Miracle-dz/UniFAR-FTUs huggingface dataset:
Amburana cearensis leaf extract protects against cisplatin-induced ovarian damage through regulation of p-PTEN and p-Akt proteins in mice

Objective(s): To evaluate the effects of Amburana cearensis leaf extract against cisplatin-induced ovarian toxicity in mice and involvement of p-PTEN and p-Akt proteins. Materials and Methods: A. cearensis ethanolic leaf extract was analyzed by high-performance liquid chromatography (HPLC). Mice were pretreated once daily for 3 days as follows: (1) the control group was pretreated with oral administration (o.p.) of saline solution, followed by intraperitoneal (IP) injection of saline solution. The other groups were pretreated (o.p.) with (2) saline solution (cisplatin group), (3) N-acetylcysteine (positive control), with (4) 50, or (5) 200 mg/kg body weight of A. cearensis extract, followed by injection of 5 mg/kg body weight (IP) of cisplatin. The ovaries were harvested and destined for histological (follicular morphology), immunohistochemistry (apoptosis and cell proliferation), and fluorescence (reactive oxygen species [ROS], glutathione concentrations [GSH], and active mitochondria) analyses. Furthermore, immunoexpression of p-PTEN and p-Akt was evaluated to elucidate a potential mechanism by which A. cearensis extract could prevent cisplatin-induced ovarian damage. Results: After HPLC analysis, protocatechuic acid was detected in the extract. The pretreatment with N-acetylcysteine or A. cearensis extract maintained the percentage of normal follicles and cell proliferation, reduced apoptosis and ROS concentrations, and increased GSH concentrations and mitochondrial activity compared with cisplatin treatment. Furthermore, pretreatment with A. cearensis extract regulated p-PTEN and p-Akt immunoexpression after cisplatin exposure. Conclusion: Pretreatment with A. cearensis extract prevented cisplatin-induced ovarian damage through its anti-oxidant actions and by modulating the expression of phosphorylated PTEN and Akt proteins.
Robust Minimax Probability Machine Regression

We formulate regression as maximizing the minimum probability (Ω) that the true regression function is within ±2 of the regression model. Our framework starts by posing regression as a binary classification problem, such that a solution to this single classification problem directly solves the original regression problem. Minimax probability machine classification (Lanckriet et al., 2002a) is used to solve the binary classification problem, resulting in a direct bound on the minimum probability Ω that the true regression function is within ±2 of the regression model. This minimax probability machine regression (MPMR) model assumes only that the mean and covariance matrix of the distribution that generated the regression data are known; no further assumptions on conditional distributions are required. Theory is formulated for determining when estimates of mean and covariance are accurate, thus implying robust estimates of the probability bound Ω. Conditions under which the MPMR regression surface is identical to a standard least squares regression surface are given, allowing direct generalization bounds to be easily calculated for any least squares regression model (which was previously possible only under very specific, often unrealistic, distributional assumptions). We further generalize these theoretical bounds to any superposition of basis functions regression model. Experimental evidence is given supporting these theoretical results.
Parkes HI observations of galaxies behind the southern Milky Way. - I. The Hydra/Antlia region (l

As part of our program to map the large-scale distribution of galaxies behind the Milky Way, we used the Parkes 210 ft (64 m) radio telescope for pointed H I observations of a sample of low surface-brightness (due to heavy obscuration) spiral galaxies selected from the deep optical Zone of Avoidance (ZOA) galaxy catalog in the Hydra/Antlia region (Kraan-Korteweg 2000a). Searching a simultaneous velocity range of either 300 to 5500 km s 1 or 300 to 10 500 km s 1 to an rms level of typically 2-4 m Jy resulted in detections in 61 of the 139 pointings, leading to a total of 66 detections (an additional detection was made in a reference position, and two other pointings revealed two and four independent signals respectively). Except for 2 strong H I emitters identified in the shallow Zone of Avoidance H I survey (Henning et al. 2000), all H I detections are new. An analysis of the properties of the observed and detected galaxies prove that pointed H I observations of highly obscured galaxies allow the tracing of a population of nearby, intrinsically large and bright spiral galaxies that otherwise would not be recovered. The new data identified a previously unrecognized nearby group at' 287:5;b 9:5;V 1700 km s 1 , the continuation of the Hydra/Antlia filament on the opposite side of the Galactic plane, and helped to delimit a distinct void in the ZOA centered at 2000 km s 1 .
SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction

Effective learning from electronic health records (EHR) data for prediction of clinical outcomes is often challenging because of features recorded at irregular timesteps and loss to follow-up as well as competing events such as death or disease progression. To that end, we propose a generative time-to-event model, SurvLatent ODE, which adopts an Ordinary Differential Equation-based Recurrent Neural Networks (ODE-RNN) as an encoder to effectively parameterize dynamics of latent states under irregularly sampled input data. Our model then utilizes the resulting latent embedding to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. We demonstrate competitive performance of our model on MIMIC-III, a freely-available longitudinal dataset collected from critical care units, on predicting hospital mortality as well as the data from the Dana-Farber Cancer Institute (DFCI) on predicting onset of Venous Thromboembolism (VTE), a life-threatening complication for patients with cancer, with death as a competing event. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying VTE risk groups, while providing clinically meaningful and interpretable latent representations.
Fabrication and transfer printing basedintegration of free-standing GaN membranemicro-lenses onto semiconductor chips

We demonstrate the back-end integration of broadband, high-NA GaN micro-lenses by micro-assembly onto non-native semiconductor substrates. We developed a highly parallel micro-fabrication process flow to suspend micron scale plano-convex lens platelets from 6"Si growth wafers and show their subsequent transfer-printing integration. A growth process targeted at producing unbowed epitaxial wafers was combined with optimisation of the etching volume in order to produce flat devices for printing. Lens structures were fabricated with 6 to 11 $\mu$m diameter, 2 $\mu$m height and root-mean-squared surface roughness below 2 nm. The lenses were printed in a vertically coupled geometry on a single crystalline diamond substrate and with $\mu$m-precise placement on a horizontally coupled photonic integrated circuit waveguide facet. Optical performance analysis shows that these lenses could be used to couple to diamond nitrogen vacancy centres at micron scale depths and demonstrates their potential for visible to infrared light-coupling applications.
Verification of the busy-forbidden protocol (using an extension of the cones and foci framework)

The busy-forbidden protocol is a new readers-writer lock with no resource contention between readers, which allows it to outperform other locks. For its verification, specifications of its implementation and its less complex external behavior are provided by the original authors but are only proven equivalent for up to 7 threads. We provide a general proof using the cones and foci proof framework, which rephrases whether two specifications are branching bisimilar in terms of proof obligations on relations between the data objects occurring in the implementation and specification. We provide an extension of this framework consisting of three additional properties on data objects, When these three additional properties also hold, the two systems are divergence-preserving branching bisimilar, a stronger version of the aforementioned relation that also distinguishes livelock. We prove this extension to be sound and use it to give a general equivalence proof for the busy-forbidden protocol.
Local Differentially Private Fuzzy Counting in Stream Data Using Probabilistic Data Structures

