Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge-Aware Neural Networks for Medical Forum Question Classification

Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically classify medical queries based on a consumer's intention, so that these questions may be directed to the right set of medical experts. Here, we develop a novel medical knowledge-aware BERT-based model (MedBERT) that explicitly gives more weightage to medical concept-bearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task. MedBERT achieves state-of-the-art performance on two benchmark datasets and performs very well in low resource settings.
MixNN: protection of federated learning against inference attacks by mixing neural network layers

Machine Learning (ML) has emerged as a core technology to provide learning models to perform complex tasks. Boosted by Machine Learning as a Service (MLaaS), the number of applications relying on ML capabilities is ever increasing. However, ML models are the source of different privacy violations through passive or active attacks from different entities. In this paper, we present MixNN a proxy-based privacy-preserving system for federated learning to protect the privacy of participants against a curious or malicious aggregation server trying to infer sensitive information (i.e., membership and attribute inferences). MixNN receives the model updates from participants and mixes layers between participants before sending the mixed updates to the aggregation server. This mixing strategy drastically reduces privacy leaks without any trade-off with utility. Indeed, mixing the updates of the model has no impact on the result of the aggregation of the updates computed by the server. We report on an extensive evaluation of MixNN using several datasets and neural networks architectures to quantify privacy leakage through membership and attribute inference attacks as well the robustness of the protection. We show that MixNN significantly limits both the membership and attribute inferences compared to a baseline using model compression and noisy gradient (well known to damage the utility) while keeping the same level of utility as classic federated learning.
The ordered Bell numbers as weighted sums of odd or even Stirling numbers of the second kind

For the Stirling numbers of the second kind S(n, k) and the ordered Bell numbers B(n), we prove the identity n/2 k=1 S(n, 2k)(2k − 1)! = B(n − 1). An analogous identity holds for the sum over odd k’s.
Leveraging Multiple CNNs for Triaging Medical Workflow

High hospitalization rates due to the global spread of Covid-19 bring about a need for improvements to classical triaging workflows. To this end, convolutional neural networks (CNNs) can effectively differentiate critical from non-critical images so that critical cases may be addressed quickly, so long as there exists some representative image for the illness. Presented is a conglomerate neural network system consisting of multiple VGG16 CNNs;the system trains on weighted skin disease images re-labelled as critical or non-critical, to then attach to input images a critical index between 0 and 10. A critical index offers a more comprehensive rating system compared to binary critical/non-critical labels. Results for batches of input images run through the trained network are promising. A batch is shown being re-ordered by the proposed architecture from most critical to least critical roughly accurately.
A 4-DoF Parallel Origami Haptic Device for Normal, Shear, and Torsion Feedback

We present a mesoscale finger-mounted 4-degreeof-freedom (DoF) haptic device that is created using origami fabrication techniques. The 4-DoF device is a parallel kinematic mechanism capable of delivering normal, shear, and torsional haptic feedback to the fingertip. Traditional methods of robot fabrication are not well suited for designing small robotic devices because it is challenging and expensive to manufacture small, low-friction joints. Our device uses origami manufacturing principles to reduce complexity and the device footprint. We characterize the bandwidth, workspace, and force output of the device. The capabilities of the torsion-DoF are demonstrated in a virtual reality scenario. Our results show that the device can deliver haptic feedback in 4-DoFs with an effective operational workspace of 0.64 cm3 with ±30◦ rotation at every location. The maximum forces and torques the device can apply in the x-, y-, z-, and θ -directions, are ±1.5 N, ±1.5 N, 2 N, and 5 N·mm, respectively, and the device has an operating bandwidth of 9 Hz.
High-order methods beyond the classical complexity bounds, II: inexact high-order proximal-point methods with segment search

