Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiomic analysis revealed the regulatory role of the KRT14 gene in eggshell quality

Background: Eggshell strength and thickness are critical factors in reducing the egg breaking rate and preventing economic losses. The calcite biomineralization process is very important for eggshell quality. Therefore, we employed transcriptional sequencing and proteomics to investigate the differences between the uteruses of laying hens with high- and low-breaking-strength shells. Results: A total of 1,028 differentially expressed genes (DEGs) and 270 differentially expressed proteins (DEPs) were identified. The analysis results of GO terms and KEGG pathways showed that most of the DEGs and DEPs were enriched in vital pathways related to processes such as calcium metabolism, hormone and amino acid biosynthesis, and cell proliferation and apoptosis. Several DEGs and DEPs that were coexpressed at mRNA and protein levels were verified. KRT14 (keratin-14) is a candidate gene (protein) obtained by multiple omics analysis due to the fold difference of KRT14 being the largest. After the overexpression of KRT14 in uterine epithelial cells, the expressions of OC116 (ovocleididin-116), CALB1 (calbindin 1), and BST1 (ADP-ribosyl cyclase 2) were found to be increased significantly, while the expression of OC17 (ovocleididin-17) was found to be decreased significantly. Conclusion: In summary, this study confirms that during normal calcification, there are differences in ion transport between the uterus of hens producing high-breaking-strength eggshells and those producing low-breaking-strength eggshells, which may help elucidate the eggshell calcification process. The KRT14 gene may promote calcium metabolism and deposition of calcium carbonate in eggshells.
Simulation study of inverse diffusion counterbalance method for super-resolution ion mobility spectrometry

Ion mobility spectrometer (IMS) is a powerful chemical composition analysis tool working at atmospheric pressure that can be used to separate complex samples and study molecular structures. Resolution is a key parameter for evaluating the performance of IMS. However, for the pulsed sampling technique used by drift tube IMS, there is an upper limit to the resolution due to the diffusion between ions and the drift gas. In this work, an inverse diffusion counterbalance method is proposed to break the resolution limit. The method is inspired by the stimulated emission depletion (STED). In optical microscopy systems, STED is used to break the optical diffraction limit by a ring of depleted light to counteract diffraction effects of the excited light. We modified this strategy and applied it to an IMS system for counteracting the diffusion effect of the pulsed ion packet. The method can increase the resolution up to 1.55 times through theoretical analysis, and the improvement is verified by simulations. The simulation results find that the initial width of the ion packet has an influence on the effectiveness of the method, and the narrower the initial width, the better the effect. The proposed inverse counterbalance strategy may also be applied to other spectral analysis instruments to break the resolution limit.
Clinical application of endoscopic surgery using a gasless unilateral transaxillary approach in the treatment of primary hyperparathyroidism

Objectives To investigate the safety and feasibility of gasless axillary parathyroid surgery in the treatment of primary hyperparathyroidism. Methods A total of 12 patients who received gasless axillary parathyroidectomy (endoscope group) and 14 patients who received traditional open parathyroidectomy (open group) from January 2019 to April 2022 were screened and included. The differences in baseline characteristics, surgical efficiency, incidence rate of complications, changes in biochemical indicators, and incision satisfaction between the two groups were analyzed and compared. Results The proportion of young patients was higher in the endoscopic group than in the open group, and the difference was statistically significant [(41.33 ± 13.65) years vs. (58.00 ± 9.44) years, P < 0.01]. The differences in operation time, intra-operative blood loss, post-operative drainage volume, hospital stay, and surgical efficiency between the two groups yielded no statistical significance (P > 0.05). Patients in the open group had more significant neck pain 3 days after surgery (P = 0.046), but the degree of pain 3 months after surgery was the same in the 2 groups (P = 0.432). Evaluation of post-operative mature stage scar and incision satisfaction regarding aesthetics in the endoscope group were significantly superior to that in the open group [(1.92 ± 0.92) points vs. (0.92 ± 1.00) points, P = 0.017 and (1.57 ± 0.51) points vs. (1.00 ± 0.013) points, P = 0.013, respectively]. No statistical significance was found in terms of incidence rate of post-operative fever (P > 0.05). No temporary recurrent laryngeal nerve injury, post-operative bleeding, incision hematoma infection, or other complications were observed. Comparing the two groups, the extent of the level decrease of PTH was similar to that of serum calcium and phosphorus (P < 0.05), where most patients experienced transient hypocalcemia after operation yielding no significant difference in incidence (P = 0.225). During a follow-up period of 3 to 36 months, a total of 1 patient in the open group experienced recurrence at 10 months after surgery and was treated non-surgically. Conclusion Gasless axillary approach to parathyroid surgery for primary hyperparathyroidism possesses good safety and patient satisfaction in terms of aesthetics.
Effectiveness of using e-government platform “Absher” as a tool for noncommunicable diseases survey in Saudi Arabia 2019–2020: A cross-sectional study