Privacy-preserving estimation of counts of items in streaming data finds applications in several real-world scenarios including word auto-correction and traffic management applications. Recent works of RAPPOR Erlingsson et al. (2014) and Apple's count-mean sketch (CMS) algorithm D. P. T. Apple, (2017) propose privacy preserving mechanisms for count estimation in large volumes of data using probabilistic data structures like counting Bloom filter and CMS. However, these existing methods fall short in providing a sound solution for real-time streaming data applications. Since the size of the data structure in these methods is not adaptive to the volume of the streaming data, the utility (accuracy of the count estimate) can suffer over time due to increased false positive rates. Further, the lookup operation needs to be highly efficient to answer count estimate queries in real-time. More importantly, the local Differential privacy mechanisms used in these approaches to provide privacy guarantees come at a large cost to utility (impacting the accuracy of count estimation). In this work, we propose a novel (local) Differentially private mechanism that provides high utility for the streaming data count estimation problem with similar or even lower privacy budgets while providing: a) fuzzy counting to report counts of related or similar items (for instance to account for typing errors and data variations), and b) improved querying efficiency to reduce the response time for real-time querying of counts. Our algorithm uses a combination of two probabilistic data structures Cuckoo filter and Bloom filter. We provide formal proofs for privacy and utility guarantees and present extensive experimental evaluation of our algorithm using real and synthetic English words datasets for both the exact and fuzzy counting scenarios. Our privacy preserving mechanism substantially outperforms the prior work in terms of lower querying time, significantly higher utility (accuracy of count estimation) under similar or lower privacy guarantees, at the cost of communication overhead.
Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features

Due to label scarcity and covariate shift happening frequently in real-world studies, transfer learning has become an essential technique to train models generalizable to some target populations using existing labeled source data. Most existing transfer learning research has been focused on model estimation, while there is a paucity of literature on transfer inference for model accuracy despite its importance. We propose a novel $\mathbf{D}$oubly $\mathbf{R}$obust $\mathbf{A}$ugmented $\mathbf{M}$odel $\mathbf{A}$ccuracy $\mathbf{T}$ransfer $\mathbf{I}$nferen$\mathbf{C}$e (DRAMATIC) method for point and interval estimation of commonly used classification performance measures in an unlabeled target population using labeled source data. Specifically, DRAMATIC derives and evaluates the risk model for a binary response $Y$ against some low dimensional predictors $\mathbf{A}$ on the target population, leveraging $Y$ from source data only and high dimensional adjustment features $\mathbf{X}$ from both the source and target data. The proposed estimators are doubly robust in the sense that they are $n^{1/2}$ consistent when at least one model is correctly specified and certain model sparsity assumptions hold. Simulation results demonstrate that the point estimation have negligible bias and the confidence intervals derived by DRAMATIC attain satisfactory empirical coverage levels. We further illustrate the utility of our method to transfer the genetic risk prediction model and its accuracy evaluation for type II diabetes across two patient cohorts in Mass General Brigham (MGB) collected using different sampling mechanisms and at different time points.
Differentiable Inference of Temporal Logic Formulas

We demonstrate the first recurrent neural network architecture for learning signal temporal logic (TL) formulas, and present the first systematic comparison of formula inference methods. Legacy systems embed much expert knowledge which is not explicitly formalized. There is great interest in learning formal specifications that characterize the ideal behavior of such systems—that is, formulas in TL that are satisfied by the system’s output signals. Such specifications can be used to better understand the system’s behavior and improve the design of its next iteration. Previous inference methods either assumed certain formula templates, or did a heuristic enumeration of all possible templates. This work proposes a neural network architecture that infers the formula structure via gradient descent, eliminating the need for imposing any specific templates. It combines the learning of formula structure and parameters in one optimization. Through systematic comparison, we demonstrate that this method achieves similar or better misclassification rates (MCRs) than enumerative and lattice methods. We also observe that different formulas can achieve similar MCR, empirically demonstrating the under-determinism of the problem of TL inference.
Correcting for small-displacement interlopers in BAO analyses