A bi-level optimization framework (BiOPT) was proposed in [3] for convex composite optimization, which is a generalization of bi-level unconstrained minimization framework (BLUM) given in [20]. In this continuation paper, we introduce a $p$th-order proximal-point segment search operator which is used to develop novel accelerated methods. Our first algorithm combines the exact element of this operator with the estimating sequence technique to derive new iteration points, which are shown to attain the convergence rate $\mathcal{O}(k^{-(3p+1)/2})$, for the iteration counter $k$. We next consider inexact elements of the high-order proximal-point segment search operator to be employed in the BiOPT framework. We particularly apply the accelerated high-order proximal-point method at the upper level, while we find approximate solutions of the proximal-point segment search auxiliary problem by a combination of non-Euclidean composite gradient and bisection methods. For $q=\lfloor p/2 \rfloor$, this amounts to a $2q$th-order method with the convergence rate $\mathcal{O}(k^{-(6q+1)/2})$ (the same as the optimal bound of $2q$th-order methods) for even $p$ ($p=2q$) and the superfast convergence rate $\mathcal{O}(k^{-(3q+1)})$ for odd $p$ ($p=2q+1$).
Finite State Machine Policies Modulating Trajectory Generator

—Deep reinforcement learning (deep RL) has emerged as an effective tool for developing controllers for legged robots. However, a simple neural network representation is known for its poor extrapolation ability, making the learned behavior vulnerable to unseen perturbations or challenging terrains. Therefore, researchers have investigated a novel architecture, Policies Modulating Trajectory Generators (PMTG), which combines trajectory generators (TG) and feedback con-trol signals to achieve more robust behaviors. In this work, we propose to extend the PMTG framework with a ﬁnite state machine PMTG by replacing simple TGs with asynchronous ﬁnite state machines (Async FSMs). This invention offers an explicit notion of contact events to the policy to negotiate unexpected perturbations. We demonstrated that the proposed architecture could achieve more robust behaviors in various scenarios, such as challenging terrains or external perturbations, on both simulated and real robots. The supplemental video can be found at: http://youtu.be/XUiTSZaM8f0.
How Complete Are Surveys for Nearby Transiting Hot Jupiters?

Hot Jupiters are a rare and interesting outcome of planet formation. Although more than 500 hot Jupiters (HJs) are known, most of them were discovered by a heterogeneous collection of surveys with selection biases that are difficult to quantify. Currently, our best knowledge of HJ demographics around FGK stars comes from the sample of ≈40 objects detected by the Kepler mission, which have a well-quantified selection function. Using the Kepler results, we simulate the characteristics of the population of nearby transiting HJs. A comparison between the known sample of nearby HJs and simulated magnitude-limited samples leads to four conclusions. (1) The known sample of HJs appears to be ≈75% complete for stars brighter than Gaia G ≤ 10.5, falling to ≲50% for G ≤ 12. (2) There are probably a few undiscovered HJs with host stars brighter than G ≈ 10 located within 10° of the Galactic plane. (3) The period and radius distributions of HJs may differ for F-type hosts (which dominate the nearby sample) and G-type hosts (which dominate the Kepler sample). (4) To obtain a magnitude-limited sample of HJs that is larger than the Kepler sample by an order of magnitude, the limiting magnitude should be approximately G ≈ 12.5. This magnitude limit is within the range for which NASA’s Transiting Exoplanet Survey Satellite can easily detect HJs, presenting the opportunity to greatly expand our knowledge of hot-Jupiter demographics.
MIIDL: a Python package for microbial biomarkers identification powered by interpretable deep learning

Detecting microbial biomarkers used to predict disease phenotypes and clinical outcomes is crucial for disease early-stage screening and diagnosis. Most methods for biomarker identification are linear-based, which is very limited as biological processes are rarely fully linear. The introduction of machine learning to this field tends to bring a promising solution. However, identifying microbial biomarkers in an interpretable, data-driven and robust manner remains challenging. We present MIIDL, a Python package for the identification of microbial biomarkers based on interpretable deep learning. MIIDL innovatively applies convolutional neural networks, a variety of interpretability algorithms and plenty of pre-processing methods to provide a one-stop and robust pipeline for microbial biomarkers identification from high-dimensional and sparse data sets.
Measurement of the absolute branching fractions of Jpsi to gamma eta and eta decay modes