Background E-government platforms provide an opportunity to use a novel data source for population health surveillance (also known as e-health). Absher is a Saudi e-government platform with 23 million authenticated users, including residents and citizens in Saudi Arabia. All Absher users were invited to participate in a web-based survey to estimate the prevalence of noncommunicable diseases and their risk factors in Saudi Arabia. Objective To assess the potential of using an e-government platform (Absher) to administer web-based health surveys. Methods A cross-sectional, web-based health survey was administered to Absher users between April 2019 and March 2020. The survey instrument included eight items and took <5 min to complete. The respondents' data were compared to Saudi Arabia's 2016 census. Descriptive summary statistics of the prevalence of major noncommuncable diseases are presented and compared to population-based prevalence data from Saudi Arabia's World Health Survey (WHS) 2019. All analysis was conducted using Stata 13.0. Results Overall, the Absher health survey had a 24.6% response rate, with most respondents being male (84%), Saudi (67%), and between 30 and 44 years of age (49%). Overall, the prevalence of noncommunicable diseases and risk factors among respondents was high for overweight (35%) and obesity (30%) and low for asthma (6%). The prevalence of diabetes, dyslipidemia, and hypertension was between 15 and 17% on average, and 26.5% were smokers. In comparison to population-based World Health Survey estimates, the Absher survey overestimated obesity, diabetes, dyslipidemia, hypertension, and smoking rates, and underestimated overweight, whereas asthma prevalence was similar for Absher and the WHS. Conclusions With improvements in the study design, the use of e-government platforms can provide a useful and potentially low-cost data source for public health research.
Influence of the stratospheric QBO on ENSO variability

A hypothetical mechanism is described whereby the Quasi-Biennial Oscillation (QBO) of Zonal winds in the equatorial stratosphere actively modulates the timing of El Nino-Southern Oscillation (ENSO) events. The mechanism involves the meridional redistribution of deep convective activity throughout the tropical Pacific warm pool region in response to variable wind shear processes which are linked to the opposing phases of the QBO. Hydrostatic conditions favoring deep convective activity within approximately *7* of the equator develop in response to the easterly shear phase of the QBO. At the same time, deep convection is inhibited in the monsoon-convergence zones farther off the equator (8-18* latitude) during the east phase of the QBO. The opposite trends occur during the westerly shear phase of the QBO wherein deep equatorial convection is suppressed while off equator monsoon convection is enhanced. It is shown that during the east phase of the QBO, the Pacific regional pressure and circulation anomalies which arise in response to QBO-linked trends in convective activity are consistent with conditions leading to warm ENSO events (i.e., El Nino). If the heat content of the warm pool is sufficient, a warm event will occur. Conversely, conditions favoring the development of cold (or La Nina) events tend to occur in association with the westerly phase of the QBO. Although several aspects of the hypothetical mechanism remain tentative, extensive empirical results present a compelling argument for the QBO as an active and fundamental component of ENSO variability
Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics

Modern machine learning research relies on relatively few carefully curated datasets. Even in these datasets, and typically in `untidy' or raw data, practitioners are faced with significant issues of data quality and diversity which can be prohibitively labor intensive to address. Existing methods for dealing with these challenges tend to make strong assumptions about the particular issues at play, and often require a priori knowledge or metadata such as domain labels. Our work is orthogonal to these methods: we instead focus on providing a unified and efficient framework for Metadata Archaeology -- uncovering and inferring metadata of examples in a dataset. We curate different subsets of data that might exist in a dataset (e.g. mislabeled, atypical, or out-of-distribution examples) using simple transformations, and leverage differences in learning dynamics between these probe suites to infer metadata of interest. Our method is on par with far more sophisticated mitigation methods across different tasks: identifying and correcting mislabeled examples, classifying minority-group samples, prioritizing points relevant for training and enabling scalable human auditing of relevant examples.
Subject Verb Agreement Error Patterns in Meaningless Sentences: Humans vs. BERT

