Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrapleural use of urokinase and DNase in pleural infections managed with repeated thoracentesis: A comparative cohort study

Introduction Evacuation of infected fluid in pleural infections is essential. To date, the use of an intrapleural fibrinolytic agent such as urokinase and DNase has not yet been assessed in infections managed by repeated therapeutic thoracentesis (RTT). Methods We performed a retrospective comparative study of two successive cohorts of consecutive patients with pleural infections from 2001 to 2018. Between 2001 and 2010, patients had RTT with intrapleural urokinase (RTT-U). After 2011, patients received intrapleural urokinase and DNase with RTT (RTT-UD). Data were collected through a standardized questionnaire. Results One hundred and thirty-three patients were included: 93 were men and the mean age was 59 years (standard deviation 17.2). Eighty-one patients were treated with a combination of intrapleural urokinase and DNase, and 52 were treated with intrapleural urokinase only. In the RTT-UD, RTT failure occurred in 14 patients (17%) compared to 10 (19%) in the RTT-U group (P = 0.82). There was no difference between the two groups in intensive care unit admission, surgical referrals or in-hospital mortality. RTT-UD was associated with faster time to apyrexia (aOR = 0.51, 95%CI [0.37–0.72]), a reduced length of hospital stay (aOR = 0.61, 95%CI [0.52–0.73]) and a higher volume of total pleural fluid retrieved (aOR = 1.38, 95%CI [1.02–1.88]). Complications were rare with only one hemothorax in the RTT-UD group and no pneumothorax requiring drainage in either group. Conclusion Compared to urokinase only, intrapleural use of urokinase and DNase in RTT was associated with quicker defervescence, shorter hospital stay and increased volumes of pleural fluid drained. Randomized controlled trials evaluating urokinase and DNase with RTT technique would be required to confirm these results.
Profile of Women Seeking Treatment for Substance Use Disorder in Tertiary Care Government De-Addiction Centre

Background: There is an increasing trend among women using substance. However, very few women seek treatment for substance use disorder in Government de-addiction centre. There is limited literature available regarding the same. Aim: To examine the profile of women seeking treatment for substance use disorder in Government De-addiction Centre, Bengaluru. Methods: The study was retrospective in nature. All the female patients who sought in-patient treatment from centre for addiction medicine, NIMHANS, Bengaluru from Apr 2015- Sept 2016 were included in the study. Method of data collection: Content analysis was used to collect the secondary data from the hospital record. Descriptive statistics such as mean, frequency distribution was used for statistical analysis. Results: Mean age of women who sought treatment for substance use disorder in the centre was 42 ±14 years. The mean age at initiation of alcohol was 27 ±9 years, alcohol dependence was 34 ±10.6 years. Majority (65%) belonged below poverty line. 59% were married and living with their spouse. 55% of them sought treatment either on their own or brought by family members, 38% referred by the psychiatrist. More than one-third (36%) had primary school education. Nearly one-third (27%) of them had repeated admissions more than once. More than one-third of them (34%) were homemakers, nearly one-fourth (24%) of them were lost their previous job, 6% of them students and professionals respectively. Conclusion: The majority (68%) of women had diagnosis of alcohol dependence, nicotine (44%), benzodiazepine (14%), opioid dependence (11%), cannabis dependence (1.6%), less than 1% had other forms behavioural addictions.
New Immunotherapeutic Approaches for Glioblastoma

Glioblastoma (GBM) is the most common primary malignant brain tumor with a high mortality rate. The current treatment consists of surgical resection, radiation, and chemotherapy; however, the median survival rate is only 12–18 months despite these alternatives, highlighting the urgent need to find new strategies. The heterogeneity of GBM makes this tumor difficult to treat, and the immunotherapies result in an attractive approach to modulate the antitumoral immune responses favoring the tumor eradication. The immunotherapies for GMB including monoclonal antibodies, checkpoint inhibitors, vaccines, and oncolytic viruses, among others, have shown favorable results alone or as a multimodal treatment. In this review, we summarize and discuss promising immunotherapies for GBM currently under preclinical investigation as well as in clinical trials.
Targeting Inflammatory Cytokines to Improve Type 2 Diabetes Control