Based on a data sample of (1.0087+-0.0044)x10^10 Jpsi events collected by the BESIII detector at the BEPCII accelerator, the absolute branching fraction (BF) of the decay Jpsi->gamma eta is measured with high precision using events in which the radiative photon converts to e+e-. Using the measured absolute BF of Jpsi->gamma eta, the absolute BFs of four dominant eta decay modes are measured for the first time. The results are B(Jpsi->gamma eta) = (1.067+-0.005+-0.023)x10^-3, B(eta->gamma gamma) = (39.86+-0.04+-0.99)%, B(eta->pi0pi0pi0) = (31.96+-0.07+-0.84)%, B(eta->pi+pi-pi0) = (23.04+-0.03+-0.54)%, and B(eta->pi+pi-gamma) = (4.38+-0.02+-0.10)%, where the first and second uncertainties are statistical and systematic, respectively. The results are consistent with the world average values within two standard deviations.
Benchmarking the Status of Default Pseudorandom Number Generators in Common Programming Languages

The ever-increasing need for random numbers is clear in many areas of computer science, from neural networks to optimization. As such, most common programming language provide easy access to Pseudorandom Number Generators. However, these generators are not all made equal, and empirical verification has previously shown some to be flawed in key ways. Because of the constant changes in programming languages, we perform the same empirical benchmarking using large batteries of statistcal tests on a wide array of PRNGs, and identify that while some languages have improved significantly over the years, there are still cases where the default PRNG fails to deliver sufficiently random results.
Efficient ILC analysis on polarization maps after EB leakage correction

The Internal Linear Combination (ILC) is widely used to extract the cosmic microwave background (CMB) signal from multi-frequency observation maps, especially for Satellite experiments with quasi-full sky coverage. We extend ILC method to CMB polarization map analysis with a small sky patch which is especially typical for ground-based experiments, by combing ILC with a template cleaning method which can give pure B map free from EB leakage caused by partial sky coverage. The feature of our methods is that we do the ILC analysis on pseudo-scalar B maps, and the advantage is that it totally avoids the impact of EB leakage on ILC, so that it can improve the efficiency of component separation dramatically. We demonstrate our methods with mock data of a future ground-based experiment with a deep survey on a clean patch in the northern sky, and the results show that the level of foreground residual can be well controlled, it biases the tensor to scalar ratio (r) at the order of 10-3 which is comparable to the statistical error by noise.
Observational scalings testing modified gravity

We consider different observational effects to test a modified gravity approach involving the cosmological constant in the common description of dark matter and dark energy.We obtain upper limits for the cosmological constant by studying the scaling relations for 12 nearby galaxy clusters, the radiated power from gravitational waves and the Tully-Fisher relation for super spiral galaxies. Our estimations reveal that, for all these cases, the upper limits for Λ are consistent with its actual value predicted by cosmological observations.
Constructing Regression Dataset from Code Evolution History

Bug datasets consisting of real-world bugs are important artifacts for researchers and programmers, which lay empirical and experimental foundation for various SE/PL research such as fault localization, software testing, and program repair. All known state-of-the-art datasets are constructed manually , which inevitably limits their scalability, representativeness, and the support for the emerging data-driven research. In this work, we propose an approach to automate the process of harvesting replicable regression bugs from the code evolutionary history. We focus on regression bug dataset, as they (1) manifest how a bug is introduced and fixed (as normal bugs), (2) support regression bug analysis, and (3) incorporate a much stronger specification (i.e., the original passing version) for general bug analysis. Technically, we address an information retrieval problem on code evolution history. Given a code repository, we search for regressions where a test can pass a regression-fixing commit, fail a regression-inducing commit, and pass a working commit. In this work, we address the challenges of (1) identifying potential regression-fixing commits from the code evolution history, (2) migrating the test and its code dependencies over the history, and (3) minimizing the compilation overhead during the regression search. We build our tool, RegMiner, which harvested 537 regressions over 66 projects for 3 weeks, created the largest replicable regression dataset within shortest period, to the best of our knowledge. Moreover, our empirical study on our regression dataset shows a gap between the popular regression fault localization techniques (e.g, delta-debugging) and the real fix, revealing new data-driven research opportunities.
ICMT: Item Cluster-Wise Multi-Objective Training for Long-Tail Recommendation