Both humans and neural language models are able to perform subject verb number agreement (SVA). In principle, semantics shouldn’t interfere with this task, which only requires syntactic knowledge. In this work we test whether meaning interferes with this type of agreement in English in syntactic structures of various complexities. To do so, we generate both semantically well-formed and nonsensical items. We compare the performance of BERT-base to that of humans, obtained with a psycholinguistic online crowdsourcing experiment. We find that BERT and humans are both sensitive to our semantic manipulation: They fail more often when presented with nonsensical items, especially when their syntactic structure features an attractor (a noun phrase between the subject and the verb that has not the same number as the subject). We also find that the effect of meaningfulness on SVA errors is stronger for BERT than for humans, showing higher lexical sensitivity of the former on this task.
Toward interactive bug reporting for (android app) end-users

Many software bugs are reported manually, particularly bugs that manifest themselves visually in the user interface. End-users typically report these bugs via app reviewing websites, issue trackers, or in-app built-in bug reporting tools, if available. While these systems have various features that facilitate bug reporting (e.g., textual templates or forms), they often provide limited guidance, concrete feedback, or quality verification to end-users, who are often inexperienced at reporting bugs and submit low-quality bug reports that lead to excessive developer effort in bug report management tasks. We propose an interactive bug reporting system for end-users (Burt), implemented as a task-oriented chatbot. Unlike existing bug reporting systems, Burt provides guided reporting of essential bug report elements (i.e., the observed behavior, expected behavior, and steps to reproduce the bug), instant quality verification, and graphical suggestions for these elements. We implemented a version of Burt for Android and conducted an empirical evaluation study with end-users, who reported 12 bugs from six Android apps studied in prior work. The reporters found that Burt’s guidance and automated suggestions/clarifications are useful and Burt is easy to use. We found that Burt reports contain higher-quality information than reports collected via a template-based bug reporting system. Improvements to Burt, informed by the reporters, include support for various wordings to describe bug report elements and improved quality verification. Our work marks an important paradigm shift from static to interactive bug reporting for end-users.
The rank 2 classification problem II: mapping scale-invariant solutions to SCFTs

This is the second of a series of papers outlining an approach to the classification of $\mathcal{N}{=}2$ superconformal field theories at rank 2 via a systematic analysis of their Coulomb branches, mathematically described by special K\"ahler scale invariant geometries. Here we describe how to make the translation between geometry and field theory data. We apply this strategy to the special K\"ahler geometries found in the first paper of the series where we made strong simplifying assumptions on the form of the solutions. Remarkably, we find that our bottom-up classification strategy pays off even in this simplified setup. All scale invariant solutions in the first paper of the series have an interpretation as the Coulomb branch of at least one $\mathcal{N}{=}2$ superconformal field theory, many matching what is already known. But we also predict the existence of six new rank 2 theories, including an entire new set closed under renormalization group flow. We characterise and discuss in detail each one of these new $\mathcal{N}{=}2$ superconformal field theories. To our knowledge none of them have known string theoretic or higher dimensional realisations.
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces

Clustered federated learning (FL) has been shown to produce promising results by grouping clients into clusters. This is especially effective in scenarios where separate groups of clients have significant differences in the distributions of their local data. Existing clustered FL algorithms are essentially trying to group together clients with similar distributions so that clients in the same cluster can leverage each other's data to better perform federated learning. However, prior clustered FL algorithms attempt to learn these distribution similarities indirectly during training, which can be quite time consuming as many rounds of federated learning may be required until the formation of clusters is stabilized. In this paper, we propose a new approach to federated learning that directly aims to efficiently identify distribution similarities among clients by analyzing the principal angles between the client data subspaces. Each client applies a truncated singular value decomposition (SVD) step on its local data in a single-shot manner to derive a small set of principal vectors, which provides a signature that succinctly captures the main characteristics of the underlying distribution. This small set of principal vectors is provided to the server so that the server can directly identify distribution similarities among the clients to form clusters. This is achieved by comparing the similarities of the principal angles between the client data subspaces spanned by those principal vectors. The approach provides a simple, yet effective clustered FL framework that addresses a broad range of data heterogeneity issues beyond simpler forms of Non-IIDness like label skews. Our clustered FL approach also enables convergence guarantees for non-convex objectives.
In-Network Accumulation: Extending the Role of NoC for DNN Acceleration

Network-on-Chip (NoC) plays a significant role in the performance of a DNN accelerator. The scalability and modular design property of the NoC help in improving the performance of a DNN execution by providing flexibility in running different kinds of workloads. Data movement in a DNN workload is still a challenging task for DNN accelerators and hence a novel approach is required. In this paper, we propose the In-Network Accumulation (INA) method to further accelerate a DNN workload execution on a many-core spatial DNN accelerator for the Weight Stationary (WS) dataflow model. The INA method expands the router’s function to support partial sum accumulation. This method avoids the overhead of injecting and ejecting an incoming partial sum to the local processing element. The simulation results on AlexNet, ResNet-50, and VGG-16 workloads show that the proposed INA method achieves 1.22× improvement in latency and ×2.16 improvement in power consumption on the WS dataflow model.
Generalized Gloves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance

For many years, machine learning methods have been used in a wide range of fields, including computer vision and natural language processing. While machine learning methods have significantly improved model performance over traditional methods, their black-box structure makes it difficult for researchers to interpret results. For highly regulated financial industries, transparency, explainability, and fairness are equally, if not more, important than accuracy. Without meeting regulated requirements, even highly accurate machine learning methods are unlikely to be accepted. We address this issue by introducing a novel class of transparent and interpretable machine learning algorithms known as generalized gloves of neural additive models. The generalized gloves of neural additive models separate features into three categories: linear features, individual nonlinear features, and interacted nonlinear features. Additionally, interactions in the last category are only local. The linear and nonlinear components are distinguished by a stepwise selection algorithm, and interacted groups are carefully verified by applying additive separation criteria. Empirical results demonstrate that generalized gloves of neural additive models provide optimal accuracy with the simplest architecture, allowing for a highly accurate, transparent, and explainable approach to machine learning.
A new small Dowker space

It is proved that if there exists a Luzin set, or if either the stick principle or diamond(b) hold, then a strong instance of the guessing principle $\clubsuit_{AD}$ holds at the first uncountable cardinal. In particular, any of the above hypotheses entails the existence of a Dowker space of size $\aleph_1$.
Ultrafast dynamics of cold Fermi gas after a local quench

We consider non-equilibrium dynamics of two initially independent reservoirs $A$ and $B$ filled with a cold Fermi gas coupled and decoupled by two quantum quenches following one another. We find that the von Neumann entropy production induced by the quench is faster than thermal transport between the reservoirs and defines the short-time dynamics of the system. We analyze the energy change in the system which adds up the heat transferred between $A$ and $B$ and the work done by the quench to uncouple the reservoirs. In the case when $A$ and $B$ interact for a short time, we notice an energy increase in both reservoirs upon decoupling. This energy gain results from the quench's work and does not depend on the initial temperature imbalance between the reservoirs. We relate the quench's work to the mutual correlations of $A$ and $B$ expressed through their von Neumann entropies. Utilizing this relation, we show that once $A$ and $B$ become coupled, their entropies grow (on a timescale of the Fermi time) faster than the heat flow within the system. This result may provide a track of quantum correlations' generation at finite temperatures which one may probe in ultracold atoms, where we expect the characteristic timescale of correlations' growth to be $\sim 0.1 {\rm ms}$.
Optical–Radio Position Offsets Are Inversely Correlated with AGN Photometric Variability

Using photometric variability information from the new Gaia DR3 release, I show for the first time that photometric variability is inversely correlated with the prevalence of optical–radio position offsets in the active galactic nuclei (AGNs) that comprise the International Celestial Reference Frame (ICRF). While the overall prevalence of statistically significant optical–radio position offsets is 11%, objects with the largest fractional variabilities exhibit an offset prevalence of only ∼2%. These highly variable objects have redder optical color and steeper optical spectral indices indicative of blazars, in which the optical and radio emission is dominated by a line-of-sight jet, and indeed nearly ∼100% of the most variable objects have γ-ray emission detected by the Fermi Large Area Telescope. This result is consistent with selection on variability preferentially picking jets pointed closest to the line of sight, where the projected optical–radio position offsets are minimized and jet emission is maximally boosted in the observed frame. While only ∼9% of ICRF objects exhibit such large photometric variability, these results suggest that taking source variability into account may provide a means of optimally weighting the optical–radio celestial reference frame link.
Intermediate logics in the setting of team semantics

Several authors have recently deﬁned intuitionistic logic based on team semantics ( tIPC ). In this paper we provide two alternative approaches to intermediate logics in the team semantics setting. We do this by modifying tIPC with axioms written with two different versions of disjunction in the logic, a local one and global one. We prove a characterization theorem in the ﬁrst approach and we introduce a generalized team semantics in the second one.
Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification

We present an algorithm that given any n-vertex, m-edge, rank r hypergraph constructs a spectral sparsifier with O(n ε−2 logn logr) hyperedges in nearly-linear O(mr) time. This improves in both size and efficiency over a line of work [Bansal-Svensson-Trevisan 2019, Kapralov-Krauthgamer-Tardos-Yoshida 2021] for which the previous best size was O(min{n ε−4 log3 n,nr3 ε−2 logn}) and runtime was O(mr + nO(1)).