Type 2 diabetes (T2D) is one of the most common chronic metabolic disorders in adulthood worldwide, whose pathophysiology includes an abnormal immune response accompanied by cytokine dysregulation and inflammation. As the T2D-related inflammation and its progression were associated with the balance between pro and anti-inflammatory cytokines, anticytokine treatments might represent an additional therapeutic option for T2D patients. This review focuses on existing evidence for antihyperglycemic properties of disease-modifying antirheumatic drugs (DMARDs) and anticytokine agents (anti-TNF-α, anti-interleukin-(IL-) 6, -IL-1, -IL-17, -IL-23, etc.). Emphasis is placed on their molecular mechanisms and on the biological rationale for clinical use. Finally, we briefly summarize the results from experimental model studies and promising clinical trials about the potential of anticytokine therapies in T2D, discussing the effects of these drugs on systemic and islet inflammation, beta-cell function, insulin secretion, and insulin sensitivity.
Healthcare, Medication Utilization and Outcomes of Patients with COPD by GOLD Classification in England

Background Available data on the relationship between COPD symptoms, disease outcomes, and mortality are currently limited. This study investigated the clinical characteristics, outcomes, healthcare utilization, and prescribing practices across GOLD 2017 groups (A, B, C, and D) in a large-scale, population-based cohort of COPD patients managed in an English primary care setting. Patients and Methods This retrospective analysis included patients aged ≥35 years, with a confirmed diagnosis of COPD and ≥1 record of pulmonary function testing in their medical history. Medical Research Council dyspnea score and exacerbation history were used to define patients’ GOLD 2017 classification. Patients were identified using the UK Clinical Practice Research Database and were followed for 12 months. Results Eligible COPD patients’ (N=42,331; mean [SD] age, 69.5 [10.7] years; 54% males), GOLD 2017 categorizations were: Group A: 49.1%, Group B: 30.5%, Group C: 8.2%, Group D: 12.1%. Overall, 37.7% of patients experienced ≥1 moderate COPD exacerbation. The rate of moderate exacerbations per person per year (PPPY) was highest in GOLD group D (0.72), followed by C (0.53), B (0.22), and A (0.15), while the rate of exacerbations leading to hospitalization PPPY was much higher in D (0.27) than in B (0.10), C (0.08), or A (0.03). Overall, 56.4% of patients visited their general practitioner ≥5 times in the 12 months of follow-up. Time-to-event analysis suggested that breathlessness contributed to exacerbation severity and frequency. One-year mortality was highest in GOLD groups D and B. The most frequent prescribed maintenance therapies were inhaled corticosteroids with long-acting β2-agonists, multiple-inhaler triple therapy, or long-acting muscarinic antagonist, irrespective of GOLD classification. Conclusion The burden of COPD remains substantial in England. Stratification of this large primary care population according to GOLD criteria predicted the risk of COPD exacerbations. Understanding populations of patients with COPD may enable the optimization of patient care.
Entropic Issues in Likelihood-Based OOD Detection

Deep generative models trained by maximum likelihood remain very popular methods for reasoning about data probabilistically. However, it has been observed that they can assign higher likelihoods to out-of-distribution (OOD) data than in-distribution data, thus calling into question the meaning of these likelihood values. In this work we provide a novel perspective on this phenomenon, decomposing the average likelihood into a KL divergence term and an entropy term. We argue that the latter can explain the curious OOD behaviour mentioned above, suppressing likelihood values on datasets with higher entropy. Although our idea is simple, we have not seen it explored yet in the literature. This analysis provides further explanation for the success of OOD detection methods based on likelihood ratios, as the problematic entropy term cancels out in expectation. Finally, we discuss how this observation relates to recent success in OOD detection with manifold-supported models, for which the above decomposition does not hold directly.
Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-Based Convolutional Neural Networks

