Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications

Federated learning (FL) is a decentralized learning framework wherein a parameter server (PS) and a collection of clients collaboratively trains a model via minimizing a global objective. Communication bandwidth is a scarce resource; in each round, the PS aggregates the updates from a subset of clients only. In this paper, we focus on non-convex minimization that is vulnerable to non-uniform and time-varying communication failures between the PS and the clients. Specifically, in each round $t$, the link between the PS and client $i$ is active with probability $p_{i}^{t}$, which is unknown to both the PS and the clients. This arises when the channel conditions are heterogeneous across clients and are changing over time. We show that when the $p_{i}^{t}$ 's are not uniform, Federated Average (FedAvg) - the most widely adopted FL algorithm - fails to minimize the global objective. Observing this, we propose Federated Postponed Broadcast (FedPBC) which is a simple variant of FedAvg. It differs from FedAvg in that the PS postpones broadcasting the global model till the end of each round. We show that FedPBC converges to a stationary point of the original objective. The introduced staleness is mild and there is no noticeable slowdown. Both theoretical analysis and numerical results are provided. On the technical front, postponing the global model broadcasts enables implicit gossiping among the clients with active links at round $t$. Despite ${p}_{i}^{t}$ 's are time-varying, we are able to bound the perturbation of the global model dynamics via the techniques of controlling the gossip-type information mixing errors.
Analysis of low-pass filters for approximate deconvolution closure modelling in one-dimensional decaying Burgers turbulence

The idea of spatial filtering is central in approximate deconvolution large-eddy simulation (AD-LES) of turbulent flows. The need for low-pass filters naturally arises in the approximate deconvolution approach which is based solely on mathematical approximations by employing repeated filtering operators. Two families of low-pass spatial filters are studied in this paper: the Butterworth filters and the Padé filters. With a selection of various filtering parameters, variants of the AD-LES are systematically applied to the decaying Burgers turbulence problem, which is a standard prototype for more complex turbulent flows. Comparing with the direct numerical simulations, it is shown that all forms of the AD-LES approaches predict significantly better results than the under-resolved simulations at the same grid resolution. However, the results highly depend on the selection of the filtering procedure and the filter design. It is concluded that a complete attenuation for the smallest scales is crucial to prevent energy accumulation at the grid cut-off.
“Let’s not Quote out of Context”: Unified Vision-Language Pretraining for Context Assisted Image Captioning

Well-formed context aware image captions and tags in enterprise content such as marketing material are critical to ensure their brand presence and content recall. Manual creation and updates to ensure the same is non trivial given the scale and the tedium towards this task. We propose a new unified Vision-Language (VL) model based on the One For All (OFA) model, with a focus on context-assisted image captioning where the caption is generated based on both the image and its context. Our approach aims to overcome the context-independent (image and text are treated independently) nature of the existing approaches. We exploit context by pretraining our model with datasets of three tasks- news image captioning where the news article is the context, contextual visual entailment, and keyword extraction from the context. The second pretraining task is a new VL task, and we construct and release two datasets for the task with 1.1M and 2.2K data instances. Our system achieves state-of-the-art results with an improvement of up to 8.34 CIDEr score on the benchmark news image captioning datasets. To the best of our knowledge, ours is the first effort at incorporating contextual information in pretraining the models for the VL tasks.
Differentiable Tree Operations Promote Compositional Generalization

In the context of structure-to-structure transformation tasks, learning sequences of discrete symbolic operations poses significant challenges due to their non-differentiability. To facilitate the learning of these symbolic sequences, we introduce a differentiable tree interpreter that compiles high-level symbolic tree operations into subsymbolic matrix operations on tensors. We present a novel Differentiable Tree Machine (DTM) architecture that integrates our interpreter with an external memory and an agent that learns to sequentially select tree operations to execute the target transformation in an end-to-end manner. With respect to out-of-distribution compositional generalization on synthetic semantic parsing and language generation tasks, DTM achieves 100% while existing baselines such as Transformer, Tree Transformer, LSTM, and Tree2Tree LSTM achieve less than 30%. DTM remains highly interpretable in addition to its perfect performance.
From sparse to dense functional data in high dimensions: Revisiting phase transitions from a non-asymptotic perspective