Due to the low resolution of slitless spectroscopy, future surveys including those made possible by the Roman and Euclid space telescopes will be prone to line mis-identification, leading to interloper galaxies at the wrong redshifts in the large-scale structure catalogues. The most pernicious of these have a small displacement between true and false redshift such that the interloper positions are correlated with the target galaxies. We consider how to correct for such contaminants, focusing on Hβ interlopers in [Oiii] catalogues as will be observed by Roman, which are misplaced by Δd = 97 h -1 Mpc at redshift z = 1. Because this displacement is close to the BAO scale, the peak in the interloper-target galaxy cross-correlation function at the displacement scale can change the shape of the BAO peak in the auto-correlation of the contaminated catalogue, and lead to incorrect cosmological measurements if not accounted for properly. We consider how to build a model for the monopole and quadrupole moments of the contaminated correlation function, including an additional free parameter for the fraction of interlopers. The key input to this model is the cross-correlation between the population of galaxies forming the interlopers and the main target sample. It will be important to either estimate this using calibration data or to use the contaminated small-scale auto-correlation function to model it, which may be possible if a number of requirements about the galaxy populations are met. We find that this method is successful in measuring the BAO dilation parameters without significant degradation in accuracy, provided the cross-correlation function is accurately known.
Towards a Comprehensive View of Accretion, Inner Disks, and Extinction in Classical T Tauri Stars: An ODYSSEUS Study of the Orion OB1b Association

The coevolution of T Tauri stars and their surrounding protoplanetary disks dictates the timescales of planet formation. In this paper, we present magnetospheric accretion and inner disk wall model fits to near-UV (NUV) to near-IR (NIR) spectra of nine classical T Tauri stars in Orion OB1b as part of the Outflows and Disks around Young Stars: Synergies for the Exploration of ULLYSES Spectra (ODYSSEUS) survey. Using NUV–optical spectra from the Hubble UV Legacy Library of Young Stars as Essential Standards (ULLYSES) Director’s Discretionary Program and optical–NIR spectra from the PENELLOPE VLT Large Programme, we find that the accretion rates of these targets are relatively high for the region’s intermediate age of 5.0 Myr; rates are in the range of (0.5–17.2) × 10−8 M ☉ yr−1, with a median value of 1.2 × 10−8 M ☉ yr−1. The NIR excesses can be fit with 1200–1800 K inner disk walls located at 0.05–0.10 au from the host stars. We discuss the significance of the choice in extinction law, as the measured accretion rate depends strongly on the adopted extinction value. This analysis will be extended to the complete sample of T Tauri stars being observed through ULLYSES to characterize accretion and inner disks in star-forming regions of different ages and stellar populations.
Atmospheric axionlike particles at Super-Kamiokande

We consider a muonphilic axion-like-particle (ALP), denoted as a , lighter than twice the muon mass. ALPs of this mass range dominantly decay into a pair of photons, induced by a triangular muon loop. Such light muonphilic ALPs are naturally long-lived. At the atmosphere, the ALPs are copiously produced from charged-meson decays in air showers, such as π ± → µ ± νa , via the ALP-muon coupling g aµµ . After propagating tens of kilometers, the ALPs decay with a → γγ inside large-volume Cherenkov detectors near the Earth surface, such as Super-Kamiokande (SK). We ﬁnd the present SK observation constrains on muonphilic ALPs of mass range [1 MeV, 30 MeV] and ALP-muon coupling [10 − 3 , 10 2 ], assuming the proper decay length cτ a in [10 − 3 km, 10 6 km] either correlated or uncorrelated with g aµµ . We conclude that atmospheric searches of such exotic states can be complementary to collider and beam-dump experiments as well as astrophysical probes.
Machine Learning-based EEG Applications and Markets

： This paper addresses both the various EEG applications and the current EEG market ecosystem propelled by machine learning. Increasingly available open medical and health datasets using EEG encourage data-driven research with a promise of improving neurology for patient care through knowledge discovery and machine learning data science algorithm development. This effort leads to various kinds of EEG developments and currently forms a new EEG market. This paper attempts to do a comprehensive survey on the EEG market and covers the six significant applications of EEG, including diagnosis/screening, drug development, neuromarketing, daily health, metaverse, and age/disability assistance. The highlight of this survey is on the compare and contrast between the research field and the business market. Our survey points out the current limitations of EEG and indicates the future direction of research and business opportunity for every EEG application listed above. Based on our survey, more research on machine learning-based EEG applications will lead to a more robust EEG-related market. More companies will use the research technology and apply it to real-life settings. As the EEG-related market grows, the EEG-related devices will collect more EEG data, and there will be more EEG data available for researchers to use in their study, coming back as a virtuous cycle. Our market analysis indicates that research related to the use of EEG data and machine learning in the six applications listed above points toward a clear trend in the growth and development of the EEG ecosystem and machine learning world.
Quantum transport and mobility spectrum of topological carriers in (001) SnTe/PbTe heterojunctions