Lane change (LC) is one of the safety-critical manoeuvres in highway driving according to various road accident records. Thus, reliably predicting such manoeuvre in advance is critical for the safe and comfortable operation of automated driving systems. The majority of previous studies rely on detecting a manoeuvre that has been already started, rather than predicting the manoeuvre in advance. Furthermore, most of the previous works do not estimate the key timings of the manoeuvre (e.g., crossing time), which can actually yield more useful information for the decision making in the ego vehicle. To address these shortcomings, this paper proposes a novel multi-task model to simultaneously estimate the likelihood of LC manoeuvres and the time-to-lane-change (TTLC). In both tasks, an attention-based convolutional neural network (CNN) is used as a shared feature extractor from a bird’s eye view representation of the driving environment. The spatial attention used in the CNN model improves the feature extraction process by focusing on the most relevant areas of the surrounding environment. In addition, two novel curriculum learning schemes are employed to train the proposed approach. The extensive evaluation and comparative analysis of the proposed method in existing benchmark datasets show that the proposed method outperforms state-of-the-art LC prediction models, particularly considering long-term prediction performance.
Enriching and Controlling Global Semantics for Text Summarization

Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Nevertheless, these models still suffer from the short-range dependency problem, causing them to produce summaries that miss the key points of document. In this paper, we attempt to address this issue by introducing a neural topic model empowered with normalizing flow to capture the global semantics of the document, which are then integrated into the summarization model. In addition, to avoid the overwhelming effect of global semantics on contextualized representation, we introduce a mechanism to control the amount of global semantics supplied to the text generation module. Our method outperforms state-of-the-art summarization models on five common text summarization datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed.
Identifying Fast/Slow Thinking without Prior

System 1 vs. 2 theory [6, 19] describes two modes of thought, a fast, instinctive one and a slow, logical one. When we ask a question (e.g. A bat and ball cost $1.10. The bat costs $1 more than the ball. How much does the ball cost? ), with prior, we can identify fast/slow thinking ($.10/$.05). But what if we do not have prior? A very clever method, surprisingly popular [13], additionally asks what percentage of other people answer $. 10/$. 05 and selects the answer that is more popular than people predict. However, the distribution report is non-minimal for many people especially for non-binary choices, the choices design requires prior and only the best answer is selected. Here we propose a simple minimal paradigm that elicits the full hierarchy of the collected answers: we ask a single open response question and elicit each respondent’s answer (e.g. $.05) and guess(es) for other people’s answers (e.g. $.10). We record the number of people who report a specific answer-guess pair (e.g. 10 people answer $.05 and guess $.10) by an answer-guess matrix. By ranking the answers to maximize the sum of the upper triangular area of the matrix, we obtain and visualize the hierarchy of the answers without any prior. Our paradigm has minimal requirement for both the respondent (no distribution report) and the requester (no choices design; check the hierarchy visually) and can be also used to research how people reason about other people’s minds [14]. We conduct four studies including math problems, Life-and-death Go problems, general knowledge and pronunciation questions. The empirical results not only validate our approach but also show that more sophisticated people can reason about less sophisticated people’s minds.
Photon rings of spherically symmetric black holes and robust tests of non-Kerr metrics

Under very general assumptions on the accretion flow geometry, images of a black hole illuminated by electromagnetic radiation display a sequence of photon rings (demagnified and rotated copies of the direct image) which asymptotically approach a purely theoretical critical curve - the outline of the black hole photon shell. To a distant observer, these images appear dominated by the direct emission, which forms a ring whose diameter is primarily determined by the effective radius of the emitting region. For that reason, connecting the image diameter seen by a distant observer to the properties of the underlying spacetime crucially relies on a calibration that necessarily depends on the assumed astrophysical source model. On the other hand, the diameter of the photon rings depends more on the detailed geometry of the spacetime than on the source structure. As such, a photon ring detection would allow for the spacetime metric to be probed in a less model-dependent way, enabling more robust tests of General Relativity (GR) and the Kerr hypothesis. Here we present the photon ring structure of several spherically symmetric black hole spacetimes and perform comparisons with the Schwarzschild/Kerr case. We offer our perspective on future tests of the spacetime metric with photon rings, discussing challenges and opportunities involved.
Who's the Daddy? Infidelity Favours Father-Son Facial Dissimilarity