Nonparametric estimation of the mean and covariance functions is ubiquitous in functional data analysis and local linear smoothing techniques are most frequently used. Zhang and Wang (2016) explored different types of asymptotic properties of the estimation, which reveal interesting phase transition phenomena based on the relative order of the average sampling frequency per subject $T$ to the number of subjects $n$, partitioning the data into three categories: ``sparse'', ``semi-dense'' and ``ultra-dense''. In an increasingly available high-dimensional scenario, where the number of functional variables $p$ is large in relation to $n$, we revisit this open problem from a non-asymptotic perspective by deriving comprehensive concentration inequalities for the local linear smoothers. Besides being of interest by themselves, our non-asymptotic results lead to elementwise maximum rates of $L_2$ convergence and uniform convergence serving as a fundamentally important tool for further convergence analysis when $p$ grows exponentially with $n$ and possibly $T$. With the presence of extra $\log p$ terms to account for the high-dimensional effect, we then investigate the scaled phase transitions and the corresponding elementwise maximum rates from sparse to semi-dense to ultra-dense functional data in high dimensions. Finally, numerical studies are carried out to confirm our established theoretical properties.
Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking

We propose a simple approach for the abstractive summarization of long legal opinions that considers the argument structure of the document. Legal opinions often contain complex and nuanced argumentation, making it challenging to generate a concise summary that accurately captures the main points of the legal opinion. Our approach involves using argument role information to generate multiple candidate summaries, then reranking these candidates based on alignment with the document's argument structure. We demonstrate the effectiveness of our approach on a dataset of long legal opinions and show that it outperforms several strong baselines.
Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review

As the adoption of machine learning models increases, ensuring robust models against adversarial attacks is increasingly important. With unsupervised machine learning gaining more attention, ensuring it is robust against attacks is vital. This paper conducts a systematic literature review on the robustness of unsupervised learning, collecting 86 papers. Our results show that most research focuses on privacy attacks, which have effective defenses; however, many attacks lack effective and general defensive measures. Based on the results, we formulate a model on the properties of an attack on unsupervised learning, contributing to future research by providing a model to use.
A Survey on Fairness-aware Recommender Systems

As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era. However, as people become more dependent on them, recent studies show that recommender systems potentially own unintentional impacts on society and individuals because of their unfairness (e.g., gender discrimination in job recommendations). To develop trustworthy services, it is crucial to devise fairness-aware recommender systems that can mitigate these bias issues. In this survey, we summarise existing methodologies and practices of fairness in recommender systems. Firstly, we present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, after introducing datasets and evaluation metrics applied to assess the fairness of recommender systems, we will delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications. Subsequently, we highlight the connection between fairness and other principles of trustworthy recommender systems, aiming to consider trustworthiness principles holistically while advocating for fairness. Finally, we summarize this review, spotlighting promising opportunities in comprehending concepts, frameworks, the balance between accuracy and fairness, and the ties with trustworthiness, with the ultimate goal of fostering the development of fairness-aware recommender systems.
Communication-Efﬁcient Federated DNN Training: Convert, Compress, Correct

This paper introduces CO 3 – an algorithm for communication-efﬁcient federated Deep Neural Network (DNN) training. CO 3 takes its name from three processing applied which reduce the communication load when transmitting the local DNN gradients from the remote users to the Parameter Server. Namely: (i) gradient quantization through ﬂoating-point conversion, (ii) lossless compression of the quantized gradient, and (iii) quantization error correction. We carefully design each of the steps above to assure good training performance under a constraint on the communication rate. In particular, in steps (i) and (ii), we adopt the assumption that DNN gradients are distributed according to a generalized normal distribution, which is validated numerically in the paper. For step (iii), we utilize an error feedback with memory decay mechanism to correct the quantization error introduced in step (i). We argue that the memory decay coefﬁcient, similarly to the learning rate, can be optimally tuned to improve convergence. A rigorous convergence analysis of the proposed CO 3 with SGD is provided. Moreover, with extensive simulations, we show that CO 3 offers improved performance when compared with existing gradient compression schemes in the literature which employ sketching and non-uniform quantization of the local gradients.
Severe hypocalcemia after denosumab administration in a patient with chronic kidney disease: a case report

Patients with altered kidney function are at increased risk of hypocalcemia after denosumab administration. There is however a small number of studies and case reports describing hypocalcemia refractory to treatment. We describe a case of severe hypocalcemia, after the administration of three doses of denosumab, in a young patient with lupus nephritis under corticosteroid coverage and osteopenia. However, more studies are needed in order to extract a safe conclusion about the factors that contribute to the development of severe hypocalcemia in this group of patients.
Quantum Phase Transitions in d-wave Superconductors and Antiferromagnetic Kagome Lattices