Measurements of magnetotransport in SnTe/PbTe heterojunctions grown by the MBE technique on (001) undoped CdTe substrates were performed. At low magnetic fields, quantum corrections to conductivity were observed that may be attributed to the presence of topological states at the junction interface. For a sample with 5 nm thick SnTe layer, the data analysis suggests that midgap states are actually gapped. However, the phase coherence effects in 10 nm and 20 nm SnTe/PbTe samples are fully explained assuming existence of gapless Dirac cones. Magnetotransport at higher magnetic fields is described in the framework of mobility spectrum analysis (MSA). We demonstrate that the electron- and hole-like peaks observed simultaneously for all SnTe/PbTe heterojunctions may originate from the concave and convex parts of the energy isosurface for topological states -- and not from the existence of quasiparticles both carrying negative and positive charges. This interpretation is supported by numerical calculations of conductivity tensor components for gapless (100) Dirac cones, performed within a classical model and based on the solutions of Boltzmann transport equation. Our approach shows the feasibility of MSA in application to magnetotransport measurements on topological matter.
Learning Quantization in LDPC Decoders

Finding optimal message quantization is a key requirement for low complexity belief propagation (BP) decoding. To this end, we propose a floating-point surrogate model that imitates quantization effects as additions of uniform noise, whose amplitudes are trainable variables. We verify that the surrogate model closely matches the behavior of a fixed-point implementation and propose a hand-crafted loss function to realize a trade-off between complexity and error-rate performance. A deep learning-based method is then applied to optimize the message bitwidths. Moreover, we show that parameter sharing can both ensure implementation-friendly solutions and results in faster training convergence than independent parameters. We provide simulation results for 5G low-density parity-check (LDPC) codes and report an error-rate performance within 0.2 dB of floating-point decoding at an average message quantization bitwidth of 3.1 bits. In addition, we show that the learned bitwidths also generalize to other code rates and channels
Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework

Vision-based human activity recognition (HAR) has emerged as one of the essential research areas in video analytics. Over the last decade, numerous advanced deep learning algorithms have been introduced to recognize complex human actions from video streams. These deep learning algorithms have shown impressive performance for the video analytics task. However, these newly introduced methods either exclusively focus on model performance or the effectiveness of these models in terms of computational efficiency, resulting in a biased trade-off between robustness and computational efficiency in their proposed methods to deal with challenging HAR problem. To enhance both the accuracy and computational efficiency, this paper presents a computationally efficient yet generic spatial–temporal cascaded framework that exploits the deep discriminative spatial and temporal features for HAR. For efficient representation of human actions, we propose an efficient dual attentional convolutional neural network (DA-CNN) architecture that leverages a unified channel–spatial attention mechanism to extract human-centric salient features in video frames. The dual channel–spatial attention layers together with the convolutional layers learn to be more selective in the spatial receptive fields having objects within the feature maps. The extracted discriminative salient features are then forwarded to a stacked bi-directional gated recurrent unit (Bi-GRU) for long-term temporal modeling and recognition of human actions using both forward and backward pass gradient learning. Extensive experiments are conducted on three publicly available human action datasets, where the obtained results verify the effectiveness of our proposed framework (DA-CNN+Bi-GRU) over the state-of-the-art methods in terms of model accuracy and inference runtime across each dataset. Experimental results show that the DA-CNN+Bi-GRU framework attains an improvement in execution time up to 167× in terms of frames per second as compared to most of the contemporary action-recognition methods.