Item recommendation based on historical user-item interactions is of vital importance for web-based services. However, the data used to train a recommender system (RS) suffers from severe popularity bias. The item frequency distribution is a highly skewed long-tail distribution. Interactions of a small fraction of popular (head) items account for almost the whole training data. Normal training methods from such biased data tend to repetitively generate recommendations from the head items, which further exacerbates the data bias and affects the exploration of potentially interesting items from niche (tail) items. In this paper, we explore the central theme of long-tail recommendation. Through an empirical study, we find that head items are very likely to be recommended due to the fact that the gradients coming from head items dominate the overall gradient update process, which further affects the optimization of tail items. To this end, we propose a general framework namely Item Cluster-Wise Multi-Objective Training (ICMT) for longtail recommendation. Firstly, the disentangled representation learning is utilized to identify the popularity impact behind useritem interactions. Then item clusters are adaptively formulated according to the disentangled popularity representation. After that, we consider the learning over the whole training data as a weighted aggregation of multiple (item) cluster-wise objectives, which can be resolved through a Pareto-Efficient solver for a harmonious overall gradient direction. Besides, a contractive loss focusing on model robustness is derived as a regularization term. We instantiate ICMT with three state-of-the-art recommendation models and conduct experiments on three real-world datasets. Experimental results demonstrate that the proposed ICMT significantly improves the overall recommendation performance, especially on tail items. Codes will be open-sourced.
CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure

Multi-beam LiDAR sensors are increasingly used in robotics, particularly with autonomous cars for localization and perception tasks, both relying on the ability to build a precise map of the environment. For this, we propose a new real-time LiDAR-only odometry method called CT-ICP (for Continuous-Time ICP), completed into a full SLAM with a novel loop detection procedure. The core of this method, is the introduction of the combined continuity in the scan matching, and discontinuity between scans. It allows both the elastic distortion of the scan during the registration for increased precision, and the increased robustness to high frequency motions from the discontinuity. We build a complete SLAM on top of this odometry, using a fast pure LiDAR loop detection based on elevation image 2D matching, providing a pose graph with loop constraints. To show the robustness of the method, we tested it on seven datasets: KITTI, KITTI-raw, KITTI-360, KITTICARLA, ParisLuco, Newer College, and NCLT in driving and high-frequency motion scenarios. Both the CT-ICP odometry and the loop detection are made available online. CT-ICP is currently first, among those giving access to a public code, on the KITTI odometry leaderboard, with an average Relative Translation Error (RTE) of 0.59% and an average time per scan of 60ms on a CPU with a single thread.
Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge

The enormous space and diversity of natural images is usually represented by a few small-scale human-rated image quality assessment (IQA) datasets. This casts great challenges to deep neural network (DNN) based blind IQA (BIQA), which requires large-scale training data that is representative of the natural image distribution. It is extremely difficult to create human-rated IQA datasets composed of millions of images due to constraints of subjective testing. While a number of efforts have focused on design innovations to enhance the performance of DNN based BIQA, attempts to address the scarcity of labeled IQA data remain surprisingly missing. To address this data challenge, we construct so far the largest IQA database, namely Waterloo Exploration-II, which contains 3,570 pristine reference and around 3.45 million singly and multiply distorted images. Since subjective testing for such a large dataset is nearly impossible, we develop a novel mechanism that synthetically assigns perceptual quality labels to the distorted images. We construct a DNN-based BIQA model called EONSS, train it on Waterloo Exploration-II, and test it on nine subject-rated IQA datasets, without any retraining or fine-tuning. The results show that with a straightforward DNN architecture, EONSS is able to outperform the very state-of-the-art in BIQA, both in terms of quality prediction performance and execution speed. This study strongly supports the view that the quantity and quality of meaningfully annotated training data, rather than a sophisticated network architecture or training strategy, is the dominating factor that determines the performance of DNN-based BIQA models. (Note: Since this is an ongoing project, the final versions of Waterloo Exploration-II database, quality annotations, and EONSS, will be made publicly available in the future when it culminates.)
The RBC’s road to ghost and removal: splenic clearance