Strongly correlated systems are of interest due to their exotic collective behavior. In this thesis we study low energy effective theory and quantum phase transitions of d-wave superconductors and spin liquids. First we examine the quantum theory of the spontaneous breaking of lattice rotation symmetry in d-wave superconductors on the square lattice. This is described by a field theory of an Ising nematic order parameter coupled to the gapless fermionic quasiparticles. We determine the structure of the renormalization group to all orders in a 1/Nf expansion, where Nf is the number of fermion spin components. Asymptotically exact results are obtained for the quantum critical theory in which, as in the large Nf theory, the nematic order has a large anomalous dimension, and the fermion spectral functions are highly anisotropic. Next we study quantum phase transitions in antiferromagnetic kagome lattices. Due to the high geometric frustration, this system poses as a good candidate for a spin liquid with exotic excitations. Here we look at physics of the spinon and vison sector. In the spinon sector, we investigate the zero-temperature phase diagram of the nearest-neighbor kagome antiferromagnet in the presence of Dzyaloshinksii-Moriya interaction. We develop a theory for the transition between Z2 spin liquids with bosonic spinons and a phase with antiferromagnetic long-range order. Connections to recent numerical studies and experiments are discussed. Finally in the vison sector, we present a projective symmetry group (PSG) analysis of the spinless excitations of Z2 spin liquids on the kagome lattice. In the simplest case, vortices carrying Z2 magnetic flux (‘visons’) are shown to transform under the
Early-type stars in the open cluster NGC 4852

The southern open cluster NGC 4852 was re-observed in the UBVRI passbands to refine its intrinsic fundamental parameters. An extended spectral coverage was also undertaken aimed at obtaining a clear picture of the kind of stars inhabiting the cluster upper main sequence. We detected an unusual number of B-type stars including a new weak-emission Be star that raises to three the number of confirmed emission objects known in the cluster area. Our analysis, however, is conclusive in that none of them belongs to the cluster. With the new data set, we improved the fundamental parameters of NGC 4852: it is little bit farther than formerly assumed; the reddening, which increases smoothly with the distance, is lower than early estimates; and more importantly, it is really a young object of nearly 40-60 Myr, contrary to the age of 200 Myr stated before. By means of spectroscopic parallaxes we secured the star distances, finding a good agreement between spectroscopic and zero-age main-sequence fitting distances, within the errors. The spatial distribution of the stars allows us to infer that NGC 4852 is the final, closer to the Sun, structure of a star-forming region and that a distant group of early-B-type stars, located immediately beyond the cluster, seems to be part of an extended OB association. The three emission stars detected in the region are related to this farther group. Finally, two extremely faint and blue stars were also found in the cluster zone. They have no connection with NGC 4852 but seem to be white-dwarf candidates.
Effect of experimental toothpaste containing hydroxyapatite nanoparticles and propolis, with and without fluoride, on the microcosm biofilm and enamel demineralization

Abstract This study evaluated the antimicrobial and anticaries effects of toothpaste containing hydroxyapatite nanoparticles (nanoHAP − 5 or 10%), xylitol (2 or 3%) and propolis (1 or 2%), associated or not with 1500 ppm fluoride (F). An in vitro model was used with microcosm biofilm produced from a pool of human saliva and McBain saliva (1:50) in the first 8 h of culture on 162 bovine enamel specimens. At the end of the experimental period, analyses of metabolic activity, colony forming units (CFU) and transverse microradiography (TMR) were performed. This study showed a possible decrease in demineralization and increase in remineralization by the commercial toothpaste (1500 ppm F) and for the experimental toothpaste containing the highest concentration of all agents, combined with F. In addition, a reduction in antimicrobial activity possibly caused by propolis and xylitol, mainly in relation to cariogenic bacteria, was observed.
Performance of satellite-based ocean forecasting (SOFT) systems: A study in the Adriatic Sea

Abstract Continuous monitoring by satellites of the ocean spatiotemporal variability allows empirical forecasting of satellite-observed data. The forecast of satellite data is achieved in three major phases: decomposition of the spatiotemporal variability of the satellite data, noise reduction of the encoded space, and time variability and the prediction, by a nonlinear forecasting technique, of the time variability. Different techniques can be employed in each processing phase. Specifically, decomposition can be carried out using spatial or temporal variance EOF decomposition. With spatial variance EOFs, performance of the empirical forecasting is associated with the predictability of the dynamics of spatial structures with strong spatial gradients (fronts, eddies, etc. . . .). Conversely, if temporal variance decomposition is employed, the success of the empirical prediction system will be related to features with strong temporal variability. Both approaches have been employed to empirically forecast sa.
Effect of Fetuin-A and oxidative stress on the occurrence of unexplained infertility

