the limiting probability measures for q>1$$ q>1 $$ and n$$ n $$ odd versus n$$ n $$ even.
Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences

Hyperdimensional computing (HDC), also known as vector symbolic architectures (VSA), is a computing framework used within artificial intelligence and cognitive computing that operates with distributed vector representations of large fixed dimensionality. A critical step in designing the HDC/VSA solutions is to obtain such representations from the input data. Here, we focus on a wide-spread data type of sequences and propose their transformation to distributed representations that both preserve the similarity of identical sequence elements at nearby positions and are equivariant with respect to the sequence shift. These properties are enabled by forming representations of sequence positions using recursive binding as well as superposition operations. The proposed transformation was experimentally investigated with symbolic strings used for modeling human perception of word similarity. The obtained results are on a par with more sophisticated approaches from the literature. The proposed transformation was designed for the HDC/VSA model known as Fourier Holographic Reduced Representations. However, it can be adapted to some other HDC/VSA models.
Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?

APIs (Application Programming Interfaces) are reusable software libraries and are building blocks for modern rapid software development. Previous research shows that programmers frequently share and search for reviews of APIs on the mainstream software question and answer (Q&A) platforms like Stack Overflow, which motivates researchers to design tasks and approaches related to process API reviews automatically. Among these tasks, classifying API reviews into different aspects (e.g., performance or security), which is called the aspect-based API review classification, is of great importance. The current state-of-the-art (SOTA) solution to this task is based on the traditional machine learning algorithm. Inspired by the great success achieved by pre-trained models on many software engineering tasks, this study fine-tunes six pre-trained models for the aspect-based API review classification task and compares them with the current SOTA solution on an API review benchmark collected by Uddin et al. The investigated models include four models (BERT, RoBERTa, ALBERT and XLNet) that are pre-trained on natural languages, BERTOverflow that is pre-trained on text corpus extracted from posts on Stack Overflow, and CosSensBERT that is designed for handling imbalanced data. The results show that all the six fine-tuned models outperform the traditional machine learning-based tool. More specifically, the improvement on the F1-score ranges from 21.0% to 30.2%. We also find that BERTOverflow, a model pre-trained on the corpus from Stack Overflow, does not show better performance than BERT. The result also suggests that CosSensBERT also does not exhibit better performance than BERT in terms of F1, but it is still worthy of being considered as it achieves better performance on MCC and AUC.
Bandit Learning in Convex Non-Strictly Monotone Games

We address learning Nash equilibria in convex games under the payoff information setting. We consider the case in which the game pseudo-gradient is monotone but not necessarily strictly monotone. This relaxation of strict monotonicity enables application of learning algorithms to a larger class of games, such as, for example, a zero-sum game with a merely convex-concave cost function. We derive an algorithm whose iterates provably converge to the least-norm Nash equilibrium in this setting. {From the perspective of a single player using the proposed algorithm, we view the game as an instance of online optimization}. Through this lens, we quantify the regret rate of the algorithm and provide an approach to choose the algorithm's parameters to minimize the regret rate.
In Vivo and In Silico Assessment of Diabetes Ameliorating Potentiality and Safety Profile of Gynura procumbens Leaves

Background Diabetes mellitus is one of the most notable health dilemmas. Analyzing plants for new antidiabetic remedies has become an impressive territory for life science researchers. Gynura procumbens has long been used to treat diabetes. Thus, we strived to ascertain the hypoglycemic potentiality of extract of leaves of G. procumbens by in vivo and in silico approaches. Methods Fresh leaves of G. procumbens were collected and shade-dried to prepare ethanolic extracts to evaluate pharmacological parameters. Diabetes was induced in rats via injecting alloxan through the intraperitoneal route at a dose of 150 mg/kg body weight. Humalyzer 3000 was used to perform a biochemical assay of collected samples from rats. Anti-hyperglycemic activity study along with overdose toxicity test was performed. The pharmacological activity of this plant was also evaluated through a molecular docking study. This in silico study investigated the binding affinity of natural ligands from G. procumbens against glycoside hydrolase enzymes. Results We detected a peak plasma concentration of G. procumbens at 3 hours 45 minutes that is roughly similar to the peak plasma concentration of metformin. Again, in OGTT and anti-hyperglycemic tests, it has been ascertained that both plant extract and metformin can exert significant (P < 0.05) and highly significant (P < 0.01) hypoglycemic activity in a dose-dependent manner. Metformin exhibited better therapeutic efficacy than that of plant extract, but it possessed null statistical significance. Also, our safety profile expressed that, similar to metformin, the plant extract can restore the disturbed pathological state in a dose-oriented approach with a wide safety margin. In silico study also validated the potentialities of natural constituents of G. procumbens. Conclusion. This study suggested that G. procumbens can be considered as potential antidiabetic plant. Robust and meticulous investigation regarding plant chemistry and pharmacology in the future may bring about a new dimension that will aid in discovering antidiabetic drugs from this plant in the diabetes management system.
Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs

Abstract Objective: To explore the value of quantitative parameters of artificial intelligence (AI) and computed tomography (CT) signs in identifying pathological subtypes of lung adenocarcinoma appearing as ground-glass nodules (GGNs). Methods: CT images of 224 GGNs from 210 individuals were collected retrospectively and classified into atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) groups. AI was used to identify GGNs and to obtain quantitative parameters, and CT signs were recognized manually. The mixed predictive model based on logistic multivariate regression was built and evaluated. Results: Of the 224 GGNs, 55, 93, and 76 were AAH/AIS, MIA, and IAC, respectively. In terms of AI parameters, from AAH/AIS to MIA, and IAC, there was a gradual increase in two-dimensional mean diameter, three-dimensional mean diameter, mean CT value, maximum CT value, and volume of GGNs (all P<0.0001). Except for the CT signs of the location, and the tumor–lung interface, there were significant differences among the three groups in the density, shape, vacuolar signs, air bronchogram, lobulation, spiculation, pleural indentation, and vascular convergence signs (all P<0.05). The areas under the curve (AUC) of predictive model 1 for identifying the AAH/AIS and MIA and model 2 for identifying MIA and IAC were 0.779 and 0.918, respectively, which were greater than the quantitative parameters independently (all P<0.05). Conclusion: AI parameters are valuable for identifying subtypes of early lung adenocarcinoma and have improved diagnostic efficacy when combined with CT signs.
Effects of Nanoceria on Human Platelet Functions and Blood Coagulation

Purpose Cerium oxide nanoparticles (nanoceria) exist in either 3+ or 4+ oxidation state with interesting redox properties and exhibit both oxidant and antioxidant attributes based on concentration, environment and pH. Thanks to their wide spread use in fuel, cosmetics and other industries as well as in biomedical field, nanoceria particles get released into environment with risk of significant human exposure, and thus necessitate careful examination and optimization of their biomedical applications. Methods This research was planned to explore effects of nanoceria on human platelet functions and whole blood coagulation. Platelets and whole blood from volunteer healthy donors were treated with different doses of nanoceria and various platelet functions, fibrin polymerization and blood coagulation were tested. Results Nanoceria particles were found to reduce platelet aggregation and secretion of granule contents in a dose-dependent manner, impaired fibrin polymerization and retraction of fibrin-rich thrombi, associated with reduced cytosolic filamentous actin. Remarkably, nanoceria instigated early clot formation in whole blood attributable to putative activation of coagulation cascade. Conclusion Above observations provide cautionary framework to critically re-evaluate and accordingly plan biomedical applications of nanoceria.
Lifetime cardiovascular risk factors and maternal and offspring birth outcomes: Bogalusa Babies

Both cardiovascular and reproductive complications may have origins in utero or in early life. Women in the Bogalusa Heart Study (n = 1401) had been linked to birth certificates for birthweight and gestational data, which were examined relative to childhood (ages 4–16) cardiometabolic indicators, indicated by mean levels overall and total risk factor burden as estimated by area under the curve (AUC) computed from longitudinal quadratic random-effects growth models. Women reported the birthweight and gestational age of each of their own pregnancies, and delivery medical records were linked to interview data where possible. Path analyses were conducted to examine the relationships among a woman’s own birth outcomes, childhood and preconception adult cardiovascular health, and birth outcomes. Mean blood pressure (systolic blood pressure (SBP) adjusted relative risk (aRR) per 1-SD increase, 1.27, 95% CI 1.04–1.57) and low-density lipoprotein (aRR 1.21, 95% CI 1.02–1.44) in childhood predicted preterm birth (PTB), while mean SBP (aRR 1.33, 95% CI 1.02–1.74) predicted term low birthweight. The AUC data suggested an association between blood pressure and PTB (aRR for SBP top 10%, 1.86, 95% CI 1.08–3.21). Pre-pregnancy total cholesterol was negatively associated with gestational age. In path analyses, positive associations were found for each step between own birthweight, childhood BMI, pre-pregnancy BMI, and child’s birthweight. Childhood levels of some, though not all, cardiovascular risk factors may predict adverse birth outcomes (preterm birth and reduced fetal growth).
Side effects among healthcare workers from a large Milan university hospital after second dose of BNT162b2 mRNA COVID-19 vaccine

Background: In Italy, healthcare workers (HCWs) were among the first to receive COVID-19 vaccination. Aim of the present study is to evaluate frequency and severity of adverse events (AEs) following the second dose of BNT162b2 vaccine among HCWs of a large university hospital in Milan, Italy. Methods: One month after having received the second dose of vaccine, HCWs filled-in a form about type, severity, and duration of post-vaccination local and systemic symptoms. We calculated the overall frequency of AEs and used multivariable Poisson regression models (adjusted for sex, age, BMI, smoking, allergy history, previous SARS-CoV-2 infection, anti-hypertensive therapy, and occupation) to calculate risk ratios (RR) and 95% confidence intervals (CI) of AEs according to selected variables. Results: We included 3659 HCWs. Overall, 2801 (76.6%) experienced at least one local event, with pain at injection site being the most frequent (2788, 76.2%). Systemic events were reported by 2080 (56.8%) HCWs, with fatigue (52.3%), muscle pain (42.2%), headache (37.7%), joint pain (31.9%), and fever (26.2%) being the most frequent. Risks of systemic events were associated with female gender (RR=1.14, CI: 1.06-1.24), age (strong decrease with increasing age, p-trend<0.001), allergy history (RR=1.13, CI: 1.05-1.20), SARS-CoV-2 infection more than 180 days before second dose (RR=1.16, CI:1.01-1.32), and current smoking (RR=0.90, CI: 0.84-0.97). Conclusions: Both local and systemic acute effects after second dose of BNT162b2 vaccine were frequently reported. However, symptoms were mostly light/mild and of short duration. Thus, our findings support the safety of COVID-19 vaccination in adults in relatively good health.