Red blood cell (RBC) clearance occurs principally in the spleen and liver, 1 although the precise mechanisms remain unclear. In sinusoidal spleens, as in humans, rats, and dogs, attention has focused on the interendothelial slits of the red pulp where passage may become dif ﬁ cult because of reduced deformability due to ageing or disease states (eg, sickle cell disease 2 or malaria 3,4 ). However, even if blocked in the red pulp, the question of how RBCs are ﬁ nally removed remains open. This same question is of para-mount importance for clearance in the spleens of mice, horses, cats, or even zebra ﬁ sh, whose spleens are nonsinusoidal. 5 Various mechanisms have been proposed that include, among other things, exposure of phosphatidylserine, 1,6 conformational changes in protein CD47, 7 oxidation of proteins (eg, band 3), 8 reduction in membrane or whole cell deformability, 9 and, importantly for what follows, expression of adhesion molecules. 10 However, in all of this, a complete mechanistic pathway for clearance remains unde ﬁ ned. Klei et al 11 recently proposed that the pathway to RBC clearance involves cell adherence to the reticular meshwork of the red pulp or to the sinuses, while under shear ﬂ ow, that leads to hemolysis and the formation of ghost cells; their hypothesis envisions that ghost cells, and not intact red cells, are ingested by red pulp macrophages. This schema may indeed provide a unifying concept that carries over for RBC clearance in a wide range of species. Klei et al 11 support their hypothesis by demonstrating that only a low percentage of intact RBCs are ingested by red pulp macrophages of the human spleen and that hemolysis and transition to ghost cells occur in senescent-like RBCs when subjected to sustained shear ﬂ
Squamous cell carcinoma antigen combined with HPV-16 infection in predicting high-grade squamous intraepithelial lesions of the cervix

Abstract An early screening of HPV and the Thinprep Cytology Test (TCT) can effectively prevent cervical cancer. However, patients with high-grade cervical intraepithelial neoplasia usually escape current screening methods and commonly develop cervical cancer. Hence, to identify effective and specific screening methods for high-grade cervical intraepithelial neoplasia is of vital necessity. In this study, 541 patients collected in Sun Yat-Sen hospital from January 2007 to December 2016 were selected. HPV genotype detection and SCC-ag detection were done in these patients. It was found that when serum SCC-ag level exceeded over 0.39 ng/ml in HPV-16 positive patients, the sensitivity and specificity of this novel approach to predict high-grade cervical intraepithelial neoplasia could reach to 83.1% and 62.1%, respectively. The result suggested that the combination of serum SCC-ag level and HPV-16 infection could be used as a novel approach for high-grade cervical intraepithelial neoplasia screening. Impact statement What is already known on this subject? Patients with a high-grade cervical intraepithelial neoplasia usually escape current screening methods. What do the results of this study add? When serum SCC-ag level exceeded over 0.39 ng/ml in HPV-16 positive patients, the sensitivity and specificity to predict high-grade cervical intraepithelial neoplasia could reach to 83.1 and 62.1%, respectively. What are the implications of these findings for clinical practice and/or further research? Combination of serum SCC-ag level and HPV-16 infection could be used to screen high-grade cervical intraepithelial neoplasia.