Objective: Unexplained infertility refers to a diagnosis in which all standard examinations are usually normal and is statistically seen in approximately 30-40% of infertile couples and endometriosis encountered in 25-50% of patients with unexplained infertility. Unexplained infertility is thought to be closely associated with endometriosis and serum and peritoneal fluid levels of Fetuin-A is increased in patients with endometriosis. Fetuin-A is proposed as a diagnostic marker for endometriosis and has anti-inflammatory effects on several diseases. Oxidative stress also is central to the etiopathogenesis of infertility in women. The aim of this study was to evaluate serum Fetuin-A and oxidative stress parameter concentrations impact on unexplained infertility. Materials and Methods: In the study, serum Fetuin-A, IL-1β, CA I, TAS, TOS levels, and PON and ARE enzyme activities were measured using the Enzyme-Linked ImmunoSorbent Assay in the sera of diagnosed unexplained infertility females (n=44) and controls (n=41). Results: There was no statistically significant difference between unexplained infertile patients and control groups in terms of serum IL-1β, CA I, OSI, and PON levels (p>0.05). Serum Fetuin-A and ARE levels were significantly higher in unexplained infertility compared with the control, whereas serum TAS and TOS levels were lower (p<0.05, p<0.01, p<0.05, p<0.05, respectively). Conclusion: It is thought that increased Fetuin-A levels may be a response to the inflammatory process and increased ARE activity may be a sign of the impaired oxidant-antioxidant balance in unexplained infertility. This may contribute to the pathogenesis of infertility, and the data obtained will make a significant contribution to new works to be done in this sense.
Aspherical grads method for calculating best fitting sphere of ultra-thin mirror and finite element analysis

Using the plastic deformation of ultra-thin mirror,the ultra-thin sphere is deformed into asphere by active optics technology,which can avoid the manufacturing problem of large aspheric mirror.Some principles of choosing the best fitting sphere for ultra-thin mirrors are discussed.The reasons to choose aspherical grads as principle are presented.The two spheres which are gotten from aspherical grads method and least square method are analyzed by the finite element method after loading displacements,and the surface errors relative the desired asphere and the maximum stresses are compared.The results show that the best fitting sphere gotten by aspherical grads method has less surface error and stress under same confined condition,same loading position and loading points.
Treatment strategies and survival outcomes of primary mediastinal large B-cell lymphoma

ABSTRACT Objectives Primary mediastinal large B-cel l lymphoma (PMBCL) is a rare subtype of B-cell lymphoma that is not yet fully understood. This population-based study aimed to assess the latest survival and treatment strategies for patients with PMBCL. Methods The study used the dataset from the Surveillance, Epidemiology, and End Results Program registry to retrospectively analyze adult patients diagnosed with PMBCL between 2001 and 2018. The primary outcome measures included overall survival (OS) and disease-specific survival (DSS). Results Among the 814 identified cases, the study revealed a 5-year OS rate of 86.7% and a 5-year DSS rate of 88.2% after a median follow-up of 54 months. Cox regression analysis indicated that age over 60 years, pre-2010 diagnosis, non-White ethnicity, advanced stage, and absence of chemotherapy significantly reduced both OS and DSS. It also found that chemotherapy has remained the primary therapeutic protocol for PMBCL over the last 20 years, whereas the utilization of surgery and radiation declined significantly. Patients diagnosed with PMBCL between 2010 and 2018 had a significantly reduced mortality risk (∼50%) compared to those diagnosed between 2001 and 2009. Notably, in the era of rituximab’s widespread usage, recipients of radiotherapy exhibited a poorer OS rate than non-recipients. Conclusion Survival outcomes for patients with PMBCL have significantly improved in the current era, possibly due to the evolving treatment paradigm. The value of radiotherapy in PMBCL is still debated and requires further prospective evaluation.
Symptoms of anxiety and depression in women with gestational trophoblastic disease compared to women who had a miscarriage: a cross-sectional study

Abstract This study was conducted between March 2020 and February 2021 to analyze anxiety and depression symptoms in 64 women with gestational trophoblastic disease (GTD) and 99 women who had miscarried. The Hospital Anxiety and Depression Scale (HADS) was applied by telephone three months after pregnancy loss. Multivariate analysis was performed using hierarchical logistic regression to evaluate associations between variables. Probable anxiety (HADS-A ≥ 8) and depression (HADS-D ≥ 8) were found in 53.1% and 43.8% of the GTD group and 49.5% and 39.4% of the miscarriage group, with no difference between the groups. Severe symptoms of anxiety (HADS-A 15-21) and depression (HADS-D 15-21) were found, respectively, in 12.5% and 4.7% of the GTD group and in 9.1% and 4.0% of the miscarriage group, also with no difference between the groups. Lack of partner support proved a risk factor for anxiety and depression, while poor education increased the risk of depression symptoms 3.43-fold following pregnancy loss. In conclusion, three months after pregnancy loss the frequency of anxiety and depression symptoms was similarly high in both groups, with poor education and lack of partner support being significant risk factors for the subsequent development of psychiatric morbidity.