Evolutionary theory predicts that children fare better if they resemble their father. However, if a man is promiscuous then his children tend to be (unwittingly) raised within other families; such children should fare better if they do not resemble their father. This suggests that father-son facial similarity should decrease as a function of male promiscuity. To test this hypothesis, the degree of similarity between each of 37 pairs of father-son photographs was rated by 83 participants. The promiscuity score of a father was defined as the number of sexual relationships he had been involved in. A linear regression of father-son similarity ratings against fathers’ promiscuity scores indicated that the slope of the fitted line was significantly less than zero (slope=-0.176, p<0.001), and that fathers’ promiscuity scores account for 75% of the variance father-son facial similarity ratings. Additionally, a significant negative non-parametric correlation (Spearman’s rho ρ=-0.886, p=0.019) between father-son similarity ratings and fathers’ promiscuity scores was found.
Estimating Gibbs partition function with quantum Clifford sampling

The partition function is an essential quantity in statistical mechanics, and its accurate computation is a key component of any statistical analysis of quantum systems and phenomena. However, for interacting many-body quantum systems, its calculation generally involves summing over an exponential number of terms and can thus quickly grow to be intractable. Accurately and efficiently estimating the partition function of its corresponding system Hamiltonian then becomes the key in solving quantum many-body problems. In this paper we develop a hybrid quantum–classical algorithm to estimate the partition function, utilising a novel quantum Clifford sampling technique. Note that previous works on the estimation of partition functions require O(1/ϵΔ) -depth quantum circuits (Srinivasan et al 2021 IEEE Int. Conf. on Quantum Computing and Engineering (QCE) pp 112–22; Montanaro 2015 Proc. R. Soc. A 471 20150301), where Δ is the minimum spectral gap of stochastic matrices and ϵ is the multiplicative error. Our algorithm requires only a shallow O(1) -depth quantum circuit, repeated O(n/ϵ2) times, to provide a comparable ϵ approximation. Shallow-depth quantum circuits are considered vitally important for currently available noisy intermediate-scale quantum devices.
Mira Ceti, Atypical Archetype

With the aim of unraveling the complexity of the morphokinematics of the circumstellar envelope (CSE) of Mira Ceti, we review, extend, and in some cases revisit Atacama Large Millimeter/submillimeter Array observations of the emission of the SiO(5–4) and CO(3–2) molecular lines. In addition, we present a detailed analysis of the optically thin 13CO(3–2) emission, which provides several important new results. In agreement with observations at infrared and visible wavelengths, we give evidence for the confinement and probably rotation of a dense gas volume within ∼50 au from the star and for a large SiO line width within ∼15 au. We show that the mass-loss process is episodic and takes the form of clumps having a very low SiO/CO abundance ratio compared with similar oxygen-rich long-period variables, probably a result of depletion on dust grains and photodissociation. We evaluate the mass-loss rate associated with the main clumps and compare it with values obtained from single-dish observations. We argue that the SiO emission observed in the southwestern quadrant is not related to the mechanism of generation of the nascent wind but to a mass ejection that occurred 11 years before the observations. We remark that Mira Ceti is not a good archetype in terms of its wind: models aiming at describing the very complex gas-dust chemistry in action in the CSE of oxygen-rich AGB stars may find it difficult to account for its peculiar features and small variations in the parameters deciding when and where mass loss can proceed significantly.
K-AID: Enhancing Pre-trained Language Models with Domain Knowledge for Question Answering

Knowledge enhanced pre-trained language models (K-PLMs) are shown to be effective for many public tasks in the literature, but few of them have been successfully applied in practice. To address this problem, we propose K-AID, a systematic approach that includes a low-cost knowledge acquisition process for acquiring domain knowledge, an effective knowledge infusion module for improving model performance, and a knowledge distillation component for reducing the model size and deploying K-PLMs on resource-restricted devices (e.g., CPU) for real-world application. Importantly, instead of capturing entity knowledge like the majority of existing K-PLMs, our approach captures relational knowledge, which contributes to better improving sentence-level text classification and text matching tasks that play a key role in question answering (QA). We conducted a set of experiments on five text classification tasks and three text matching tasks from three domains, namely E-commerce, Government, and Film&TV, and performed online A/B tests in E-commerce. Experimental results show that our approach is able to achieve substantial improvement on sentence-level question answering tasks and bring beneficial business value in industrial settings.
Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesis

Edge devices in federated learning usually have much more limited computation and communication resources compared to servers in a data center. Recently, advanced model compression methods, like the Lottery Ticket Hypothesis, have already been implemented on federated learning to reduce the model size and communication cost. However, Backdoor Attack can compromise its implementation in the federated learning scenario. The malicious edge device trains the client model with poisoned private data and uploads parameters to the center, embedding a backdoor to the global shared model after unwitting aggregative optimization. During the inference phase, the model with backdoors classifies samples with a certain trigger as one target category, while shows a slight decrease in inference accuracy to clean samples. In this work, we empirically demonstrate that Lottery Ticket models are equally vulnerable to backdoor attacks as the original dense models, and backdoor attacks can influence the structure of extracted tickets. Based on tickets' similarities between each other, we provide a feasible defense for federated learning against backdoor attacks on various datasets.
Minimax rates for conditional density estimation via empirical entropy

We consider the task of estimating a conditional density using i.i. d. samples from a joint distribution, which is a fundamental problem with applications in both classification and uncertainty quantification for regression. For joint density estimation, minimax rates have been characterized for general density classes in terms of uniform (metric) entropy, a well-studied notion of statistical capacity. When applying these results to conditional density estimation, the use of uniform entropy -- which is infinite when the covariate space is unbounded and suffers from the curse of dimensionality -- can lead to suboptimal rates. Consequently, minimax rates for conditional density estimation cannot be characterized using these classical results. We resolve this problem for well-specified models, obtaining matching (within logarithmic factors) upper and lower bounds on the minimax Kullback--Leibler risk in terms of the empirical Hellinger entropy for the conditional density class. The use of empirical entropy allows us to appeal to concentration arguments based on local Rademacher complexity, which -- in contrast to uniform entropy -- leads to matching rates for large, potentially nonparametric classes and captures the correct dependence on the complexity of the covariate space. Our results require only that the conditional densities are bounded above, and do not require that they are bounded below or otherwise satisfy any tail conditions.
Distributions on partitions arising from Hilbert schemes and hook lengths

Abstract Recent works at the interface of algebraic combinatorics, algebraic geometry, number theory and topology have provided new integer-valued invariants on integer partitions. It is natural to consider the distribution of partitions when sorted by these invariants in congruence classes. We consider the prominent situations that arise from extensions of the Nekrasov–Okounkov hook product formula and from Betti numbers of various Hilbert schemes of n points on ${\mathbb {C}}^2$ . For the Hilbert schemes, we prove that homology is equidistributed as $n\to \infty $ . For t-hooks, we prove distributions that are often not equidistributed. The cases where $t\in \{2, 3\}$ stand out, as there are congruence classes where such counts are zero. To obtain these distributions, we obtain analytic results of independent interest. We determine the asymptotics, near roots of unity, of the ubiquitous infinite products $$ \begin{align*}F_1(\xi; q):=\prod_{n=1}^{\infty}\left(1-\xi q^n\right), \ \ \ F_2(\xi; q):=\prod_{n=1}^{\infty}\left(1-(\xi q)^n\right) \ \ \ {\mathrm{and}}\ \ \ F_3(\xi; q):=\prod_{n=1}^{\infty}\left(1-\xi^{-1}(\xi q)^n\right). \end{align*} $$